Abstract
Neuroscientists and cognitive scientists credit the dorsal and ventral pathways for the capability of detecting both still salient and motion salient objects. In this work, a framework is developed to explore potential models of still and motion saliency and is an extension of the original VENUS system. The early visual pathway is modeled by using Independent Component Analysis to learn a set of Gabor-like receptive fields similar to those found in the mammalian visual pathway. These spatial receptive fields form a set of 2D basis feature matrices, which are used to decompose complex visual stimuli into their spatial components. A still saliency map is formed by combining the outputs of convoluting the learned spatial receptive fields with the input stimuli. The dorsal pathway is primarily focused on motion-based information. In this framework, the model uses simple motion segmentation and tracking algorithms to create a statistical model of the motion and color-related information in video streams. A key feature of the human visual system is the ability to detect novelty. This framework uses a set of Gaussian distributions to model color and motion. When a unique event is detected, Gaussian distributions are created and the event is declared novel. The next time a similar event is detected the framework is able to determine that the event is not novel based on the previously created distributions. A forgetting term is also included that allows events that have not been detected for a long period of time to be forgotten.
Library of Congress Subject Headings
Motion perception (Vision)--Computer simulation; Video recordings--Data processing; Neural networks (Computer science); Image processing
Publication Date
2006
Document Type
Thesis
Department, Program, or Center
Computer Science (GCCIS)
Advisor
Gaborski, Roger
Advisor/Committee Member
Reynolds, Carl
Advisor/Committee Member
Hemaspaandra, Edith
Recommended Citation
Burlone, David, "A biologically plausible system for detecting saliency in video" (2006). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/256
Campus
RIT – Main Campus
Comments
Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works. Physical copy available through RIT's The Wallace Library at: TA1634 .B87 2006