projects
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====== Proposed Projects for Summer 2015 ====== | ====== Proposed Projects for Summer 2015 ====== | ||
- | ====== | + | ======Projects for Summer 2015 ====== |
\\ | \\ | ||
------------ | ------------ | ||
+ | \\ | ||
+ | |||
+ | =======Scouting Stone Project====== | ||
+ | **Supervisor**: | ||
+ | |||
+ | **Short Description**: | ||
+ | A unique 4th year project... | ||
+ | Interested in robotics, robot-human interaction, | ||
+ | |||
+ | The Scouting Stone Project is a robotic work of art which fuses sensory tracking of people in public space, autonomous exploratory movement, and stone sculpture. A large, hollow boulder housing a robot base will move through its environment based on live data collected about the cumulative travels of people in the robot’s surroundings. This data — obtained through a network of placed video cameras — will be used to identify the common lanes of travel that most people follow in the space. The robot will integrate this information and then move blocking a different area which will in turn disrupt the motion of people in the environment. | ||
+ | The intended presentation of this work is in public where the work can engage with an unsuspecting public, and the intended movement of the robot will be glacially slow so that the movement of the robot is only apparent after repeated visits to the space. The robot has numerous challenges for its design including the ability to carry heavy weight, highly articulate motion, object avoidance, data analysis, power supply management, and object stability among others. The ultimate goal of this work is to exhibit it across Canada both in Artist Run Centres and in public venues. This is a great opportunity to work on what will be a highly visible project which people from all walks of life will see and interact with. | ||
+ | |||
+ | If you are interested in working on this project, as either as part of a 4th year thesis in Computer Science, or as a capstone engineering (ENG4000) project please contact Michael Jenkin (jenkin@cse.yorku.ca). | ||
+ | |||
+ | \\ | ||
+ | ----------- | ||
\\ | \\ | ||
+ | =====Virtual Worlds for Immersive Visual Displays===== | ||
+ | |||
+ | **Supervisor**: | ||
+ | |||
+ | **Desired background**: | ||
+ | |||
+ | **Short Description**: | ||
+ | \\ | ||
+ | ------------ | ||
+ | \\ | ||
+ | |||
+ | =====Locomotion Interface for Immersive Visual Displays===== | ||
+ | |||
+ | **Supervisor**: | ||
+ | |||
+ | **Desired background**: | ||
+ | |||
+ | Short Description: | ||
+ | \\ | ||
+ | ------------ | ||
+ | \\ | ||
+ | |||
+ | =====Implementation of a Monte Carlo simulator for heat transport problems===== | ||
+ | |||
+ | **Supervisor**: | ||
+ | |||
+ | **Desired background**: | ||
+ | |||
+ | **Short Description**: | ||
+ | \\ | ||
+ | ------------ | ||
+ | \\ | ||
+ | |||
+ | =====Ranking salient contours for object segmentation===== | ||
+ | **Supervisors**: | ||
+ | |||
+ | The ultimate goal of Computer Vision is to enable a machine to see and understand an image or scene, at least as well as a human. An important step towards this goal is to partition an image into regions, each corresponding to an object or entity. This is referred to as image segmentation in the computer vision community. Segmentation is an important step towards image understanding and can enhance the performance of many applications such as object detection, object tracking, surveillance, | ||
+ | |||
+ | 1- Finding a set of line segments in the image that represent color or texture discontinuities (edges) | ||
+ | 2- Representing the line segments in a sparse graph model | ||
+ | 3- Extracting simple closed contours (cycles) in the above graph | ||
+ | 4- Ranking the set of closed contours for output | ||
+ | |||
+ | |||
+ | The goal of the proposed project is to research and develop an automated method for ranking object hypotheses (stage 4 in the above framework). Given a set of closed contours found in an image, the problem under consideration is to rank these contours for output, based on how well they bound the salient object in the image. This ranking stage is important since it defines the final output of the object segmentation method. | ||
+ | In particular, working with a senior graduate student or postdoctoral fellow, the successful applicant will: | ||
+ | |||
+ | 1. Conduct a literature review of the state of the art ranking methods. These methods can include general ranking methods as well as those specifically designed to perform best for top ranked output such as those used for web searches (e.g. Google or Bing). | ||
+ | |||
+ | 2. Develop and implement an efficient method for ranking closed contours. | ||
+ | |||
+ | 3. Identify the quantity and distribution of data required for training above method, and prepare training data accordingly. | ||
+ | |||
+ | 4. Compare ranking results with the currently available (and implemented) ranking method. | ||
+ | |||
+ | Other possible extensions to this project can include: | ||
+ | |||
+ | 5. Study of ranking feature, and their inferential power | ||
+ | |||
+ | 6. Study of the distribution of contour hypotheses and their power in representing whole objects or parts | ||
+ | |||
+ | Required skills: | ||
+ | |||
+ | 1. Good programming skills | ||
+ | |||
+ | 2. Good math skills | ||
+ | |||
+ | 3. Knowledge of MATLAB programming language | ||
+ | |||
+ | For more information about the lab, visit www.elderlab.yorku.ca. | ||
+ | |||
+ | \\ | ||
+ | ------------ | ||
+ | \\ | ||
+ | |||
+ | =====Hierarchical grouping of salient paths===== | ||
+ | **Supervisors**: | ||
+ | |||
+ | The ultimate goal of Computer Vision is to enable a machine to see and understand an image or scene, at least as well as a human. An important step towards this goal is to partition an image into regions, each corresponding to an object or entity. This is referred to as image segmentation in the computer vision community. Segmentation is an important step towards image understanding and can enhance the performance of many applications such as object detection, object tracking, surveillance, | ||
+ | |||
+ | |||
+ | 1- Finding a set of line segments in the image that represent color or texture discontinuities (edges) | ||
+ | |||
+ | 2- Representing the line segments in a sparse graph model | ||
+ | |||
+ | 3- Extracting simple closed contours (cycles) in the above graph | ||
+ | |||
+ | 4- Ranking the set of closed contours for output | ||
+ | |||
+ | |||
+ | |||
+ | The goal of the proposed project is to research and develop a hierarchical method for extracting object hypotheses (stage 3 in the above framework). Given a set of open paths of arbitrary length in the graph, the problem under consideration is to group them to find plausible closed contours bounding the salient object in the image. This grouping stage is important, since i) the performance of the final product depends on the quality of the hypotheses formed in this stage, and ii) lowering the computational complexity of this stage results in significant speed up of the solution. | ||
+ | In particular, working with a senior graduate student or postdoctoral fellow, the successful applicant will: | ||
+ | |||
+ | 1. Run our current method for collecting open paths at different lengths. | ||
+ | |||
+ | 2. Develop a method for finding intersections of paths at a certain length (or up to a certain length) with lower complexity than simple line intersection methods | ||
+ | |||
+ | 3. Analyze the open paths for completeness- given paths of a certain length (or up to a certain length), what is the lowest achievable error of object contour hypotheses that can be formed by them? | ||
+ | |||
+ | 4. Develop a method for combining short open paths for obtaining longer ones, or closed contours | ||
+ | |||
+ | 5. Compare results with the currently available (and implemented) greedy method. | ||
+ | |||
+ | |||
+ | Required skills: | ||
+ | |||
+ | 1. Good programming skills | ||
+ | |||
+ | 2. Good math skills | ||
+ | |||
+ | 3. Knowledge of MATLAB programming language | ||
+ | |||
+ | For more information about the lab, visit www.elderlab.yorku.ca. | ||
+ | \\ | ||
+ | ------------ | ||
+ | \\ | ||
+ | |||
+ | =====Using neural models for natural language processing===== | ||
+ | |||
+ | **Supervisor**: | ||
+ | |||
+ | **Required Background**: | ||
+ | |||
+ | Deep neural networks (DNNs) have achieved huge successes in many pattern classification tasks, including | ||
+ | speech recognition and computer vision. In this project, the student will study DNN-based models for an | ||
+ | interesting natural language processing (NLP) task, selecting from word embedding, language modelling, | ||
+ | paraphrase detection or others. More specifically, | ||
+ | and performance evaluation for the selected NLP task using some standard corpora. | ||
+ | \\ | ||
+ | ----------------------------- | ||
+ | \\ | ||
=====Data Visualization Project===== | =====Data Visualization Project===== | ||
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**Required Background**: | **Required Background**: | ||
- | We have a large project in data visualization projects and may have a couple of projects for 4080 this summer. If interested in learning more details about what a project in this area would involve, please contact Professor Gryz by emai. jarek at eecs.yorku.ca | + | We have a large project in data visualization projects and may have a couple of projects for 4080 this summer. |
+ | |||
+ | Skydive - Sample projects: | ||
+ | |||
+ | 1. Skydive’s modules API specification - refactoring of existing, and specification of new interfaces for communication between Skydive’s modules | ||
+ | |||
+ | 2. 3D viewer - improving users experiece with 3D visualizations (e.g. panning, better zooming, translation, | ||
+ | |||
+ | 3. Data wizard - to guide a user from defining a connection to a data base, through selecting measures, to visualization - a kind of pyramid configuration module | ||
+ | |||
+ | 4. Support for additional channels (e.g. normals map, specular map) | ||
+ | |||
+ | 5. User’s interface - more options for users (e.g. defining aggregates, selecting different measures, automatic texture generation) | ||
+ | |||
+ | 6. Pyramid generation support for different data bases (e.g. Empress DB, PostgreSQL, MySQL) | ||
+ | |||
+ | Technologies: | ||
+ | If interested in learning more details about what a project in this area would involve, please contact Professor Gryz by emai. jarek at eecs.yorku.ca | ||
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+ | |||
=====Mining Software Repositories Data===== | =====Mining Software Repositories Data===== | ||
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- | ** More project Proposals | + | ** Additional proposals |
- | + | You may also wish to look at [[former]] for projects proposed in Winter 2015. | |
- | Meantime you may wish to look at [[former]] for projects proposed in Winter 2015. | + | |
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projects.txt · Last modified: 2015/04/24 03:09 by pd