Mallikarjun B.R., C.V. Jawahar - Efficient Face Frontalization in Unconstrained Images Proceedings of the Fifth National Conference on Computer Vision Pattern Recognition, Image Processing and Graphics (NCVPRIPG 2015), 16-19 Dec 2015, Patna, India.
An exemplar based approach to detect the facial landmarks. We show that by using a very simple SIFT and HOG based descriptor, it is possible to identify the most accurate fiducial outputs from a set of results produced by regression and mixture of trees based algorithms (which we call candidate algorithms) on any given test image. Our approach manifests as two algorithms, one based on optimizing an objective function with quadratic terms and the other based on simple kNN.
Areas of Interest: Using Deep Learning for Multi-modal Gesture Recognition, Machine Learning
Jitendra Yasaswi, Suresh Purini and C. V. Jawahar - Plagiarism detection in Programming Assignments Using Deep Features 4th Asian Conference on Pattern Recognition (ACPR 2017), Nanjing, China, 2017.[PDF]
Jitendra Yasaswi Bharadwaj katta, Srikailash G, Anil Chilupuri, Suresh Purini and C.V. Jawahar - Unsupervised Learning Based Approach for Plagiarism Detection in Programming Assignments ISEC. 2017. [PDF]
Areas of Interest: Medical Image Processing
Karthik G, Rangrej S and Jayanthi Sivaswamy - A deep learning framework for segmentation of retinal layers from OCT images ACPR, Nanjing [PDF]
Karthik Gopinath, Jayanthi Sivaswamy
and Tarannum Mansoori - Automatic Glaucoma Assessment from Angio-OCT ImagesProc. of IEEE International Symposium on Bio-Medical Imaging(ISBI), 2016, 13 - 16 April, 2016, Prague. [PDF]
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.
Areas of Interest: Computer vision
Gaurav Mishra, Saurabh Saini, Kiran Varanasi P.J. Narayanan - Human Shape Capture and Tracking at Home IEEE Winter Conference on Applications of Computer Vision (WACV 2018), Lake Tahoe, CA, USA, 2018. [PDF]
Human body tracking typically requires specialized capture set-ups. Although pose tracking is available in consumer devices like Microsoft Kinect, it is restricted to stick figures visualizing body part detection. In this paper, we propose a method for full 3D human body shape and motion capture of arbitrary movements from the depth channel of a single Kinect, when the subject wears casual clothes