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: 来自澳大利亚Qeensland大学的计算机视觉Matlab工具箱。
This Toolbox provides a number of functions that are useful in computer vision,
machine vision and related areas. It is a somewhat eclectic collection reflecting
the author s personal interest in areas of photometry, photogrammetry, colorimetry. It
covers functions such as image file reading and writing, filtering, segmentation,
feature extraction, camera calibration, camera exterior orientation, display,
color space conversion and blackbody radiators. The Toolbox, combined
with MATLAB and a modern workstation computer, is a useful and convenient
environment for investigation of machine vision algorithms. It is possible to use
MEX files to interface with image acquisition hardware ranging from simple
framegrabbers to Datacube servers.-from Australia Qeensland University of Computer Vision Matlab toolbox. This Toolbox provides a number of functions tha t are useful in computer vision, machine vision and related areas. It is a somewh at eclectic collection reflecting the author's personal interest in areas of photometry. photogrammetry, colorimetry. It covers functions such as image file reading and writing, filtering, segmentation and feature extraction. camera calibration. camera exterior orientation, display, color space conversion and blackbody radiator s. The Toolbox. MATLAB and combined with a modern workstation c omputer. is a useful and convenient environment for OAS stigation of machine vision algorithms. It is p ossible to use MEX files to interface with image acquisition hardware ranging from simple fram eg Platform: |
Size: 2459648 |
Author:杨愚根 |
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Description: 数据是来源于图像的不变矩提取，这里是用matlab实现的，后面可能需要用vc实现，不过也不一定，数据是由房房同学提供的，说不定不用做
利用最简单的bp神经网络来实现分类，一共两类车辆，这里是模拟实现把，识别效果还能用，先凑合着把，这里的特征提取也够玄乎。
-Data is derived from the image moment invariants extraction, this is achieved using matlab, behind vc may need to achieve, but not necessarily, the data is provided by atrial students might not need to do the simplest use of bp neural network to achieve the classification, a total of two types of vehicles, this is the realization of the simulation, also used to identify the effect, first make do with the, where feature extraction玄乎enough. Platform: |
Size: 1186816 |
Author:车林 |
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Description: 影像特征提取是影像匹配和三维信息提取的基础,是影像分析与单像处理技术领域中最重要的任务之一。有效的特
征提取算法是影像分析与处理的关键。从点特征定义出发,叙述了两种常用的点特征提取算子———Moravec算子和Forstner
算子的原理,利用Moravec算子和Forstner算子可以实现影像点特征的提取,并从提取点特征的准确度、提取速度对两种算子
进行比较,最后得出了有益的结论。-Image feature extraction and image matching are three-dimensional information extraction is based on image analysis and single-treatment technology as the most important tasks. Effective feature extraction algorithm for image analysis and processing are the key. Characteristics from the point of definition, describes the two commonly used feature extraction operator points--- Moravec operator and Forstner operator principle, using Moravec Operator and Forstner operator can realize image feature extraction point, and from the extraction point characteristics of accuracy, speed of extraction operator to compare the two, and finally arrive at a useful conclusion. Platform: |
Size: 778240 |
Author:刘镖峰 |
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Description: 数字图像处理骨架提取，能将一个二值图像的骨架提取出来进行后续处理。骨架是一种非常重要的图像几何特征，在图像几何形态分析及相关领域具有非常广泛的应用，利用骨架表示原始图像，可以在保持图像重要拓扑特征的前提下，减少图像中的冗余信息。骨架既能压缩图像数据，保留区域的连通性和拓扑性，描述区域轮廓的几何特征，又能使原始图像完全或部分地从骨架中恢复或重构。-Skeleton extraction of digital image processing, to a binary image of the skeleton extracted follow-up to deal with. Skeleton is a very important feature of the image geometry, geometry in image analysis and related fields with a very wide range of applications, the use of the original image frame that may be important in maintaining the topological characteristics of the image under the premise of reducing the redundant information in images. Skeleton can compress image data, the reserved area of connectivity and topology, and describes the outline of the geometric characteristics of the region, and it also enabled the original image in whole or in part to recover from the skeleton or remodeling. Platform: |
Size: 72704 |
Author:hujiang |
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Description:
在特征提取阶段，研究了PCA, 2DPCA, (2D) 2PCA, DiagPCA, DiagPCA-F-2DPCA等多
种方法。不同于基于图象向量的PCA特征提取，由于2DPCA, (2D) ZPCA, DiagPCA和
DiagPCA-I-2DPCA的特征提取都直接基于图象矩阵，计算量小，所以特征的提取速度明
显高于PCA方法。-In the feature extraction stage, the study of the PCA, 2DPCA, (2D) 2PCA, DiagPCA, DiagPCA-F-2DPCA and other methods. Vector is different from the PCA-based image feature extraction, as 2DPCA, (2D) ZPCA, DiagPCA and DiagPCA-I-2DPCA the feature extraction are directly based on image matrix, a small amount of calculation, so the speed of feature extraction method was significantly higher than PCA . Platform: |
Size: 45056 |
Author:付采 |
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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: This project deals with the tracking and following of single object in a sequence of frames
and the velocity of the object is determined. Algorithms are developed for improving
the image quality, segmentation, feature extraction and for deterring the velocity. The
developed algorithms are implemented and evaluated on TMS320C6416T DSP Starter
Kit (DSK). Segmentation is performed to detect the object after reducing the noise from
that scene. The object is tracked by plotting a rectangular bounding box around it in
each frame. The velocity of the object is determined by calculating the distance that the
object moved in a sequence of frames with respect to the frame rate that the video is
recorded. The algorithms developed can also be used for other applications (real time,
object classi cation, etc.). Platform: |
Size: 1197056 |
Author:vikas |
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Description: 基于内容的图像检索主颜色直方图的提取，从图像的获取到直方图现实到特征的提取，最后根据图像检索出与所给图像最相似的图像。-Content-based image retrieval dominant color histogram extracted from the images to get to the real histogram feature extraction, the last based on the image to retrieve the image with the most similar images. Platform: |
Size: 7168 |
Author:Jennifer |
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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: Gabor滤波器经常被用于形状检测和特征提取，比如增强指纹图像。本代码用matlab实现了一个二维Gabor滤波器。
代码使用如下：
function [G,gabout] = gaborfilter1(I,Sx,Sy,f,theta)
from gaborfilter1 with different f(Frequency) and theta(Angle).
for example
f:0,2,4,8,16,32
theta = 0,pi/3,pi/6,pi/2,3pi/4
then for any input image like(eg. stereo.jpg)
you have 6x5 = 30 filtered images.
You can choose your desired angles or frequencies.
You can put nominaly Sx & Sy = 2,4 or some one else.
For instance I tested above example on ( cameraman.tif )(in MATLAB pictures)
I = imread( cameraman.tif )
[G,gabout] = gaborfilter1(I,2,4,16,pi/3)
figure,imshow(uint8(gabout)) -Gabor filters are often used for shape detection and feature extraction, such as the enhanced fingerprint image. Matlab implementation of the code is a two-dimensional Gabor filter. Use the following code: function [G, gabout] = gaborfilter1 (I, Sx, Sy, f, theta) from ' gaborfilter1' with different f (Frequency) and theta (Angle). For example f: 0,2,4, 8,16,32 theta = 0, pi/3, pi/6, pi/2,3 pi/4 then for any input image like (eg. stereo.jpg) you have 6x5 = 30 filtered images. You can choose your desired angles or frequencies. You can put nominaly Sx & Sy = 2,4 or some one else. For instance I tested above example on (' cameraman.tif' ) (in MATLAB pictures) I = imread (' cameraman.tif' ) [G, gabout] = gaborfilter1 (I, 2,4,16, pi/3) figure, imshow (uint8 (gabout)) Platform: |
Size: 1024 |
Author:郑碧波 |
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Description: Melanoma is one of the deadly diseases among skin
cancer. Melanoma detection can be done by dermatological
screening and biopsy tests which are time consuming and
expensive that requires experts from medical field. Due to cost of
dermatologist to screen every patient, an automated system is
needed for melanoma detection so that death rates can be
minimized if detected early. It can be done using various image
processing techniques. An important step in the automated
system of melanoma detection is the segmentation process which
locates the border of skin lesion in order to separate the lesion
part from background skin for further feature extraction. Platform: |
Size: 2911232 |
Author:Deep123
|
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Description: 本文主要对指纹图像进行三方面处理：图像预处理、特征提取和特征匹配。图像预处理包括四个步骤：图像分割、滤波增强、二值化、细化，对指纹图像进行预处理后，去除了原图像的冗余部分，方便后续的识别处理；特征提取主要是提取指纹图像细化后的端点和分叉点；特征匹配是利用两个指纹的图像进行特征点比较，来确定两幅图像是否来自于同一手指。
该课题为基于MATLAB的指纹识别系统，带有丰富的人机交互GUI界面。目前毕业设计选题中，传统指纹识别不易得到高分，基本都是对指纹进行处理，而没有比对两者指纹是不是同一个人，轻易被导师被否决。因此建议做成两个指纹进行比对，输出结果，以文字和声音方式播报结果。整个设计在一个GUI界面上完成。(This paper mainly deals with fingerprint image in three aspects: image preprocessing, feature extraction and feature matching. Image preprocessing includes four steps: image segmentation, filter enhancement, binarization and thinning. After preprocessing fingerprint image, the redundant part of the original image is removed to facilitate the subsequent recognition processing; feature extraction is mainly to extract the endpoint and bifurcation points after thinning fingerprint image; feature matching is to determine two images by comparing the feature points of two fingerprint images Whether it comes from the same finger.) Platform: |
Size: 2781184 |
Author:王春水 |
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Description: 本设计为基于MATLAB的指纹识别系统。本设计系统主要对指纹图像进行三方面处理：图像预处理、特征提取和特征匹配。图像预处理包括四个步骤：图像灰度化、滤波增强、二值化、细化，对指纹图像进行预处理后，去除了原图像的冗余部分，方便后续的识别处理；特征提取主要是提取指纹图像细化后的端点和分叉点；特征匹配是利用两个指纹的图像进行特征点比较，来确定两幅图像是否来自于同一手指。(This design is a fingerprint identification system based on MATLAB. This system mainly deals with fingerprint image in three aspects: image preprocessing, feature extraction and feature matching. Image preprocessing includes four steps: image grayscale, filter enhancement, binarization and thinning. After preprocessing the fingerprint image, the redundant part of the original image is removed for the convenience of subsequent recognition processing. Feature extraction is mainly to extract the endpoint and bifurcation points after thinning the fingerprint image. Feature matching is to determine the two images by comparing the feature points of the two fingerprint images Whether the image comes from the same finger.) Platform: |
Size: 3789824 |
Author:www.wobishe.com |
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Description: 本设计为基于MATLAB的指纹识别系统。带GUI可视化平台。本设计系统主要对指纹图像进行三方面处理：图像预处理、特征提取和特征匹配。图像预处理包括四个步骤：图像灰度化、滤波增强、二值化、细化，对指纹图像进行预处理后，去除了原图像的冗余部分，方便后续的识别处理；特征提取主要是提取指纹图像细化后的端点和分叉点；特征匹配是利用两个指纹的图像进行特征点比较，来确定两幅图像是否来自于同一手指。(This design is based on Matlab fingerprint identification system. With GUI visualization platform. This design system mainly processes fingerprint image in three aspects: image preprocessing, feature extraction and feature matching. Image preprocessing includes four steps: image graying, filtering enhancement, binarization and thinning. After preprocessing the fingerprint image, the redundant part of the original image is removed to facilitate the subsequent identification processing; feature extraction is mainly to extract the refined endpoint and bifurcation point of fingerprint image; feature matching is to determine two images by comparing the feature points of two fingerprint images If it comes from the same finger.) Platform: |
Size: 3466240 |
Author:MATLAB道长 |
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Description: 本设计为基于MATLAB特征点匹配的指纹识别系统。带有一个GUI界面。主要对指纹图像进行三方面处理：图像预处理、特征提取和特征匹配。图像预处理包括四个步骤：图像分割、滤波增强、二值化、细化，对指纹图像进行预处理后，去除了原图像的冗余部分，方便后续的识别处理；特征提取主要是提取指纹图像细化后的端点和分叉点；特征匹配是利用两个指纹的图像进行特征点比较，来确定两幅图像是否来自于同一手指。(This design is a fingerprint recognition system based on MATLAB feature point matching. With a GUI interface. Fingerprint image is mainly processed in three aspects: image preprocessing, feature extraction and feature matching. Image preprocessing includes four steps: image segmentation, filter enhancement, binarization and thinning. After preprocessing the fingerprint image, the redundant part of the original image is removed to facilitate the subsequent identification processing; feature extraction is mainly to extract the refined endpoint and bifurcation point of fingerprint image; feature matching is to determine the two images by comparing the feature points of two fingerprint images Is it from the same finger.) Platform: |
Size: 3682304 |
Author:可乐一生 |
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Search - feature extraction from image matlab