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LEARNING CONTOUR STATISTICS FROM NATURAL IMAGES
by
Chaithanaya Amai Ramachandra
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOMEDICAL ENGINEERING)
December 2012
Copyright 2012 Chaithanaya Amai Ramachandra
Object Description
| Title | Learning contour statistics from natural images |
| Author | Ramachandra, Chaithanya Amai |
| Author email | cramacha@usc.edu;chaithanya@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Biomedical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2012-10-19 |
| Date submitted | 2012-11-12 |
| Date approved | 2012-11-13 |
| Restricted until | 2012-11-13 |
| Date published | 2012-11-13 |
| Advisor (committee chair) | Mel, Bartlett W. |
| Advisor (committee member) |
Grzywacz, Norberto M. Tjan, Bosco S. |
| Abstract | Vision is one of our most important senses, and accordingly, a large fraction of our cerebral cortex is devoted to visual processing.One of the key computations in the early stages of the visual system is the extraction of object contours, since the occluding boundaries and orientation discontinuities of objects that make up the ""line drawing"" of a scene are the most important and direct cues to object shape. Contour detection in natural scenes has proven to be a difficult technical problem, however, mainly because the existence (or not) of a contour at a given location, orientation and scale depends, probabilistically, on a large number of cues covering a large fraction of the visual field.Worse, the cues interact with each other in a multitude of ways and combinations, leading to an enormously complex cue integration problem. ❧ In this work, we attempt to break down the contour extraction problem into natural, tractable, modular subcomputations.In chapter 2, we describe a novel approach to combining local edge cues from the area generally orthogonal to the orientation of the contour.Key aspects of the approach are the (1) tabulation and modeling of contour statistics at fixed values of local edge contrast, to reduce higher-order dependencies within the population of local edge cues, and (2) picking the most informative contour cues from the de-correlated local edge population. The resulting contour operator has no parameters and has significantly improved localization and sharpened orientation tuning compared to the raw local filter values. In chapter 3, we describe a novel approach to combining cues from the area generally tangential to the contour.In this case, we have developed an approach to efficiently gather the contour statistics needed to optimally use ""contextual"" cues from aligned high-resolution flankers and the superimposed coarse-scale edges.In the process, we have found evidence that the integration of these contextual cues across scales can be achieved by a cascade of simple 2-input functions, greatly simplifying our statistics-driven approach.We found that the interaction between two collinear flankers is similar to a minimum like operation, center response and flankers interaction is conjunctive/contextual, and for the particular case of cross scale interaction we looked at there was minimal interaction.We generalized the approach developed for collinear cues to cues for curved contours.The resulting contextually-boosted contour operator strongly emphasizes spatially-extended contours found in natural scenes, again with zero parameters.We also describe a novel image enhancement method based on Cornsweet illusion using contours obtained from the above two methods. |
| Keyword | edge detection; natural image statistics; contour extraction; Bayesian inference; image enhancement |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Contributing entity | University of Southern California |
| Rights | Ramachandra, Chaithanya Amai |
| Physical access | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@lib.usc.edu |
| Archival file | uscthesesreloadpub_Volume4/etd-Ramachandr-1287.pdf |
Description
| Title | Page 1 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | LEARNING CONTOUR STATISTICS FROM NATURAL IMAGES by Chaithanaya Amai Ramachandra A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (BIOMEDICAL ENGINEERING) December 2012 Copyright 2012 Chaithanaya Amai Ramachandra |
