Description: We propose an algorithm for facial expression recognition which can classify the given image into one of the seven basic facial expression categories (happiness, sadness, fear, surprise, anger, disgust and neutral). PCA is used for dimensionality reduction in input data while retaining those characteristics of the data set that contribute most to its variance, by keeping lower-order principal components and ignoring higher-order ones. Such low-order components contain the "most important" aspects of the data. The extracted feature vectors in the reduced space are used to train the supervised Neural Network classifier. This approach results extremely powerful because it does not require the detection of any reference point or node grid. The proposed method is fast and can be used for real-time applications. Platform: |
Size: 21504 |
Author:mhm |
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Description: this code can recognize geometrical objects like squares triangles rectangles circuls ovals from an image Platform: |
Size: 1024 |
Author:Abdijjeh |
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Description: Gabor小波变换技术对医学CT图像进行纹理特征分类时，由于图像拍摄角度的变化会造成分类的误差。针对以上问题，在Gabor小波变换的基础上提出一种用于分析旋转不变医学图像的方法。该方法采用旋转规范化，即特征元素的循环移位使规范化后所有的图像都具有相同的主方向。实验结果表明，加入旋转规范化循环算子的Gabor小波变换在医学CT图像纹理特征分类时能够达到较好的精确度。-Gabor wavelet transform lacks in its ability to classify the medical CT image if it’s rotation invariant image. Aiming at the problem, an approach is presented for rotation invariant medical texture classification based on Gabor wavelet transform. Rotation normalization is achieved by circular shift of the feature elements, so that all images have the same dominant direction. Experimental result shows that Gabor wavelet transform with circular operator of rotation normalization has well precision to classify the medical CT image. Platform: |
Size: 407552 |
Author:li |
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Description: 以镜头光圈分类 镜头有手动光圈（manual iris）和自动光圈（auto iris）之分，配合摄象机使用，手动光圈镜头适合于亮度不变的应用场合，自动光圈镜头因亮度变更时其光圈亦作自动调整，故适用亮度变化的场合。-This paper describes an end-to-end method for extracting
moving targets from a real-time video stream, classifying
them into predefined categories according to imagebased
properties, and then robustly tracking them. Moving
targets are detected using the pixel wise difference between
consecutive image frames. A classification metric is applied
these targets with a temporal consistency constraint
to classify them into three categories: human, vehicle or
background clutter. Once classified, targets are tracked by
a combinationof temporal differencing and templatematching. Platform: |
Size: 401408 |
Author:陈思宇 |
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Description: We present a method to learn and recognize object class
models from unlabeled and unsegmented cluttered scenes
in a scale invariant manner. Objects are modeled as flexible
constellations of parts. A probabilistic representation is
used for all aspects of the object: shape, appearance, occlusion
and relative scale. An entropy-based feature detector
is used to select regions and their scale within the image. In
learning the parameters of the scale-invariant object model
are estimated. This is done using expectation-maximization
in a maximum-likelihood setting. In recognition, this model
is used in a Bayesian manner to classify images. The flexible
nature of the model is demonstrated by excellent results
over a range of datasets including geometrically constrained
classes (e.g. faces, cars) and flexible objects (such
as animals). Platform: |
Size: 3409920 |
Author:Daria |
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Description: Image Texture Classification Using Combined Grey Level Co-Occurrence Probabilities and Support Vector Machines
Texture refers to properties that represent the surface or structure of an object and is defined as something consisting of mutually related elements. The main focus in this study is to do texture segmentation and classification for texture digital images. Grey level co-occurrence probabilities (GLCP) method is being used to extract features from texture image. Gaussian support vector machines (GSVM) have been proposed to do classification on the extracted features. A popular Brodatz texture album had been chosen to test out the result. In this study, a combined GLCP-GSVM shows an improvement over GLCP in terms of classification accuracy. Platform: |
Size: 659456 |
Author:Chetna Kharkar |
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Description: With the fast development of the computer technology and information processing technology, the problem of information security is becoming more and more important. Information hiding is usually used to protect the important information from disclosing when it is transmitting over an insecure channel. Digital image encryption is one of the most important methods of image information hiding and camouflage. The image encryption techniques mainly include compression methodology, modern cryptography mechanism, chaos techniques, DNA techniques, and so on. In this paper, we summarize the main encryption algorithms and classify them based on the means. In particular, chaos-based and DNA cryptography-based image encryption algorithms are illustrated and analyzed in detail. Finally, the future direction in this field is discussed. Platform: |
Size: 76800 |
Author:mrinal |
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Description: Diabetic retinopathy contributes to serious health problem in many parts of the world. With the motivation of the needs of the medical community system for early screening of diabetics and other diseases, a computer aided diagnosis system is proposed. This work is aimed to develop an automated system to analyze the retinal images for important features of diabetic retinopathy using image processing techniques and an image classifier based on artificial neural network which classify the images according to the disease conditions.-Diabetic retinopathy contributes to serious health problem in many parts of the world. With the motivation of the needs of the medical community system for early screening of diabetics and other diseases, a computer aided diagnosis system is proposed. This work is aimed to develop an automated system to analyze the retinal images for important features of diabetic retinopathy using image processing techniques and an image classifier based on artificial neural network which classify the images according to the disease conditions. Platform: |
Size: 1200128 |
Author:arun |
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Description: 针对目前传统的枸杞分级主要采用人工方法， 费时费力且效率不高的缺点， 提出了一种基于机器视觉技术对枸杞
进行自动分类的方法。 采用数字图像处理技术对枸杞图像进行了预处理、 分割 ， 从而提取枸杞的色泽、 大小及形状等特征
参数； 用 K-means 算法对特征进行聚类， 得到枸杞相应等级的基准； 根据聚类分析得到的基准采用最小距离分类器对枸杞
进行分级。 实验结果表明 ， 该方法能够准确快速地对不同色泽和大小的枸杞进行分类。-Traditional wolfberry sorting primarily uses artificial method. It has time-consuming and inefficient shortcomings. An
automatic wolfberry classification method based on machine vision is proposed. This paper uses digital image processing technology for wolfberry image pre-processing, segmentation and extraction of characteristic parameters of color, size and shape it
uses the K-means clustering feature to get the baseline of wolfberry appropriate level it grades wolfberry by minimum distance
classifier based on the trained benchmark. The experimental results show that this method can classify different colors and sizes
of wolfberry more accurately and quickly. Platform: |
Size: 1451008 |
Author:李祥龙 |
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Description: 将一幅图像划分成8*8图像块，计算灰度梯度共生矩阵，基于混合熵对每个图像块分类-divide an image into 8*8 image blocks,calculate gray-gradient-matrix, classify image blocks based on the mixed entropy Platform: |
Size: 1024 |
Author:王小卷 |
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Description: 如何编图形软件开发程序，画图过程的显示，图像分层显示
图层软件架构：
大型图形软件通用的架构，是用一个抽象类（CLayer）的多态派生类对不同图层代码分类管理。
（如果将各种图形绘制以及拖放过程在一个类中编写，最后这个类的代码将无比庞大而且难于维护）
a)支持画直线、矩形和椭圆等多种图形的软件，必须将鼠标按下、释放和拖动等事件联合处理；
b)支持绘图过程中的显示；
c)支持鼠标光标掠过某个图层时显示热点跟踪（HotTrack）状态；
d)单击某个图层时显示选中状态；
e)拖动某个选中状态图层的功能（根据鼠标落点和起点的距离进行偏移）；
f)新绘制的图层和已绘制好选中的图层边框和填充颜色管理。(How to make graphical software development procedures, drawing process display, image layered display
Layer software architecture:
The general architecture of large graphics software is to classify and manage different layers of code using a polymorphic class derived from an abstract class (CLayer).
(if the various graphic drawing and drag and drop processes are written in a class, the code for that class will be extremely large and difficult to maintain)
A) software that supports graphics such as straight lines, rectangles, and ellipses. It must combine the mouse, press, release, and drag events together;
B) support display in drawing process;
C) displays the hot spot tracking (HotTrack) state when the mouse cursor is passed over a layer;
D) displays a selected state when clicked on a layer;
E) drag the function of a selected layer (offset by the distance between the mouse and the starting point);
F) the new layer and the selected layer border and fill color management.) Platform: |
Size: 53248 |
Author:天下001
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Search - classify an image