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Air pollution is an olfactory pollution because many polluting gases have a strong odor even at low concentrations. These pollutants are natural or anthropogenic emission sources. This pollution has many harmful effects on human health or upon the environment. So it is necessary to detect the pollution to reduce its effects. An electronic nose is capable of detecting the presence of gas after learning...
This paper presents speaker recognition system possessing very good generalization properties. Relatively low equal error rate for speaker verification and high identification rate for identification are achieved for very short training and testing sequences. This behaviour is achieved for the kernel modification of a classic Ho-Kashyap linear classifier. Achieved results for the new approach are...
Learning algorithms of the support vector machine is to map the input vector to a high dimensional space through certain kernel function and separate the image of the original linear input vector with the maximum of interval under consideration. This paper is about the limb motion recognition problem of stroke patients, mapping the input vector to the reproducing kernel RKHS (reproducing Kernel Hilbert...
One of the fundamental issues of human and computational cognitive psychology is pattern or shape recognition. Various applications in image processing and computer vision rely on skeleton-like shape features A possible technique for extracting these feautures is thinning. Although the majority of 2D thinning algorithms work on digital pictures sampled on the conventional square grid, the role of...
In this paper, a new multi-scale deblurring method is proposed to remove the motion blur. The method estimates the blur kernel by an alternative algorithm at scales from coarse to fine. After the blur kernel is estimated in the finest scale, the blurred image is restored via image deconvolution. To remove the ringing artifacts, we propose a smooth regions constraint. Combining with the noise prior,...
The enormous amount of data has been boundlessly growing over the last few decades and expected to exponentially do so in the future. However, a substantial size of this accumulated amount is discarded anyhow. The processing capabilities have been considered as one of the major barriers in the way of exploiting this priceless mine. Therefore, the issue has absorbed considerable part of researchers'...
In the last few years there has been growing interest in the use of functional Magnetic Resonance Imaging (fMRI) for brain mapping. To decode brain patterns in fMRI data, we need reliable and accurate classifiers. Towards this goal, we compared performance of eleven popular pattern recognition methods. Before performing pattern recognition, applying the dimensionality reduction methods can improve...
In this paper, we have presented a new and faster word retrieval approach, which is able to deal with heterogeneous document image collections. A certain amount of image features (statistical and Gabor Wavelet) are extracted, which inherently represent word's images. These features are used for generating hash table for fast retrieval of similar image from a very large image dataset. The decomposition...
A new set of orthogonal moment functions named as GF moments (GFMs) was proposed in this paper. The kernel functions of GFMs is GF-system, which is a class of complete orthogonal spline function set of degree k(k=0,1,2,?). The implementation of GFMs does not involve any numerical approximation and has a rather low computation complexity, since the basis set has the advantages of lower order. These...
Manual video surveillance is highly expensive and inconvenient in continuous monitoring by security personnel. So automatic video surveillance is needed. A large amount of security measure is required in public and private sectors due to terrorist activities. In this paper, an automatic activity recognition approach is proposed. The difference image is used to extract the motion information based...
In this paper, Persian handwritten digits reorganization by using zoning features and projection histogram for extracting feature vectors with 69-dimensions is presented. In classification stage, support vector machines (SVM) with three linear kernels, polynomial kernel and Gaussian kernel have been used as classifier. We tested our algorithm on the dataset that contained 8600 samples of Persian handwritten...
This paper presents a method to recognize attentional behaviors from a head-mounted binocular eye tracker in triadic interactions. By taking advantage of the first-person view, we simultaneously estimate the first-person and third-person gaze. The first-person gaze is computed using an appearance-based method relying on local features. In parallel, head pose tracking allows determining the coarse...
Automatic adult video detection is a problem of interest to many organizations around the world. The aim is to restrict the easy access of underage youngsters to such potentially harmful material. Most of the existing techniques are mere extensions of image categorization approaches. In this paper we propose a video genre classification technique tuned specifically for adult content detection by considering...
Based the analysis of the effect of kernel parameters and penalty parameters on the performance of support vector machine(SVM), the paper has proposed a new method of hydroid simulated annealing technology. The experiment run on the datasets of UCI with the algorithm has shown the result with higher accuracy.
This paper shows the possibility of classifying the surface of locomotion of a modular snake-like robot only from torque and current sensors in the servo-motors. Locomotion in modular snake-like robots is made from gaits that involve the entire body structure, in this particular work we use a modular snake-like robot consisting of 16 modules located 90 degrees rotated one with respect to the previous,...
Feature fusion can effectively improve the steganographic detection capability, but the previous researches of feature fusion in JPEG image steganography detection rarely considered the nonlinear correlation of features. This paper analyzes the correlation of JPEG image steganographic features and fuses features with lowest correlation to obtain better detection capability based on KCCA (Kernel canonical...
A sparse algorithm, based on empirical feature selection, is investigated from the viewpoint of learning theory. It is a novel way to realize sparse empirical feature-based learning different from the regularized kernel projection machines. Représenter theorem and error analysis of this algorithm are established without sparsity assumption of regression function. An empirical study verifies our theoretical...
Edge detection is one of the most commonly used operations in image analysis. Most algorithms contain two basic steps: denoise and derivative computing. We apply kernel regression to remove noise and to get gray-level and derivative intensity surface of images. We explore the Nadaraya-Watson kernel regression which conquers the more negative impact caused by noises for derivative computing than general...
We use a Ground Penetrating Radar (GPR) to localize eggs of sea turtles laid in sand. GPR technology has been developed to detect subsurface structures, and successfully applied in archeology, civil engineering, and demining. Typical uses rely on relatively strong signals due to high contrast in dielectric properties of the buried manmade objects and the soil. Signal to noise ratios in our task are...
The methods based on empirical risk minimization are often applied to hydrocarbon discriminant in oil and gas exploration. But the predictive validities of these methods are not perfect with small sample data. This paper introduces a nonlinear support vector machine (SVM) based on structural risk minimization which can obtain global optimization other than local one and better generalization. The...
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