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Drag and Drop Component Suite Version 4.1 Field test 5, released 16-dec-2001 ?1997-2001 Angus Johnson & Anders Melander http://www.melander.dk/delphi/dragdrop/ ------------------------------------------- Table of Contents: ----------------------
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边缘提取算法提取细胞轮廓 适用于前景背景反差巨大-An object can be easily detected in an image if the object has sufficient
contrast from the background. We use edge detection and basic morphology
tools to detect a prostate cancer cell.
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求一个图像的最大内接矩形面积。
步骤：
1.相机标定。首先根据物像关系式求出其中的参数。注意参数求出后要进行参数校验。
2.从背景中分离出目标
3.边缘检测
4.目标形状参数计算。-For an image of the largest inscribed rectangle area. Steps: 1. Camera calibration. First of all object-image relationship in accordance with the par
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The various tasks described above—content-based image retrieval, object-level image subregion
querying, object localization, cut detection, and semantic organization of an image collection —
become especially challenging when the image database i
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采用 CAMSHIFT 算法快速跟踪和检测运动目标的 C/C++ 源代码，OPENCV BETA 4.0 版本在其 SAMPLE 中给出了这个例子。算法的简单描述如下-This application demonstrates a fast, simple color tracking algorithm that can be used to track faces, hands . The CAMSHIFT algorithm is a modification of the Meanshi
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人脸检测的研究具有重要的学术价值，人脸是一类具有相当复杂的细节变化的自然结构目标，对此类目标的挑战性在于：人脸由于外貌、表情、肤色等不同，具有模式的可变性；一般意义下的人脸上，可能存在眼镜、胡须等附属物；作为三维物体的人脸影像不可避免地受由光照产生的阴影的影响。因此，如果能够找到解决这些问题的方法，成功地构造出人脸检测系统，将为解决其他类似的复杂模式的检测问题提供重要的启示。-Face detection can be regarded as a specific case of object-
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图像自相似性计算。基于全局的图像自相似性计算的目标检测-
Global and Efficient Self-Similarity for Object Classification and Detection
From the methods in [1] this code allows to compute self-similarity
hypercubes (SSHs). These SSHs can be obtained from a prototype
as
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“Fast Tracking via Dense Spatio-Temporal Context Learning,” In ECCV 2014的源代码，效果非常好。-In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-te
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In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its local context based on a
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