基于部件的自动目标检测方法研究

张正; 王宏琦; 孙显; 巩大亮; 胡岩峰

doi:10.3724/SP.J.1146.2009.00552

基于部件的自动目标检测方法研究

doi: 10.3724/SP.J.1146.2009.00552

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出版历程
- 收稿日期: 2009-04-14
- 修回日期: 2009-11-12
- 刊出日期: 2010-05-19

An Automatic Method for Targets Detection Using a Component-Based Model

摘要

摘要: 该文提出了一种新的自动目标检测算法，实现对自然场景图像及高分辨率遥感图像中结构相对复杂的人造目标的自动检测。该方法基于组成物体的几何部件处理问题，降低了对训练样本数量的需求。首先选择两类典型特征，基于机器学习训练对应的分类器，有效地减少了背景中某些物体与前景目标部分特性相似对检测方法准确率的影响；然后利用标值点过程对问题建模，以对目标分布的先验约束和分类器的响应作为数据能量，自顶向下地自动检测目标。实验结果表明，该方法准确率高、鲁棒性好，具有较强的实际应用价值。
- 目标检测; 基于部件; 协方差矩阵; 标值点过程
Abstract: A novel target automatic detection algorithm is proposed in this paper, and it is mainly used for the processing of the man-made targets with a relatively complex structure in natural scenes images and high-resolution remote sensing images. Based on each geometric component of objects, this method needs less training samples. First of all, it selects two sorts of typical features and trains classifiers by machine learning correspondingly, which can effectively prevent the decrease of accuracy for the similarities between interest objects and some objects in background. Then the method detects targets top-down and automatically with the marked point process model, whose data terms consist of the priori constraint on the objects distribution and respondences of trained classifiers. The experimental results demonstrate the precision, robustness, and effectiveness of the proposed method.

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Opelt A and Pinz A. Fusing shape and appearance information for object category detection[C]. Proc. British Machine Vision Conference, Edinburgh, UK, 2006: 117-126.[2]孙显, 王宏琦, 张正. 基于对象的Boosting方法自动提取高分辨率遥感图像中建筑物目标[J].电子与信息学报.2009, 31(1):177-181浏览Sun Xian, Wang Hong-qi, and Zhang Zheng. Automatic building extraction in high resolution remote sensing image using object-based Boosting method[J].Journal of Electronics Information Technology.2009, 31(1):177-181[3]Fergus R, Perona P, and Zisserman A. A sparse object category model for efficient learning and exhaustive recognition[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, 2005: 380-387.[4]Felzenszwalb P, McAllester D, and Ramanan D. A discriminatively trained, multiscale, deformable part model[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, USA, 2008: 1-8.[5]Opelt A, Pinz A, and Zisserman A. A boundary-fragment model for object detection[C]. Proc. European Conference on Computer Vision, Graz, Austria, 2006: 575-588.Tuzel O, Porikli F, and Meer P. Region covariance: A fast descriptor for detection and classification[C]. Proc. European Conference on Computer Vision, Graz, Austria, 2006: 589-600.[6]Ortner M, Descombe X, and Zerubia, J. A marked point process of rectangles and segments for automatic analysis of digital elevation models[J].IEEE Transactions on Pattern Analysis and Machine Intelligence.2008, 30(1):105-119[7]Agarwal S, Awan A, and Roth D. Learning to detect objects in images via a sparse, part-based representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence.2004, 26(11):1475-1490[8]Hoiem D, Rother C, and Winn J. 3D layoutCRF for multi-view object class recognition and segmentation[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, 2007: 1-8.Ahuja N and Todorovic S. Learning the taxonomy and models of categories present in arbitrary images[C]. Proc. of the Eleventh IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, 2007: 1-8.[9]Amores J, Sebe N, and Radeva P. Fast spatial pattern discovery integrating boosting with constellations of contextual descriptor[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, 2005: 769-774.Shotton J, Blake A, and Cipolla R. Contour-based learning for object detection[C]. Proc. of the Tenth IEEE International Conference on Computer Vision, Beijing, China, 2005: 503-510.

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