Description: 此设计要求能够实现将医学图像进行识别的过程，包括了图像预处理、图像特征提取及分类判决三大模块。在预处理这一步中主要实现的是将彩色图像转换为灰度图像，灰度图像的二值化，直方图修正，去除干扰、噪声以及差异，边缘增强等；第二模块是图像的特征提取。由于对象的物理与几何特性差异，在影像中表现为局部区域的灰度产生明显变化，形成影像特征，而图像特征提取就是对其进行加工、整理、分析、归纳以便提取构成目标影像的特征，得到能反映图像内容区别于其他事物的本质特征；分类判决作为第三模块，则是要在第二步的基础上采用某种分类判别函数与判别规则，通过对目标特征的分析和匹配来识别目标。-this design requirements to achieve medical image identification process, including the image preprocessing, Image feature extraction and classification ruling three modules. This pretreatment step in the main achievement of the color image is converted to grayscale images, the two gray-scale image value, histogram amendment to remove interference and noise variance Edge Enhancement; the second module is the image feature extraction. Because of the physical objects with geometric characteristics difference in the images showed local area Gray significant changes, the video features, feature extraction and image is its processing, compilation, analysis, summarized in order to extract constitute the target image features can be reflected image as distinct from the other characteristics of th Platform: |
Size: 6144 |
Author:uhih |
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Description: 一个提取图像小波纹理特征和颜色特征的源码-a wavelet extraction image texture features and color characteristics of the source Platform: |
Size: 1024 |
Author:陈晶晶 |
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Description: 能够提取出图像I的HOG特征，返回其幅值，角度和在各个角度上的幅值。
是进行其他针对图像的HOG特征提取的前一步。-I can extract images of HOG features, return to their amplitude, angle and perspective in all of the amplitude. Is the other for image feature extraction HOG step forward. Platform: |
Size: 1024 |
Author:郑帅 |
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Description: 子模式主成分分析首先对原始图像分块，然后对相同位置的子图像分别建立子图像集，在每一个子图像集内使用PCA方法提取特征，建立子空间。对待识别图像，经相同分块后，分别将子图像向对应的子空间投影，提取特征。最后根据最近邻原则进行分类。-Sub-mode principal component analysis first of the original image block, and then the same sub-image, respectively, the location of the establishment of sub-image set, in each sub-image set to use PCA to extract the features, the establishment of sub-space. Treatment to identify images, by the same block, the respective sub-image to the corresponding sub-space projection, feature extraction. Finally, according to the principle of nearest neighbor classification. Platform: |
Size: 165888 |
Author:tanghui |
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Description: matlab编写的图像处理程序，用于提取图像的纹理、颜色特征。-matlab image processing procedures to prepare for the extraction of image texture and color features. Platform: |
Size: 2048 |
Author:杨 |
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Description: Based on the information processing functionalities of spiking neurons, a spiking neural network model is proposed to extract features from a visual image. The network is constructed with a conductance-based integrate-and-fire neuron model and a set of specific receptive fields. The properties of the network are detailed in this paper. Simulation results show that the network is able to perform image feature extraction within a time interval of 100 ms. This processing time is consistent with the human visual system. The demonstrations show how the network can extract right-angle contours in a visual image. Based on this principle, many other image features can be extracted by analogy. The parallel processing mechanism of this network model is very promising for a hardware implementation based on VLSI or FPGA technology. Platform: |
Size: 288768 |
Author:sacoura31 |
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Description: 图像特征识别通过神经网络训练方法实现，是学习参考的好资料-you will need first to run the file that name "charGUI4.fig" and on the right side there is a load training set where you have to train the system first, run any data that is should be from 1 to 9 and 0 like ( 1 2 3 4 5 5 6 7 8 9 0) as many line as you want so if you have a traning data that is 10 columns by 5 lines then you have to assign that to the small boxes that name column and line. right now keep the#words as is because I plan to use it but I change my mind so that it is automatically count the word for you.
after you train the system then load any image test, from the "Load Image" once you upload the image then choose the "select" and then go to the uploaded image and select whatever you want to recognize, after that select "crop" to crop the exact part that you want to recognize after that selcet "Features Extraction" to extract the features after that choose the "recognize" to see the results up on the result box.
P.S. keep in mind that the program has been tested on the Platform: |
Size: 709632 |
Author: 反对撒 |
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Description: Feature extraction is a key issue in contentbased
image retrieval (CBIR). In the past, a number of
texture features have been proposed in literature,
including statistic methods and spectral methods.
However, most of them are not able to accurately capture
the edge information which is the most important texture
feature in an image. Recent researches on multi-scale
analysis, especially the curvelet research, provide good
opportunity to extract more accurate texture feature for
image retrieval. Curvelet was originally proposed for
image denoising and has shown promising performance.
In this paper, a new image feature based on curvelet
transform has been proposed. We apply discrete curvelet
transform on texture images and compute the low order
statistics from the transformed images. Images are then
represented using the extracted texture features. Retrieval
results show, it significantly outperforms the widely used
Gabor texture feature. Platform: |
Size: 1426432 |
Author:Swati |
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Description: 采用matlab，实现了gabor滤波的核心功能-gabor filter is very famous and really useful for texture feature extraction and image retrivel. attached code is wrriten well using matlab. More detail can be referenced in the paper <texture features for browsing and retrieval of image data> Platform: |
Size: 1209344 |
Author:rocky |
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Description: 近年来，随着互联网的高速发展，网上的多媒体信息也急剧增加，这些多媒体信息以图像为主。如何从浩瀚的图像数据库中快速、准确地找出所需要的图像，己成为一个备受关注的研究课题。有效地组织、管理和检索大规模的图像数据成为迫切需要解决的问题。于是基于内容的图像检索(Content-Based Image Retrieval: CBIR)作为一个崭新的研究领域出现了。
本课题拟研究、分析彩色图像红、绿、蓝三基色直方图的生成、特征提取和相似度等问题，应用图像的颜色信息—三基色直方图对图像进行检索。针对基于颜色的图像检索，本文采用应用广泛的RGB颜色空间来表示图像的颜色特征，对颜色分量进行等间隔量化并形成特征矢量并对特征矢量进行归一化处理，采用图像均匀分块的方法引入图像中色彩所处的位置信息，用距离度量函数进行图像的相似性匹配。在此基础上实现了基于三基色直方图算法的检索系统。
本文的研究和实践对于促进基于内容的图像数据库检索技术的应用具有一定的参考价值和实践意义。-With the rapid development of Internet, the multimedia information is booming. All this information is mostly images. Effective recognizing, management and searching all these images have been an emergent problem. This has led the rise of a new research and development field: Content-Based Image Retrieval (CBIR).
The topics to research, analysis color images red, green and blue color histogram generation, feature extraction and the similarity of the issues, application of the color image- trichromatic histogram of the image retrieval. Based on the color against the static image retrieval, this paper application of a wide range of RGB color space to indicate the color image features, the color components, such as spacing and quantitative characteristics of a feature vector and a normalization of vector processing, using uniform image block the introduction of the method in which the color image Location information, and distance measuring function similar to the image of the match. On Platform: |
Size: 408576 |
Author:qichao |
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Description: 基于susan算法的角点检测程序,提供对图像特征准确，鲁棒，快速的提取-Susan algorithm based on corner detection procedures, providing an accurate image features, robust, fast extraction Platform: |
Size: 6144 |
Author:陈晓广 |
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Description: 计算机人脸识别技术( Face Reocgnition)就利用计算机分析人脸图像,从中提取出有效的识别信息,用来辨认身份的一门技术。[ 1 ]即对已知人脸进行标准化处理后,通过某种方法和数据库中的人脸样本进行匹配,寻找库中对应人脸及该人脸相关信息。人脸自动识别系统有两个主要技术环节,一是人脸定位,即从输入图像中找到人脸存在的位置,将人脸从背景中分割出来,二是对标准化后的人脸图像进行特征提取和识别。本文中介绍的PCA (特征脸)方法就是一种常用的人脸
特征提取方法。-Computer Face Recognition Technology (Face Reocgnition) on the use of computer analysis of facial image, to extract the valid identification information used to identify the status of a technology. [1] that is known to standardize treatment of face, through a method and a database of face samples for matching, search library, the corresponding face and the face-related information. Automatic face recognition system has two main technical aspects, first, face location, that is, from the input image to find the location of the face there, the faces will be split out from the background, the second is, the standard features of face images extraction and recognition. Described in this paper PCA (Eigenfaces) method is a common facial feature extraction method. Platform: |
Size: 224256 |
Author:Highjoe |
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Description: 毕设内容，高光谱图像基本处理，主要实现单波段提取与伪彩色图像合成功能，提供程序框架，更多功能只需嵌入即可。另用MATLAB写了其GUI。-Complete set content, the basic processing of hyperspectral images, mainly for single-band pseudo-color image extraction and synthesis, to provide procedural framework, more features can only embed. MATLAB was also used their GUI. Platform: |
Size: 12288 |
Author:张磊 |
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Description: 对空间卫星、飞机等图像进行特征提取，提取的特征包括面积，周长，紧密度，及离心率-Space satellites, aircraft and other image feature extraction, the extracted features include area, perimeter, compactness, and eccentricity Platform: |
Size: 98304 |
Author:陈铭 |
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Description: 对图像的纹理特征进行提取，主要基于灰度共生矩阵，然后计算二次统计量（即特征）-Texture features for image extraction, mainly based on gray level co-occurrence matrix, and then calculate the amount of secondary statistics (ie features) Platform: |
Size: 7168 |
Author:亚会 |
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Description: MATLAB提取图像特征中的基于矩特征的图像特征提取，对图像识别很有用-MATLAB image feature extraction based on the characteristics of the moment of image features extraction on image recognition is very useful Platform: |
Size: 51200 |
Author:王东明 |
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Description: 纹理图像的特征提取，这对日后用MATLAB提取相关特征值有一定的引导作用-Texture image feature extraction, which extracts relevant features using MATLAB future value of certain guide Platform: |
Size: 20043776 |
Author:葛静 |
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Search - image features extraction matlab