Description: canny edge detector，下面是canny edge detector的算法： 1.将图像与高斯函数做卷积，获得平滑图像。 2.用基于平滑梯度方向的方法确定图像局部边缘的法向。 3.根据局部边缘的法线方向求解边缘位置。 4.计算边缘强度，并对梯度幅值进行非极大值抑制。 5.用双阈值算法检测和连接边缘。该文件提供了canny edge detector的VC实现代码。-canny edge detector, the following is a canny edge detector algorithm: 1. will make the image with the Gaussian convolution function to obtain a smooth image. 2. Gradient-based smoothing method to determine the direction of the edge of the image of local law to. 3. According to the local normal direction to solve the edge of the edge location. 4. Calculation of the edge strength, gradient amplitude and non-maxima suppression. 5. To use dual-threshold algorithm for edge detection and connection. The document provides a canny edge detector realize the VC code. Platform: |
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Author:桂祖恒 |
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Description: canny算子进行边缘检测的源代码。
（1）首先对图像进行高斯滤波，去除噪声的影响；
（2）对滤波后图像计算梯度的幅值和方向
（3）对梯度幅值进行模极大值抑制
（4）双阈值确定边缘-canny edge detection operator to the source code. (1) First of all Gaussian image filtering to remove noise (2) after filtering the image calculated gradient amplitude and direction of (3) of the gradient amplitude for modulus maxima suppression (4) dual-threshold determine the edge Platform: |
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Author:章格 |
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Description: 极大值抑制与双阈值门限得到图像边缘：
* nonmaxsuppts.m Code for performing non-maxima suppression and thresholding of points generated by a feature/corner detector. It optionally returns sub-pixel feature locations.-Maxima suppression and dual-threshold threshold obtained Edge:* nonmaxsuppts.m Code for performing non-maxima suppression and thresholding of points generated by a feature/corner detector. It optionally returns sub-pixel feature locations. Platform: |
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Author:asdasdasd |
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Description: 利用openCV找出已知图像中的角点， 利用slider bar 控制临界值， 并显示出通过临界值的角点像素， 并用non-maxima suprression 使之变稀疏并显示-routines in OpenCV to demonstrate the operations of the Harris Corner detector. 1. Open the image box_in_scene.jpg and show the image in a window.
2. Compute the minimum eigenvalue of that image.
3. Threshold the minimum eigenvalue, and draw the pixels that pass this threshold test in white, and the rest of the pixels in black. The actual threshold should be set by a slider which is on top of the window. I have found that a threshold of 0.01 for the minimum eigenvalue gives good results as the middle value of the slider.
4. Take the pixels that pass this threshold test and use a non-maxima suppression algorithm to thin out the potential corners. Draw the corners that pass the non-maxima suppression test in another window. These are the final corners. Platform: |
Size: 84992 |
Author:Ke Li |
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Description: Non maxima suppression and thresholding for points generated by a feature
or corner detector. Platform: |
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Author:mahmoud |
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Description: 采用Canny算法进行边缘检测。1用高斯滤波器平滑图像。2用一阶偏导的有限差分3对梯度幅度进行非极大值抑制。4用双阈值。5采用高斯平滑函数-Canny edge detection algorithm used. A smooth image with a Gaussian filter. 2 with the first-order partial derivatives of the finite difference gradient magnitude 3 on the non-maxima suppression. 4 with a double threshold. 5 Gaussian smoothing function Platform: |
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Author:吴婵 |
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Description: harry角点检测c++源代码，利用差分算子对图像进行滤波，对Ix2/Iy2/Ixy进行高斯平滑，以去除噪声，计算角点量，进行局部非极大值抑制以获得最终角点-harry corner detection c++ source code, the use of differential operators on image filtering, Gaussian smoothing of Ix2/Iy2/Ixy to remove noise, calculate the amount of corners, the local non-maxima suppression to get the final corner Platform: |
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Author:欣晨 |
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Description: Many state-of-the-art approaches for object recognition
reduce the problem to a 0-1 classifi cation task. Such re-
ductions allow one to leverage sophisticated classifi ers for
learning. These models are typically trained independently
for each class using positive and negative examples cropped
from images. At test-time, various post-processing heuris-
tics such as non-maxima suppression (NMS) are required
to reconcile multiple detections within and between differ-
ent classes for each image. Though crucial to good perfor-
mance on benchmarks, this post-processing is usually de-
fi ned heuristically.-Many state-of-the-art approaches for object recognition reduce the problem to a 0-1 classification task. Such re-ductions allow one to leverage sophisticated classifiers for learning. These models are typically trained independently for each class using positive and negative examples cropped from images. At test-time, various post-processing heuris-tics such as non-maxima suppression (NMS) are required to reconcile multiple detections within and between differ-ent classes for each image. Though crucial to good perfor-mance on benchmarks, this post-processing is usually de-fined heuristically. Platform: |
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Author:xukaijun |
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Description: 采用Canny算法进行边缘检测。主要包括以下几个步骤：1、用高斯滤波器平滑图像。2、用一阶偏导的有限差分计算梯度的幅值和方向。3、对梯度幅值进行非极大值抑制。4、用双阈值算法检测和连接边缘。5、采用高斯平滑函数-Using the Canny edge detection algorithm. Include the following steps: 1, smooth image with a Gaussian filter. 2, first-order partial derivatives of finite-difference calculation of the magnitude and direction of the gradient. 3, the gradient amplitude, non-maxima suppression. 4, dual-threshold algorithm to detect and connect edge. 5 Gaussian smoothing function Platform: |
Size: 84992 |
Author:王云舒 |
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Description: harris角点提取算法，可以设置高斯差分窗口大小、非极大值抑制的邻域大小、高斯函数方差大小-harris corner extraction algorithm, you can set the window size of the Gaussian difference, non-maxima suppression of the size of the neighborhood, the size of the variance of the Gaussian function Platform: |
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Author:qss |
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Search - maxima suppression