Inquiries for Bachelor and Master projects are always welcome.
We have also done research in many other fields. If you are interested in any of these, do not hesitate to contact us - we are always happy to get back to those topics and do exciting research there!
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How to find a good thesis topic in computer vision.
“What are some good thesis topics in Computer Vision?”
This is a common question that people ask in forums – and it’s an important question to ask for two reasons:
For these reasons, it’s best to do as much research as you can to avoid the above pitfalls or your days of research will slowly become torturous for you – and that would be a shame because computer vision can truly be a lot of fun 🙂
So, down to business.
The purpose of this post is to propose ways to find that one perfect topic that will keep you engaged for months (or years) to come – and something you’ll be proud to talk about amongst friends and family.
I’ll start the discussion off by saying that your search strategy for topics depends entirely on whether you’re preparing for a Master’s thesis or a PhD. The former can be more general, the latter is (nearly always) very fine-grained specific. Let’s start with undergraduate topics first.
I’ll propose here three steps you can take to assist in your search: looking at the applications of computer vision, examining the OpenCV library, and talking to potential supervisors.
Computer Vision has so many uses in the world. Why not look through a comprehensive list of them and see if anything on that list draws you in? Here’s one such list I collected from the British Machine Vision Association :
Go through this list and work out if something stands out for you. Perhaps your family is involved in agriculture? Look up how computer vision is helping in this field! The Economist wrote a fascinating article entitled “ The Future of Agriculture ” in which they discuss, among other things, the use of drones to monitor crops, create contour maps of fields, etc. Perhaps Computer Vision can assist with some of these tasks? Look into this!
OpenCV is the best library out there for image and video processing (I’ll be writing a lot more about it on this blog). Other libraries do exist that do certain specific things a little better, e.g. Tracking.js , which performs things like tracking inside the browser, but generally speaking, there’s nothing better than OpenCV.
On the topic of searching for thesis topics, I recall once reading a suggestion of going through the functions that OpenCV has to offer and seeing if anything sticks out at you there. A brilliant idea. Work down the list of the OpenCV documentation . Perhaps face recognition interests you? There are so many interesting projects where this can be utilised!
You can’t go past this suggestion. Every academic has ideas constantly buzzing around his head. Academics are immersed in their field of research and are always to talking to people in the industry to look for interesting projects that they could get funding for. Go and talk to the academics at your university that are involved in Computer Vision. I’m sure they’ll have at least one project proposal ready to go for you.
You should also run any ideas of yours past them that may have emerged from the two previous steps. Or at least mention things that stood out for you (e.g. agriculture). They may be able to come up with something themselves.
Well, if you’ve reached this far in your studies then chances are you have a fairly good idea of how this all works now. I won’t patronise you too much, then. But I will mention three points that I wish someone had told me prior to starting my PhD adventure:
Spending time looking for a thesis topic is time worth spending. It could save you from future pitfalls. With respect to undergraduate thesis topics looking at Computer Vision applications is one place to start. The OpenCV library is another. And talking to potential supervisors at your university is also a good idea.
With respect to PhD thesis topics, it’s important to take into consideration what the fields of expertise of your potential supervisors are and then searching for topics in these areas. If these supervisors have ready-made topics for you, it is worth considering them to save you a lot of time and stress in the first year or so of your studies. Finally, it’s usually good to avoid trending topics because of the people you will be competing against for publications.
But the bottom line is, devote time to finding a topic that truly interests you . It’ll be the difference between wanting to get out of bed to do more and more research in your field or dreading each time you have to walk into your Computer Science building in the morning.
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Biomedical Imaging
The current plethora of imaging technologies such as magnetic resonance imaging (MR), computed tomography (CT), position emission tomography (PET), optical coherence tomography (OCT), and ultrasound provide great insight into the different anatomical and functional processes of the human body.
Computer Vision
Computer vision is the science and technology of teaching a computer to interpret images and video as well as a typical human. Technically, computer vision encompasses the fields of image/video processing, pattern recognition, biological vision, artificial intelligence, augmented reality, mathematical modeling, statistics, probability, optimization, 2D sensors, and photography.
Image Segmentation/Classification
Extracting information from a digital image often depends on first identifying desired objects or breaking down the image into homogenous regions (a process called 'segmentation') and then assigning these objects to particular classes (a process called 'classification'). This is a fundamental part of computer vision, combining image processing and pattern recognition techniques.
Multiresolution Techniques
The VIP lab has a particularly extensive history with multiresolution methods, and a significant number of research students have explored this theme. Multiresolution methods are very broad, essentially meaning than an image or video is modeled, represented, or features extracted on more than one scale, somehow allowing both local and non-local phenomena.
Remote Sensing
Remote sensing, or the science of capturing data of the earth from airplanes or satellites, enables regular monitoring of land, ocean, and atmosphere expanses, representing data that cannot be captured using any other means. A vast amount of information is generated by remote sensing platforms and there is an obvious need to analyze the data accurately and efficiently.
Scientific Imaging
Scientific Imaging refers to working on two- or three-dimensional imagery taken for a scientific purpose, in most cases acquired either through a microscope or remotely-sensed images taken at a distance.
Stochastic Models
In many image processing, computer vision, and pattern recognition applications, there is often a large degree of uncertainty associated with factors such as the appearance of the underlying scene within the acquired data, the location and trajectory of the object of interest, the physical appearance (e.g., size, shape, color, etc.) of the objects being detected, etc.
Video Analysis
Video analysis is a field within computer vision that involves the automatic interpretation of digital video using computer algorithms. Although humans are readily able to interpret digital video, developing algorithms for the computer to perform the same task has been highly evasive and is now an active research field.
Evolutionary Deep Intelligence
Deep learning has shown considerable promise in recent years, producing tremendous results and significantly improving the accuracy of a variety of challenging problems when compared to other machine learning methods.
Discovery Radiomics
Radiomics, which involves the high-throughput extraction and analysis of a large amount of quantitative features from medical imaging data to characterize tumor phenotype in a quantitative manner, is ushering in a new era of imaging-driven quantitative personalized cancer decision support and management.
Sports Analytics
Sports Analytics is a growing field in computer vision that analyzes visual cues from images to provide statistical data on players, teams, and games. Want to know how a player's technique improves the quality of the team? Can a team, based on their defensive position, increase their chances to the finals? These are a few out of a plethora of questions that are answered in sports analytics.
The University of Waterloo acknowledges that much of our work takes place on the traditional territory of the Neutral, Anishinaabeg, and Haudenosaunee peoples. Our main campus is situated on the Haldimand Tract, the land granted to the Six Nations that includes six miles on each side of the Grand River. Our active work toward reconciliation takes place across our campuses through research, learning, teaching, and community building, and is co-ordinated within the Office of Indigenous Relations .
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Are you looking for some interesting project ideas for your thesis, project or dissertation? Then be sure that a machine learning topic would be a very good topic to write on. I have outlined 10 different topics. These topics are really good because you can easily obtain the dataset (i will provide the link to the dataset) and you can as well get some support from me. Let me know if you need any support in preparing your thesis.
You can leave a comment below in the comment area.
The data is provided by the Oncology department and details instances and related attributes which are nine in all.
You can obtain the dataset from here
This is one of the most interesting topics for me. The reason is because the revenue generated or expended by ads campaign depends not just on the volume of the ads, but also on the relevance of the ads. Therefore it is possible to increase revenue and reduce spending by developing a Machine Learning model that select relevants ads with a high level of accuracy. The dataset provides a collection of ads as well as the structure and geometry of the ads.
Get the ads dataset from here
This looks like big data stuff. But no! It’s simply dataset you can use for analysis. It is the actual data obtained from the US census in 1990. There are 68 attributes for each of the records and clustering would be performed to identify trends in the data.
You can obtain census the dataset from here
This is quite a tasking project but its quite interesting. Before now, there exists models to predict the ratings of movies on a scale of 0 to 10 or 1 to 5. But this takes it a step further. You actually need to determine the outcome of the movie. The data set is a large multivariate dataset of movie director, cast, individual roles of the actor, remarks, studio and relevant documents.
You can get the movies dataset from here
This project have been classified as difficult but I don’t think so. The objective to predict the the area affected by forest fires. Dataset include relevant meteological information and other parameters taken from a region of Portugal.
You can get the fire dataset from here
Two ground ozone datasets are provided for this. Data includes temperatures at various times of the day as well as wind speed. The data included in the dataset was collected in a span of 6 years from 1998 to 2004.
You can get the Ozone dataset from here
If you have watched the movie, ‘Person of Interest’ directed by Jonathan Nolan, then you will appreciate the fact that there is a possibility of predicting violent criminal activities before they actually occur. Dataset would contain historical data on crime rate, types of crimes occurrence per region.
You can get the crime dataset from here
The dataset for this project is derived from user review comments from Amazon users. The model should be able to perform analysis on the training dataset and come up with a model that classifies the reviews based on sentiments. Granularity can be improved by generating predictions based on location and other factors.
You can get the reviews dataset from here
Everyone uses electricity at home. Or rather, almost everyone! Would is not be great to have a system that helps to predict electricity consumption. Training dataset provided for this project includes feature set such as the size of the home, duration and more
You can get the dataset from here
Here the available dataset provide a collection of data about an individual on a subject matter. You are required to create a model that would try to quantify the amount of knowledge the individual have on the given subject. You can be creating by trying to also infer the performance of the user on certain exams.
I hope these 10 Machine Learning Project topic would be helpful to you.
Thanks for reading and do leave a comment below if you need some support
Kindson Munonye is currently completing his doctoral program in Software Engineering in Budapest University of Technology and Economics
Machine learning 101 – equation for a line and regression line, simple linear regression in machine learning (a simple tutorial), pca tutorial 1 – introduction to pca and dimensionality reduction, 2 thoughts on “ 10 machine learning project (thesis) topics for 2020 ”.
Is there any suggestion related to educational data mining?
I’m working on this. You can subscribe to my channel so when I make the update, you can get notified https://www.youtube.com/channel/UCvHgEAcw6VpcOA3864pSr5A
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This list includes topics for potential bachelor or master theses, guided research, projects, seminars, and other activities. Search with Ctrl+F for desired keywords, e.g. ‘machine learning’ or others.
PLEASE NOTE: If you are interested in any of these topics, click the respective supervisor link to send a message with a simple CV, grade sheet, and topic ideas (if any). We will answer shortly.
Of course, your own ideas are always welcome!
Type of work:.
Machine learning models designed and trained to work on a specific regions are not necessarily transferable to other spatially different region. Include a spatially explicit component is mandatory to differentiates behaviors and predictions according to spatial locations. However, it is no clear what is the best way to use this spatial information or which kind of models work best for spatial transferability. In this topic, global remote sensing data will be used for supervised learning in different Earth observation applications.
Feel free to reach out if you have any question or ideas regarding the topic.
The goal of this project is to develop and evaluate a novel dual-decoder architecture for image super-resolution (SR) [1]. This architecture will utilize a single encoder to extract features from an input image, followed by two decoders: one trained to map the features to a low-resolution (LR) output, and the other to map the features to a high-resolution (HR) output. This approach aims to enhance the SR performance by leveraging the complementary learning objectives of both decoders. The goal of the work is to try different architectures and to analyze different loss formulations as well as the feature space learned by the encoder.
The aim of this project is to integrate the TaylorShift [1] attention mechanism into the SwinIR model to enhance the efficiency and performance of image super-resolution (SR) [2]. By leveraging the linear complexity of TaylorShift, we intend to improve the processing speed and reduce the memory footprint of SwinIR without compromising its high accuracy in generating high-resolution images from low-resolution inputs. Image super-resolution is a crucial task in computer vision that aims to enhance the resolution of images, making them clearer and more detailed. SwinIR (Swin Transformer for Image Restoration) has shown state-of-the-art performance in various image restoration tasks, including super-resolution. However, the quadratic complexity of its attention mechanism can be a bottleneck, especially for high-resolution images. TaylorShift, a novel reformulation of the Taylor softmax function, addresses this issue by reducing the complexity of the attention mechanism from quadratic to linear. This enables efficient processing of long sequences and high-resolution images while maintaining the ability to capture intricate token-to-token interactions.
Sherlock Holmes is taking the statement of the witness. The witness is describing the appearance of the perpetrator and the forensic setting they still remember. Your task as the AI investigator will be to generate a comic sketch of the scene and phantom images of the accused person based on the spoken statement of the witness. For this you will use state-of-the-art transformers and visualize the output in an application. As AI investigator you will detect, qualify and quantify bias in the images which are produced by different generation models you have chosen.
This work is embedded in the DFKI KI4Pol lab together with the law enforcement agencies. The stories are fictional you will not work on true crime.
Requirements:
Das Management von Immobilien ist komplex und umfasst verschiedenste Informationsquellen und -objekte zur Durchführung der Prozesse. Ein Corporate Memory kann hier unterstützen in der Analyse und Abbildung des Informationsraums um Wissensdienste zu ermöglichen. Aufgabe ist es, eine Ontologie für das Immobilienmanagement zu entwerfen und beispielhaft ein Szenario zu entwickeln. Für die Materialien und Anwendungspartner sind gute Deutschkenntnisse erforderlich.
High use of resources are thought to be an indirect cause of failures in large cluster systems, but little work has systematically investigated the role of high resource usage on system failures, largely due to the lack of a comprehensive resource monitoring tool which resolves resource use by job and node. This project studies log data of the DFKI Kaiserslautern high performance cluster to consider the predictability of adverse events (node failure, GPU freeze), energy usage and identify the most relevant data within. The second supervisor for this work is Joachim Folz.
Data is available via Prometheus -compatible system:
Feel free to reach out if the topic sounds interesting or if you have ideas related to this work. We can then brainstorm a specific research question together. Link to my personal website.
In recent years knowledge graphs received a lot of attention as well in industry as in science. Knowledge graphs consist of entities and relationships between them and allow integrating new knowledge arbitrarily. Famous instances in industry are knowledge graphs by Microsoft, Google, Facebook or IBM. But beyond these ones, knowledge graphs are also adopted in more domain specific scenarios such as in e-Procurement, e-Invoicing and purchase-to-pay processes. The objective in theses and projects is to explore particular aspects of constructing and/or applying knowledge graphs in the domain of purchase-to-pay processes and e-Invoicing.
Working on deep neural networks for making the time-series anomaly detection process more robust. An important aspect of this process is explainability of the decision taken by a network.
Transformer networks have emerged as competent architecture for modeling sequences. This research will primarily focus on using transformer networks for forecasting time series (multivariate/ univariate) and may also involve fusing knowledge into the machine learning architecture.
This dissertation comprises several studies addressing supervised learning problems where the supervision is imperfect. Firstly, we investigate the margin conditions in active learning. Active learning is characterized by its special mechanism where the learner can sample freely over the feature space and exploit mostly the limited labeling budget by querying the most informative labels. Our primary focus is to discern critical conditions under which certain active learning algorithms can outperform the optimal passive learning minimax rate. Within a non-parametric multi-class classification framework,our results reveal that the uniqueness of Bayes labels across the feature space serves as the pivotal determinant for the superiority of active learning over passive learning. Secondly, we study the estimation of central mean subspace (CMS), and its application in transfer learning. We show that a fast parametric convergence rate is achievable via estimating the expected smoothed gradient outer product, for a general class of covariate distribution that admits Gaussian or heavier distributions. When the link function is a polynomial with a degree of at most r and the covariates follow the standard Gaussian, we show that the prefactor depends on the ambient dimension d as d^r. Furthermore, we show that under a transfer learning setting, an oracle rate of prediction error as if the CMS is known is achievable, when the source training data is abundant. Finally, we present an innovative application involving the utilization of weak (noisy) labels for addressing an Individual Tree Crown (ITC) segmentation challenge. Here, the objective is to delineate individual tree crowns within a 3D LiDAR scan of tropical forests, with only 2D noisy manual delineations of crowns on RGB images available as a source of weak supervision. We propose a refinement algorithm designed to enhance the performance of existing unsupervised learning methodologies for the ITC segmentation problem.
We offer these current topics directly for Bachelor and Master students at TU Darmstadt who can feel free to DIRECTLY contact the thesis advisor if you are interested in one of these topics. Excellent external students from another university may be accepted but are required to first email Jan Peters before contacting any other lab member for a thesis topic. Note that we cannot provide funding for any of these theses projects.
We highly recommend that you do either our robotics and machine learning lectures ( Robot Learning , Statistical Machine Learning ) or our colleagues ( Grundlagen der Robotik , Probabilistic Graphical Models and/or Deep Learning). Even more important to us is that you take both Robot Learning: Integrated Project, Part 1 (Literature Review and Simulation Studies) and Part 2 (Evaluation and Submission to a Conference) before doing a thesis with us.
In addition, we are usually happy to devise new topics on request to suit the abilities of excellent students. Please DIRECTLY contact the thesis advisor if you are interested in one of these topics. When you contact the advisor, it would be nice if you could mention (1) WHY you are interested in the topic (dreams, parts of the problem, etc), and (2) WHAT makes you special for the projects (e.g., class work, project experience, special programming or math skills, prior work, etc.). Supplementary materials (CV, grades, etc) are highly appreciated. Of course, such materials are not mandatory but they help the advisor to see whether the topic is too easy, just about right or too hard for you.
Only contact *ONE* potential advisor at the same time! If you contact a second one without first concluding discussions with the first advisor (i.e., decide for or against the thesis with her or him), we may not consider you at all. Only if you are super excited for at most two topics send an email to both supervisors, so that the supervisors are aware of the additional interest.
FOR FB16+FB18 STUDENTS: Students from other depts at TU Darmstadt (e.g., ME, EE, IST), you need an additional formal supervisor who officially issues the topic. Please do not try to arrange your home dept advisor by yourself but let the supervising IAS member get in touch with that person instead. Multiple professors from other depts have complained that they were asked to co-supervise before getting contacted by our advising lab member.
NEW THESES START HERE
Scope: Bachelor/Master thesis Advisor: Vignesh Prasad and Alap Kshirsagar Added: 2024-04-25 Start: ASAP Topic: Topic:
Grasping is one of the most fundamental and challenging tasks in the robotic manipulation of objects. Most of the prior work on robotic grasping has focused on grasping with a single gripper and several large-scale datasets have been developed in recent years to tackle the problem of single-arm grasping in 3D by utilizing deep-learning techniques [1,2]. But many tasks in industrial and domestic environments require bimanual grasps. Bimanual grasps are required for manipulation of large, deformable or fragile objects. This project seeks to develop a data-driven technique for bimanual robotic grasp generation from visual input. We will utilize a large-scale dataset of simulated bimanual grasps [3] to train a bimanual grasp pose generation model. The method will be evaluated in simulation as well as on a real robot.
Requirements
Interested students can apply by sending an e-mail to [email protected] and attaching the documents mentioned below:
References [1] C. Eppner, A. Mousavian, and D. Fox, “ACRONYM: A Large-Scale Grasp Dataset Based on Simulation,” in Proceedings - IEEE International Conference on Robotics and Automation, 2021, vol. 2021-May, pp. 6222–6227, doi: 10.1109/ICRA48506.2021.9560844. [2] A. Mousavian, C. Eppner, and Di. Fox, “6-DOF GraspNet: Variational grasp generation for object manipulation,” in Proceedings of the IEEE International Conference on Computer Vision, 2019, vol. 2019-Octob, pp. 2901–2910, doi: 10.1109/ICCV.2019.00299. [3] G. Zhai et al., “{DA2} Dataset: Toward Dexterity-Aware Dual-Arm Grasping,” IEEE Robot. Autom. Lett., vol. 7, no. 4, pp. 8941–8948, 2022.
Scope: Master thesis Advisor: Puze Liu and Julen Urain De Jesus Start: ASAP Topic:
High-speed reactive motion is one of the fundamental capabilities of robots to achieve human-level behavior. Optimization-based methods suffer from real-time requirement when the problem is non-convex and contains constraints. Reinforcement learning requires extensive reward engineering to achieve the desired performance. Imitation learning, on the other hand, gathers human knowledge directly from data collection and enables robots to learn natural movements efficiently. In this paper, we explore how imitation learning can be performed in a complex robot Air Hockey Task. The robot needs to learn not only low-level skills, but also high-level tactics from human demonstrations.
References * Chi, Cheng, et al. "Diffusion policy: Visuomotor policy learning via action diffusion." arXiv preprint arXiv:2303.04137 (2023). * Liu, Puze, et al. "Robot reinforcement learning on the constraint manifold." Conference on Robot Learning. PMLR (2022). * Pan, Yunpeng, et al. "Imitation learning for agile autonomous driving." The International Journal of Robotics Research 39.2-3 (2020). Interested students can apply by sending an e-mail to [email protected] and attaching the required documents mentioned above.
Scope: Master thesis Advisor: Theo Vincent Start: Flexible Topic:
Q-learning methods are at the heart of Reinforcement Learning. They have been shown to outperform humans on some complex tasks such as playing video games [1]. In robotics, where the action space is in most cases continuous, actor-critic methods are relying on Q-learning methods to learn the critic [2]. Although Q-learning methods have been extensively studied in the past, little focus has been placed on the way the online neural network is exploring the space of Q functions. Most approaches focus on crafting a loss that would make the agent learn better policies [3]. Here, we offer a thesis that focuses on the position of the online Q neural network in the space of Q functions. The student will first investigate this idea on simple problems before comparing the performance to strong baselines such as DQN or REM [1, 4] on Atari games. Depending on the result, the student might as well get into MuJoCo and compare the results with SAC [2]. The student will be welcome to propose some ideas as well.
Highly motivated students can apply by sending an email to [email protected] . Please attach your CV, a grade sheet and clearly state why you are interested in this topic. Students who have followed the Reinforcement Learning or Robot Learning course will be prioritized.
References [1] Mnih, Volodymyr, et al. "Human-level control through deep reinforcement learning." nature 518.7540 (2015): 529-533. [2] Haarnoja, Tuomas, et al. "Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor." International conference on machine learning. PMLR, 2018. [3] Hessel, Matteo, et al. "Rainbow: Combining improvements in deep reinforcement learning." Proceedings of the AAAI conference on artificial intelligence. Vol. 32. No. 1. 2018. [4] Agarwal, R., Schuurmans, D. & Norouzi, M.. (2020). An Optimistic Perspective on Offline Reinforcement Learning International Conference on Machine Learning (ICML).
This project aims to advance deformable object manipulation by co-optimizing robot gripper morphology and control policies. The project will involve utilizing existing simulation environments for deformable object manipulation [2] and implementing a method to jointly optimize gripper morphology and grasp policies within the simulation.
Required Qualification:
Preferred Qualification:
Application Requirements:
Interested students can apply by sending an e-mail to [email protected] and attaching the required documents mentioned above.
References: [1] Xu, Jie, et al. "An End-to-End Differentiable Framework for Contact-Aware Robot Design." Robotics: Science & Systems. 2021. [2] Huang, Isabella, et al. "DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets." arXiv preprint arXiv:2303.16138 (2023).
In this thesis, you will work on developing an imitation learning algorithm using diffusion models for robotic manipulation tasks, such as the ones in [2, 3, 4], but taking into account the geometry of the task space.
If this sounds interesting, please send an email to [email protected] and [email protected] , and possibly attach your CV, highlighting the relevant courses you took in robotics and machine learning.
What's in it for you:
Requirements:
References: [1] https://arxiv.org/abs/2112.10752 [2] https://arxiv.org/abs/2308.01557 [3] https://arxiv.org/abs/2209.03855 [4] https://arxiv.org/abs/2303.04137 [5] https://arxiv.org/abs/2205.09991
Interested students can apply by sending an E-Mail to [email protected] and attaching the required documents mentioned below.
References: [1] Ho and Ermon. "Generative adversarial imitation learning" [2] Arenz, et al. "Efficient Gradient-Free Variational Inference using Policy Search"
Interested students can apply by sending an E-Mail to [email protected] and attaching the required documents mentioned below.
References: [1] Maki, et al. "Fear of Falling and Postural Performance in the Elderly" [2] Davis et al. "The relationship between fear of falling and human postural control" [3] Ho and Ermon. "Generative adversarial imitation learning"
To tackle this problem we want to utilize Central Pattern Generators (CPGs), which can generate timings for ground contacts for the four feet. The policy gets rewarded for complying with the contact patterns of the CPGs. This leads to a straightforward way of regularizing and steering the policy to a natural gait without posing too strong restrictions on it. We first want to manually find fitting CPG parameters for different gait velocities and later move to learning those parameters in an end-to-end fashion.
Highly motivated students can apply by sending an E-Mail to [email protected] and attaching the required documents mentioned below.
Minimum Qualification:
References: [1] Cheng, Xuxin, et al. "Extreme Parkour with Legged Robots."
Goal of this thesis will be the development and application of a model-based reinforcement learning method on real robots. Your tasks will include: 1. Setting up a simulation environment for deformable object manipulation 2. Utilizing existing models for stress and deformability prediction[1] 3. Implementing a reinforcement learning method to work in simulation and, if possible, on the real robot methods.
If you are interested in this thesis topic and believe you possess the necessary skills and qualifications, please submit your application, including a resume and a brief motivation letter explaining your interest and relevant experience. Please send your application to [email protected].
Required Qualification :
Desired Qualification :
References: [1] Huang, I., Narang, Y., Bajcsy, R., Ramos, F., Hermans, T., & Fox, D. (2023). DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets. arXiv preprint arXiv:2303.16138.
The objective of this thesis is to build upon prior research [2, 3] to establish a connection between Diffusion Models and Imitation Learning. We aim to explore how to exploit Diffusion Models and improve the performance of Imitation learning algorithms that interact with the world.
We welcome highly motivated students to apply for this opportunity by sending an email expressing their interest to Firas Al-Hafez ( [email protected] ) Julen Urain ( [email protected] ). Please attach your letter of motivation and CV, and clearly state why you are interested in this topic and why you are the ideal candidate for this position.
Required Qualification : 1. Strong Python programming skills 2. Basic Knowledge in Imitation Learning 3. Interest in Diffusion models, Reinforcement Learning
Desired Qualification : 1. Attendance of the lectures "Statistical Machine Learning", "Computational Engineering and Robotics" and/or "Reinforcement Learning: From Fundamentals to the Deep Approaches"
References: [1] Song, Yang, and Stefano Ermon. "Generative modeling by estimating gradients of the data distribution." Advances in neural information processing systems 32 (2019). [2] Ho, Jonathan, and Stefano Ermon. "Generative adversarial imitation learning." Advances in neural information processing systems 29 (2016). [3] Garg, D., Chakraborty, S., Cundy, C., Song, J., & Ermon, S. (2021). Iq-learn: Inverse soft-q learning for imitation. Advances in Neural Information Processing Systems, 34, 4028-4039. [4] Chen, R. T., & Lipman, Y. (2023). Riemannian flow matching on general geometries. arXiv preprint arXiv:2302.03660.
Scope: Bachelor / Master thesis Advisor: Joe Watson Added: 2023-10-07 Start: ASAP Topic: In a previous project [1], I found that behavior cloning (BC) was a surprisingly poor baseline for imitating humanoid locomotion. I suspect the issue may lie in the challenges of regularizing high-dimensional regression.
The goal of this project is to investigate BC for humanoid imitation, understand the scaling issues present, and evaluate possible solutions, e.g. regularization strategies from the regression literature.
The project will be building off Google Deepmind's Acme library [2], which has BC algorithms and humanoid demonstration datasets [3] already implemented, and will serve as the foundation of the project.
To apply, email [email protected] , ideally with a CV and transcript so I can assess your suitability.
References: [1] https://arxiv.org/abs/2305.16498 [2] https://github.com/google-deepmind/acme [3] https://arxiv.org/abs/2106.00672
Scope: Bachelor/Master thesis Advisor: Alap Kshirsagar , Dorothea Koert Added: 2023-09-27 Start: ASAP
Topic: In order to operate close to non-experts, future robots require both an intuitive form of instruction accessible to lay users and the ability to react appropriately to a human co-worker. Instruction by imitation learning with probabilistic movement primitives (ProMPs) [1] allows capturing tasks by learning robot trajectories from demonstrations including the motion variability. However, appropriate responses to human co-workers during the execution of the learned movements are crucial for fluent task execution, perceived safety, and subjective comfort. To facilitate such appropriate responsive behaviors in human-robot interaction, the robot needs to be able to react to its human workspace co-inhabitant online during the execution. Also, the robot needs to communicate its motion intent to the human through non-verbal gestures such as eye and head gazes [2][3]. In particular for humanoid robots, combining motions of arms with expressive head and gaze directions is a promising approach that has not yet been extensively studied in related work.
Goals of the thesis:
Highly motivated students can apply by sending an email to [email protected]. Please attach your CV and transcript, and clearly state your prior experiences and why you are interested in this topic.
References : [1] Koert, Dorothea, et al. "Learning intention aware online adaptation of movement primitives." IEEE Robotics and Automation Letters 4.4 (2019): 3719-3726. [2] Admoni, Henny, and Brian Scassellati. "Social eye gaze in human-robot interaction: a review." Journal of Human-Robot Interaction 6.1 (2017): 25-63. [3] Lemasurier, Gregory, et al. "Methods for expressing robot intent for human–robot collaboration in shared workspaces." ACM Transactions on Human-Robot Interaction (THRI) 10.4 (2021): 1-27.
Topic: Tactile sensing is a crucial sensing modality that allows humans to perform dexterous manipulation[1]. In recent years, the development of artificial tactile sensors has made substantial progress, with current models relying on cameras inside the fingertips to extract information about the points of contact [2]. However, robotic tactile sensing is still a largely unsolved topic despite these developments. A central challenge of tactile sensing is the extraction of usable representations of sensor readings, especially since these generally contain an incomplete view of the environment.
Recent model-based reinforcement learning methods like Dreamer [3] leverage latent state-space models to reason about the environment from partial and noisy observations. However, more work has yet to be done to apply such methods to real-world manipulation tasks. Hence, this thesis will explore whether Dreamer can solve challenging real-world manipulation tasks by leveraging tactile information. Initial results suggest that tasks like peg-in-a-hole can indeed be solved with Dreamer in simulation (see figure above), but the applicability of this method in the real world has yet to be shown.
In this work, you will work with state-of-the-art hardware and compute resources on a hot research topic with the option of publishing your work at a scientific conference.
Highly motivated students can apply by sending an email to [email protected]. Please attach a transcript of records and clearly state your prior experiences and why you are interested in this topic.
References [1] 2S Match Anest2, Roland Johansson Lab (2005), https://www.youtube.com/watch?v=HH6QD0MgqDQ [2] Gelsight Inc., Gelsight Mini, https://www.gelsight.com/gelsightmini/ [3] Hafner, D., Lillicrap, T., Ba, J., & Norouzi, M. (2019). Dream to control: Learning behaviors by latent imagination. arXiv preprint arXiv:1912.01603.
Robots are expected to soon leave their factory/laboratory enclosures and operate autonomously in everyday unstructured environments such as households. Semantic information is especially important when considering real-world robotic applications where the robot needs to re-arrange objects as per a set of language instructions or human inputs (as shown in the figure). Many sophisticated semantic segmentation networks exist [1]. However, a challenge when using such methods in the real world is that the semantic classes rarely align perfectly with the language input received by the robot. For instance, a human language instruction might request a ‘glass’ or ‘water’, but the semantic classes detected might be ‘cup’ or ‘drink’.
Nevertheless, with the rise of large language and vision-language models, we now have capable segmentation models that do not directly predict semantic classes but use learned associations between language queries and classes to give us ’open-vocabulary’ segmentation [2]. Some models are especially powerful since they can be used with arbitrary language queries.
In this thesis, we aim to build on advances in 3D vision-based robot manipulation and large open-vocabulary vision models [2] to build a full pick-and-place pipeline for real-world manipulation. We also aim to find synergies between scene reconstruction and semantic segmentation to determine if knowing the object semantics can aid the reconstruction of the objects and, in turn, aid manipulation.
Highly motivated students can apply by sending an e-mail expressing their interest to Snehal Jauhri (email: [email protected]) or Ali Younes (email: [email protected]), attaching your letter of motivation and possibly your CV.
Topic in detail : Thesis_Doc.pdf
Requirements: Enthusiasm, ambition, and a curious mind go a long way. There will be ample supervision provided to help the student understand basic as well as advanced concepts. However, prior knowledge of computer vision, robotics, and Python programming would be a plus.
References: [1] Y. Wu, A. Kirillov, F. Massa, W.-Y. Lo, and R. Girshick, “Detectron2”, https://github.com/facebookresearch/detectron2 , 2019. [2] F. Liang, B. Wu, X. Dai, K. Li, Y. Zhao, H. Zhang, P. Zhang, P. Vajda, and D. Marculescu, “Open-vocabulary semantic segmentation with mask-adapted clip,” in CVPR, 2023, pp. 7061–7070, https://github.com/facebookresearch/ov-seg
Linear approximators in Reinforcement Learning are well-studied and come with an in-depth theoretical analysis. However, linear methods require defining a set of features of the state to be used by the linear approximation. Unfortunately, the feature construction process is a particularly problematic and challenging task. Deep Reinforcement learning methods have been introduced to mitigate the feature construction problem: these methods do not require handcrafted features, as features are extracted automatically by the network during learning, using gradient descent techniques.
In simple reinforcement learning tasks, however, it is possible to use tile coding as features: Tiles are simply a convenient discretization of the state space that allows us to easily control the generalization capabilities of the linear approximator. The objective of this thesis is to design a novel algorithm for automatic feature extraction that generates a set of features similar to tile coding, but that can arbitrarily partition the state space and deal with arbitrary complex state space, such as images. The idea is to combine the feature extraction problem directly with Linear Reinforcement Learning methods, defining an algorithm that is able both to have the theoretical guarantees and good convergence properties of these methods and the flexibility of Deep Learning approaches.
Minimum knowledge
Preferred knowledge
Accepted candidate will
This work considers policies as learnable inductive guidance for shared control. In particular, we use the class of Riemannian motion policies [3] and consider them as differentiable optimization layers [4]. We analyze (i) if RMPs can be pre-trained by learning from demonstrations [5] or reinforcement learning [6] given a specific context; (ii) and subsequently employed seamlessly for human-guided teleoperation thanks to their physically consistent properties, such as stability [3]. We believe this step eliminates the laborious process of constructing complex policies and leads to improved and generalizable shared control architectures.
Highly motivated students can apply by sending an e-mail expressing your interest to [email protected] and [email protected] , attaching your letter of motivation and possibly your CV.
References: [1] Niemeyer, Günter, et al. "Telerobotics." Springer handbook of robotics (2016); [2] Selvaggio, Mario, et al. "Autonomy in physical human-robot interaction: A brief survey." IEEE RAL (2021); [3] Cheng, Ching-An, et al. "RMP flow: A Computational Graph for Automatic Motion Policy Generation." Springer (2020); [4] Jaquier, Noémie, et al. "Learning to sequence and blend robot skills via differentiable optimization." IEEE RAL (2022); [5] Mukadam, Mustafa, et al. "Riemannian motion policy fusion through learnable lyapunov function reshaping." CoRL (2020); [6] Xie, Mandy, et al. "Neural geometric fabrics: Efficiently learning high-dimensional policies from demonstration." CoRL (2023).
This work focuses on arbitration between the user and assistive policy, i.e., shared autonomy. Various works allow the user to influence the dynamic behavior explicitly and, therefore, could not satisfy stability guarantees [3]. We pursue the idea of formulating arbitration as a trajectory-tracking problem that implicitly considers the user's desired behavior as an objective [4]. Therefore, we extend the work of Hansel et al. [5], who employed probabilistic inference for policy blending in robot motion control. The proposed method corresponds to a sampling-based online planner that superposes reactive policies given a predefined objective. This method enables the user to implicitly influence the behavior without injecting energy into the system, thus satisfying stability properties. We believe this step leads to an alternative view of shared autonomy with an improved and generalizable framework.
Highly motivated students can apply by sending an e-mail expressing your interest to [email protected] or [email protected] , attaching your letter of motivation and possibly your CV.
References: [1] Niemeyer, Günter, et al. "Telerobotics." Springer handbook of robotics (2016); [2] Selvaggio, Mario, et al. "Autonomy in physical human-robot interaction: A brief survey." IEEE RAL (2021); [3] Dragan, Anca D., and Siddhartha S. Srinivasa. "A policy-blending formalism for shared control." IJRR (2013); [4] Javdani, Shervin, et al. "Shared autonomy via hindsight optimization for teleoperation and teaming." IJRR (2018); [5] Hansel, Kay, et al. "Hierarchical Policy Blending as Inference for Reactive Robot Control." IEEE ICRA (2023).
In this thesis, we want to investigate the effectiveness of vision-based tactile sensors for solving dynamic tasks (igniting matches). Since the whole task is difficult to simulate, we directly collect real-world data to learn policies from the human demonstrations [2,3]. We believe that this work is an important step towards more advanced tactile skills.
Highly motivated students can apply by sending an e-mail expressing your interest to [email protected] and [email protected] , attaching your letter of motivation and possibly your CV.
References: [1] https://www.youtube.com/watch?v=HH6QD0MgqDQ [2] Learning Compliant Manipulation through Kinesthetic and Tactile Human-Robot Interaction; Klas Kronander and Aude Billard. [3] https://www.youtube.com/watch?v=jAtNvfPrKH8
Within this thesis, the problems of learning from observations and efficient exploration in overactued systems should be addressed. Regarding the former, novel methods incorporating inverse dynamics models into the inverse reinforcement learning problem [1] should be adapted and applied. To address the problem of efficient exploration in overactuted systems, two approaches should be implemented and compared. The first approach uses a handcrafted action space, which disables and modulates actions in different phases of the gait based on biomechanics knowledge [2]. The second approach uses a stateful policy to incorporate an inductive bias into the policy [3]. The thesis will be supervised in conjunction with Guoping Zhao ( [email protected] ) from the locomotion lab.
Highly motivated students can apply by sending an e-mail expressing their interest to Firas Al-Hafez ( [email protected] ), attaching your letter of motivation and possibly your CV. Try to make clear why you would like to work on this topic, and why you would be the perfect candidate for the latter.
Required Qualification : 1. Strong Python programming skills 2. Knowledge in Reinforcement Learning 3. Interest in understanding human locomotion
Desired Qualification : 1. Hands-on experience on robotics-related RL projects 2. Prior experience with different simulators 3. Attendance of the lectures "Statistical Machine Learning", "Computational Engineering and Robotics" and/or "Reinforcement Learning: From Fundamentals to the Deep Approaches"
References: [1] Al-Hafez, F.; Tateo, D.; Arenz, O.; Zhao, G.; Peters, J. (2023). LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning, International Conference on Learning Representations (ICLR). [2] Ong CF; Geijtenbeek T.; Hicks JL; Delp SL (2019) Predicting gait adaptations due to ankle plantarflexor muscle weakness and contracture using physics-based musculoskeletal simulations. PLoS Computational Biology [3] Srouji, M.; Zhang, J:;Salakhutdinow, R. (2018) Structured Control Nets for Deep Reinforcement Learning, International Conference on Machine Learning (ICML)
Goals of the thesis
Desired Qualifications
Literature [1] Lederman and Klatzky, “Haptic perception: a tutorial” [2] Seminara et al., “Active Haptic Perception in Robots: A Review” [3] Chu et al., “Using robotic exploratory procedures to learn the meaning of haptic adjectives” [4] Kerzel et al., “Neuro-Robotic Haptic Object Classification by Active Exploration on a Novel Dataset”
Highly motivated students can apply by sending an e-mail expressing your interest to [email protected] , attaching your letter of motivation and possibly your CV.
References: [1] Learn2Assemble with Structured Representations and Search for Robotic Architectural Construction; Niklas Funk et al. [2] Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery; Niklas Funk et al. [3] Structured agents for physical construction; Victor Bapst et al.
The proposed architecture can be broken down into the following sub-tasks: 1. Multi-object 6D pose estimation from video: Identify the object 6D poses in each video frame to generate the object trajectories 2. Action segmentation from video: Classify the action being performed in each video frame 3. High-level task representation learning: Learn the sequence of robotic movement primitives with the associated object poses such that the robot completes the demonstrated task 4. Low-level movement primitives: Create a database of low-level robotic movement primitives which can be sequenced to solve the long-horizon task
Desired Qualification: 1. Strong Python programming skills 2. Prior experience in Computer Vision and/or Robotics is preferred
During the project, we will create a large-scale dataset of videos of humans demonstrating industrial assembly sequences. The dataset will contain information of the 6D poses of the objects, the hand and body poses of the human, the action sequences among numerous other features. The dataset will be open-sourced to encourage further research on VIL.
[1] F. Sener, et al. "Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities". CVPR 2022. [2] P. Sharma, et al. "Multiple Interactions Made Easy (MIME) : Large Scale Demonstrations Data for Imitation." CoRL, 2018.
References: [1] Xu, Anqi, and Gregory Dudek. "Optimo: Online probabilistic trust inference model for asymmetric human-robot collaborations." ACM/IEEE HRI, IEEE, 2015; [2] Kwon, Minae, et al. "When humans aren’t optimal: Robots that collaborate with risk-aware humans." ACM/IEEE HRI, IEEE, 2020; [3] Chen, Min, et al. "Planning with trust for human-robot collaboration." ACM/IEEE HRI, IEEE, 2018; [4] Poole, Ben et al. “On variational bounds of mutual information”. ICML, PMLR, 2019.
Highly motivated students can apply by sending an e-mail expressing your interest to [email protected] , attaching your a letter of motivation and possibly your CV.
Scope: Master Thesis Advisor: Dorothea Koert , Joni Pajarinen Added: 2021-06-08 Start: ASAP
The ability to model the beliefs and goals of a partner is an essential part of cooperative tasks. While humans develop theory of mind models for this aim already at a very early age [1] it is still an open question how to implement and make use of such models for cooperative robots [2,3,4]. In particular, in shared workspaces human robot collaboration could potentially profit from the use of such models e.g. if the robot can detect and react to planned human goals or a human's false beliefs during task execution. To make such robots a reality, the goal of this thesis is to investigate the use of first and second order mental models in a cooperative manipulation task under partial observability. Partially observable Markov decision processes (POMDPs) and interactive POMDPs (I-POMDPs) [5] define an optimal solution to the mental modeling task and may provide a solid theoretical basis for modelling. The thesis may also compare related approaches from the literature and setup an experimental design for evaluation with the bi-manual robot platform Kobo.
Highly motivated students can apply by sending an e-mail expressing your interest to [email protected] attaching your CV and transcripts.
References:
ml. Beginners please see learnmachinelearning
Hello, I'm currently a university student entering into their Master's in Computer Science. I am heavily interested in the field of Deep Learning and in particular, Computer Vision.
For my Bachelor's dissertation I worked on the classification of Alzheimer's Disease using MRI scans, and I heavily enjoyed the process. I find the field of computer vision interesting, not just due to the applications, but due to interest in the methods and algorithms behind it. However, the main issue that I had with my Bachelor's project is that ultimately it felt quite ameteurish and not as cutting edge as I would want - I want to work on something that is somewhat new, with the possibility of creating a truly great paper.
I've had many ideas of different routes to go down, however I'm not fully sure what would be the best route for me, and if there's areas in the field of computer vision I am neglecting. In particular I have been interested in applications of Visual Transformers as I've recently learnt about them, however I am interested in any project or ideas within the field of computer vision, regardless of if they employ Visual Transformers or not.
Some ideas I was considering:
- 3D Object Generation using Visual Transformers: Would use Shapenet dataets to generate 3d objects, employing the visual transformer architecture. This could be difficult (dataset size constraints), however, my supervisor has experience with her PHD students working on this
- Brain MRI Upscaling using Visual Transformers: Self explanatory. I was inspired for this project when working on my bachelor's thesis. My main concerns with this is that it could be very out of my depth and too much for me to do in one year.
- Bone fracture classification & segmentation (custom dataset): As part of my supervisor's research, she has commissioned a custom bone fracture dataset, which would have a segmentation and classification task. This could be implemented with a number of different algorithms. This is very interesting to me as working with such an exclusive dataset is very cool, however I'm not sure if this is the part of the field I'm most interested in - however it is likely a more doable project.
This list is limited by what I know, so if there's any fields that are on the cutting edge and could be interesting, please do leave a comment. Thanks!
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Each thesis idea includes an introduction, which presents a brief overview of the topic and the research objectives. The ideas provided are related to different areas of machine learning and deep learning, such as computer vision, natural language processing, robotics, finance, drug discovery, and more.
A list of completed theses and new thesis topics from the Computer Vision Group. ... Manual phenotyping is highly time-consuming; therefore, many computer vision and machine learning based methods have been proposed in the past years to perform this task automatically based on images of the plants. However, the publicly available datasets (in ...
Thesis topics could focus on the enhancement of machine perception through computer vision and sensor fusion, the development of more sophisticated AI-driven decision frameworks, or ethical considerations in the deployment of autonomous systems.
How to Contact Faculty for IW/Thesis Advising. Send the professor an e-mail. When you write a professor, be clear that you want a meeting regarding a senior thesis or one-on-one IW project, and briefly describe the topic or idea that you want to work on. ... Computer Vision, Machine Learning. Independent Research Topics: 3D Vision; Object ...
If you're new or learning computer vision, these projects will help you learn a lot. 1. Edge & Contour Detection. If you're new to computer vision, this project is a great start. CV applications detect edges first and then collect other information. There are many edge detection algorithms, and the most popular is the Canny edge detector ...
2. Adversarial Examples that Fool both Computer Vision and Time-Limited Humans, by Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian Goodfellow, Jascha Sohl-Dickstein Original Abstract. Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus ...
The thesis discusses a system named Myriad, a distributed computing framework for Machine Vision applications. Myriad is composed components, such as image processing engines and equipment controllers, which behave as enhanced web servers and communicate using simple HTTP requests.
Research Areas Research Areas Our research group is working on a range of topics in Computer Vision and Image Processing, many of which are using Artifical Intelligence. Computer Vision is about interpreting images. More specifically the goal is to infer properties of the observed world from an image or a collection of images. Our work combines a range of mathematical domains including ...
widely used in machine vision problems. Today, the use of deep machine learning is a priority in the problems of classification and tracking, which is confirmed by the results of competitions at Kaggle (www.kaggle.com) and Image.net. The most popular neural network used in classification tasks is the convolutional neural network (CNN).
Machine Vision Thesis Topics - Free download as PDF File (.pdf), Text File (.txt) or read online for free. The document discusses selecting thesis topics in machine vision and the assistance available. It notes that machine vision is a rapidly evolving field with many possibilities for research but narrowing options can be challenging. It then describes how the company HelpWriting.net ...
With respect to undergraduate thesis topics looking at Computer Vision applications is one place to start. The OpenCV library is another. And talking to potential supervisors at your university is also a good idea. With respect to PhD thesis topics, it's important to take into consideration what the fields of expertise of your potential ...
Computer Vision. Computer vision is the science and technology of teaching a computer to interpret images and video as well as a typical human. Technically, computer vision encompasses the fields of image/video processing, pattern recognition, biological vision, artificial intelligence, augmented reality, mathematical modeling, statistics, probability, optimization, 2D sensors, and photography.
Below you can see the thesis topics for 2022-2023. We offer 3 different thesis formats: - Format 1 : Regular thesis (fully supervised by KU Leuven) - Format 2 : Thesis in cooperation with a company (supervised by KU Leuven and the company) - Format 3 : Thesis with a company project within a company (supervised by the company) NOTE: Additional ...
The deadline for submitting this form is 30th of October, 2023. (!!) Below you can see the thesis topics for 2023-2024. We offer 3 different thesis formats: - Format 1 : Regular thesis (fully supervised by KU Leuven) - Format 2 : Thesis in cooperation with a company (supervised by KU Leuven and the company) - Format 3 : Thesis with a company ...
2. Intelligent Internet Ads Generation (Classification) This is one of the most interesting topics for me. The reason is because the revenue generated or expended by ads campaign depends not just on the volume of the ads, but also on the relevance of the ads. Therefore it is possible to increase revenue and reduce spending by developing a ...
Thesis Topics. This list includes topics for potential bachelor or master theses, guided research, projects, seminars, and other activities. Search with Ctrl+F for desired keywords, e.g. 'machine learning' or others. PLEASE NOTE: If you are interested in any of these topics, click the respective supervisor link to send a message with a ...
For example, perhaps take a walk through a park, take pictures of all of the plants of one species, and see if you can use machine learning that can figure out things like degree of branching, age, pest prevalence, etc., from images of the plant. Undergrad ML TA. I suggest you find a researcher at your university, preferably in biology ...
Machine Vision Thesis - Free download as PDF File (.pdf), Text File (.txt) or read online for free. This document discusses the challenges of writing a thesis on machine vision and provides suggestions for getting help. It notes that machine vision is a complex, specialized topic requiring extensive research, data analysis, and expertise.
Your thesis could be based on UI and computer vision as they really are changing the land scape and help an open source project in the process. We also want to add image homography and feature tracking to the next release (1.3). We have quick release cycles as well (about every 3 months).
Thesis focus with in the field of Machine Vision that is used for optical online quality inspection of the cutting knifes in a wood chipper that is also the title of the thesis.The work is focused on measuring the quality of the cutting knifes that are moving with the speed of 45 m/s in a real time wood chipper.
This dissertation comprises several studies addressing supervised learning problems where the supervision is imperfect. Firstly, we investigate the margin conditions in active learning. Active learning is characterized by its special mechanism where the learner can sample freely over the feature space and exploit mostly the limited labeling budget by querying the most informative labels.
In this thesis, we aim to build on advances in 3D vision-based robot manipulation and large open-vocabulary vision models [2] to build a full pick-and-place pipeline for real-world manipulation. We also aim to find synergies between scene reconstruction and semantic segmentation to determine if knowing the object semantics can aid the ...
I am struggling to find a research topic for my masters thesis in Artificial Intelligence (computer vision topics). With a plethora of research already published and requirement of novelty, it's a real struggle finding a proper and practical research topic. Some topics I'v currently shortlisted are facial expression / emotion recognition ...
Project. Hello, I'm currently a university student entering into their Master's in Computer Science. I am heavily interested in the field of Deep Learning and in particular, Computer Vision. For my Bachelor's dissertation I worked on the classification of Alzheimer's Disease using MRI scans, and I heavily enjoyed the process.