Mohamed El Ansari

Bio: Mohamed El Ansari is an academic researcher from Institut national des sciences appliquées de Rouen. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 13, co-authored 48 publications receiving 517 citations. Previous affiliations of Mohamed El Ansari include University of Bordeaux & Intelligence and National Security Alliance.

Traffic sign detection and recognition based on random forests

Abstract: Graphical abstractDisplay Omitted In this paper we present a new traffic sign detection and recognition (TSDR) method, which is achieved in three main steps. The first step segments the image based on thresholding of HSI color space components. The second step detects traffic signs by processing the blobs extracted by the first step. The last one performs the recognition of the detected traffic signs. The main contributions of the paper are as follows. First, we propose, in the second step, to use invariant geometric moments to classify shapes instead of machine learning algorithms. Second, inspired by the existing features, new ones have been proposed for the recognition. The histogram of oriented gradients (HOG) features has been extended to the HSI color space and combined with the local self-similarity (LSS) features to get the descriptor we use in our algorithm. As a classifier, random forest and support vector machine (SVM) classifiers have been tested together with the new descriptor. The proposed method has been tested on both the German Traffic Sign Detection and Recognition Benchmark and the Swedish Traffic Signs Data sets. The results obtained are satisfactory when compared to the state-of-the-art methods.

Computer-aided diagnosis system for colon abnormalities detection in wireless capsule endoscopy images

Abstract: Wireless capsule endoscopy (WCE) is a novel imaging technique that can travel through human body and image the small bowel entirely. Therefore, it has been gradually adopted compared with traditional endoscopies for gastrointestinal diseases. However, the big number of the produced images by a WCE test makes their review exhaustive for the physicians. It is helpful for clinicians if we can develop a computer-aided diagnosis system for the task of identifying the images with potential problems. The aim of this paper is to automatize the process of WCE images abnormalities detection by presenting a new texture extraction scheme for pathological inflammation, polyp, and bleeding regions discrimination in WCE images. A new approach based on local binary pattern variance and discrete wavelet transform is proposed. The new textural features scheme has many advantages, e.g., it detects multi-directional characteristics and overcomes the illuminations changes in WCE images. Intensive experiments are conducted on two datasets constructed from several WCE exams. The promising results make the presented method suitable for abnormalities detection in WCE images.

Mean shift and log-polar transform for road sign detection

Abstract: Road sign detection is an important function for driver assistance systems. Although it has been studied for many years, it still has some performance limitations. This paper proposes a new method for road sign detection by employing both color and shape cues. The proposed method consists of three steps. First, the initial image is pre-processed using mean shift clustering algorithm. The clustering is carried out based on color information. Second, a random forest classifier is used to segment the clustered image. In the final step, a shape based classification is performed using a log-polar transform and cross correlation technique. The proposed detection method has been tested on both the German traffic sign detection benchmark (GTSDB) and the Swedish traffic signs (STS) datasets, and yields to 93.50 % and 94.22 % on the GTSDB dataset in terms of F-measure and area under curve(AUC), respectively. These results are satisfactory when compared to recent state-of-the-art methods.

Multi-scale completed local binary patterns for ulcer detection in wireless capsule endoscopy images

Abstract: This paper deals with ulcer abnormalities detection of small bowel, from wireless capsule endoscopy images (WCE). We propose a multi-scale approach based on completed local binary patterns, and laplacian pyramid (MS-CLBP). The proposed approach captures additional information about the magnitude as a robust descriptor against illuminations changes in WCE images. In addition, ulcer detection, was performed using the Green component and Cr components of RGB and YCbCr color spaces, respectively. Using the support vector machine (SVM) classifier, we conduct several experiments on two datasets. The results obtained validate the efficiency of the proposed system with an average accuracy of 95.11 and 93.88% for both datasets. Finally, a comparison with the state of the art methods shows that the proposed method is superior to the other approaches.

A new thermal infrared and visible spectrum images-based pedestrian detection system

Abstract: In this paper, we propose a hybrid system for pedestrian detection, in which both thermal and visible images of the same scene are used. The proposed method is achieved in two basic steps: (1) Hypotheses generation (HG) where the locations of possible pedestrians in an image are determined and (2) hypotheses verification (HV), where tests are done to check the presence of pedestrians in the generated hypotheses. HG step segments the thermal image using a modified version of OTSU thresholding technique. The segmentation results are mapped into the corresponding visible image to obtain the regions of interests (possible pedestrians). A post-processing is done on the resulting regions of interests to keep only significant ones. HV is performed using random forest as classifier and a color-based histogram of oriented gradients (HOG) together with the histograms of oriented optical flow (HOOF) as features. The proposed approach has been tested on OSU Color-Thermal, INO Video Analytics and LITIV data sets and the results justify its effectiveness.

Deep Learning for Large-Scale Traffic-Sign Detection and Recognition

Abstract: Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory. It provides an accurate and timely way to manage traffic-sign inventory with a minimal human effort. In the computer vision community, the recognition and detection of traffic signs are a well-researched problem. A vast majority of existing approaches perform well on traffic signs needed for advanced driver-assistance and autonomous systems. However, this represents a relatively small number of all traffic signs (around 50 categories out of several hundred) and performance on the remaining set of traffic signs, which are required to eliminate the manual labor in traffic-sign inventory management, remains an open question. In this paper, we address the issue of detecting and recognizing a large number of traffic-sign categories suitable for automating traffic-sign inventory management. We adopt a convolutional neural network (CNN) approach, the mask R-CNN, to address the full pipeline of detection and recognition with automatic end-to-end learning. We propose several improvements that are evaluated on the detection of traffic signs and result in an improved overall performance. This approach is applied to detection of 200 traffic-sign categories represented in our novel dataset. The results are reported on highly challenging traffic-sign categories that have not yet been considered in previous works. We provide comprehensive analysis of the deep learning method for the detection of traffic signs with a large intra-category appearance variation and show below 3% error rates with the proposed approach, which is sufficient for deployment in practical applications of the traffic-sign inventory management.

Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images.

Abstract: Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these images suffer from low contrast, complex background, variations in lesion shape and color, which affect the accuracy of their segmentation and subsequent classification. This research proposes an automated system for detection and classification of ulcers in WCE images, based on state-of-the-art deep learning networks. Deep learning techniques, and in particular, convolutional neural networks (CNNs), have recently become popular in the analysis and recognition of medical images. The medical image datasets used in this study were obtained from WCE video frames. In this work, two milestone CNN architectures, namely the AlexNet and the GoogLeNet are extensively evaluated in object classification into ulcer or non-ulcer. Furthermore, we examine and analyze the images identified as containing ulcer objects to evaluate the efficiency of the utilized CNNs. Extensive experiments show that CNNs deliver superior performance, surpassing traditional machine learning methods by large margins, which supports their effectiveness as automated diagnosis tools.

VIFB: A Visible and Infrared Image Fusion Benchmark

Abstract: Visible and infrared image fusion is an important area in image processing due to its numerous applications. While much progress has been made in recent years with efforts on developing image fusion algorithms, there is a lack of code library and benchmark which can gauge the state-of-the-art. In this paper, after briefly reviewing recent advances of visible and infrared image fusion, we present a visible and infrared image fusion benchmark (VIFB) which consists of 21 image pairs, a code library of 20 fusion algorithms and 13 evaluation metrics. We also carry out extensive experiments within the benchmark to understand the performance of these algorithms. By analyzing qualitative and quantitative results, we identify effective algorithms for robust image fusion and give some observations on the status and future prospects of this field.