zimushibie0505

说明：  边缘特征的提取就是求图像梯度的局部最大值和方向。实际计算中，以微分算子的形式表示，并采用快速卷积函数来实现。常用的算子有微分算子，拉普拉斯算子，Canny算子等。其中Canny边缘检测是一种较新的边缘检测算子，具有较好的边缘检测性能，得到越来越广泛的应用。Canny边缘检测法利用高斯函数的一阶微分，它能在噪声抑制和边缘检测之间取得较好的平衡(Edge feature extraction is to seek the local maximum of image gradient and orientation. The actual calculation to the form of differential operator representation, and using fast convolution function to achieve. Commonly used operators are differential operators, Laplace operator, Canny operator and so on. Canny edge detection which is a relatively new edge detection operator, and has good edge detection performance, get more and more widely used. Canny edge detection method using first derivative of Gaussian function, it can in the noise suppression and edge detection to achieve a better balance between)

蚁群算法，图像边缘检测，用于模式识别，可以编译通过！(Ant colony algorithm for image edge detection for pattern recognition can be compiled by!)

说明：  使用gpu、cpu并行进行sift算子计算匹配，能够在原来的基础上加速处理，但对显卡要求较高，具体环境配置使用方法可以参照mannual(SiftGPU is an implementation of SIFT [1] for GPU. SiftGPU processes pixels parallely to build Gaussian pyramids and detect DoG Keypoints. Based on GPU list generation[3], SiftGPU then uses a GPU/CPU mixed method to efficiently build compact keypoint lists. Finally keypoints are processed parallely to get their orientations and descriptors. SiftGPU is inspired by Andrea Vedaldi s sift++[2] and Sudipta N Sinha et al s GPU-SIFT[4] . Many parameters of sift++ ( for example, number of octaves, number of DOG levels, edge threshold, etc) are also available in SiftGPU. The shader programs are dynamically generated according to the parameters that user specified. SiftGPU also includes a GPU exhaustive/guided sift matcher SiftMatchGPU. It basically multiplies the descriptor matrix on GPU and find closest feature matches on GPU. Both GLSL and CUDA implementations are provided. )

C#实现给照片批量添加水印功能 实例源码，可添加文字水印和图片水印等，其中还有透明度以及水印字体设置等功能

通过利用自动阈值法，获得图像中颗粒粒径的分布，并将粒径分布进行排序。(By using automatic threshold method to obtain the particle size distribution of the image, and the particle size distribution of the sort.)

从串口实时采集数据，读取家速度传感器的值，并且显示波形(Real-time collection of data from the serial port, read the value of home speed sensor, and display the waveform)

该文件夹包括代码及其对应的论文。其作用在于模拟人类视觉系统的生理特性--视觉注意机制，按照人眼观察外界的方式，检测视觉显著性物体和区域，并阐述了显著性区域的显著性密度和尺度之间的关系，可应用于生物视觉模拟、视觉目标检测、视觉目标跟踪、视觉智能监控，以及视觉生理学和视觉心理学等的研究中。(This document contains codes and the corresponding paper. The aim is to simulate a physiological characteristic of human visual system called visual attention mechanism. The code is used to detect salient objects or regions following the way human eye observes the world. The relation between density and scale of salient object/region is described. It has found widespread use in numerous applications such as biological vision simulation, visual object detection, visual object tracking, visual intelligent surveillance, visual physiological, and visual psychology.)

灰度双线性插值，缩放图像。可以对图像进行几乎无损的小倍数放大，倍数大时有损。(Gray bilinear interpolation, zooming the image. Can be almost non-destructive image to enlarge small multiples, multiples of large detrimental.)

品质因子可调的离散小波变换工具箱，可通过参数灵活控制小波函数的频域窗宽(Discrete wavelet transform toolbox with quality factor adjustable, flexible control parameters through the wavelet function in the frequency domain window width)

说明：  该课题为基于MATLAB的汽车出入库识别系统，带有丰富的人机交互GUI界面。目前毕业设计选题中，传统的中规中矩的车牌识别不易得到高分，必须要在此基础上有所创新方得可以避开其他雷同课题，不会轻易被导师被否决而导致毕设不过。因此建议在车牌识别基础上加入多种方法的对比，看看哪种方法的精度高。虽然目前有很多方法的车牌识别，但是都是在各自的测试库里面测试的，比如测试的车牌图像不同，字符库不同，导致无法做对比。整个设计在一个GUI界面上完成。(This project is based on MATLAB vehicle in and out of the warehouse identification system, with rich human-computer interaction GUI interface. At present, in the topic selection of graduation project, the traditional regular license plate recognition is not easy to get high scores, so it is necessary to innovate on this basis to avoid other similar topics, and it will not be rejected easily by the tutor, leading to the failure of graduation project. Therefore, it is suggested to add a variety of methods on the basis of license plate recognition to see which method has high accuracy. Although there are many methods of license plate recognition, they are tested in their own test library, for example, the test license plate image is different, the character library is different, so it can not be compared. The whole design is completed on a GUI interface.)