Relative advantage of touch over vision in the exploration of texture
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.
Bai, Yoon Ho (2008). Relative advantage of touch over vision in the exploration of texture. Master's thesis, Texas A&M University. Texas A&M University. Available electronically from