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Detecting Salient Objects Of Natural Scene In A Video'S Using Spatio-Temporal Saliency & Colour Map

Authors: SUHAS B. KHADAKE;

Detecting Salient Objects Of Natural Scene In A Video'S Using Spatio-Temporal Saliency & Colour Map

Abstract

Detection of spatio temporal saliency is the main aim of this paper. It uses system where it utilizes the human eye perceptive action. This uses the signal coming from human eye, for detection of saliency, for that a large database are used (about 15000 frames). For Saliency detection two pathways are decided. This uses the data on the basis of same thing on images and in accordance similarity will be calculated. According to it visual summary data are used. It gives much amount of data, which gives a lot of research in computer vision and results are used for calculation of saliency. We have take the view of all method used for calculation of saliency. So by using the data of different method we develop a Novel method which will give best results of detection of saliency. Here technique used is calculation of saliency by using spatio temporal saliency and colour map of video or images. In this method number of experiment are in progress for saliency. This method has fast and robust result. https://journalnx.com/journal-article/20150109

Keywords

Spatio-Temporal saliency, phase spectrum, Particle filter, Gaussian, pixel-level,, HVS, resolution

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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