Recogniztion_2004_05_18

运用GVF snake进行图像分割。效果好，可用于学习借鉴。(image segmentantion based on GVF snake.)

用于人脸识别，检测驾驶员人员是否疲劳，在现实中有很好的应用，其算法简单易懂，适合初学者(For face recognition, detection of driver fatigue, in reality has a very good application, the algorithm is simple, easy to understand, suitable for beginners)

人头检测 通过颜色分割产生人头头发部分的二值图像，计算此部分的面积和长宽比来判断是否有人头(human detection)

说明：  识别指纹端点分叉点以及奇异点，其中还包括指纹方向场和指纹梯度场的计算(It also includes the calculation of fingerprint direction field and fingerprint gradient field)

avi视频提取出书帧图像，然后对图像进行处理，经过处理后的图像帧再写成另一段的avi视频。(avi video video frame extracted, processed image frame, and then the images will be referred to the district into another frame of video avi.)

局部二进制模式，LBP，是已被用于纹理特征的一个 分类。在本文中，提出了一种基于使用这些功能的方法 检测缺陷图案的面料。在培训阶段，在第一个步骤LBP算子是 施加到无缺陷织物样品，逐个象素和参考的所有的行（列） 特征矢量的计算。那么这个图像被分为Windows和LBP算子是 应用这些窗口的每一行（列）。根据与参考比较 特征向量一个合适的阈值，无缺陷的窗户被发现。在检测阶段中，一个 测试图像被划分成的窗户，并使用阈值时，有缺陷的窗口可以 检测到。该方法简单，灰度不变。由于其简单性， 线上实现是可能的。(Local Binary Patterns, LBP, is one of the features which has been used for texture classification. In this paper, a method based on using these features is proposed for detecting defects in patterned fabrics. In the training stage, at first step LBP operator is applied to all rows (columns) of a defect free fabric sample, pixel by pixel, and the reference feature vector is computed. Then this image is divided into windows and LBP operator is applied to each row (column) of these windows. Based on comparison with the reference feature vector a suitable threshold for defect free windows is found. In the detection stage, a test image is divided into windows and using the threshold, defective windows can be detected. The proposed method is simple and gray scale invariant. Because of its simplicity, online implementation is possible as well. )

著名专家写的计算机视觉方面的原著，希望对大家有所帮助(Writing a well-known experts in the computer vision aspects of the original, and they hope to help everyone)

说明：  用于医学图像融合CT和MRI灰度图像，MRI和PET的彩色图像融合(For medical image fusion)

主分量分析对SAR图像目标进行特征提取，用最近临方法进行分类(Principal component analysis（PCA） of SAR image target feature extraction, classification using nearest-neighbor method)

可以去除不均匀光照、噪声的预处理算法，利用双边滤波和中值滤波的优势。(You can remove the uneven illumination, noise preprocessing algorithm, taking advantage of bilateral filtering and median filtering.)