Fethi Smach

An FPGA-based accelerator for Fourier Descriptors computing for color object recognition using SVM

Fourier Descriptors (FD) can be used as feature vector components in various applications, such as real-time color object recognition or image retrieval. The full process is composed of the feature extraction followed by a classification step performed using support vector machine (SVM). In order to accelerate the computation of FD, a hardware implementation using FPGA technology is presented in this paper. We evaluated classification performance with respect to lighting variations and noise sensibility. Several experiments were carried out on three databases. Then an efficient architecture for FD computation on FPGAs is proposed and designed as accelerator. The WildCard is used to prototype this implementation. This design can have an operation speed up of approximately 10 compared to the standard software PC implementation.

Real time hardware co-simulation of Edge Detection for video processing system

A methodology for implementing real-time DSP applications on a field programmable gate arrays (FPGA) using Xilinx System Generator (XSG) for Matlab is presented in this paper. It presents architecture for Edge Detection using Sobel Filter for image processing using Xilinx System Generator. The design was implemented targeting a Spartan3A DSP 3400 device (XC3SD3400A-4FGG676C) then a Virtex 5 (xc5vlx50-1ff676). The Edge Detection method has been verified successfully with no visually perceptual errors in the resulted images.

Embedded real-time video processing system on FPGA

Image Processing algorithms implemented in hardware have emerged as the most viable solution for improving the performance of image processing systems. The introduction of reconfigurable devices and high level hardware programming languages has further accelerated the design of image processing in FPGA. This paper briefly presents the design of Sobel edge detector system on FPGA. The design is developed in System Generator and integrated as a dedicated hardware peripheral to the Microblaze 32 bit soft RISC processor with the EDK embedded system. The input comes from a live video acquired from a CMOS camera and the detected edges are displayed on a DVI display screen.

Colour Object recognition combining Motion Descriptors, Zernike Moments and Support Vector Machine

Fourier descriptors have been used successfully in the past to grey-level images, rigid bodied object. Here we used motion descriptors (MD) introduced recently by Gauthier et al., combined with Zernike Moments (ZM), in order to perform a recognition task in colour images. The feature vector for the MD obtained for each object appears to be unique and can be used for shape recognition. The MD, alone or combined with ZM, are used as an input of a support vector machine (SVM) based classifier. We illustrate results on three available datasets: ORL faces database, COIL-100, which consists of 3D objects and A R faces

Efficient and fast multi-modal foreground-background segmentation using RGBD data

Abstract This paper addresses the problem of foreground and background segmentation. Multi-modal data specifically RGBD data has gain many tasks in computer vision recently. However, techniques for background subtraction based only on single-modality still report state-of-the-art results in many benchmarks. Succeeding the fusion of depth and color data for this task requires a robust formalization allowing at the same time higher precision and faster processing. To this end, we propose to make use of kernel density estimation technique to adapt multi-modal data. To speed up kernel density estimation, we explore the fast Gauss transform which allows the summation of a mixture of M kernel at N evaluation points in O(M+N) time as opposed to O(MN) time for a direct evaluation. Extensive experiments have been carried out on four publicly available RGBD foreground/background datasets. Results demonstrate that our proposal outperforms state-of-the-art methods for almost all of the sequences acquired in challenging indoor and outdoor contexts with a fast and non-parametric operation.

The multi-scale covariance descriptor: Performances analysis in human detection

This paper presents a study on human detection using the multi-scale covariance descriptor (MSCOV) proposed in a previous work [1] in which we showed the performance of this descriptor for human re-identification. In this work, we evaluate its performance in human detection. We propose a fast tree based method for multi-scale features covariance computation. This method considerably speed up the image scan process for fast object detection. Furthermore, we experimentally evaluate the human detection performance using region covariance descriptor (COV), multi-scale covariance descriptor (MSCOV) and histogram of oriented gradients (HOG). In term of classifier, we consider the popular Support Vector Machines (SVM). The experiments are performed on both benchmarking datasets INRIA and MIT CBCL. Experiments on both datasets show the high detection performance of the MSCOV based detector.

3D face landmark auto detection

This paper presents our methodology for Landmark Point detection to improve 3D face recognition in a presence of variant facial expression. The objective was to develop an automatic process for distinguishing and segmenting to be embedded in a 3D face recognition system using only 3D Point Distribution Model (PDM) as input. The approach used hydride method to extract this features from the surface curvature information. Landmark Localization is done on the segmented face via finding the change that decreases the deviation of the model from the mean profile. Face registering is achieved using previous anthropometric information and the localized landmarks. The results confirm that the method used is accurate and robust for the proposed application.

Finding Invariants of Group Actions on Function Spaces, a General Methodology from Non-Abelian Harmonic Analysis

In this paper, we describe a general method using the abstract non-Abelian Fourier transform to construct “rich” invariants of group actions on functional spaces.

Complex-Valued Representation for RGB-D Object Recognition

Object recognition methods usually tend to focus on single cues coming from traditional vision based systems but ignore to incorporate multi-modal data. With the advent of depth RGB-D sensors which provide synchronized multi-modal data with good quality, new opportunities have been emerged. In this paper, we make use of RGB and depth images to propose a new object recognition approach. Using a pixel-wise scheme, we propose a novel method to describe RGB-D images with a complex-valued representation. By means of neural network, we introduce a new CVNN (Complex-Valued Neural Network) with RBF neurons. Different from many RGB-D features, the proposed approach is able to jointly use RGB and depth data within a unified end-to-end learning framework. Category and instance object recognition tasks are evaluated through experiments carried out on a large scale RGB-D object dataset. Results show that our method can efficiently recognize objects in RGB-D images and outperforms state-of-the-art approaches.

Automatic landmark detection and 3D Face data extraction

Abstract This paper contributes to 3D facial synthesis by presenting a novel method for parameterization using Landmark Point detection. The approach presented aims at improving facial recognition even in varying facial expressions, and missing data in 3D facial models. As such, the prime objective was to develop an automatically embedded process that can detect any frontal face in 3D face recognition systems, with face segmentation and surface curvature information. Using the hybrid interpolation method, experiments on facial landmarks were performed on 4950 images from Face Recognition Grand Challenge database (FRGC). Distinctive facial landmarks from the nose–tips, Limits mouth and two eye corners formed the statistical inputs for Iterative Closest Point (ICP) in the Point Distribution Model (PDM). Performance or landmark localization is reported by using percentage deviation from the mean 3D profile. Localization results and estimated data on landmark locations demonstrate that the method confirms its effectiveness for proposed application.