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[Picture Viewer] 2D-lacal-steering-kernel DL : 0: 读取文件夹下的所有相应格式的图像组合成为一个图像序列-read image
Date : 2026-01-10 Size : 1kb User : 刘刚
[Picture Viewer] hough DL : 0: 用于对图像的边缘检测（采用canny算法）。 image 输入图像，这个必须是单通道的，即灰度图 edges 输出的边缘图像，也是单通道的，但是是黑白的 threshold1 第一个阈值 threshold2 第二个阈值 aperture_size Sobel 算子内核大小 -Using canny algorithm for image edge detection (). image input image, this must be a single-channel output edge image that grayscale edges, single-channel, but it is a black and white threshold1 threshold threshold2 of a second threshold aperture_size Sobel operator kernel size
Date : 2026-01-10 Size : 358kb User : panrujie
[Picture Viewer] ROI2 DL : 0: 从眼动仪提取数据，根据高斯核函数得到感兴趣区域-Extract data from the eye tracker, based on the Gaussian kernel function to get the area of interest
Date : 2026-01-10 Size : 1kb User : rose
[Picture Viewer] ksr DL : 0: 数据平滑函数，已经测试过了，采用高斯核函数方法，很好用 -Data smoothing function, it has been tested using Gaussian kernel function method, easy to use
Date : 2026-01-10 Size : 2kb User : 周小光
[Picture Viewer] ksrlin DL : 0: 数据平滑函数，在高斯核函数的基础上进行的方法改进，-Data smoothing function, a method based on the Gaussian kernel function were improved,
Date : 2026-01-10 Size : 2kb User : 周小光
[Picture Viewer] ksr DL : 0: 数据平滑函数，在高斯核函数的基础上进行的改进-Data smoothing function, based on the Gaussian kernel function were improved
Date : 2026-01-10 Size : 1kb User : 周小光
[Picture Viewer] meanshift DL : 0: meanshift均值平移跟踪算法中核函数窗宽的自动选取代码，根据目标大小变化核窗宽，使得当目标出现大小变化时准确跟踪到目标中心-Meanshift mean shift tracking algorithm kernel bandwidth automatic code is selected, according to the objective changes in the size of the kernel bandwidth, making when target size change accurate tracking to the target center
Date : 2026-01-10 Size : 12kb User : 笨小猫
[Picture Viewer] wnrigpppi DL : 0: we present a nonlinear version of the well-known anomaly detection method referred to as the RX-algorithm. Extending this algorithm to a feature space associated with the original input space via a certain nonlinear mapping function can provide a nonlinear version of the RX-algorithm. This nonlinear RX-algorithm, referred to as the kernel RX-algorithm, is basically intractable mainly due to the high dimensionality of the feature space produced by the nonlinear mapping function. However, in this paper it is shown that the kernel RX-algorithm can easily be implemented by kernelizing the RX-algorithm
Date : 2026-01-10 Size : 1.53mb User : yangs
[Picture Viewer] aareition DL : 0: er than mean squared error (MSE) function only. As an additional merit, it is also revealed that rigorous Mercer kernel condition is not required in FKNN networks. When the proposed architecture of FKNN networks is constructed in a layer-by-layer way, i.e., the number of the hidden nodes of every hidden layer may be determined only in terms of the extracted principal com- ponents after the explicit execution of a KPCA, we can develop FKNN’s deep architecture such that its deep learning framework (DLF) has strong theoretical guarantee. Our experimental results about image classification manifest that the proposed FKNN’s deep architecture and its DLF based learning indeed enhance the classification performance
Date : 2026-01-10 Size : 1.51mb User : yangs