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Moving Object detection is a challenging tool because shape and size of the object in a video vary significantly according to camera direction, partial occlusion and poses. Significant research has been carried out for detecting people in videos. Traditional methods for detecting human used sliding window approach which involved scanning various sizes of windows across an image. Hence in this work...
Features and Properties of an image represents the quality of the given image. In this paper Analysis of Image Properties for Classifying Different Images into Clusters (AIPCDIC) we present the overview of various Features and properties which can be extracted from a gray image are presented. The basic image properties like Brightness, Contrast, Entropy, Skewness, Kurtosis, Spatial Frequency, Visibility...
A hybrid scheme for the image segmentation of high-resolution images is proposed in this study. Our methodology is based on combining both supervised and unsupervised segmentation. The entire process is performed in the frequency domain, rather than the spatial domain, using the Shift Invariant Shearlet Transform (SIST). Initially, the input image is filtered using an anisotropic filter to enhance...
The most widely used in the field of visual object recognition descriptive features are shape based features. Identify objects in the image, contour and region shape descriptors based on two main topics to be examined. In order to describe objects with lesser number of descriptors, linear or cubic curves are fitted to the contours of the objects. The end points of these finite length curves are usually...
This paper presents a novel framework for Content Based Image Retrieval(CBIR), which combines color, texture and spatial structure of image. The proposed method uses color, texture and spatial structure descriptors to form a feature vector. Images are segmented into regions to extract local color, texture and CENTRIST(CENsus Transform hISTogram) features respectively. Multiple-instance learning (MIL)...
This paper proposes a method to segment the exudates and lesions in retinal fundus images and classify using selective brightness feature. The exudates are segmented from background and their size is also measured. The segmentation is done by extraction of pixels which fall in the color range of the spots. The essential features inferred from the segmented image include the count of the exudates,...
Gesture recognition system is an important link between humans and machines, in this paper we are going to present a new gesture system that used to control the human made machine by teleoperations, we have applied in this system the HSV color model that used for segmentation operation with some modification to overcome the problem of incomplete segmentation, the input gesture is divided into blocks...
The contour matching is a hot issue and a difficult problem of image processing, and the accuracy and the efficient of the algorithms are the most two critical factors. To obtain the most likely matching part,a curve matching algorithm that based on the multiscale space is proposed. Firstly the line length and the angle are calculated as the feature vectors, secondly by setting different angle threshold...
Image segmentation can be considered as the process of clustering image pixels of different image features. Clustering algorithm based on ant behaviors is a parallel, self-organized algorithm with sound discreteness, positive feedback and robustness. The basic ant colony algorithm is redundant in futile program loop, random in search mechanism and of which, the result is sensitive to the initial parameters...
In this paper, a computer approach is proposed for recognition of retina layers on optical coherence tomography (OCT) images. OCT uses the optical backscattering of light to scan the eye and describe a pixel representation of the anatomic layers within the retina. Our approach is based on co-occurrence matrix for feature extraction and a supervised learning method for classification, which four features...
To segment magnetic resonance image series is an interdisciplinary topic that involves both medical and computer science. It is one of the most important steps for medical diagnosis and quantitative analysis. This paper proposes an automatic segmentation method based on support vector machine (SVM). Feature vectors are generated according to both grayscale value and texture pattern of MR brain images...
During the past few years, advancements in high end computers and sensor techniques made it possible to develop a real-time odontological biometric identification and verification system apart from the existing offline forensic odontological systems. However, this requires highly automated teeth image segmentation and feature extraction algorithms. In this paper, we propose a novel non-forensic biometric...
This paper presents a feature extraction method for hand gesture based on multi-layer perceptron. The feature of hand skin color in the YCbCr color space is used to detect hand gesture. The hand silhouette and features can be accurately extracted in means of binarizing the hand image and enhancing the contrast. Median and smoothing filters are integrated to remove the noise. Combinational parameters...
The process of learning models from raw data typically requires a substantial amount of user input during the model initialization phase. We present an assistive visualization system which greatly reduces the load on the users and makes the process of model initialization and refinement more efficient, problem-driven, and engaging. Utilizing a sequence segmentation task with a Hidden Markov Model...
In this paper an off-line Arabic/Farsi handwritten recognition Algorithm on a subset of Farsi name is proposed. In this system, There is no sub-word segmentation phase. Script database includes 3300 images of 30 Farsi common names. The features are wavelet coefficients extracted from smoothed word image profiles in four standard directions. The Centers of competitive layer of RBF neural network have...
In this paper, we propose a texture feature-based language identification using wavelet-domain BDIP (block difference of inverse probabilities), BVLC (block variance of local correlation coefficients), and NRMA (normalized magnitude) features. The proposed method includes three special operations of NRMA, Donoho's soft-thresholding, and variance thresholding. In the proposed method, wavelet subbands...
This paper proposes a novel framework for color texture segmentation based on Discrete Reduced Biquaternion Fourier Transform (DRBFT) and Discrete Wavelet Transform (DWT). Reduced biquaternions (RB), which are an extension of the complex numbers, are used to characterize the color information. Multi-Channel wavelet filtering is used to extract the texture information at various scales from the LUV...
Support vector machines (SVM) is gaining a considerable attention as an approach to improvement performance of the content-based image retrieval (CBIR). Most SVM for CBIR rely on global feature, which length of the feature representation is fixed. However, region-based image retrieval (RBIR) use variable length representation, and common kernel utilize the inner product or lp norm in input space,...
While existing image retrieval techniques primarily use distance to judge whether two images are similar or not, we propose to introduce an additional rotational correlation test to formulate a combined content similarity detection. We show that, with the same content based image retrieval algorithm, adding rotational correlation to the existing operational process can improve the performance of content...
Food recognition is difficult because food items are de-formable objects that exhibit significant variations in appearance. We believe the key to recognizing food is to exploit the spatial relationships between different ingredients (such as meat and bread in a sandwich). We propose a new representation for food items that calculates pairwise statistics between local features computed over a soft...
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