A. Ben Hamza

Image denoising: a nonlinear robust statistical approach

Nonlinear filtering techniques based on the theory of robust estimation are introduced. Some deterministic and asymptotic properties are derived. The proposed denoising methods are optimal over the Huber /spl epsi/-contaminated normal neighborhood and are highly resistant to outliers. Experimental results showing a much improved performance of the proposed filters in the presence of Gaussian and heavy-tailed noise are analyzed and illustrated.

Geodesic Object Representation and Recognition

This paper describes a shape signature that captures the intrinsic geometric structure of 3D objects. The primary motivation of the proposed approach is to encode a 3D shape into a one-dimensional geodesic distribution function. This compact and computationally simple representation is based on a global geodesic distance defined on the object surface, and takes the form of a kernel density estimate. To gain further insight into the geodesic shape distribution and its practicality in 3D computer imagery, some numerical experiments are provided to demonstrate the potential and the much improved performance of the proposed methodology in 3D object matching. This is carried out using an information-theoretic measure of dissimilarity between probabilistic shape distributions.

A multiresolution descriptor for deformable 3D shape retrieval

In this paper, we present a spectral graph wavelet framework for the analysis and design of efficient shape signatures for nonrigid 3D shape retrieval. Although this work focuses primarily on shape retrieval, our approach is, however, fairly general and can be used to address other 3D shape analysis problems. In a bid to capture the global and local geometry of 3D shapes, we propose a multiresolution signature via a cubic spline wavelet generating kernel. The parameters of the proposed signature can be easily determined as a trade-off between effectiveness and compactness. Experimental results on two standard 3D shape benchmarks demonstrate the much better performance of the proposed shape retrieval approach in comparison with three state-of-the-art methods. Additionally, our approach yields a higher retrieval accuracy when used in conjunction with the intrinsic spatial partition matching.

Image watermarking scheme using nonnegative matrix factorization and wavelet transform

We introduce a robust image watermarking scheme for copyright protection using discrete wavelet transform (DWT) and nonnegative matrix factorization (NMF). The core idea of the proposed approach is to decompose an image into four wavelet sub-bands and then apply NMF to the blocks of each sub-band, followed by an eigendecomposition distortion step. The experimental results clearly show a better visual imperceptibility and an excellent resiliency of the proposed watermarking scheme against intentional and geometric attacks.

Intrinsic spatial pyramid matching for deformable 3D shape retrieval

In this paper, we present an intrinsic spatial pyramid matching approach for 3D shape retrieval. Motivated by the fact that the second eigenfunction of Laplace–Beltrami operator not only can capture the global topological structure information, but also is intrinsic, we propose to adopt its level sets as cuts to perform surface partition. The resulting matching scheme is able to consistently estimate the approximate global geometric correspondence among 3D shapes. In particular, we can leverage recent developments in intrinsic shape analysis and perform intrinsic spatial pyramid matching based on dense spectral shape descriptors such as scale-invariant heat kernel signature. Our experiments demonstrate a significant improvement of 3D shape retrieval on two standard benchmarks.

Information divergence measure for ISAR image registration

Entropy-based divergence measures have shown promising results in many areas of engineering and image processing. In this paper, a new generalized divergence measure, divergence, is proposed. Some properties such as convexity and its upper bound are derived. Based on the Jensen-Renyi divergence, we propose a new approach to the problem of ISAR (Inverse Synthetic Aperture Radar) image registration. The goal is to estimate the target motion during the imaging time. Our approach applies Jensen-Renyi divergence to measure the statistical dependence between consecutive ISAR image frames, which would be maximal if the images are geometrically aligned. Simulation results demonstrate that the proposed method is efficient and effective.

Spatially aggregating spectral descriptors for nonrigid 3D shape retrieval: a comparative survey

This paper presents a comprehensive review and analysis of recent spectral shape descriptors for nonrigid 3D shape retrieval. More specifically, we compare the latest spectral descriptors based on the Laplace–Beltrami (LB) operator, including ShapeDNA, heat kernel signature, scale invariant heat kernel signature, heat mean signature, wave kernel signature, and global point signature. We also include the eigenvalue descriptor (EVD), which is a geodesic distance-based shape signature. The global descriptors ShapeDNA and EVD are compared via the chi-squared distance, while all local descriptors are compared using the codebook model. Moreover, we investigate the ambiguity modeling of codebook for the densely distributed low-level shape descriptors. Inspired by the ability of spatial cues to improve discrimination between shapes, we also propose to adopt the isocontours of the second eigenfunction of the LB operator to perform surface partition, which can significantly ameliorate the retrieval performance of the time-scaled local descriptors. In addition, we introduce an intrinsic spatial pyramid matching approach in a bid to further enhance the retrieval accuracy. Extensive experiments are carried out on two 3D shape benchmarks to assess the performance of the spectral descriptors. Our proposed approach is shown to provide the best performance.

Nonextensive information-theoretic measure for image edge detection

We propose a nonextensive information-theoretic measure called Jensen-Tsallis divergence, which may be defined between any arbitrary number of probability distributions, and we analyze its main theoretical properties. Using the theory of majorization, we also derive its upper bounds performance. To gain further insight into the robustness and the application of the Jensen-Tsallis divergence measure in imaging, we provide some numerical experiments to show the power of this entopic measure in image edge detection.

Image Text Detection Using a Bandlet-Based Edge Detector and Stroke Width Transform.

In this paper, we propose a text detection method based on a feature vector generated from connected components produced via the stroke width transform. Several properties, such as variant directionality of gradient of text edges, high contrast with background, and geometric properties of text components jointly with the properties found by the stroke width transform are considered in the formation of feature vectors. Then, k-means clustering is performed by employing the feature vectors in a bid to distinguish text and non-text components. Finally, the obtained text components are grouped and the remaining components are discarded. Since the stroke width transform relies on a precise edge detection scheme, we introduce a novel bandlet-based edge detector which is quite effective at obtaining text edges in images while dismissing noisy and foliage edges. Our experimental results indicate a high performance for the proposed method and the effectiveness of our proposed edge detector for text localization purposes.

Dynamic independent component analysis approach for fault detection and diagnosis

In this paper, we introduce a novel fault detection and diagnosis method using a dynamic independent component analysis-based approach. We also present an innovative mechanism for detecting and diagnosing the faults. The proposed approach is able to accurately detect and isolate the root causes for each individual fault. The Tennessee Eastman challenge process is used to demonstrate the much improved performance of our proposed technique in comparison with other currently existing statistical monitoring and fault detection methods.