Applying Histogram Contrast Analysis to Texture Perception

将直方图对比度分析应用于纹理感知

• 批准号: 9203291
• 负责人: Charles Chubb
• 金额: $ 22.49万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-08-15 至 1996-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9203291&HistoricalAwards=false
• 关键词: Applying; Histogram; Contrast; Analysis; Texture

Many brain processes operate in parallel on sensory input to draw distinctions (boundaries, gradations, modulations, etc.) in the fabric of perceptual space/time. For instance, there are visual processes that operate on the spatiotemporal variations in light intensity to generate brightness distinctions. Other processes operate on variations in the spectral content of light over space and time to construct color distinctions. Still other processes draw distinctions based on spatiotemporal variations in textural properties (e.g., texture contrast, granularity and/or orientation). It is this last class of distinction-drawing processes, or channels, that these experiments will investigate. Histogram contrast analysis is a new branch of measurement theory whose purpose is to (i) isolate individual perceptual channels, and (ii) precisely characterize the computations those channels perform. No one knows exactly how many distinct channels are devoted to drawing texture-based perceptual distinctions, though there is general agreement that multiple channels are involved. Nor is there any data to indicate what specific computations are performed by any one of the putative channels used in texture perception. Heretofore, models of texture perception have relied on arbitrary, ad hoc guesses as to the nature of the channels used in drawing textural distinctions. These experiments will use histogram contrast analysis (i) to isolate individual channels used in drawing textural distinctions and (ii) to determine their computational characteristics. These experiments will thus make a start at the task of constructing a table of the computational "elements of texture perception" analogous to the periodic table of physical elements. Any theory of texture perception precise and comprehensive enough to predict performance probabilities on actual texture discrimination tasks must have such a table of elements as its cornerstone.

许多大脑过程在感觉输入上并行运作， 区别（边界、层次、调制等）在 感知空间/时间的结构。 例如，有视觉 在光的时空变化上运作的过程 强度以产生亮度区别。 其他进程 利用空间中光的光谱成分的变化 和时间来区分颜色。 还有其他过程 根据纹理的时空变化绘制区别 属性（例如，纹理对比度、粒度和/或 方向）。 这是最后一类的区别画 这些实验将研究的过程或通道。 直方图对比分析是测量理论的一个新的分支 其目的是（i）隔离各个感知通道，以及 (ii)精确地描述这些通道的计算 表演。 没有人知道到底有多少不同的渠道， 致力于绘制基于纹理的感知区别， 人们普遍认为涉及多个渠道。 也没有任何数据表明具体的计算是什么 由纹理中使用的任何一个假定通道执行 perception. 因此，纹理感知模型依赖于 对所使用的信道的性质进行任意的、特别的猜测， 来区分纹理。 这些实验将使用直方图对比度分析（i）， 隔离用于绘制纹理区别的各个通道 以及（ii）确定它们的计算特性。 这些 因此，实验将开始构建一个 计算“纹理感知要素”表 类似于物理元素周期表。 任何理论 纹理感知的精确性和全面性足以预测 在实际纹理辨别任务上的性能概率 必须有这样一个要素表作为其基石。