Bai, Yoon Ho (2008-08). Relative advantage of touch over vision in the exploration of texture. Master's Thesis. Thesis uri icon


  • 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.

publication date

  • August 2008