Vibha Vyas

Work place: College of Engineering Pune,India



Research Interests: Image Processing, Image Manipulation, Image Compression, Computer Vision, Computer systems and computational processes


Vibha Vyas did her BE from Nagpur university in 1995, M.Tech in 2002 and PhD in 2010 from COEP. Her area of interest are Signal and Image processing with various applications of machine vision and multidimensional processes. She is guiding various UG and PG level students in said area.

Author Articles
Velocity and Orientation Detection of Dynamic Textures Using Integrated Algorithm

By Shilpa Paygude Vibha Vyas Chinmay Khadilkar

DOI:, Pub. Date: 8 Dec. 2018

Dynamic Texture Analysis is a hotspot field in Computer Vision. Dynamic Textures are temporal extensions of static Textures. There are broadly two cat-egories of Dynamic Textures: natural and manmade. Smoke, fire, water and tree are natural while traffic and crowd are manmade Dynamic Textures. In this paper, an integrated efficient algorithm is discussed and proposed which is used for detecting two features of objects in Dynamic Textures namely, velocity and orientation. These two features can be used in identifying the velocity of vehicles in traffic, stampede prediction and cloud movement direction. Optical flow technique is used to obtain the velocity feature of the objects in motion.  Since optical flow is computationally complex, it is applied after background subtraction. This reduces the number of computations. Variance feature of Gabor filter is used to find the orientation which gives direction of movement of majority objects in a video. The combination of optical flow and Gabor filter technique together gives accurate orientation and velocity of Dynamic Texture with less number of computations in terms of time and algorithm.. Proposed algorithm can be used in real time applications. Velocity detection is done using Farneback Optical flow and orientation or angle detection is done using Bank of Gabor Filters The existing methods are used to calculate either velocity or orientation accurately individually. Varied datasets are used for experimentation and acquired results are validated for the selected database. 

[...] Read more.
Transfer Subspace Learning Model for Face Recognition at a Distance

By Alwin Anuse Nilima Deshmukh Vibha Vyas

DOI:, Pub. Date: 8 Jan. 2017

Many machine learning algorithms work under the assumption that the training and testing data are drawn from the same distribution. However, in practice the assumption might not hold. Transfer subspace learning algorithms aims at utilizing knowledge gained in source domain to learn a task in target domain. The main objective of this work is to apply transfer subspace learning framework on face recognition task at a distance. In this paper we identify face recognition at distance as a transfer learning problem. We show that if the face recognition task is modeled as transfer learning problem, the overall classification rate is increased significantly compared to traditional brute force approach. We also discuss a data set which is unique and meant to advance this research. The novelty of this work lies in modeling face recognition task at distance as a transfer subspace learning problem.

[...] Read more.
Other Articles