Description: This algorithm will perform hough transform and detect shape of an object.-This algorithm will perform hough transfo rm and detect shape of an object. Platform: |
Size: 4284416 |
Author:Kent |
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Description: 广义Hough变换_多个圆的快速随机检测。基本上解决了多个元检测的问题-Generalized Hough Transform _ more than a round of rapid random testing. Basically solved the problem of multiple element detection Platform: |
Size: 363520 |
Author:崔少云 |
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Description: 根据：一种可识别破碎图形的特殊广义Hough变换方法 论文，在matlab上做的实验源码，有模版生成，广义哈夫变换代码-In accordance with: A broken identifiable graphics special generalized Hough transform methods papers, in matlab to do experiments on the source, there are template generation, generalized Hough transform code Platform: |
Size: 471040 |
Author:wanxl_xjtu |
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Description: 一种基于广义Hough变换的破碎图形的识别方法-Generalized Hough transform based on the recognition of broken graphics Platform: |
Size: 3072 |
Author:Lucy |
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Description: 广义霍夫变换边缘提取代码，目标是针对图像中某一标识，进行识别和提取-Generalized Hough transform edge detection code, the goal is for the image of a logo, to identify and extract Platform: |
Size: 4096 |
Author:吴安琪 |
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Description: 本压缩包是24篇关于HOUGH变换算法的小论文，里面有经典，广义，改进的方法。希望对写论文的朋友有所帮助。欢迎下载-This archive is a 24 HOUGH transform algorithm on a small paper, which has classical, generalized, improved methods. Friends want to help write the paper. Welcome to download Platform: |
Size: 8071168 |
Author:李克文 |
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Description: 这是一个自己写的广义霍夫变换提取不规则不提边缘的方法，希望大家喜欢-This is a generalized Hough transform to write their own not to mention irregular edge extraction method, hope you like Platform: |
Size: 114688 |
Author:蒋飞云 |
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Description: 一种基于随机Hough 变换圆检测的改进算法广义的 Hough 变换可以推广至检验任意形状。-Based on randomized Hough transform circle detection algorithm improved generalized Hough transform can be extended to test any shape. Platform: |
Size: 177152 |
Author:vicky |
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Description: 自己编写的广义霍夫变换，里面有测试图片，可以作为学习霍夫变换的入门-I have written a generalized Hough transform, there are test images, the Hough transform can be started as a learning Platform: |
Size: 106496 |
Author:蒋飞云 |
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Description: SIFT keypoints of objects are first extracted from a set of reference images[4] and stored in a database. An object is recognized in a new image by individually comparing each feature from the new image to this database and finding candidate matching features based on Euclidean distance of their feature vectors. From the full set of matches, subsets of keypoints that agree on the object and its location, scale, and orientation in the new image are identified to filter out good matches. The determination of consistent clusters is performed rapidly by using an efficient hash table implementation of the generalized Hough transform. Platform: |
Size: 700416 |
Author:ahmed |
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