Texture segmentation is an effortless process in scene analysis, yet its mechanisms
have not been sufficiently understood. Several theories and algorithms exist
for texture discrimination based on vision. These models diverge from one another in
algorithmic approaches to address texture imagery using spatial elements and their
statistics. Even though there are differences among these approaches, they all begin
from the assumption that texture segmentation is a visual task.
However, considering that texture is basically a surface property, this assumption
can at times be misleading. An interesting possibility is that since surface properties
are most immediately accessible to touch, texture perception may be more intimately
associated with texture than with vision (it is known that tactile input can affect
vision). Coincidentally, the basic organization of the touch (somatosensory) system
bears some analogy to that of the visual system. In particular, recent neurophysiological
findings showed that receptive fields for touch resemble that of vision, albeit
with some subtle differences.
The main novelty and contribution of this thesis is in the use of tactile receptive
field responses for texture segmentation. Furthermore, we showed that touch-based
representation is superior to its vision-based counterpart when used in texture boundary
detection. Tactile representations were also found to be more discriminable (LDA
and ANOVA). We expect our results to help better understand the nature of texture
perception and build more powerful texture processing algorithms. The results suggest that touch has an advantage over vision in texture processing.
Findings in this study are expected to shed new light on the role of tactile perception
of texture and its interaction with vision, and help develop more powerful, biologically
inspired texture segmentation algorithms.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/85999 |
| Date | 10 October 2008 |
| Creators | Bai, Yoon Ho |
| Contributors | Choe, Yoonsuck |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | electronic, born digital |
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