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Image analysis using visual saliency with applications in hazmat sign detection and recognition
| Content Provider | Semantic Scholar |
|---|---|
| Author | Zhao, Bin |
| Copyright Year | 2014 |
| Abstract | Zhao, Bin Ph.D., Purdue University, December 2014. Image Analysis Using Visual Saliency with Applications in Hazmat Sign Detection and Recognition. Major Professor: Edward J. Delp. Visual saliency is the perceptual process that makes attractive objects “stand out” from their surroundings in the low-level human visual system. Visual saliency has been modeled as a preprocessing step of the human visual system for selecting the important visual information from a scene. We investigate bottom-up visual saliency using spectral analysis approaches. We present separate and composite model families that generalize existing frequency domain visual saliency models. We propose several frequency domain visual saliency models to generate saliency maps using new spectrum processing methods and an entropy-based saliency map selection approach. A group of saliency map candidates are then obtained by inverse transform. A final saliency map is selected among the candidates by minimizing the entropy of the saliency map candidates. The proposed models based on the separate and composite model families are also extended to various color spaces. We develop an evaluation tool for benchmarking visual saliency models. Experimental results show that the proposed models are more accurate and efficient than most state-of-the-art visual saliency models in predicting eye fixation. We use the above visual saliency models to detect the location of hazardous material (hazmat) signs in complex scenes. We develop a hazmat sign location detection and content recognition system using visual saliency. Saliency maps are employed to extract salient regions that are likely to contain hazmat sign candidates and then use a Fourier descriptor based contour matching method to locate the border of hazmat signs in these regions. This visual saliency based approach is able to increase the |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1605&context=open_access_dissertations |
| Alternate Webpage(s) | https://redpill.ecn.purdue.edu/~zhao61/phd/defense_slides.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
