Feature extraction for image recognition and computer vision

Xudong Jiang
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引用次数: 36

Abstract

Feature extraction and classifier design are two main processing blocks in all pattern recognition and computer vision systems. For visual patterns, extracting robust and discriminative features from image is the most difficult yet the most critical step. Several typical and advanced approaches of feature extraction from image are explored, some of which are analyzed in depth. Various techniques of feature extraction from image are organized in four categories: human expert knowledge based methods, image local structure based approaches, image global structure based techniques and machine learning based statistical approaches. We will show examples of applying these feature extraction approaches to solve problems of the image based biometrics, including fingerprint verification/identification and face detection/recognition. These illustrative application examples unveil the ideas, principles and advancements of feature extraction techniques and demonstrate their effectiveness and limitations in solving real-world problems.
用于图像识别和计算机视觉的特征提取
特征提取和分类器设计是所有模式识别和计算机视觉系统的两个主要处理模块。对于视觉模式,从图像中提取鲁棒性和判别性特征是最困难也是最关键的一步。探讨了几种典型和先进的图像特征提取方法,并对其中一些方法进行了深入分析。各种图像特征提取技术分为四类:基于人类专家知识的方法、基于图像局部结构的方法、基于图像全局结构的方法和基于机器学习的统计方法。我们将展示应用这些特征提取方法来解决基于图像的生物识别问题的例子,包括指纹验证/识别和人脸检测/识别。这些说明性的应用实例揭示了特征提取技术的思想、原理和进展,并展示了它们在解决现实问题方面的有效性和局限性。
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