Description: 本代码可以运行出结果，用于对比两个图像的对比匹配-The code can run results for the contrast comparing the two images match Platform: |
Size: 1744896 |
Author:chai rugang |
Hits:
Description: Images passed to your application are not always of the same size as your
templates. To compare them you might need to up scale or down scale them to
the template size. It is preferred that when comparing you check the size of the
template to be compared then adjust the size of the incoming image to be
compared. For simplicity you can do it for the following sample only.
Hints:
• Upsampling by interpolation/replacement
• Downsampling by interpolation/replacement
• Refer to sample code given in first tutorial for ways to create
images of similar width and height.-Images passed to your application are not always of the same size as your
templates. To compare them you might need to up scale or down scale them to
the template size. It is preferred that when comparing you check the size of the
template to be compared then adjust the size of the incoming image to be
compared. For simplicity you can do it for the following sample only.
Hints:
• Upsampling by interpolation/replacement
• Downsampling by interpolation/replacement
• Refer to sample code given in first tutorial for ways to create
images of similar width and height. Platform: |
Size: 3468288 |
Author:marwa |
Hits:
Description: 基于肤色模型和迭代阈值的人脸轮廓提取VC 6.0源码，用于真彩色图像的人脸轮廓提取，并集合四个经典轮廓提取算法效果对比程序-Based on skin color model and the iterative threshold of the human face contour extraction VC 6.0 source code, true color images for face contour extraction, and a collection of four classic contour extraction algorithm for comparing program results Platform: |
Size: 4045824 |
Author:孟盼盼 |
Hits:
Description: This paper presents a new approach to image segmentation using Pillar K-means algorithm. This
segmentation method includes a new mechanism for grouping the elements of high resolution images in order to
improve accuracy and reduce the computation time. The system uses K-means for image segmentation optimized by
the algorithm after Pillar. The Pillar algorithm considers the placement of pillars should be located as far from each
other to resist the pressure distribution of a roof, as same as the number of centroids between the data distribution. This
algorithm is able to optimize the K-means clustering for image segmentation in the aspects of accuracy and
computation time. This algorithm distributes all initial centroids according to the maximum cumulative distance metric.
This paper evaluates the proposed approach for image segmentation by comparing with K-means clustering
algorithm and Gaussian mixture model and the participation of RGB, HSV, HSL and CIELAB color spaces.
-This paper presents a new approach to image segmentation using Pillar K-means algorithm. This
segmentation method includes a new mechanism for grouping the elements of high resolution images in order to
improve accuracy and reduce the computation time. The system uses K-means for image segmentation optimized by
the algorithm after Pillar. The Pillar algorithm considers the placement of pillars should be located as far from each
other to resist the pressure distribution of a roof, as same as the number of centroids between the data distribution. This
algorithm is able to optimize the K-means clustering for image segmentation in the aspects of accuracy and
computation time. This algorithm distributes all initial centroids according to the maximum cumulative distance metric.
This paper evaluates the proposed approach for image segmentation by comparing with K-means clustering
algorithm and Gaussian mixture model and the participation of RGB, HSV, HSL and CIELAB color spaces.
Platform: |
Size: 974848 |
Author:Deepesh |
Hits:
Search - code for comparing images