Description: Edge detection is one of the most commonly used operations in image analysis, and
there are probably more algorithms in the literature for enhancing and detecting edges
than any other single subject. The reason for this that edges form the outline of an
object. An edge is the boundary between an object and the background, and indicates
the boundary between overlapping objects. This means that if the edges in an image can
be identified accurately, all of the objects can be located and basic properties such as
area, perimeter, and shape can be measured. Since computer vision involves the
identification and classification of objects in an image, edge detections is an essential
tool. In this paper, we have compared several techniques for edge detection in image
processing. We consider various well-known measuring metrics used in image
processing applied to standard images in this comparison- Edge detection is one of the most commonly used operations in image analysis, and
there are probably more algorithms in the literature for enhancing and detecting edges
than any other single subject. The reason for this is that edges form the outline of an
object. An edge is the boundary between an object and the background, and indicates
the boundary between overlapping objects. This means that if the edges in an image can
be identified accurately, all of the objects can be located and basic properties such as
area, perimeter, and shape can be measured. Since computer vision involves the
identification and classification of objects in an image, edge detections is an essential
tool. In this paper, we have compared several techniques for edge detection in image
processing. We consider various well-known measuring metrics used in image
processing applied to standard images in this comparison Platform: |
Size: 448512 |
Author:Image |
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Description: 主要讲述基于图像的形状分析和分类的理论和实践方法，是图像处理和模式识别方面的好书-Mainly about theory and practice for image-based shape analysis and classification. A good book for image processing and pattern recognition. Platform: |
Size: 9947136 |
Author:fruitqin |
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Description: Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which is promising in exposing the more complicated correlation between original high-dimensional features. In this paper, we first talk about the basic ideas of PCA and kernel PCA, and then focus on the reconstruction of pre-images for kernel PCA. We also give an introduction on how PCA is used in active shape models (ASMs), and discuss how kernel PCA can be applied to improve traditional ASMs. Then we show some experiment results to compare the performance of kernel PCA and traditional PCA for pattern classification. We also implement the kernel PCA-based ASMs, and use it to construct human face models. Platform: |
Size: 403456 |
Author:Shinva |
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Description: 背景:快速的将心脏按其特征进行聚类可为后续统计分析和研究带来很大的便利.系统聚类法是将样品或变量按照其性质上的亲疏相似程度进行分类的一种多元统计方法.目的:提出用主成分一聚类分析的方法来描述心脏形态学形状并进行分类,对中国健康成年人的心脏X射线测量的各项指标进行综合评价.方法:搜集了36例健康成年人的胸片,并用MxLiteView软件手动测量了每幅胸片中代表心脏形态学形状常用的10个指标,用Matlab软件对测量指标进行主成分分析,然后对提取出的主成分进行聚类.结果与结论:主成分分析后提取出3个主成分变量,将36例样本用提取的主成分进行聚类,可将样本分为3类,分别代表了心脏的3类不同的心型.用该方法对心脏形态学形状进行快速分类,对心脏的统计和分类研究提供了一定的参考价值.-Background: The heart of their fast clustering features can bring great convenience for subsequent statistical analysis and research system clustering method is variable according to the sample or the nature of the closeness of the similarity of a multivariate statistical classification. methods Objective: proposed using principal component analysis of a clustering method to describe the shape of cardiac morphology and classification of the indicators Chinese healthy adult heart X-ray measurement of comprehensive evaluation methods: collected 36 cases of healthy adult person s chest and measuring MxLiteView software manual chest represents the heart of each piece of morphological shape commonly used 10 indicators to measure using Matlab software principal component analysis and principal components of the extracted clustering results aND CONCLUSION: after principal component analysis to extract the three main components of the variables, the 36 cases with samples extracted principal co Platform: |
Size: 666624 |
Author:王斌 |
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Description: 实现简单的灰度处理,主要调用BItmap函数库的内容，实现位处理功能。-This system mainly uses LabWindows/CVI and the visual software package Vision design system program. Vision has a wide range of machine vision development environment, the visual software package Vision machine vision algorithm contains all kinds of filtering, mathematical transformation, morphological analysis, calibration, classification identification, shape search and other basic computing functions. Process is as follows: image acquisition, gray processing, image segmentation, filtering denoising, computing area. Platform: |
Size: 2780160 |
Author:王寿春 |
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Search - Shape Analysis and Classification