Description: This sample program loads an image and plots the graph of its horizontal or vertical projection
The Document contains an image and a projection vector.
When the Open command is sued, the image is loaded from a file.
Then, a projection along a row or a column is applied and stored in the vector.
The code relative to the eVision functions has been generated
by EasyAccess while executing the lesson Sample programs -> ImgProjection Platform: |
Size: 25571 |
Author:magnilog |
Hits:
Description: This sample program loads an image and plots the graph of its horizontal or vertical projection
The Document contains an image and a projection vector.
When the Open command is sued, the image is loaded from a file.
Then, a projection along a row or a column is applied and stored in the vector.
The code relative to the eVision functions has been generated
by EasyAccess while executing the lesson Sample programs -> ImgProjection -This sample program loads an image and plots the graph of its horizontal or vertical projectionThe Document contains an image and a projection vector.When the Open command is sued, the image is loaded from a file.Then, a projection along a row or a column is applied and stored in the vector.The code relative to the eVision functions has been generated by EasyAccess while executing the lesson Sample programs-> ImgProjection Platform: |
Size: 25600 |
Author:magnilog |
Hits:
Description: 一个是求一个图像的垂直和水平的灰度积分投影，一个是将视频转化成帧。可运行-One is seeking an image of the vertical and horizontal integral projection of the gray one is framing the video conversion. Run Platform: |
Size: 1024 |
Author:晓晖 |
Hits:
Description: 图像拼接原代码，ＶＣ＋＋，图像拼接在制作全景图的过程中具有重要作用。对多幅图像进行特定模式投影后,用约束的相位相关度法求取水平垂直偏移量,然后寻找最佳缝合线,实现图像拼接,最后采用多分辨率算法对全图进行拼接处理去除曝光差异和鬼影。整个过程用VisualC++加以实现,实验结果验证了算法的有效性。-The original code image mosaic, VC , image mosaic panorama in the production process plays an important role. Of multiple images for a particular mode of projection, the use of constrained phase correlation method to strike a horizontal and vertical offset, and then find the best suture to achieve image mosaic, and finally the use of multi-resolution algorithm to deal with the whole graph splicing to remove exposure differences and ghosting. The whole process used VisualC++ Be achieved, experimental results verify the effectiveness of the algorithm. Platform: |
Size: 212992 |
Author:jms |
Hits:
Description: 本程序通过水平和垂直灰度投影，框选出灰度面部图像中的眼睛区域。-This program is used to locate eye region in gray facial image by means of horizontal and vertical projection. Platform: |
Size: 64512 |
Author:胡刚 |
Hits:
Description: 实现水槽沙波激光照片沙地波形提取功能。主要用到了水平投影的算法实现了对图像很好的处理。-To achieve laser tank sandwave Extraction feature photos sand wave. The main use of the horizontal projection of the algorithm achieved a very good handle on the image. Platform: |
Size: 1024 |
Author:li ying yan |
Hits:
Description: 该函数是对图像进行处理得到水平和垂直方向的投影曲线图，（有详细解释）并对图像进行骨架处理-The function is processed by the image horizontal and vertical projection graphs, (with detailed explanation) and image processing framework Platform: |
Size: 2048 |
Author:Simon |
Hits:
Description: 图像拼接在制作全景图的过程中具有重要作用。对多幅图像进行特定模式投影后,用约束的相位相关度法求取水平垂直偏移量,然后寻找最佳缝合线,实现图像拼接,最后采用多分辨率算法对全图进行拼接处理去除曝光差异和鬼影。-Image matching plays an important role in the process of producing panorama. After specific pattern of multiple image projection, with the constraints of the phase correlation method to calculate the horizontal vertical offsets, and look for the best sutures, realize image mosaicing, and finally the multiresolution algorithm is adopted to map splicing processing which can reduce exposure difference and ghost. Platform: |
Size: 4367360 |
Author:amy |
Hits:
Description: The basic steps of the connected-component text extraction algorithm are given below,
and diagrammed in Figure 10. The details are discussed in the following sections.
1. Convert the input image to YUV color space. The luminance(Y) value is used for
further processing. The output is a gray image.
2. Convert the gray image to an edge image.
3. Compute the horizontal and vertical projection profiles of candidate text regions
using a histogram with an appropriate threshold value.
4. Use geometric properties of text such as width to height ratio of characters to
eliminate possible non-text regions.
5. Binarize the edge image enhancing only the text regions against a plain black
background.
6. Create the Gap Image (as explained in the next section) using the gap-filling
process and use this as a reference to further eliminate non-text regions the
output.
-The basic steps of the connected-component text extraction algorithm are given below,
and diagrammed in Figure 10. The details are discussed in the following sections.
1. Convert the input image to YUV color space. The luminance(Y) value is used for
further processing. The output is a gray image.
2. Convert the gray image to an edge image.
3. Compute the horizontal and vertical projection profiles of candidate text regions
using a histogram with an appropriate threshold value.
4. Use geometric properties of text such as width to height ratio of characters to
eliminate possible non-text regions.
5. Binarize the edge image enhancing only the text regions against a plain black
background.
6. Create the Gap Image (as explained in the next section) using the gap-filling
process and use this as a reference to further eliminate non-text regions the
output.
Platform: |
Size: 41984 |
Author:Lee Kurian |
Hits:
Description: This paper presents a novel approach for detecting vehicles for driver assistance. Assuming flat
roads, vanishing point is first estimated using Hough transform space to reduce the
computational complexity. Localization of vehicles is carried using horizontal projection on the
horizontal gradient image below vanishing point. An uppermost and lowermost peak in the
horizontal profile corresponds to search space of vehicles. Binarization of search space on the
horizontal gradient image is done using Otsu algorithm. Verification of vehicles is carried
through a series of rule based classifiers constructed using statistical moments, observing peaks
in vertical profiling, vehicle texture, symmetry and shadow property. Experimentation was
carried out on flat highway roads and detection rate of vehicles is nearly found to be 88.23 -This paper presents a novel approach for detecting vehicles for driver assistance. Assuming flat
roads, vanishing point is first estimated using Hough transform space to reduce the
computational complexity. Localization of vehicles is carried using horizontal projection on the
horizontal gradient image below vanishing point. An uppermost and lowermost peak in the
horizontal profile corresponds to search space of vehicles. Binarization of search space on the
horizontal gradient image is done using Otsu algorithm. Verification of vehicles is carried
through a series of rule based classifiers constructed using statistical moments, observing peaks
in vertical profiling, vehicle texture, symmetry and shadow property. Experimentation was
carried out on flat highway roads and detection rate of vehicles is nearly found to be 88.23 Platform: |
Size: 188416 |
Author:Chidanand |
Hits:
Search - horizontal projection of an image