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    • 8. 发明授权
    • System for identifying objects and features in an image
    • 用于识别图像中的对象和特征的系统
    • US6151424A
    • 2000-11-21
    • US709918
    • 1996-09-09
    • Shin-yi Hsu
    • Shin-yi Hsu
    • G06K9/00G06K9/64G06T5/00G06K9/36
    • G06T7/0083G06K9/00201G06K9/0063G06K9/3241G06K9/6206G06T7/0081G06T7/0087G06T7/0097G06T2207/10028G06T2207/10036G06T2207/20016G06T2207/20084G06T2207/30181G06T2207/30212
    • The present invention features the use of the fundamental concept of color perception and multi-level resolution to perform scene segmentation and object/feature extraction in the context of self-determining and self-calibration modes. The technique uses only a single image, instead of multiple images as the input to generate segmented images. Moreover, a flexible and arbitrary scheme is incorporated, rather than a fixed scheme of segmentation analysis. The process allows users to perform digital analysis using any appropriate means for object extraction after an image is segmented. First, an image is retrieved. The image is then transformed into at least two distinct bands. Each transformed image is then projected into a color domain or a multi-level resolution setting. A segmented image is then created from all of the transformed images. The segmented image is analyzed to identify objects. Object identification is achieved by matching a segmented region against an image library. A featureless library contains full shape, partial shape and real-world images in a dual library system. The depth contours and height-above-ground structural components constitute a dual library. Also provided is a mathematical model called a Parzen window-based statistical/neural network classifier, which forms an integral part of this featureless dual library object identification system. All images are considered three-dimensional. Laser radar based 3-D images represent a special case.
    • 本发明的特征在于使用颜色感知和多级分辨率的基本概念来在自我确定和自校准模式的上下文中执行场景分割和对象/特征提取。 该技术仅使用单个图像,而不是多个图像作为输入来生成分割图像。 此外,并入了灵活和任意的方案,而不是固定的分割分析方案。 该过程允许用户在分割图像之后使用任何适当的对象提取方式执行数字分析。 首先,检索图像。 然后将图像转换成至少两个不同的带。 然后将每个变换图像投影到彩色域或多级分辨率设置中。 然后从所有变换图像创建分割图像。 分析分割的图像以识别对象。 通过将分割区域与图像库匹配来实现对象识别。 一个无特征库在双库系统中包含完整的形状,部分形状和真实世界的图像。 深度轮廓和高度地面结构构件构成双重库。 还提供了一种称为Parzen基于窗口的统计/神经网络分类器的数学模型,其形成该无特征的双库对象识别系统的组成部分。 所有图像都被认为是三维的。 基于激光雷达的3-D图像代表特殊情况。