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In this paper, we propose a new multi-class steganalysis for binary image. The proposed method can identify the type of steganographic technique used by examining on the given binary image. In addition, our proposed method is also capable of differentiating an image with hidden message from the one without hidden message. In order to do that, we will extract some features from the binary image. The feature extraction method used is a combination of the method extended from our previous work and some new methods proposed in this paper. Based on the extracted feature sets, we construct our multi-class steganalysis from the SVM classifier. We also present the empirical works to demonstrate that the proposed method can effectively identify five different types of steganography. [ABSTRACT FROM AUTHOR]

Co-occurrence matrix is traditionally used for the representation of texture information. In this paper, the co-occurrence matrix is combined with Gaussian curvature for 3D shape representation and a novel 3D shape description approach named Gaussian curvature co-occurrence matrix is proposed. Normalization process to Gaussian curvature co-occurrence matrix and the invariants independence of the translation, scaling and rotation transforms are demonstrated. Experiments indicate a better classification rate and running complexity to objects with slight different shape characteristic compared with traditional methods. [ABSTRACT FROM AUTHOR]

This paper presents a biometric recognition based on the iris of a human eye using gray-level co-occurrence matrix (GLCM). A new approach of GLCM, called 3D-GLCM, which is expanded from the original 2D-GLCM is proposed and used to extract the iris features. The experimental results show that the proposed approach gains an encouraging performance on the UBIRIS iris database. The recognition rate up to 99.65% can be achieved. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to detect pedestrians from images. This paper proposes a method for extracting feature descriptors consisting of co-occurrence histograms of oriented gradients (CoHOG). Including co-occurrence with various positional offsets, the feature descriptors can express complex shapes of objects with local and global distributions of gradient orientations. Our method is evaluated with a simple linear classifier on two famous pedestrian detection benchmark datasets: ˵DaimlerChrysler pedestrian classification benchmark dataset″ and ˵INRIA person data set″. The results show that proposed method reduces miss rate by half compared with HOG, and outperforms the state-of-the-art methods on both datasets. [ABSTRACT FROM AUTHOR]

The selection of the optimal feature subset and the classification has become an important issue in the field of iris recognition. In this paper we propose several methods for iris feature subset selection and vector creation. In this paper we propose a new feature extraction method for iris recognition based on contourlet transform. Contourlet transform captures the intrinsic geometrical structures of iris image. For reducing the feature vector dimensions we use the method for extract only significant bit and information from normalized iris images. In this method we ignore fragile bits. At last, the feature vector is created by two methods: Co-occurrence matrix properties and contourlet coefficients. For analyzing the desired performance of our proposed method, we use the CASIA dataset, which is comprised of 108 classes with 7 images in each class and each class represented a person. Experimental results show that the proposed increase the classification accuracy and also the iris feature vector length is much smaller versus the other methods. [ABSTRACT FROM AUTHOR]

The novel technologies used in different application domains allow to obtain digital images with a high complex informative content. These meaningful information are expressed by textural skin that covers the objects represented inside the images. The textural information can be exploited to interpret the semantic meaning of the images themselves. This paper provides a mathematical characterization, based on texture analysis, of the craniopharyngioma pathology distinguishing it from other kinds of primary cerebral tumors. By this characterization a prototype has been developed, which has primarily allowed to identify potential abnormal masses inside the cerebral tissue and subsequently to possibly classify them as craniopharyngiomas. [ABSTRACT FROM AUTHOR]

In different areas of Biometrics, recognition by iris images in nowadays has been taken into consideration by researchers as one of the common methods of identification like passwords, credit cards or keys. Iris recognition a novel biometric technology has great advantages such as variability, stability and security. Although the area of the iris is small it has enormous pattern variability which makes it unique for every one and hence leads to high reliability. In this paper we propose a new feature extraction method for iris recognition based on contourlet transform. Contourlet transform captures the intrinsic geometrical structures of iris image. It decomposes the iris image into a set of directional sub-bands with texture details captured in different orientations at various scales so for reducing the feature vector dimensions we use the method for extract only significant bit and information from normalized iris images. In this method we ignore fragile bits. At last, the feature vector is created by using Co-occurrence matrix properties. For analyzing the desired performance of our proposed method, we use the CASIA dataset, which is comprised of 108 classes with 7 images in each class and each class represented a person. And finally we use SVM and KNN classifier for approximating the amount of people identification in our proposed system. Experimental results show that the proposed increase the classification accuracy and also the iris feature vector length is much smaller versus the other methods. [ABSTRACT FROM AUTHOR]

The existing use of summary statistics from co-occurrence matrices of images for texture recognition and classification has inadequacies when dealing with non-uniform and colored texture such as traditional `Batik΄ and `Songket΄ cloth motifs. This study uses the Tchebichef orthogonal polynomial as a way to preserve the shape information of co-occurrence matrices generated using the RGB multispectral method; allowing prominent features and shapes of the matrices to be preserved while discarding extraneous information. The decomposition of the six multispectral co-occurrence matrices yields a set of moment coefficients which can be used to quantify difference between textures. The proposed method have yielded very good recognition rate when used with the BayesNet classifier. [ABSTRACT FROM AUTHOR]

Differential statistics were proposed in this paper to disclose the existence of hidden data in grayscale raw images. Meanwhile, differential statistics were utilized to improve the algorithm introduced by Fridrich to attack steganographic schemes in grayscale JPEG images. In raw images, to describe the correlation between data and their spatial positions, co-occurrence matrix based on intensities of adjacent pixels was adopted and the use of co-occurrence matrix was extended to high-order differentiations. The COMs (center of mass) of HCFs (histogram character function) were calculated from these statistics to form a 30-dimensional feature vector for steganalysis. For JPEG files, differential statistics were collected from boundaries of DCT blocks in their decompressed images. The COM of HCF was computed for each of these differential statistics and statistics from DCT domain so that a 28-dimensional feature vector can be extracted from a JPEG image. Two blindly steganalytic algorithms were constructed based on Support Vector Machine and the two kinds of feature vectors respectively. The presented methods demonstrate higher detecting rates with lower false positives than known schemes. [ABSTRACT FROM AUTHOR]