Object recognition with color cooccurrence histograms

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Pub Date : 1999-06-23 DOI:10.1109/CVPR.1999.784727

Peng Chang, J. Krumm

{"title":"Object recognition with color cooccurrence histograms","authors":"Peng Chang, J. Krumm","doi":"10.1109/CVPR.1999.784727","DOIUrl":null,"url":null,"abstract":"We use the color cooccurrence histogram (CH) for recognizing objects in images. The color CH keeps track of the number of pairs of certain colored pixels that occur at certain separation distances in image space. The color CH adds geometric information to the normal color histogram, which abstracts away all geometry. We compute model CHs based on images of known objects taken from different points of view. These model CHs are then matched to subregions in test images to find the object. By adjusting the number of colors and the number of distances used in the CH, we can adjust the tolerance of the algorithm to changes in lighting, viewpoint, and the flexibility of the object We develop a mathematical model of the algorithm's false alarm probability and use this as a principled way of picking most of the algorithm's adjustable parameters. We demonstrate our algorithm on different objects, showing that it recognizes objects in spite of confusing background clutter partial occlusions, and flexing of the object.","PeriodicalId":20644,"journal":{"name":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"226","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPR.1999.784727","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 226

Abstract

We use the color cooccurrence histogram (CH) for recognizing objects in images. The color CH keeps track of the number of pairs of certain colored pixels that occur at certain separation distances in image space. The color CH adds geometric information to the normal color histogram, which abstracts away all geometry. We compute model CHs based on images of known objects taken from different points of view. These model CHs are then matched to subregions in test images to find the object. By adjusting the number of colors and the number of distances used in the CH, we can adjust the tolerance of the algorithm to changes in lighting, viewpoint, and the flexibility of the object We develop a mathematical model of the algorithm's false alarm probability and use this as a principled way of picking most of the algorithm's adjustable parameters. We demonstrate our algorithm on different objects, showing that it recognizes objects in spite of confusing background clutter partial occlusions, and flexing of the object.

查看原文本刊更多论文

具有颜色共现直方图的物体识别

我们使用颜色共现直方图(CH)来识别图像中的物体。颜色CH跟踪在图像空间中某些分离距离上出现的某些彩色像素对的数量。颜色CH将几何信息添加到正常的颜色直方图中，抽象掉所有的几何信息。我们根据从不同角度拍摄的已知物体的图像计算模型CHs。然后将这些模型CHs与测试图像中的子区域进行匹配以找到目标。通过调整CH中使用的颜色数量和距离数量，我们可以调整算法对照明，视点和对象灵活性变化的容忍度。我们开发了算法虚警概率的数学模型，并将其用作选择大多数算法可调参数的原则方法。我们在不同的对象上演示了我们的算法，表明它可以在令人困惑的背景杂波、部分遮挡和对象弯曲的情况下识别目标。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)

自引率

0.00%

发文量