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+====== Ongoing Research (Not Frequently Updated! See Individual web pages) ======
-====== Ongoing Projects ======
-| {{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. |
-| {{vigil:research:ongoing_projects_images:sail.png?150}} | ** [[http://www.cse.iitb.ac.in/~chirag/MTP/index.html|Structure From Motion in Isometry Shape Space]]([[http://www.cse.iitb.ac.in/~chirag/|Chirag Patel]])**: Structure from Motion refers to the process of estimating the camera motion and the rigid or deforming three-dimensional structure from image measurements taken over a period of time. The problem is the one of the most difficult problem in the field of Computer Vision and many researchers have worked on it. In this project, we introduce a novel framework to solve to the problem. We reduce Structure from Motion Problem into an optimization problem in a Shape Space with a Riemannian metric which discourages non-isometric deformations. Proposed approach is expected to work better with most of the deforming objects we see in our surroundings. |
-| {{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. |
-| {{vigil:research:ongoing_projects_images:sfm.jpg?150}} | ** [[http://www.cse.iitb.ac.in/~ashwinkp/btp/btp.html|Isometry-based Structure from Motion]]([[http://www.cse.iitb.ac.in/~ashwinkp/|K. P. Ashwin]])**:Structure from motion refers to the process of estimating the 3D structure of a moving object using image measurements taken over a period of time and. This has been an active area of research for a lot of years and is generally considered a hard problem to solve. In this work, we propose a novel solution to solving the age-old Structure from motion problem. We review this problem in the setting of Riemannian Geometry, in which shapes are represented by points on the surface of a high dimensional space. We use isometric constraints on the shape to identify the most likely deformation of the model. |
-| {{vigil:research:ongoing_projects_images:q_1_27_s.jpg?150}} | **[[http://www.cse.iitb.ac.in/~kkrai/mtp|Object Recognition and Content Based Image Retrieval]] ([[http://www.cse.iitb.ac.in/~kkrai/|Krishna Kumar Rai]])**:\\ This work explores the task of searching images in small databases based on the user's interest. Given a query image we try to find similar images. To do so we need to analyze the visual content of the images. Here the term 'visual content' refers to the color, texture, shape, spatial layout or any other visual information that can be derived from the image. Such an approach is generally called content based image retrieval or CBIR. CBIR has wide range of applications including remote sensing, art collection, photo archives, medical records etc. There are various ways to perform CBIR, including object recognition, region matching and metadata search etc. Object recognition is the most semantically rich way to search images but at the same time it is the most difficult one to perform effectively. Although this work is more focused upon object recognition, We have developed two techniques for CBIR. The first one is based on recognizing objects in the images to find similar images, while the second technique is based on finding similar regions (or segments) in the images.|
-| {{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.|
-| {{vigil:research:ongoing_projects_images:10.jpg?150}} | **[[http://www.cse.iitb.ac.in/appu/papers/bmvc07.pdf|Human Pose Extraction from Monocular Videos using Non-rigid Factorization ]] ([[http://www.cse.iitb.ac.in/~appu/|Appu Shaji]] and [[http://www.cse.iitb.ac.in/~sharat/|Sharat Chandran]] )**:\\ We focus on the problem of automatically extracting the 3D configuration of human poses from 2D image features tracked over a finite interval of time . This problem is highly non-linear in nature and confounds standard regression techniques. Our approach effectively marries a non-rigid factorization algorithm with prior learned statistical models from archival motion capture database. We show that a stand alone non-rigid factorization algorithm is highly unsuitable for this problem. However, when coupled with the learned statistical model in the form of a constrained non- linear programming method, it yields a substantially better solution. |
-| {{vigil:research:ongoing_projects_images:factintution.png?150}} | **[[http://www.cse.iitb.ac.in/appu/papers/icpr08.pdf|Motion Factorisation by Geometrical Optimisation on SE3 Manifold]] ([[http://www.cse.iitb.ac.in/~appu/|Appu Shaji]] and [[http://www.cse.iitb.ac.in/~sharat/|Sharat Chandran]] and [[http://www.batman.eng.monash.edu.au/suter_research/suter_research.html|David Suter]] )**:\\  We presents a novel formulation for the popular factorisation based solution for Structure from Motion.  Since our measurement matrices are populated with incomplete and inaccurate data, SVD based total least squares solution are less than appropriate.  Instead, we approach the problem as a non-linear unconstrained minimisation problem on the product manifold of the Special Euclidean Group ($SE_3$).  The restriction of the domain of  optimisation to the $SE_3$ product manifold not only implies that each intermediate solution is a plausible object motion, but also ensures better intrinsic stability for the minimisation algorithm. |
-| {{vigil:research:ongoing_projects_images:rain.jpeg?150}} | ** [[http://www.it.iitb.ac.in/~pisith/mtp/|Rendering Rain Falling Effect]] ([[http://www.it.iitb.ac.in/~pisith|Pisith Hao]])**:\\ There has been a manyfold increase in the computational speed of graphics hardware in recent times. This power afforded by modern graphics cards enables the possibility of simulating complex environmental phenomena, such as atmospheric special effects.\\ The main objective of the project is to create realistic rain falling effect in real time taking into account the refraction and reflection of raindrop by modeling the world as a cube map, and using environment mapping technique. |
-| {{vigil:research:ongoing_projects_images:brahm.png?150}} | ** [[http://brahm.pbwiki.com/|Committee Based Active Learning for CBIR]]([[http://www.cse.iitb.ac.in/~brahm/|Brahm Kiran Singh]])**:\\ Active Learning has been shown to be a very effective tool for enhancing retrieval results in text retrieval. In Content-Based Image Retrieval(CBIR) it is more and more frequently used and very good results have been obtained.\\ This project is an extension to Committee-Based Active Learning approach for CBIR systems. Though this project aims at employing this approach using SVMs, the approach, like the traditional committee-based approach, is much more general and puts no restriction on the type of learning machines used. The aim is to employ committee-based method of Active Learning to improve the performance of CBIR systems. This report discusses a modification of the classical committee-based approach in that the initial sample selection for training the learners is modified — a sampling of the database is done, which is not entirely random. |
-| {{vigil:research:ongoing_projects_images:nekhil.png?150}} | ** [[http://www.cse.iitb.ac.in/graphics/~nekhil/website/BTP_Stage1/BTP%20Presentation%20version%20final.pdf|Building Vision Based Interaction Systems]]([[http://www.cse.iitb.ac.in/~nekhil/|Nekhil Agrawal]])**:\\ The main aim of this project is to build a virtual model and enable multiple users to interact with the model, using concepts of computer vision and computer graphics. The development of project can be divided into two major portions.\\  1. When the user presses trigger of the camera mounted on his gun, then using techniques of camera calibration we figure out where the user is located in the room and the direction of hit.\\ 2. Then corresponding location and orientation in the model and the point of hit is calculated. |
-| {{vigil:research:ongoing_projects_images:chetan.jpg?150}} | ** [[http://www.cse.iitb.ac.in/graphics/~chetandewan/website/index.html|Customised 3D rendering engine]]([[http://www.cse.iitb.ac.in/~chetandewan/|Lt Col Chetan Dewan]])**:\\ The objective of the project is to customise any suitable open source rendering engine and transform it into a Graphics development toolkit/rendering engine for developing simulations for armed forces training. The development is part of previous ongoing project in which Coin3D a open source engine was selected and some customisation was carried out. The previous work was shelved as in the light of current survey carried out by the author it was realised that Coin3D had stagnated with time and a number of new opensource projects had far surpassed the enhanced Coin3D project both in features as well as performance. For more information you can also visit http://chetandewan.diinoweb.com/files/ or http://chetandewan.drivehq.com/. |
-| {{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. |
-| {{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 effective 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-effective 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 offers 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.  |