Finding Texture Boundaries in Images
Texture provides one cue for identifying the physical cause of an intensity edge, such as occlusion, shadow, surface orientation or reflectance change. Marr, Julesz, and others have proposed that texture is represented by small lines or blobs, called 'textons' by Julesz [1981a], toge...
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| Language: | en_US |
| Published: |
2004
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| Online Access: | http://hdl.handle.net/1721.1/6956 |
| _version_ | 1811071872809304064 |
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| author | Voorhees, Harry |
| author_facet | Voorhees, Harry |
| author_sort | Voorhees, Harry |
| collection | MIT |
| description | Texture provides one cue for identifying the physical cause of an intensity edge, such as occlusion, shadow, surface orientation or reflectance change. Marr, Julesz, and others have proposed that texture is represented by small lines or blobs, called 'textons' by Julesz [1981a], together with their attributes, such as orientation, elongation, and intensity. Psychophysical studies suggest that texture boundaries are perceived where distributions of attributes over neighborhoods of textons differ significantly. However, these studies, which deal with synthetic images, neglect to consider two important questions: How can these textons be extracted from images of natural scenes? And how, exactly, are texture boundaries then found? This thesis proposes answers to these questions by presenting an algorithm for computing blobs from natural images and a statistic for measuring the difference between two sample distributions of blob attributes. As part of the blob detection algorithm, methods for estimating image noise are presented, which are applicable to edge detection as well. |
| first_indexed | 2024-09-23T08:57:23Z |
| id | mit-1721.1/6956 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:57:23Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/69562019-04-10T19:56:25Z Finding Texture Boundaries in Images Voorhees, Harry Texture provides one cue for identifying the physical cause of an intensity edge, such as occlusion, shadow, surface orientation or reflectance change. Marr, Julesz, and others have proposed that texture is represented by small lines or blobs, called 'textons' by Julesz [1981a], together with their attributes, such as orientation, elongation, and intensity. Psychophysical studies suggest that texture boundaries are perceived where distributions of attributes over neighborhoods of textons differ significantly. However, these studies, which deal with synthetic images, neglect to consider two important questions: How can these textons be extracted from images of natural scenes? And how, exactly, are texture boundaries then found? This thesis proposes answers to these questions by presenting an algorithm for computing blobs from natural images and a statistic for measuring the difference between two sample distributions of blob attributes. As part of the blob detection algorithm, methods for estimating image noise are presented, which are applicable to edge detection as well. 2004-10-20T20:10:31Z 2004-10-20T20:10:31Z 1987-06-01 AITR-968 http://hdl.handle.net/1721.1/6956 en_US AITR-968 9042366 bytes 6420146 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | Voorhees, Harry Finding Texture Boundaries in Images |
| title | Finding Texture Boundaries in Images |
| title_full | Finding Texture Boundaries in Images |
| title_fullStr | Finding Texture Boundaries in Images |
| title_full_unstemmed | Finding Texture Boundaries in Images |
| title_short | Finding Texture Boundaries in Images |
| title_sort | finding texture boundaries in images |
| url | http://hdl.handle.net/1721.1/6956 |
| work_keys_str_mv | AT voorheesharry findingtextureboundariesinimages |