asimpledpcmcode

测距的相关内容以及采用三角测量的方法测量出来(Ranging content and the use of the triangulation method is measured)

针对最低有效位 LSB方法保密性较差的问题, 本文提出了一种复合Henon混沌序列和LSB的图像加密解密算法(For the least significant bit LSB method less confidentiality issues, this paper proposes a complex Henon chaotic sequence and the LSB of the image encryption and decryption algorithm)

说明：  通过Logistic映射与Chen超混沌系统产生随机序列的DNA分块编解码图像加密技术(Image encryption technology of DNA block encoding and decoding by using logistic map and Chen hyperchaos system to generate random sequences)

基于小波变换模极大的多尺度图像边缘检测,比较简单，但实用，是本人模式识别程序的一部分(Based on wavelet transform modulus maxima of the multi-scale image edge detection, relatively simple, but useful, is himself a part of pattern recognition procedures)

SVM 用于基于块划分特征提取的图像分类源码(SVM for feature extraction block-based image classification into source)

进行分数傅立叶变换的算法，通过该算法可以对函数进行任意分数阶傅立叶变换(For the fractional Fourier transform algorithms, the adoption of the algorithm can function for arbitrary fractional order Fourier transform)

光流法、帧间差分法、高斯背景模型差分法，在opencv环境下实现运动目标检测(moving objective detection matlab)

Abstract—Noninvasive ultrasound imaging of carotid plaques allows for the development of plaque-image analysis methods associated with the risk of stroke. This paper presents several plaqueimage analysis methods that have been developed over the past years. The paper begins with a review of clinical methods for visual classification that have led to standardized methods for image acquisition, describes methods for image segmentation and denoizing, and provides an overview of the several texture-feature extraction and classification methods that have been applied. We provide a summary of emerging trends in 3-D imaging methods and plaque-motion analysis. Finally, we provide a discussion of the emerging trends and future directions in our concluding remarks.

图像匹配的实现，其中match.m：测试程序，sift.m :尺度不变特征变换（SIFT算法）的核心算法程序，appendimages.m: 该函数创建一个新的图像分别包含两个匹配的图像和他们之间的匹配对的连接直线，可以运行。 (The realization of image matching, which match.m: test program, sift.m: scale-invariant feature transform (SIFT algorithm) of the core algorithm, appendimages.m: This function creates a new image each consists of two images and matching match between them on the connection line, you can run.)

立体匹配中的单目标定,输入图片，得出结果，官方实例(Monocular calibration)