Description: 通过等值线的绘制与填充过程分离的编程思想,先用OpenGL硬件加速的光栅化技术实现区域快速填充,然后利用MFC下的GD I绘图功能在相同的区域上绘制等值线。该方法不涉及到复杂的算法,用很简单的代码就能实现与商业软件视觉效果相媲美的等值线填充效果,且适用于离散区域为任意形状的多边形网格系统。-Through the contour of the drawing and filling process of the separation of programming ideas, use OpenGL hardware-accelerated rasterization technology to achieve regional rapid filling, then the use of MFC under the GD I drawing function in the same region on the map contour lines. This method does not involve complex algorithms, using very simple code could be achieved with the commercial software visual effects comparable to the effect of contour filling, and applied to discrete regions of arbitrarily shaped polygon grid. Platform: |
Size: 285696 |
Author:vcgirl |
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Description: SVG元素的功能很好的帮助了我们实现这个功能。由于在界面上我们有很多辅助内容，所以只能分出一部区域用来显示地图-SVG elements function very well helped us achieve this function. The interface because we have a lot of auxiliary content, so the separation of a region can only be used to display map Platform: |
Size: 65536 |
Author:范顺龙 |
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Description: 本文提出了一种视频运动对象的提取算法，该算法采用序列图像帧间差的高阶统计量（HOS）假设检验，确定运动对象的位置，自动分离运动区域与背景！-This paper presents a video moving object extraction algorithm, which uses inter-frame sequence of images of poor higher-order statistics (HOS) hypothesis testing to determine the location of moving objects, automatic separation of the motion region and background! Platform: |
Size: 200704 |
Author:feige |
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Description: 1. 理解彩色图像的颜色分层原理和方法
2. 利用颜色分层方法对彩色图像进行分层处理
3. 实现对身份证标准图像的背景分离和皮肤区域提取-1 understand the color image color layering principles and methods
(2) the use of color layered approach for hierarchical processing of color images
3 to achieve the standard of identity and separation of the background image to extract the skin region Platform: |
Size: 174080 |
Author:李雪 |
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Description: 包含两个代码，m文件，基于区域的分裂合并图像分割方法，predicate规定了分裂合并准则，splitmerge用于分裂合并，数字图像处理，冈萨雷斯，Matlab版第十章第四节，区域分离与合并-Contains two code, m files, split and merge region-based image segmentation methods, predicate to the provisions of division merger guidelines, the splitmerge used to split the merger, Digital Image Processing, Gonzalez, section 2.4 of Matlab version, regional separation and merge Platform: |
Size: 2048 |
Author:陶胡子 |
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Description: 首先利用人脸的色彩特征和自适应阈值法实现特征候选区域和人脸肤色区域的分离，然后利用人脸的几何特性将连通的特征候选
区域保留下来作为人脸特征区域。后续的特征提取可以在这些人脸特征区域中完成。一般的人脸特征提取方法都可以将该方法作为提高效
率的预处理操作。实验证明，该方法具有高效率、低计算量的特点，并且受人脸表情、图像角度和背景的影响较小。-First, the use of the color characteristics of the human face and adaptive thresholding feature candidate region and the skin color of the face area of separation, then the use of the geometric characteristics of the face to the connectivity features of candidate regions preserved as a facial feature region. Subsequent feature extraction can be completed in the area of these facial feature. General facial feature extraction methods the method as a pre-processing operations to improve efficiency. The experiments show that this method has the characteristics of high efficiency, low computational smaller, and the facial expression, the image angle and background. Platform: |
Size: 108544 |
Author:东方 |
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Description: Source separation problems in digital signal processing are those which are used to separate useful signal a mix of signals of interest, other signals and noise. The typical example of a source separation problem is the cocktail party problem, where many number of peoples are talking simultaneously in a room and a listener is trying to follow one of the discussions. The human brain can separate the voices of different people (source separation) using neurons in the auditory region. But source separation using digital signals is difficult task with the available techniques. Several approaches have been proposed for solving this issue but most of the techniques are still in development stages. Some of the successfully tested approaches are principal components analysis (PCA) and Independent components analysis (ICA). Both the approaches work well when there are no delays or echoes present-Source separation problems in digital signal processing are those which are used to separate useful signal a mix of signals of interest, other signals and noise. The typical example of a source separation problem is the cocktail party problem, where many number of peoples are talking simultaneously in a room and a listener is trying to follow one of the discussions. The human brain can separate the voices of different people (source separation) using neurons in the auditory region. But source separation using digital signals is difficult task with the available techniques. Several approaches have been proposed for solving this issue but most of the techniques are still in development stages. Some of the successfully tested approaches are principal components analysis (PCA) and Independent components analysis (ICA). Both the approaches work well when there are no delays or echoes present Platform: |
Size: 1662976 |
Author:bala |
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Description: QT下基于opencv3.2的HSV图像分离，通过查找V分量上ROI区域最亮值的坐标，找到H分量上对应坐标的3*3矩形平均H值，判断出该坐标所处位置的颜色分类(QT under the opencv3.2 based HSV image separation, by looking for the V component on the ROI region of the brightest value coordinates, find the corresponding 3*3 coordinates of the H components of the rectangular average H value, to determine the location of the coordinates of color classification) Platform: |
Size: 6144 |
Author:qxz49111752
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Description: 二维连通区域的凝聚算法，解决了传统凝聚算法出现目标分离的问题。(The agglomerative algorithm of two-dimensional connected region solves the problem of separation of targets in the traditional agglomerative algorithm.) Platform: |
Size: 545792 |
Author:大龙瞄
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Description: Diabetic retinopathy is an important branch of ophthalmology. Non - proliferative diabetic retinopathy is used to detect Microaneurysms in the early stage. Microaneurysms are verified through fundus images; where in the fine red-dots near the blood vessels confirm this defect. Conventional methods and their weak resolution seldom can identify to such accuracies. In this work, we present a procedure to identify Microaneurysms with higher accuracy. The retinal vessels are extracted, from collected fundus image, using a Gabor wavelet which delivers high accuracy output. For accurate analysis the image it is sub divided into two regions, neighborhood and non-vessel neighborhood for expediting support vector machine (SVM) analysis. Further the SVM engine is trained for positive and negative samples of identified region fundus images. Then by sliding window technique, the entire test image is analyzed limiting analysis by SVM engine for near vessel region. This improves overall performance of the analysis and permits time available for a deeper/ sensitivity analysis of near vessel areas. The logic and the code has been tested on sample images and the results have been satisfactory. Platform: |
Size: 561690 |
Author:praneethtm@gmail.com |
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Search - region separation