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Neural models of color vision with applications to image processing and recognition.

机译：色彩视觉的神经模型及其在图像处理和识别中的应用。

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摘要

The early stages of color vision in the primate brain depend upon functionally distinct cells in the retina, thalamus, and primary visual cortex (V1). Within the retina, ganglion cells pool responses from red, green, and blue cones to enhance contrast and discount the illuminant. In the thalamus, single opponent cells differentiate between changes in achromatic and chromatic illumination. In V1, two types of double opponent (DO) cells have been found. The first type of DO cell has been popularized by Livingstone and Hubel. These cells are thought to support simultaneous color contrast, discounting the illuminant, and detecting material boundaries, among other things. The second DO cells, reported by T'so and Gilbert, are thought to integrate chromatic and achromatic vision.; This dissertation defines the DISCOV (DImensionless Shunting COlor Vision) system, which models a cascade of primate color vision cells: retinal ganglion, thalamic single opponent, and two classes of cortical double opponents. A unified model formalism derived from psychophysical axioms produces transparent network dynamics and principled parameter settings. DISCOV fits an array of physiological data for each cell type, and makes testable experimental predictions. Properties of DISCOV model cells are compared with properties of corresponding components in the alternative Neural Fusion model.; The Neural Fusion model has also been used to analyze multispectral and multimodal images in remote sensing and medical imagery. Images are preprocessed to create a multidimensional input vector containing local contrast, color, and texture information at each pixel. An ARTMAP network then classifies input vectors into target and not-target classes.; Benchmark testbeds permit the comparison of DISCOV and Neural Fusion for data fusion. The first testbed is composed of pixels drawn from a MassGIS orthophoto image (0.5 m resolution), taken from an airplane. The second testbed is a NASA Landsat image (15-60 m resolution), taken from a satellite. Both testbed images are views of northeast Boston and its suburbs. The marginal utility of each color vision model cell type is tested to determine its usefulness in image processing and classification. Results varied across testbeds suggesting further study.
机译：灵长类动物大脑中彩色视觉的早期阶段取决于视网膜，丘脑和初级视觉皮层（V1）中功能不同的细胞。在视网膜内，神经节细胞汇集红色，绿色和蓝色视锥细胞的反应，以增强对比度并减少光源。在丘脑中，单个对手细胞区分消色差和彩色照明的变化。在V1中，发现了两种类型的双重对手（DO）单元。第一类DO电池已被Livingstone和Hubel推广。这些单元被认为可以支持同时的颜色对比，减少光源和检测材料边界等。 T'so和Gilbert报告的第二个DO细胞被认为整合了彩色和非彩色视觉。本论文定义了DISCOV（无量纲分流彩色视觉系统）系统，该系统模拟了灵长类彩色视觉细胞的级联：视网膜神经节，丘脑单对手和两类皮质双对手。从心理物理公理派生的统一模型形式主义产生透明的网络动力学和有原则的参数设置。 DISCOV适合每种细胞类型的一系列生理数据，并进行可测试的实验预测。在替代神经融合模型中，将DISCOV模型单元的属性与相应组件的属性进行比较。神经融合模型也已用于分析遥感和医学图像中的多光谱和多峰图像。对图像进行预处理，以创建一个多维输入向量，其中包含每个像素处的局部对比度，颜色和纹理信息。然后，ARTMAP网络将输入向量分为目标和非目标类别。基准测试台可以比较DISCOV和神经融合进行数据融合。第一个测试台由从飞机上获取的MassGIS正射影像（分辨率为0.5 m）绘制的像素组成。第二个测试台是从卫星拍摄的NASA Landsat图像（分辨率为15-60 m）。两张测试台图像均为波士顿东北部及其郊区的风景。测试每种颜色视觉模型单元类型的边际效用，以确定其在图像处理和分类中的有用性。不同试验台的结果各不相同，建议进一步研究。

著录项

  • 作者

    Chelian, Suhas E.;

  • 作者单位

    Boston University.;

  • 授予单位 Boston University.;
  • 学科 Biology Neuroscience.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 59 p.
  • 总页数 59
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 神经科学;
  • 关键词

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