R
Rajkiran Kumar Gottumukkal
Researcher at Old Dominion University
Publications - 31
Citations - 1106
Rajkiran Kumar Gottumukkal is an academic researcher from Old Dominion University. The author has contributed to research in topics: Facial recognition system & Object (computer science). The author has an hindex of 15, co-authored 31 publications receiving 1078 citations.
Papers
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Journal ArticleDOI
An improved face recognition technique based on modular PCA approach
TL;DR: The proposed algorithm when compared with conventional PCA algorithm has an improved recognition rate for face images with large variations in lighting direction and facial expression and is expected to be able to cope with these variations.
Patent
Behavioral recognition system
John Eric Eaton,Wesley Kenneth Cobb,Dennis G. Urech,Blythe Bobby Ernest,David Samuel Friedlander,Rajkiran Kumar Gottumukkal,Lon W. Risinger,Kishor Adinath Saitwal,Ming-Jung Seow,David M. Solum,Gang Xu,Tao Yang +11 more
TL;DR: In this article, a method and a system for analyzing and learning behavior based on an acquired stream of video frames is presented. But the method is not suitable for real-time applications.
Patent
Semantic representation module of a machine-learning engine in a video analysis system
John Eric Eaton,Wesley Kenneth Cobb,Dennis G. Urech,David Samuel Friedlander,Gang Xu,Ming-Jung Seow,Lon W. Risinger,David M. Solum,Tao Yang,Rajkiran Kumar Gottumukkal,Kishor Adinath Saitwal +10 more
TL;DR: In this article, a machine learning engine is described that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time.
Patent
Context processor for video analysis system
John Eric Eaton,Wesley Kenneth Cobb,Bobby Ernest Blythe,Rajkiran Kumar Gottumukkal,Kishor Adinath Saitwal +4 more
TL;DR: In this article, a scene depicted in an acquired stream of video frames is segmented into plurality of regions representing various objects of the background image, and the regions are analyzed to determine their z-depth order in relation to a video capturing device providing the stream of the video frames and other regions.
Patent
Identifying anomalous object types during classification
Wesley Kenneth Cobb,David Samuel Friedlander,Rajkiran Kumar Gottumukkal,Ming-Jung Seow,Gang Xu +4 more
TL;DR: In this article, a self-organizing map and adaptive resonance theory (SOM-ART) network is used to discover object type clusters and classify objects depicted in the image data based on pixel-level micro-features that are extracted from the image.