Mohamed A. Deriche

Scale-space properties of the multiscale morphological dilation-erosion

A multiscale morphological dilation-erosion smoothing operation and its associated scale-space expansion for multidimensional signals are proposed. Properties of this smoothing operation are developed and, in particular a scale-space monotonic property for signal extrema is demonstrated. Scale-space fingerprints from this approach have advantages over Gaussian scale-space fingerprints in that: they are defined for negative values of the scale parameter; have monotonic properties in two and higher dimensions; do not cause features to be shifted by the smoothing; and allow efficient computation. The application of reduced multiscale dilation-erosion fingerprints to the surface matching of terrain is demonstrated.

A new technique for combining multiple classifiers using the dempster-shafer theory of evidence

This paper presents a new classifier combination technique based on the Dempster-Shafer theory of evidence. The Dempster-Shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. However, since each of the available methods that estimates the evidence of classifiers has its own limitations, we propose here a new implementation which adapts to training data so that the overall mean square error is minimized. The proposed technique is shown to outperform most available classifier combination methods when tested on three different classification problems.

3D registration using a new implementation of the ICP algorithm based on a comprehensive lookup matrix: Application to medical imaging

The iterative closest point (ICP) algorithm is an efficient algorithm for robust rigid registration of 3D data. Results provided by the algorithm are highly dependent upon the step of finding corresponding pairs between the two sets of 3D data before registration. In this paper, a look up matrix is introduced in the point matching step to enhance the overall ICP performance. Convergence properties and robustness are evaluated in the presence of Gaussian and impulsive noise, and under different data set sizes. The new algorithm has been evaluated on 3D medical data. It has been applied successfully to register closed surfaces acquired using different medical imaging modalities.

A bibliography of pixel-based blind image forgery detection techniques

With the advent of powerful image editing tools, manipulating images and changing their content is becoming a trivial task. Now, you can add, change or delete significant information from an image, without leaving any visible signs of such tampering. With more than several millions pictures uploaded daily to the net, the move towards paperless workplaces, and the introduction of e-Government services everywhere, it is becoming important to develop robust detection methods to identify image tampering operations and validate the credibility of digital images. This led to major research efforts in image forensics for security applications with focus on image forgery detection and authentication. The study of such detection techniques is the main focus of this paper. In particular, we provide a comprehensive survey of different forgery detection techniques, complementing the limitations of existing reviews in the literature. The survey covers image copy-move forgery, splicing, forgery due to resampling, and the newly introduced class of algorithms, namely image retouching. We particularly discuss in detail the class of pixel-based techniques which are the most commonly used approaches, as these do not require any a priori information about the type of tampering. The paper can be seen as a major attempt to provide an up-to-date overview of the research work carried in this all-important field of multimedia. HighlightsA new comprehensive survey of pixel-based forgery detection methods is presented.A framework for grouping different forgery detection algorithms is described.We outline the strengths and weaknesses of research efforts in forgery detection.Numerous tables and figures, analyzing existing algorithms, are discussed.An extensive list of references covering the work of the last decade is provided.

Unveiling the Hidden Assumptions of Energy Detector Based Spectrum Sensing for Cognitive Radios

Cognitive radio is a promising solution to current problem of spectrum scarcity. It relies on efficient spectrum sensing. Energy detection is the most dominantly used spectrum sensing approach owing to its low computational complexity and ability to identify spectrum holes without requiring a priori knowledge of primary transmission characteristics. This paper offers a comprehensive tutorial on energy detection based spectrum sensing and presents an in depth analysis of the test statistic for energy detector. General structure of the test statistic and corresponding threshold are presented to address existing ambiguities in the literature. The derivation of exact distribution of the test statistic, reported in the literature, is revisited and hidden assumptions on the primary user signal model are unveiled. In addition, the scope of detection probability results is discussed for identifying various classes of random primary transmissions. Gaussian approximations of the test statistic are investigated. Specifically, the roles of signal to noise ratio and performance constraint in terms of probability of detection or false alarm are highlighted when Normal approximations are used in place of exact expressions.

Arabic sign language recognition using the leap motion controller

Sign language is important for facilitating communication between hearing impaired and the rest of society. Two approaches have traditionally been used in the literature: image-based and sensor-based systems. Sensor-based systems require the user to wear electronic gloves while performing the signs. The glove includes a number of sensors detecting different hand and finger articulations. Image-based systems use camera(s) to acquire a sequence of images of the hand. Each of the two approaches has its own disadvantages. The sensor-based method is not natural as the user must wear a cumbersome instrument while the imagebased system requires specific background and environmental conditions to achieve high accuracy. In this paper, we propose a new approach for Arabic Sign Language Recognition (ArSLR) which involves the use of the recently introduced Leap Motion Controller (LMC). This device detects and tracks the hand and fingers to provide position and motion information. We propose to use the LMC as a backbone of the ArSLR system. In addition to data acquisition, the system includes a preprocessing stage, a feature extraction stage, and a classification stage. We compare the performance of Multilayer Perceptron (MLP) neural networks with the Nave Bayes classifier. Using the proposed system on the Arabic sign alphabets gives 98% classification accuracy with the Nave Bayes classifier and more than 99% using the MLP.

Fingerprint feature extraction using block-direction on reconstructed images

Fingerprints have been used as unique identifiers of individuals for a very long time. As fingerprint databases are characterized by their large size and may contain noisy and distorted images, an efficient representation of the images is essential for a reliable identification. Considering fingerprints as sample images from non-stationary processes with flow patterns, we propose here a robust technique to extract their features. The unique properties of fingerprint textures are used to enhance the images and improve the fidelity of their features. The ridges are extracted from enhanced foreground areas based on local dominant ridge directions. The resulting bit-mapped images are thinned and smoothed to detect structural features. A large number of false features are eliminated in the proposed post-processing stage. The proposed algorithm results in an efficient and fast representation of fingerprints which accurately retains the fidelity in minutiae.

Image-Based and Sensor-Based Approaches to Arabic Sign Language Recognition

Sign language continues to be the preferred method of communication among the deaf and the hearing-impaired. Advances in information technology have prompted the development of systems that can facilitate automatic translation between sign language and spoken language. More recently, systems translating between Arabic sign and spoken language have become popular. This paper reviews systems and methods for the automatic recognition of Arabic sign language. Additionally, this paper highlights the main challenges characterizing Arabic sign language as well as potential future research directions.

Image based arabic sign language recognition

In this paper we propose an image based system for Arabic Sign Language recognition. The recognition stage is performed using a Hidden Markov Model. We have used a Gaussian skin color model to detect the signer’s face. The detected face region is then used as a reference to track the hands movement using region growing from the sequence of images comprising the signs. A number of features are then selected from the detected hand regions across the sequence of images. Such features are then used as input to the HMM. The proposed system achieved a recognition accuracy of 98% for a data set of 50 signs.

A novel fingerprint image compression technique using wavelets packets and pyramid lattice vector quantization

A novel compression algorithm for fingerprint images is introduced. Using wavelet packets and lattice vector quantization , a new vector quantization scheme based on an accurate model for the distribution of the wavelet coefficients is presented. The model is based on the generalized Gaussian distribution. We also discuss a new method for determining the largest radius of the lattice used and its scaling factor , for both uniform and piecewise-uniform pyramidal lattices. The proposed algorithms aim at achieving the best rate-distortion function by adapting to the characteristics of the subimages. In the proposed optimization algorithm, no assumptions about the lattice parameters are made, and no training and multi-quantizing are required. We also show that the wedge region problem encountered with sharply distributed random sources is resolved in the proposed algorithm. The proposed algorithms adapt to variability in input images and to specified bit rates. Compared to other available image compression algorithms, the proposed algorithms result in higher quality reconstructed images for identical bit rates.