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Finger vein identification, as an important part in biological feature identification, has been widely used in various fields. The finger vein image shows the vein structure captured under infrared ray. This paper firstly ado pts the principal component analysis (PCA) to extract low-dimension features of vein images; and then constructs a multi-layer neural network classifier based on BP Neural Network,...
Digital image processing is one of the key feature for security, authentication and authorization over the image. Image segmentation is an essential step to evaluate images and extract data from them. The separation of an image into significant structures, image segmentation is frequently an critical step in image analysis, visualization, object representation and many other image processing tasks...
The present work represents a non contact, machine vision based method to estimate ripeness level of Guava fruit. The fruit under test is classified as green, ripe, overripe and spoiled using a web camera based computer vision system. This simple method uses a combination of digital web camera; computer and indigenously developed GUI based software to measure and analyze the surface color of the fruits...
The Cardiac Ejection Fraction (EF) is an essential criterion in cardiovascular disease prognosis. In clinical routine, EF is often computed from manually or automatically segmenting the Left Ventricle (LV) in End-dyastole and Endsystole frames, which is prohibitively time consuming and needs user interactions. In this paper, we propose a method to minimize user effort and estimate the EF directly...
The present study is conducted to assist radiologists in marking tumor boundaries and in decision making process for multiclass classification of brain tumors. Primary brain tumors and secondary brain tumors along with normal regions are segmented by Gradient Vector Flow (GVF)-a boundary based technique. GVF is a user interactive model for extracting tumor boundaries. These segmented regions of interest...
A novel image fusion algorithm using adaptive Unit-Linking Pulse Coupled Neural Networks (ULPCNN) is put forward. Firstly, ULPCNN threshold function is improved, and then the null interconnection and the adaptive interconnection ULPCNN are formed. Secondly, the nonlinear mapped ULPCNN time matrix can be obtained, which can represent the characteristic of the single pixel, but can reflect the pixel...
In this work, a method for automatic identification of round diatoms based on image texture features is presented. This method combines segmentation adjustment by curve fitting and texture measures based on the spectrum features. The classification accuracy was tested using leave on out methods. With classification carried out using a BP neural network we attained 96.2% accuracy from a set of image...
Designing an effective classifier has been a challenging task in the previous methods proposed in the literature. In this paper, we apply a combination of feature selection algorithm and neural network classifier in order to recognize five types of white blood cells in the peripheral blood. For this purpose, first nucleus and cytoplasm are segmented using Gram-Schmidt method and snake algorithm, respectively;...
This paper describes research work in developing an intelligent model for classifying selected rubber tree series clones based on shape features using image processing techniques. Sample of rubber tree seeds are captured using digital camera where the RGB color image are processed involving segmentation algorithm which includes thresholding and morphological technique. Shape features such as area,...
Line matching is useful in many computer vision tasks such as object recognition, image registration, and 3D reconstruction. The literature on line matching has advanced in recent years, nevertheless, compared to other features (such as point and region matching approaches) it has made little progress. Especially, very few algorithms address the problem of image scaling. In this paper, we present...
In this paper, we provide a method to recognize weed seeds based on computer vision. According to the stability and genetic characteristics described in phytotaxonomy for weed seeds. Image processing method encompasses threshold segmentation and smooth processing etc, nine features parameters are extracted by image processing, which keep RST invariance. The principal components analysis method is...
Segmentation is the foundation for image processing, and it is also one of the basic works in image recognition and image analysis. This paper introduces SOM neural network and takes out principal component analysis with 12 components on the four colorful spaces RGB, YIQ, XYZ and Ycbcr. Then the methods 3PCA, the 2PCA and the 1PCA are used respectively to segment the color milk somatic cell images...
In last decades, Gabor feature based face representation presented promising results in face recognition applications due to its robustness against illumination and facial expression changes. The power of Gabor lays in its properties like the computation of local structure corresponding to different spatial frequency (scale), spatial localization, orientations and inessentiality of manual annotations...
In this paper we discussed and implemented Morphological method for face recognition using fiducial points. A new technique for extracting facial features is suggested here. This method is independent of the face expressions. In recognition process, these fiducial point are fed as inputs to a Back propagation neural network for learning and identifying a person. So with the help of this technique,...
This paper presents an automated system for the classification of different digitized computed tomographic images of the human liver. The proposed system consists of four main steps. First, images were preprocessed to enhance the image contrast and segment the human liver images from background and surrounding organs. Second, five sets of features were extracted using: (1) statistical-based features,...
A novel method is proposed herein for handwritten digit segmentation in historical document images. It is based on one-class classifiers, which are used to distinguish isolated characters from touching characters. In contrast to other techniques based on feed forward neural networks, the proposed method does not require negative data in the training phase. Three methods for feature extraction and...
This paper adopts unsupervised on-line shape learning for image analysis tasks, removing the requirement for a pre-defined set of templates and allowing the system to handle novel objects. This learning approach was chosen for its simplicity and extensibility. The results show that the size and shape features are sufficient for accurate object classification. We briefly focused on how to use and work...
This paper presents an automatic semantic concept extraction method which employs low level visual feature fusion. Both static and dynamic feature fusion approaches are studied and evaluated. The main contributions of this paper are: a novel dynamic feature fusion approach inspired from coding is proposed to create compact yet rich signatures; Statistical study of descriptors with and without fusion...
Most digital still color cameras use a single electronic sensor (CCD or CMOS) overlaid with a color filter array. At each pixel location only one color sample is taken, and the other colors must be interpolated using neighboring samples. This color plane interpolation is known as demosaicking, which is one of the important tasks in a digital camera pipeline. Demosaicked images possess spatially periodic...
This paper presents an eye-gaze tracking system based on the image processing. All the computations are performed in software and the system just needs a PC camera attached to the user's computer. We first extract the facial regions form the images using the skin-color model and connected-component analysis. Then the eye regions are detected by employing the rules and area segmentation. After the...
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