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| {{vigil:research:ongoing_projects_images:image_based_anim.png?150}} \\ | {{vigil:research:ongoing_projects_images:image_based_anim.png?150}} \\ | ||
| **[[http://www.cse.iitb.ac.in/graphics/~ambareesha/website/index.html|Image Based Animations]] ([[http://www.cse.iitb.ac.in/~biswarup/|Biswarup Choudhury]] and [[http://www.cse.iitb.ac.in/~ambareesha|Ambareesha Raghothaman]])**:\\ Image-based rendering techniques enable the synthesis of novel views of a scene directly from input images, unlike traditional computer graphics techniques, where the 3D geometry and surface reflectance properties of the surfaces in the scene need to be specified. It is very time-consuming to specify a realistic 3D model. Also, accurate specification of reflectance properties of materials in the scene is difficult. \\ Our endeavour is to take the idea of Image Based Rendering a step further, by creating motion using images. Specifically given a set of images of a static object, under a carefully chosen set of "basis lighting configurations", and an arbitrary environment in the from of images again,our algorithm creates realistic motion along any arbitrary path composed realistically under novel illumination configurations. | **[[http://www.cse.iitb.ac.in/graphics/~ambareesha/website/index.html|Image Based Animations]] ([[http://www.cse.iitb.ac.in/~biswarup/|Biswarup Choudhury]] and [[http://www.cse.iitb.ac.in/~ambareesha|Ambareesha Raghothaman]])**:\\ Image-based rendering techniques enable the synthesis of novel views of a scene directly from input images, unlike traditional computer graphics techniques, where the 3D geometry and surface reflectance properties of the surfaces in the scene need to be specified. It is very time-consuming to specify a realistic 3D model. Also, accurate specification of reflectance properties of materials in the scene is difficult. \\ Our endeavour is to take the idea of Image Based Rendering a step further, by creating motion using images. Specifically given a set of images of a static object, under a carefully chosen set of "basis lighting configurations", and an arbitrary environment in the from of images again,our algorithm creates realistic motion along any arbitrary path composed realistically under novel illumination configurations. | ||
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| ===== Rendering ===== | ===== Rendering ===== | ||
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| {{vigil:research:ongoing_projects_images:xyzdragon.png?150}} \\ | {{vigil:research:ongoing_projects_images:xyzdragon.png?150}} \\ | ||
| ** [[http://www.cse.iitb.ac.in/~kashyap/rtrt/index.html|Realtime Raytracing of Point Based Models]]([[http://www.cse.iitb.ac.in/~kashyap/|Sriram Kashyap]], [[http://www.cse.iitb.ac.in/~rhushabh/|Rhushabh Goradia]])**:\\ Point-based representations of objects have been recently used as an alternative to triangle-based representations. Starting with a z-buffer style rendering, recent work has progressed to rendering point based models using raycasting, and more general raytracing, for producing photo-realistic illumination effects such as shadows and refraction. Our work advances the state of the art by showing how to render large models (several million points) in real time. We use a GPU to simultaneously provide effects involving shadows, reflection, and refraction in real time. Our system relies on an efficient way of storing and accessing hierarchical data structures on the GPU, as well as novel techniques to handle ray intersections with point based entities. | ** [[http://www.cse.iitb.ac.in/~kashyap/rtrt/index.html|Realtime Raytracing of Point Based Models]]([[http://www.cse.iitb.ac.in/~kashyap/|Sriram Kashyap]], [[http://www.cse.iitb.ac.in/~rhushabh/|Rhushabh Goradia]])**:\\ Point-based representations of objects have been recently used as an alternative to triangle-based representations. Starting with a z-buffer style rendering, recent work has progressed to rendering point based models using raycasting, and more general raytracing, for producing photo-realistic illumination effects such as shadows and refraction. Our work advances the state of the art by showing how to render large models (several million points) in real time. We use a GPU to simultaneously provide effects involving shadows, reflection, and refraction in real time. Our system relies on an efficient way of storing and accessing hierarchical data structures on the GPU, as well as novel techniques to handle ray intersections with point based entities. | ||
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| ===== Computer Aided Diagnostics ===== | ===== Computer Aided Diagnostics ===== | ||
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| {{vigil:research:ongoing_projects_images:oca150.png?150}} \\ | {{vigil:research:ongoing_projects_images:oca150.png?150}} \\ | ||
| ** [[http://www.cse.iitb.ac.in/~andrew/index.html|Hierarchical Normalized Cuts: Unsupervised Segmentation of Vascular Biomarkers from Ovarian Cancer Tissue Microarrays]] (Andrew Janowczyk)** \\ Research has shown that tumor vascular markers (TVMs) may serve as potential OCa biomarkers for prognosis prediction. The ability to quickly and quantitatively estimate vascular stained regions may yield an image based metric linked to disease survival and outcome. In this paper, we present a general, robust and efficient unsupervised segmentation algorithm, termed Hierarchical Normalized Cuts (HNCut), and show its application in precisely quantifying the presence and extent of a TVM on OCa TMAs. The strength of HNCut is in the use of a hierarchically represented data structure that bridges the mean shift (MS) and the normalized cuts (NCut) algorithms. This allows HNCut to efficiently traverse a pyramid of the input image at various color resolutions, efficiently and accurately segmenting the object class of interest (in this case ESM-1 vascular stained regions) by simply annotating half a dozen pixels belonging to the target class. | ** [[http://www.cse.iitb.ac.in/~andrew/index.html|Hierarchical Normalized Cuts: Unsupervised Segmentation of Vascular Biomarkers from Ovarian Cancer Tissue Microarrays]] (Andrew Janowczyk)** \\ Research has shown that tumor vascular markers (TVMs) may serve as potential OCa biomarkers for prognosis prediction. The ability to quickly and quantitatively estimate vascular stained regions may yield an image based metric linked to disease survival and outcome. In this paper, we present a general, robust and efficient unsupervised segmentation algorithm, termed Hierarchical Normalized Cuts (HNCut), and show its application in precisely quantifying the presence and extent of a TVM on OCa TMAs. The strength of HNCut is in the use of a hierarchically represented data structure that bridges the mean shift (MS) and the normalized cuts (NCut) algorithms. This allows HNCut to efficiently traverse a pyramid of the input image at various color resolutions, efficiently and accurately segmenting the object class of interest (in this case ESM-1 vascular stained regions) by simply annotating half a dozen pixels belonging to the target class. | ||
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| {{vigil:research:completed_projects_images:motion_synthesis.png|}} \\ | {{vigil:research:completed_projects_images:motion_synthesis.png|}} \\ | ||
| **[[http://www.it.iitb.ac.in/~svs/publications%20web/icvgip04/icvgip04.html|Synthesizing New Walk and Climb Motions from a Single Motion Captured Walk Sequence]] ([[http://www.it.iitb.ac.in/~svs/|Shrinath Shanbaug]])**:\\ We describe a method to dynamically synthesize believable, variable stride, and variable foot lift motions for human walks and climbs. Our method is derived from a single motion captured walk sequence, and is guided by a simple kinematic walk model. The method allows control in the form of stride and lift parameters. It generates a range of variations while maintaining individualistic nuances of the captured performance. | **[[http://www.it.iitb.ac.in/~svs/publications%20web/icvgip04/icvgip04.html|Synthesizing New Walk and Climb Motions from a Single Motion Captured Walk Sequence]] ([[http://www.it.iitb.ac.in/~svs/|Shrinath Shanbaug]])**:\\ We describe a method to dynamically synthesize believable, variable stride, and variable foot lift motions for human walks and climbs. Our method is derived from a single motion captured walk sequence, and is guided by a simple kinematic walk model. The method allows control in the form of stride and lift parameters. It generates a range of variations while maintaining individualistic nuances of the captured performance. | ||
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| ===== Projector Camera Systems ===== | ===== Projector Camera Systems ===== | ||
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| {{vigil:research:completed_projects_images:nilesh.png|}} \\ | {{vigil:research:completed_projects_images:nilesh.png|}} \\ | ||
| **[[http://trellis.cse.iitb.ac.in/~nekhil/website/BTP_stage2/BTP%20report%20version%203.0.pdf|Projector-camera based Solutions for Simulation System]] (Nilesh Heda)**: \\ Projector based display systems are widely used as they offer an attractive combination of dense pixels over large regions. Traditionally, the projector is used for presentation purposes on single planar surface. However, it can be used for displaying on multi planar irregular surfaces. In this work, we discuss methods to use a projector along with a camera for displaying on irregular surfaces using projector-camera homography. In particular we would like to develop a shooting-range simulator system, using projector-camera system and laser pointer based interaction. | **[[http://trellis.cse.iitb.ac.in/~nekhil/website/BTP_stage2/BTP%20report%20version%203.0.pdf|Projector-camera based Solutions for Simulation System]] (Nilesh Heda)**: \\ Projector based display systems are widely used as they offer an attractive combination of dense pixels over large regions. Traditionally, the projector is used for presentation purposes on single planar surface. However, it can be used for displaying on multi planar irregular surfaces. In this work, we discuss methods to use a projector along with a camera for displaying on irregular surfaces using projector-camera homography. In particular we would like to develop a shooting-range simulator system, using projector-camera system and laser pointer based interaction. | ||
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| ===== Signal Processing of Medical Diagnostic Data ===== | ===== Signal Processing of Medical Diagnostic Data ===== | ||
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| {{vigil:research:completed_projects_images:ayurveda.png|}} \\ | {{vigil:research:completed_projects_images:ayurveda.png|}} \\ | ||
| **[[http://www.cse.iitb.ac.in/~ajjoshi/jamnagar.ppt|5th International Seminar on Ayurvedic education, research and drug standardization]] ([[http://www.cse.iitb.ac.in/~ajjoshi/|Aniruddha Joshi]])**:\\ The "Nadi" or pulse has been used as a fundamental tool for diagnosis in "Ayurveda". We provided a systematic measurement scheme to establish an objective diagnosis. The pulse waveforms show different rhythms, intensities, frequncy contents in normals and disorders considered, and thus is capable of classified by contempory machine learning algorithms. | **[[http://www.cse.iitb.ac.in/~ajjoshi/jamnagar.ppt|5th International Seminar on Ayurvedic education, research and drug standardization]] ([[http://www.cse.iitb.ac.in/~ajjoshi/|Aniruddha Joshi]])**:\\ The "Nadi" or pulse has been used as a fundamental tool for diagnosis in "Ayurveda". We provided a systematic measurement scheme to establish an objective diagnosis. The pulse waveforms show different rhythms, intensities, frequncy contents in normals and disorders considered, and thus is capable of classified by contempory machine learning algorithms. | ||
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| {{vigil:research:ongoing_projects_images:meet.jpg?150}} \\ | {{vigil:research:ongoing_projects_images:meet.jpg?150}} \\ | ||
| ** [[http://www.cse.iitb.ac.in/~sahu/btp|Intelligent Video Capturing ]]([[http://www.cse.iitb.ac.in/~sahu|Ashutosh Sahu]])**:\\ Videoconferencing has proved to be an eff ective tool for interaction between geographically distributed work teams. It has supported the transmission and recording of meetings for many years. However, such recordings of meetings are often monotonous and tedious to watch. Quite often, just one camera is used to capture video. Without multiple views, the users at the other end may lack the visual information needed to understand the meeting in its full context. Moreover, the shot does not change often, unless managed by hired professionals. This motivates the design of an automatic meeting capture system that uses cost-eff ective equipment and unobtrusive tracking, for capturing videos both in real-time and offline environments. The camera control algorithm running the system controls shot selection and handles errors, in both cases. Unlike the real-time environment, the offline environment off ers a lot of flexibility and scope to intelligently switch between various shots captured by various cameras. Audio feed has been the tried-and-tested method to detect speakers in the meeting. The goal is to explore the extent to which vision techniques using training and machine learning concepts can be applied for the purpose of detection of speakers. | ** [[http://www.cse.iitb.ac.in/~sahu/btp|Intelligent Video Capturing ]]([[http://www.cse.iitb.ac.in/~sahu|Ashutosh Sahu]])**:\\ Videoconferencing has proved to be an eff ective tool for interaction between geographically distributed work teams. It has supported the transmission and recording of meetings for many years. However, such recordings of meetings are often monotonous and tedious to watch. Quite often, just one camera is used to capture video. Without multiple views, the users at the other end may lack the visual information needed to understand the meeting in its full context. Moreover, the shot does not change often, unless managed by hired professionals. This motivates the design of an automatic meeting capture system that uses cost-eff ective equipment and unobtrusive tracking, for capturing videos both in real-time and offline environments. The camera control algorithm running the system controls shot selection and handles errors, in both cases. Unlike the real-time environment, the offline environment off ers a lot of flexibility and scope to intelligently switch between various shots captured by various cameras. Audio feed has been the tried-and-tested method to detect speakers in the meeting. The goal is to explore the extent to which vision techniques using training and machine learning concepts can be applied for the purpose of detection of speakers. | ||
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| {{vigil:research:ongoing_projects_images:testimg-out.jpg?150}} \\ | {{vigil:research:ongoing_projects_images:testimg-out.jpg?150}} \\ | ||
| ** [[http://www.cse.iitb.ac.in/graphics/~lakulish/website/index.html|skeleton-based pose estimation of human figures]]([[http://www.cse.iitb.ac.in/~lakulish/|Lakulish Antani]])**:\\ Pose estimation of human figures is a challenging open problem. Model-based approaches, which can incorporate prior knowledge of the structure and appearance of human figures in various poses are the most promising line of research in this area. Most models of the human body represent it in terms of a tree of interconnected parts. Given such a model, two broad classes of pose estimation algorithms exist: top-down and bottom-up. Top-down algorithms locate body parts in a top-down order with respect to the tree of parts, performing a structure-guided search. Bottom-up algorithms, on the other hand, first look for potential parts irrespective of which parts they may be (usually based on local image properties such as edges), and then assemble a human figure using a subset of these candidate parts. Both approaches have pros and cons, and there are compelling reasons to develop a hybrid approach.\\ We describe a model-based pose estimation algorithm that combines top-down and bottom-up approaches in a simple manner. We describe a bottom-up part detector based on the skeleton transform, and a skeleton computation pipeline that uses existing algorithms for computing a pruned skeleton transform of any image. We describe a top-down pose estimation algorithm based on pictorial structures which we combine with the skeleton-based part detector. We also describe a way of imposing a structure on the space of candidate parts by computing a hierarchy between skeleton fragments, and use this structure to facilitate pose estimation. We compare our pose estimation algorithm with the classic pictorial structures algorithm, and compare our skeleton-based part detector with another edge-based part detector, and provide ideas for improving our method. | ** [[http://www.cse.iitb.ac.in/graphics/~lakulish/website/index.html|skeleton-based pose estimation of human figures]]([[http://www.cse.iitb.ac.in/~lakulish/|Lakulish Antani]])**:\\ Pose estimation of human figures is a challenging open problem. Model-based approaches, which can incorporate prior knowledge of the structure and appearance of human figures in various poses are the most promising line of research in this area. Most models of the human body represent it in terms of a tree of interconnected parts. Given such a model, two broad classes of pose estimation algorithms exist: top-down and bottom-up. Top-down algorithms locate body parts in a top-down order with respect to the tree of parts, performing a structure-guided search. Bottom-up algorithms, on the other hand, first look for potential parts irrespective of which parts they may be (usually based on local image properties such as edges), and then assemble a human figure using a subset of these candidate parts. Both approaches have pros and cons, and there are compelling reasons to develop a hybrid approach.\\ We describe a model-based pose estimation algorithm that combines top-down and bottom-up approaches in a simple manner. We describe a bottom-up part detector based on the skeleton transform, and a skeleton computation pipeline that uses existing algorithms for computing a pruned skeleton transform of any image. We describe a top-down pose estimation algorithm based on pictorial structures which we combine with the skeleton-based part detector. We also describe a way of imposing a structure on the space of candidate parts by computing a hierarchy between skeleton fragments, and use this structure to facilitate pose estimation. We compare our pose estimation algorithm with the classic pictorial structures algorithm, and compare our skeleton-based part detector with another edge-based part detector, and provide ideas for improving our method. | ||
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| ===== Miscellaneous ===== | ===== Miscellaneous ===== | ||
