Sidi Ahmed Mahmoudi

A Multi-Resolution FPGA-Based Architecture for Real-Time Edge and Corner Detection

This work presents a new flexible parameterizable architecture for image and video processing with reduced latency and memory requirements, supporting a variable input resolution. The proposed architecture is optimized for feature detection, more specifically, the Canny edge detector and the Harris corner detector. The architecture contains neighborhood extractors and threshold operators that can be parameterized at runtime. Also, algorithm simplifications are employed to reduce mathematical complexity, memory requirements, and latency without losing reliability. Furthermore, we present the proposed architecture implementation on an FPGA-based platform and its analogous optimized implementation on a GPU-based architecture for comparison. A performance analysis of the FPGA and the GPU implementations, and an extra CPU reference implementation, shows the competitive throughput of the proposed architecture even at a much lower clock frequency than those of the GPU and the CPU. Also, the results show a clear advantage of the proposed architecture in terms of power consumption and maintain a reliable performance with noisy images, low latency and memory requirements.

GPU-based segmentation of cervical vertebra in X-Ray images

The segmentation of cervical vertebra in X-Ray radiographs can give valuable information for the study of the vertebral mobility. One particular characteristic of the X-Ray images is that they present very low grey level variation and makes the segmentation difficult to perform. In this paper, we propose a segmentation procedure based on the Active Shape Model to deal with this issue. However, this application is seriously hampered by its considerable computation time. We present how vertebra extraction can efficiently be performed in exploiting the vast processing power of the Graphics Processing Units (GPU). We propose a CUDA-based GPU implementation of the most intensive processing steps enabling to boost performance. Experimentations have been conducted using a set of high resolution X-Ray medical images, showing a global speedup ranging from 15 to 21, by comparison with the CPU implementation.

Heterogeneous computing for vertebra detection and segmentation in x-ray images

The context of this work is related to the vertebra segmentation. The method we propose is based on the active shape model (ASM). An original approach taking advantage of the edge polygonal approximation was developed to locate the vertebra positions in a X-ray image. Despite the fact that segmentation results show good efficiency, the time is a key variable that has always to be optimized in a medical context. Therefore, we present how vertebra extraction can efficiently be performed in exploiting the full computing power of parallel (GPU) and heterogeneous (multi-CPU/multi-GPU) architectures. We propose a parallel hybrid implementation of the most intensive steps enabling to boost performance. Experimentations have been conducted using a set of high-resolution X-ray medical images, showing a global speedup ranging from 3 to 22, by comparison with the CPU implementation. Data transfer times between CPU and GPU memories were included in the execution times of our proposed implementation.

Efficient exploitation of heterogeneous platforms for images features extraction

Image processing algorithms present a necessary tool for various domains related to computer vision, such as video surveillance, medical imaging, pattern recognition, etc. However, these algorithms are hampered by their high consumption of both computing power and memory, which increase significantly when processing large sets of images. In this work, we propose a development scheme enabling an efficient exploitation of parallel (GPU) and heterogeneous platforms (Multi-CPU/Multi-GPU), for improving performance of single and multiple image processing algorithms. The proposed scheme allows a full exploitation of hybrid platforms based on efficient scheduling strategies. It enables also overlapping data transfers by kernels executions using CUDA streaming technique within multiple GPUs. We present also parallel and heterogeneous implementations of several features extraction algorithms such as edge and corner detection. Experimentations have been conducted using a set of high resolution images, showing a global speedup ranging from 5 to 30, by comparison with CPU implementations.

A new self-adapting architecture for feature detection

In this paper, we present a FPGA based flexible self-adapting architecture for two features detectors, the Canny edge detector and the Harris corner detector, with reduced latency and memory requirements, and supporting variable resolution images. The new architecture uses neighbourhood extractors that can self-adapt its parameters on-the-fly and algorithm simplifications to reduce mathematical complexity, memory requirements and latency without losing reliability.

Multi-CPU/Multi-GPU Based Framework for Multimedia Processing

Image and video processing algorithms present a necessary tool for various domains related to computer vision such as medical applications, pattern recognition and real time video processing methods. The performance of these algorithms have been severely hampered by their high intensive computation since the new video standards, especially those in high definitions require more resources and memory to achieve their computations. In this paper, we propose a new framework for multimedia (single image, multiple images, multiple videos, video in real time) processing that exploits the full computing power of heterogeneous machines. This framework enables to select firstly the computing units (CPU or/and GPU) for processing, and secondly the methods to be applied depending on the type of media to process and the algorithm complexity. The framework exploits efficient scheduling strategies, and allows to reduce significantly data transfer times thanks to an efficient management of GPU memories and to the overlapping of data copies by kernels executions. Otherwise, the framework includes several GPU-based image and video primitive functions, such as silhouette extraction, corners detection, contours extraction, sparse and dense optical flow estimation. These primitives are exploited in different applications such as vertebra segmentation in X-ray and MR images, videos indexation, event detection and localization in multi-user scenarios. Experimental results have been obtained by applying the framework on different computer vision methods showing a global speedup ranging from 5 to 100, by comparison with sequential CPU implementations.

Traitement d'images sur architectures parallèles et hétérogènes

ABSTRACT. Image processing algorithms present a necessary tool for various domains relatedto computer vision. These algorithms are hampered by their high consumption of computingtimes when processing large sets of high resolution images. In this work, we propose a deve-lopment scheme enabling an efficient exploitation of parallel (GPU) and heterogeneous (Multi-CPU/Multi-GPU) platforms, in order to improve performance of image processing algorithms.The proposed scheme enables an efficient scheduling of hybrid tasks and an effective manage-ment of heterogeneous memories. We present also parallel and hybrid implementations of edgeand corner detection methods. Experimental results showed a global speedup ranging from 5to 25, when processing different sets of images, by comparison with CPU implementations. MOTS-CLES : calcul heterogene, GPU, traitement d’images, detection des coins et contours. KEYWORDS: heterogeneous computing, GPU, image processing, corner and edge detection. DOI:10.3166/TSI.31.1183-1203 c 2012 Lavoisier

Towards a smart selection of resources in the cloud for low-energy multimedia processing: Towards a Smart Selection of Cloud Resources

Nowadays, image and video processing applications have become widely used in many domains related to computer vision. Indeed, they can come from cameras, smartphones, social networks, or from medical devices. Generally, these images and videos are used for illustrating people or objects (cars, trains, planes, etc) in many situations such as airports, train stations, public areas, sport events, and hospitals. Thus, image and video processing algorithms have got increasing importance, they are required from various computer vision applications such as motion tracking, real time event detection, database (images and videos) indexation, and medical computer‐aided diagnosis methods. The main inconvenient of image and video processing applications is the high intensity of computation and the complex configuration and installation of the related materials and libraries. In this paper, we propose a new framework that allows users to select in a smart and efficient way the computing units (CPU or/and GPU) in a cloud‐based platform, in case of processing one image (or one video in real time) or many images (or videos). This framework enables to affect the local or remote computing units for calculation after analyzing the type of media and the algorithm complexity. The framework disposes of a set of selected CPU and GPU‐based computer vision methods, such as image denoising, histogram computation, features descriptors (SIFT, SURF), points of interest extraction, edges detection, silhouette extraction, and sparse and dense optical flow estimation. These primitive functions are exploited in various applications such as medical image segmentation, videos indexation, real time motion analysis, and left ventricle segmentation and tracking from 2D echocardiography. Experimental results showed a global speedup ranging from 5× to 273×(compared to CPU versions) as result of the application of our framework for the above‐mentioned methods. In addition to these performances, the parallel and heterogeneous implementations offered lower power consumption as result of the fast treatment.

Real Time GPU-Based Segmentation and Tracking of the Left Ventricle on 2D Echocardiography

Left ventricle segmentation and tracking in ultrasound images present necessary tasks for cardiac diagnostic. These tasks are difficult due to the inherent problems of ultrasound images (i.e. low contrast, speckle noise, signal dropout, presence of shadows, etc.). In this paper, we propose an accurate and automatic method for left ventricle segmentation and tracking. The method is based on optical flow estimation for detecting the left ventricle center. Then, the contour is defined and tracked using convex hull and spline interpolation algorithms. In order to provide a real time processing of videos, we propose also an effective and adapted exploitation of new parallel and heterogeneous architectures, that consist of both central (CPU) and graphic (GPU) processing units. The latter can exploit both NVIDIA and ATI graphic cards since we propose CUDA and OpenCL implementations. This allowed to improve the performance of our method thanks to the parallel exploitation of the high number of computing units within GPU. Our experiments are conducted using a set of 11 normal and 17 disease hearts ultrasound video sequences. The related results achieved automatic and real-time left ventricle detection and tracking with a rate of 92 % of success.

A Portable Multi-CPU/Multi-GPU Based Vertebra Localization in Sagittal MR Images

Accurate Vertebra localization presents an essential step for automating the diagnosis of many spinal disorders. In case of MR images of lumbar spine, this task becomes more challenging due to vertebra complex shape and high variation of soft tissue. In this paper, we propose an efficient framework for spine curve extraction and vertebra localization in T1-weighted MR images. Our method is a fast parametrized algorithm based on three steps: 1. Image enhancing 2. Meanshift clustering [5] 3. Pattern recognition techniques. We propose also an adapted and effective exploitation of new parallel and hybrid platforms, that consist of both central (CPU) and graphic (GPU) processing units, in order to accelerate our vertebra localization method. The latter can exploit both NVIDIA and ATI graphic cards since we propose CUDA and OpenCL implementations of our vertebra localization steps. Our experiments are conducted using 16 MR images of lumbar spine. The related results achieved a vertebra detection rate of 95% with an acceleration ranging from 4 to 173 \(\times \) thanks to the exploitation of Multi-CPU/Multi-GPU platforms.