Action Recognition using Canonical Correlation Kernels|
People Involved : G Nagendar, C V Jawahar
Action recognition has gained significant attention from the computer vision
community in recent years. This is a challenging problem, mainly due to the
presence of significant camera motion, viewpoint transitions, varying
illumination conditions and cluttered backgrounds in the videos. A wide
spectrum of features and representations has been used for action recognition
in the past. Recent advances in action recognition are propelled by (i) the
use of local as well as global features, which have significantly helped in
object and scene recognition, by computing them over 2D frames or over a
3D video volume (ii) the use of factorization techniques over video volume
tensors and defining similarity measures over the resulting lower dimensional
factors. In this project, we try to take advantages of both these approaches
by defining a canonical correlation kernel that is computed from tensor
representation of the videos. This also enables seamless feature fusion
by combining multiple feature kernels.
People Involved : Yashaswi Verma, C V Jawahar
In many real-life scenarios, an object can be categorized into multiple
categories. E.g., a newspaper column can be tagged as "political", "election",
"democracy"; an image may contain "tiger", "grass", "river"; and so on. These
are instances of multi-label classification, which deals with the task of
associating multiple labels with single data. Automatic image annotation is a
multi-label classification problem that aims at associating a set of text
with an image that describes its semantics.
People Involved : Pawan Harish, P J Narayanan, Nirnimesh
Displays have seen many improvements over the years but have many
shortcomings still. These include rectangular shape, low color gamut, low
dynamic range, lack of focus and context in a scene, lack of 3D viewing,
etc. We propose Computational Displays, which employ computation to
economically alleviate some of the shortcomings of today's displays.
Scene Text Understanding|
People Involved : Anand Mishra, Karteek Alahari and C.V. Jawahar
Scene text recognition has gained significant attention from the computer
vision community in recent years. Often images contain text which gives rich
and useful information about their content. Recognizing such text is a
challenging problem, even more so than the recognition of scanned documents.
Scene text exhibits a large variability in appearances, and can prove to be
challenging even for the state-of-the-art OCR methods. Many scene understanding
methods recognize objects and regions like roads, trees, sky etc in the image
successfully, but tend to ignore the text on the sign board. Our goal is to
fill this gap in understanding the scene.
GPU Processing and CUDA |
People Involved : Pawan Harish, Suryakant Patiar, Shiben Bhattacharjee, Vaibhav Vineet, Sheetal Lahabar
Commodity graphics hardware has become a cost-effective parallel platform
for solving many general problems. New Graphics hardware by Nvidia offers
an alternate programming model called CUDA which can be used in more
flexible ways than GPGPU.
Medical Image Processing|
People Involved :Gopal Datt Joshi, Mayank Chawla, Arunava Chakravarty, Akhilesh Bontala, Shashank Mujjumdar, Rohit Gautam, Subbu, Sushma
Digital medical images are widely used for diagnostic purposes. Our goal is to develop algorithms for medical image analysis focusing on enhancement, segmentation, multi-modal registration and classification.
Terrain Rendering and Information System|
People Involved : Shiben Bhattacharjee, Suryakant Patidar
Digital Terrain Model refer to a data model that attempts to provide a three dimensional representation of a continuous surface, since terrains are two and a half dimensional rather than three dimensional.
Data Generation Tool kit|
People Involved :V Krishna
Synthetic data is very useful for validating the algorithms developed for various computer vision and image based rendering algorithms.
Recognition of Indian Language Documents|
People Involved : Million Meshesha, Balasubramanian Anand, Sesh Kumar, L. Jagannathan, Neeba N V, Venkat Rasagna
The present growth of digitization of documents demands an immediate solution to enable the archived valuable materials searchable and usable by users in order to achieve its objective.
Retrieval of Document Images|
People Involved : Million Meshesha, Balasubramanian Anand, Pramod Sankar K, Anand Kumar
Our approach towards retrieval of document images, avoids explicit recognition of the text. Instead, we perform word matching in the image space. Given a query word, we generate its corresponding word image, and compare it against the words in the documents.
Retrieval from Video Databases|
People Involved :Tarun Jain, Anurag Singh Rana, Balakrishna C., Pramod Sankar K., Saurabh Pandey, Balamanohar P., Natraj J.
Digital Libraries of broadcast videos could be easily built with existing technology. The storage, archival, search and retrieval of broadcast videos provide a large number of challenges for the research community. We address these challenges in different novel directions.
People Involved : Abdul Hafez, Visesh Uday Kumar, Supreeth, Anil, D. Santosh
Our research activity is primarily concerned with the geometric analysis of scenes captured by vision sensors and the control of a robot so as to perform set tasks by utilzing the scene intepretation.
Content Based Image Retrieval : CBIR|
People Involved : Pradhee Tandon, P. Suman Karthik, Natraj J., Dhaval Mehta, E. S. V. N. L. S. Diwakar
We strive to enable machines with subjective perception capabilities at par with human their counterparts, especially with regards to images.
Contours, Textures, Homography and Fourier Domain|
People Involved :Sujit Kuthirummal, Paresh Kumar Jain, M. Pawan Kumar, Saurabh Goyal
The aim of this study is to come up with a Fourier representation of contours and then utilise it to estimate two view relationships like homography and also come up with novel invariants.
People Involved :Vandana Roy, Sachin Gupta
The aim of the work is to develop robust and accurate biometric recognition systems, primarily
for use in civilian applications. We are currently working on enhancing soft biometric traits such
as hand geometry and palm texture, and also on gathering identity information from online handwritten
Online Handwriting Analysis|
People Involved :Naveen Chandra Tewari, Sachin Gupta
Handwritten Recognition refers to mapping of meaningful handwritten lexemes to computer understandable codes. There are many applications in which entering data using handwriting is more convenient than keyboard like in making notes or making hand sketches.
Garuda: A Scalable, Geometry Managed Display Wall|
People Involved :Pawan Harish, Nirnimesh
Cluster-based tiled display walls simultaneously provide high resolution and large display area (Focus + Context) and are suitable for many applications. They are also cost-effective and scalable with low incremental costs. Garuda is a client-server based display wall solution designed to use off-the-shelf graphics hardware and standard Ethernet network.
People Involved : Pooja Verlani, Aditi Goswami, Shekhar Dwivedi, Sireesh Reddy K, Sashi Kumar Penta
Depth Images are viable representations that can be computed from the real world using cameras and/or other scanning devices. The depth map provides a 2 and a half D structure of the scene. The depth map gives a visibility-limited model of the scene and can be rendered easily using graphics techniques.
People Involved :Gopal Datt Joshi, Kartheek N V, Varun Jampani
The perceptual mechanisms used by different organisms to negotiate the visual world are fascinatingly diverse. Even if we consider only the sensory organs of vertebrates, such as eye, there is much variety.
Learning Appearance Models|
People Involved :Karteek Alahari, Paresh Jain, Ranjeeth Kumar, Manikandan
Our reseach focuses on learning appearance models from images/videos that can be used for a variety of tasks such as recognition, detection and classification etc.
Projected Texture for 3D Object Recognition|
People Involved : Avinash Sharma, Nishant Shobhit
We are solving the problem of 3D object recognition. Instead of recovering the 3D of object we are encoding the
depth variation in object with deformation induced in pattern projected on object from certain pose. We
proposed some effective features which can effectively characterize deformation of projected pattern for the
purpose of recognition.
Security and Privacy of Visual Data|
People Involved : Maneesh Upmanyu, Narsimha Raju, Shashank Jagarlamudi, Kowshik Palivela
With a rapid development and acceptablity of computer vision based systems in one's daily life, securing of the visual data has become imperative. Security issues in computer vision primarily originates from the storage, distribution and processing of the personal data, whereas privacy concerns with tracking down of the user's activity. Through this work we address specific security and privacy concerns of the visual data. We propose application specific, computationally efficient and provably secure computer vision algorithms for the encrypted domain.