topoplot

多小波分析工具箱,请大家分享,给与反馈,谢谢大家 不错的(Wavelet analysis toolbox, please share and give the feedback, thank you for your good)

图象降维算法,比如有名的LLE,PCA,ISOMAP......等,对研究图象分析的研究人员有很大的帮助(image dimensionality reduction algorithms such as the famous LLE, PCA, ISOMAP .... Image analysis of the study researchers will be very helpful )

说明：  SVAG图形库, linux下面运行的图形库程序(SVAG graphics library, linux graphics library running the following procedure)

说明：  MPCA一个博士编写的多线程pca变换方法，内含发表论文(Dr. MPCA prepared a multi-threaded PCA transform methods, including papers)

s世界计算机视觉三大会议之CVPR会议，中国人发表的经典立体匹配的论文(s World Computer Vision Conference CVPR three meetings, the Chinese classic stereo matching published papers)

基于SVM与人工神经网络的车牌识别算法，使用了OpenCV的图像处理函数，在VS2013 + OpenCV 2.4.9平台上实现(SVM algorithm based on license plate recognition and artificial neural network, using OpenCV image processing function, implemented on VS2013+ OpenCV 2.4.9 platform)

对超声图像的去噪和分割，可以显示肿瘤图像的边缘和面积(For ultrasound image denoising and segmentation, image can show the tumor edge and area)

在对话框中实现splitter(achieve splitter)

说明：  UNet最早发表在2015的MICCAI上，短短3年，引用量目前已经达到了4070，足以见得其影响力。而后成为大多做医疗影像语义分割任务的baseline，也启发了大量研究者去思考U型语义分割网络。而如今在自然影像理解方面，也有越来越多的语义分割和目标检测SOTA模型开始关注和使用U型结构，比如语义分割Discriminative Feature Network(DFN)(CVPR2018)，目标检测Feature Pyramid Networks for Object Detection(FPN)(CVPR 2017)等。(Its influence has reached 70% in 2015. Then it became the baseline that most of the medical image semantic segmentation tasks, and inspired a large number of researchers to think about the U-shaped semantic segmentation network. In the aspect of natural image understanding, more and more SOTA models of semantic segmentation and object detection begin to pay attention to and use U-shaped structure, such as semantic segmentation, discriminative feature network (DFN) (cvpr2018), feature pyramid networks for object detection (FPN) (CVPR 2017), etc.)

这篇论文提出了一种较好的水下图像增强的方法。首先使用经过端到端训练的卷积神经网络去测量输入图片，同时以自适应双边滤波器对传输图片进行处理。接着提出一种基于白平衡的策略来消除图片的颜色偏差，用拉普拉斯金字塔融合获得无雾和色彩校正图像的融合结果。 最后，输出图像被转换为混合小波和方向滤波器组（HWD）域，用于去噪和边缘增强。 实验结果表明，该方法可以消除颜色失真，提高水下图像的清晰度。(This paper proposes a better underwater image enhancement method. Firstly, an end-to-end training convolutional neural network is used to measure the input image, and an adaptive bilateral filter is used to process the transmitted image. Then a strategy based on white balance is proposed to eliminate the color deviation of images. The fusion results of fog-free and color correction images are obtained by Laplacian pyramid fusion. Finally, the output image is converted into hybrid wavelet and directional filter bank (HWD) domain for denoising and edge enhancement. The experimental results show that the method can eliminate color distortion and improve the clarity of underwater images.)