Sourav Saha

Work place: Institute of Engineering & Management, Kolkata, 700091, India



Research Interests: 2D Computer Graphics, Computer Graphics and Visualization, Pattern Recognition, Computer Vision, Computer systems and computational processes


Sourav Saha: He is currently working as Assistant Professor in the Department of Computer Science and Engineering, Institute of Engineering and Management. He started his career working in R&D sector at various companies. Since 2011, he has been teaching in Institute of Engineering and Management, Kolkata courses like Data Structure and Algorithm, Artificial Intelligence, Computer Graphics, Image Processing both at under-graduation and post-graduation levels. He did his graduation (B.Tech) in Computer Science & Engineering from Kalyani University in 2000, and obtained his Master of Engineering (M.E.) degree in Computer Science and Engineering from Bengal Engineering and Science University in 2002. He was awarded university medal for securing highest mark in M.E. and also received award from Indian National Academy of Engineering for best innovative bachelor level project in 2000. He has numerous international and national publications in reputed journals and conferences to his credit. His research interest mostly lies in the fields of Computer Vision, Pattern Recognition and Cellular Automata etc.

Author Articles
A Heuristic Strategy for Sub-Optimal Thick-Edged Polygonal Approximation of 2-D Planar Shape

By Sourav Saha Saptarsi Goswami Priya Ranjan Sinha Mahapatra

DOI:, Pub. Date: 8 Apr. 2018

This paper presents a heuristic approach to approximate a two-dimensional planar shape using a thick-edged polygonal representation based on some optimal criteria. The optimal criteria primarily focus on derivation of minimal thickness for an edge of the polygonal shape representation to handle noisy contour. Vertices of the shape-approximating polygon are extracted through a heuristic exploration using a digital geometric approach in order to find optimally thick-line to represent a discrete curve. The merit of such strategies depends on how efficiently a polygon having minimal number of vertices can be generated with modest computational complexity as a meaningful representation of a shape without loss of significant visual characteristics. The performance of the proposed frame- work is comparable to the existing schemes based on extensive empirical study with standard data set.

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A New Evaluation Measure for Feature Subset Selection with Genetic Algorithm

By Saptarsi Goswami Sourav Saha Subhayu Chakravorty Amlan Chakrabarti Basabi Chakraborty

DOI:, Pub. Date: 8 Sep. 2015

Feature selection is one of the most important preprocessing steps for a data mining, pattern recognition or machine learning problem. Finding an optimal subset of features, among all the combinations is a NP-Complete problem. Lot of research has been done in feature selection. However, as the sizes of the datasets are increasing and optimality is a subjective notion, further research is needed to find better techniques. In this paper, a genetic algorithm based feature subset selection method has been proposed with a novel feature evaluation measure as the fitness function. The evaluation measure is different in three primary ways a) It considers the information content of the features apart from relevance with respect to the target b) The redundancy is considered only when it is over a threshold value c) There is lesser penalization towards cardinality of the subset. As the measure accepts value of few parameters, this is available for tuning as per the need of the particular problem domain. Experiments conducted over 21 well known publicly available datasets reveal superior performance. Hypothesis testing for the accuracy improvement is found to be statistically significant.

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