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H.264, MPEG-4 Part 10, is the latest digital video coding standard that achieves very high data compression by using several new coding features. One of the new feature is variable block sizes for inter frame coding to increase compression efficiency. However, to achieve this, the H.264 encoder employs a complex mode decision technique based on rate-distortion optimization (RDO) that requires high...
In the process of producing copper bar, because of the cast copper billet, rolling equipment, rolling process and other reasons, the surface of copper bar appear some defects such as crack, scarring, roller printing, scratches, holes, scales, pitting, and so on. These deficiencies not only affect the appearance of the product, but more importantly reduce the product's corrosion resistance, abrasion...
An attempt has been made in the paper to find globally optimal cluster centers for remote-sensed images with the proposed Rapid Genetic k-Means algorithm. The idea is to avoid the expensive crossover or fitness to produce valid clusters in pure GA and to improve the convergence time. The drawback of using pure GA in the problem is the usage of an expensive crossover or fitness to produce valid clusters...
As compared with text spam, the image spam is a variant which is invented to escape from traditional text-based spam classification and filtering. Various approaches to image spam filtering have been proposed with respective advantages and drawbacks in terms of time cost and efficiency. In this paper, we propose a new approach based on Base64 encoding of image files and n-gram technique for feature...
Emotional state recognition is an important component for efficient human-computer interaction. Most existing works address this problem using 2D features, but they are sensitive to head pose, clutter, and variations in lighting conditions. The general 3D based methods only consider geometric information for feature extraction. In this paper, we present a real 3D visual features based method for human...
In this work, we proposed a novel authentication system based on facial features. The proposed method is based on PCA and LDA for feature extraction, these extracted features are combined using wavelet fusion. In this work we use neural networks to classify extracted features of faces. The proposed method consists of six steps: i) Extraction of images from the database, ii) Preprocessing, iii) Feature...
A pulmonary nodule is the most common sign of lung cancer. The proposed system efficiently predicts lung tumor from Computed Tomography (CT) images through image processing techniques coupled with neural network classification as either benign or malignant. The lung CT image is denoised using non-linear total variation algorithm to remove random noise prevalent in CT images. Optimal thresholding is...
Accurate land use/cover (LUC) classification data derived from remotely sensed data are very important for land use planning and environment sustainable development. Traditionally, statistical classifiers are often used to generate these data, but these classifiers rely on assumptions that may limit their utilities for many datasets. Conversely, artificial neural network (ANN) and decision tree (DT)...
This paper proposes an Adaptive Fuzzy Classifier Approach (AFCA) to local edge detection in order to address the challenges of detecting latent fingerprint in severely degraded images. The proposed approach adapts classifier parameters to different parts of input images using the concept of reference neighborhood. Three variants of AFCAs, namely K-means-clustering AFCA, Entropy-based AFCA, and Statistical...
Efficient data mining and indexing is important for multimedia analysis and retrieval. In the field of large-scale video analysis, effective genre categorization plays an important role and serves one of the fundamental steps for data mining. Existing works utilize domain-knowledge dependent feature extraction, which is limited from genre diversification as well as data volume scalability. In this...
In many applications it is necessary to be able to classify images in a database accurately and with acceptable speed. The main problem is to assign different images to right categories. The later problem becomes more challenging while dealing with large databases with many categories and subcategories. In this paper we propose a novel classification method based on an adopted hierarchical Dirichlet...
In this paper we propose two alternatives to overcome the natural asynchrony of modalities in Audio-Visual Speech Recognition. We first investigate the use of asynchronous statistical models based on Dynamic Bayesian Networks with different levels of asynchrony. We show that audio-visual models should consider asynchrony within word boundaries and not at phoneme level. The second approach to the problem...
A partially observable Markov decision process (POMDP) is proposed to perform multi-view classification of underwater objects. The model allows one to adaptively determine which additional views of an object would be most beneficial for reducing classification uncertainty. Acquiring additional views is made possible by employing a sonar-equipped autonomous underwater vehicle (AUV) for data collection...
Singular points detection, a crucial step for fingerprint identification system, is accurately robust, and reliable. In the processing of the fingerprint image matching and classification, many method use singular points to align two fingerprint images to surmount the problems about rotation and translation. The performances of the fingerprint recognition system rely on the effect of singular points...
Classification of textures based on wavelet pattern analysis is one of the most effective methods in texture classification. However using all frequency sub-bands in decomposition for classification may increase time complexity of classification algorithms. To reduce the time complexity, sub-bands with high energy and entropy are selected for classification. Fractal dimension can be used to select...
K-Means algorithm is an unsupervised clustering algorithm that classifies the input data points into multiple classes based on their inherent distance from each other. Success of k-means color image segmentation depends on parameter k. If numbers of clusters are estimated correctly, k-means image segmentation can provide good results. This paper proposes a novel method based on edge detection to estimate...
We propose an automatic moment-based image recognition technique in this paper. The problem to be solved consists of classifying the images from a set, using the content similarity. In the feature extraction stage, we compute a set of feature vectors using area moments. An automatic unsupervised feature vector classification approach is proposed next. It uses a hierarchical agglomerative clustering...
Most pornographic image recognition researches focus on detection accuracy. However, as the highly increasing of web data, detection speed becomes a new consideration. In this paper, the new issue is discussed from the following two aspects: 1) feature extraction in compressed domain and 2) classifier design, and then a simple, novel and yet effective pornographic image recognition method in compressed...
The mining of images from several categories is a problem arisen naturally under a wide range of circumstances. Image mining concerns with extraction of image data relationships, or other patterns of images which are not explicitly stored in the images. And Image classification is a large and growing field within image processing. Image Classification is useful in CBIR (Content Based Image Retrieval)...
In this paper, a novel fuzzy support vector machine based image watermarking scheme is proposed.Since the application of support vector machine in the process of watermarking technology is only a simple classification of the image. However,the fuzzy support vector machines by selecting the appropriate degree of membership to reflect the different importance of the different sample points. In this...
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