Description: we embed watermarks in the original circles. In this way, the synchronization issue for watermark embedding and detecting can be best solved. Simulation results show that this method can resist variety of Geometry Attack, such as rotation, scaling, shearing and other common signal attacks. Platform: |
Size: 1106 |
Author:xiaxianming |
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Description: we embed watermarks in the original circles. In this way, the synchronization issue for watermark embedding and detecting can be best solved. Simulation results show that this method can resist variety of Geometry Attack, such as rotation, scaling, shearing and other common signal attacks. -we embed watermarks in the original circles. In this way, the synchronization issue for watermark embedding and detecting can be best solved. Simulation results show that this method can resist variety of Geometry Attack, such as rotation, scaling, shearing and other common signal attacks . Platform: |
Size: 1024 |
Author:xiaxianming |
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Description: The present report outlines the design, implementation and performance of the application of Hough
Transform (HT) to detect circles in arbitrary pictures.
Detecting circles in arbitrary pictures involves a two step process in this project. First, edges within the
image have to be detected then, by means of a voting mechanism the most probable circles will be
located.
Edge detection goal is to look for boundary locations that naturally happen between objects. Objects
normally have continuous intensity values therefore sudden changes in that pattern might indicate a
boundary condition. Changes of intensity in one direction can be calculated by the gradient operator
however, noise can alter meaningful edges. Convolution is then normally applied to account for a small
amount of smoothing, thus reducing noise.-The present report outlines the design, implementation and performance of the application of Hough
Transform (HT) to detect circles in arbitrary pictures.
Detecting circles in arbitrary pictures involves a two step process in this project. First, edges within the
image have to be detected then, by means of a voting mechanism the most probable circles will be
located.
Edge detection goal is to look for boundary locations that naturally happen between objects. Objects
normally have continuous intensity values therefore sudden changes in that pattern might indicate a
boundary condition. Changes of intensity in one direction can be calculated by the gradient operator
however, noise can alter meaningful edges. Convolution is then normally applied to account for a small
amount of smoothing, thus reducing noise. Platform: |
Size: 1107968 |
Author:donna |
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Description: MFC基于最小二乘法检测圆的算法，只能检测单个圆。-MFC-based least squares algorithm for detecting circles, can only detect a single round. Platform: |
Size: 4126720 |
Author:gu wei |
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Description: Hough变换检测同心圆，效果较好，附有详细的说明。-Detecting circles using hough transformation.The result seems good and details are tagged. Platform: |
Size: 1024 |
Author:李诗诗 |
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Search - detecting circles