HC-visual-detection-by-matlab

实现DCT与DWT相结合的算法,用matable及小波变换实现所想构思。(The algorithm of combining DCT with DWT is realized, and the idea is realized by matable and wavelet transform.)

一个修改后的PCA进行人脸识别的Matlab源码(A revised PCA for Face Recognition Matlab source code)

说明：  采用haar小波实现二维图像的分解与重构（mallat算法）：(Two-dimensional image using haar wavelet decomposition and reconstruction (mallat algorithm):)

转换图象为二值图象后，进行识别与标记的matlab源码。可以计算目标物个数，形心等。(image conversion value for the two images, identification and marking of Matlab source. Can be calculated from the number of goals, such as heart-shaped.)

说明：  实现图片、文本隐藏在图片中，并导出等操作(Implement images, text hidden in images, and export operations)

说明：  生成差异图，用于变化检测，包含比值法、差值法、均值比算法等(The difference graph is generated for change detection, including ratio method, difference method and mean ratio algorithm)

激光标定中得激光线检测程序，激光标定中得激光线检测程序(A laser line detection program was the laser line detection procedures, laser calibration laser calibration)

Image segmentation systems have the potential to dramatically improve the performance of Digital image processing. Segmentation procedures partition an image into its constituent parts or objects. In general, segmentation is one of the most difficult tasks in digital image processing. Detection of salient image regions is useful for applications like image segmentation, adaptive compression, and region-based image retrieval. This problem can be tackled by mapping the pixels into various feature spaces, which are subjected to various grouping algorithms It present a novel method to determine salient regions in images using low-level features of luminance and color. The method is fast, easy to implement and generates high quality saliency maps of the same size and resolution as the input image.

一种自适应chirplet分解的快速算法 针对信号自适应chirplet分解未知参数多、实现起来比较困难的特点，文献[1]提出了一种新的chirplet分解快速算法。该算法利用计算信号的二次相位函数，得到其能量分布集中于信号的调频率曲线上的结论，此时通过谱峰检测可同时获得chirplet调频率、时间中心和幅度的估计；然后通过解线性调频技术获得其初始频率和宽度的估计，仿真结果验证了本文算法的有效性。 (Chirplet an adaptive decomposition algorithm for rapid adaptive signal decomposition Chirplet many unknown parameters, is more difficult to achieve the characteristics of the literature [1] proposed a new fast algorithm Chirplet decomposition. The algorithm calculating the signal function of the second phase, its energy distribution to be concentrated in the signal modulation frequency curve on the conclusions, this time through the spectral peak detection can be obtained simultaneously Chirplet tune the frequency, timing and magnitude of the estimated center then through the solution line technology of FM frequency and the width of the initial estimates, the simulation results verify the effectiveness of the algorithm.)

这是TV模型最新的快速计算Matlab源代码，算法可以解决很多非线性扩散方程的计算。稳定性高，计算快。(This is the latest TV models fast calculation of Matlab source code, the algorithm can solve a lot of non-linear diffusion equation calculations. High stability, fast calculation.)