Projects

Object Segmentation and Matching from aerial Lidar data ( Sarnoff Corporation, Dr. Harpreet Sawhney and Dr. Bogdan Matei )

Goal is to develop a method to segment objects from aerial lidar data and identify the objects to assist in 3D modeling of cities.

Few example classes include, Buildings, Trees, Vehicles, Traffic Signs, Lamps etc. The images and videos cannot be put on the website.

On the right are few Images of part the data.

Automated Cinematography for Sports ( Sarnoff Corporation, Mr. Jayan Eledath )

www.pajamachannels.com explains the objective of this project. It is a real time sports system which uses vision algorithms for automated coverage. It has been my dream project because of two reasons, I was part of the small team to ponder on the idea and prove the first prototype. It is a live system developed from scratch and deployed at multiple colleges. For a graduate student willing to spend the entire day at work, to see it going live is a dream come true.

Fragment Tracker ( Sarnoff Innovative Tech. Pvt. Ltd., Mr. Arvind Lakshmikumar )

We combined best of object representation and object searching strategies in tracking to create a hybrid tracker. Object to be tracked is first broken down into multiple fragments in HSV space and each of the fragment is tracked using EM shift approach. The tracks of the fragments is combined to get the holistic track output.

Traffic Management system ( Sarnoff Innovative Tech. Pvt. Ltd. )

Using standard vision techniques of motion detection, motion tracking and vehicle extraction, High occupancy vehicle detection, License plate detection we created a system which uses all these to detect traffic rules violation. The links to the output videos are: Onewaydetection, SignalJump

Sports classification using Geometric properties( Work done out of interest )

This project is about classifying sports images using the invariant nature of cross-ratios under projective transformation. The algorithm works as follows: A histogram of cross-ratios computed based on the intersection of lines detected using Hough transform is used to form a feature vector of the image. A modified One-Against-All multi-class SVM classifier is used to classify the feature vector. Images of Tennis, Football, Basketball and Badminton were used for classification. This algorithm is published in Asian Conference on Computer Vision (ACCV)'07.

Stroke Classification in Tennis ( Work done out of interest )

Classifying the different tennis strokes is the prime goal of this project. We came up with an algorithm that uses the gradient information of the player's skeleton. The player is modeled using color histogram and tracked across the video using histogram back projection. An oriented histogram of the skeleton obtained in each frame forms the feature vector which is then sent to a trained SVM classifier to classify the strokes: Forehand, Backhand and No Shot. The work got published in International Conference on Image Analysis and Recognition (ICIAR) '07.

Palmprint recognition ( Final year project, Dr. Anoop M. Namboodiri )

Developed a real time biometric system which handles pose variations and deformations of the palm. Pose estimation and elastic matching techniques were used to handle the variations. Also, created a new palmprint dataset which contains the pose variations and deformations as there was none existing.

Text based video search ( Honors project, Dr. C. V. Jawahar ) 

Analyzed various text extraction methods for images and videos and designed a hybrid approach combining the advantages of bottom-up and top-down methodologies. Invariant features are computed for the extracted text regions and indexed for the search process. The novelty of the system lies in using the DTW based word-matching technique instead of extracting text for the retrieval process. The approach was tested on three languages, English, Hindi and Telugu.

Face recognition ( Pattern recognition course project and Semester project, Dr. P. J. Narayanan )

Studied and experimented with various face recognition methods to come up with an Illumination invariant recognition technique. Principal component analysis, Fischer linear discriminant, Neural networks, DCT were few of the methods tried. The concept of illumination cone was explored and combined with PCA to get higher accuracies though illumination invariance was not completely achieved. Also, created a dataset with controlled illumination conditions in the process. The report contains the work and the sample images of the dataset.[pdf][pps]

Digital watermarking and Stegenography ( Digital image processing course project, Dr. Jayanthi Sivaswamy )

The objective was to come up with a watermarking technique which has a duel role of helping in copyright protection as well as metadata storage. Explored existing watermarking techniques and adapted them to serve the purpose. %An application was also developed that allowed to embed and decode the watermark. It was tested  for medical and sports images. The system has the advantage of having the metadata in the image instead of an additional data source.[pps]

Data Compression ( Semester Project, Dr. V. Ch. Venkaiah )

Goal was to evaluate compression techniques and find the best method to encode vision datasets. Studied various compression techniques like JPEG 2000, Fractal compression, Differential RLE. The tradeoff between time and compression was the key to us. Also, evaluated PCA based compression which worked well for Face and Palm datasets.[pps]

Graphics toolkit ( Semester Project, Dr. P. J. Narayanan )

The project consisted of series of assignments involving 2D graphics, 3D graphics and finally writing own toolkit to draw lines, polygons. Polygon filling, clipping using Z-buffering were also part of the tasks.