fatma sayadi - Academia.edu (original) (raw)
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Papers by fatma sayadi
cerc.wvu.edu
This paper presents a hardware implementation of face and eyes detection algorithm. To become we ... more This paper presents a hardware implementation of face and eyes detection algorithm. To become we must go through several stages. First we have implemented an algorithm for detecting and tracking eyes written in C using an open source library of image processing and computer vision the "OpenCV". Then a profiling on HW/SW (hardware/software) partition was done. The hardware part solves the part of the algorithm with higher computational costs. The system has been implemented on Spartan3A DSP FPGA board. In order to visualize the results on real images, we have made a co-simulation of this bloc using the Simulink tool of Matlab.
Face detection in a fixed image without special hypothesis is a difficult problem due to the high... more Face detection in a fixed image without special hypothesis is a difficult problem due to the high variability of the shape to detect. Many techniques of detection and face recognition have been developed in recent years and many of which are very efficient. Among these methods, we find the method of Viola and Jones were studied in this work. The aim of our work is to study this method to implement on the CPU with C / C + +, then acceleration was presented with OpenCV. Subsequently, a second implementation in FPGA was presented
International Journal of Advanced Media and Communication, 2014
Many applications in image processing have high degrees of inherent parallelism and are thus good... more Many applications in image processing have high degrees of inherent parallelism and are thus good candidates for parallel implementation. In fact, programming tools for field programmable gate array (FPGA), SIMD instructions on CPU and a large number of cores on graphic processor unit (GPU) have been developed, but it is still difficult to achieve high performance on these platforms. This paper analyses the distinct features of compute unified device architecture (CUDA) GPU and summarises the general program mode of CUDA. Furthermore, we present three different implementations of Sobel edge detection on CPU, FPGA and GPU. Tested image data are also used in these hardware platforms to compare computational efficiency of CPU, GPU and FPGA.
cerc.wvu.edu
This paper presents a hardware implementation of face and eyes detection algorithm. To become we ... more This paper presents a hardware implementation of face and eyes detection algorithm. To become we must go through several stages. First we have implemented an algorithm for detecting and tracking eyes written in C using an open source library of image processing and computer vision the "OpenCV". Then a profiling on HW/SW (hardware/software) partition was done. The hardware part solves the part of the algorithm with higher computational costs. The system has been implemented on Spartan3A DSP FPGA board. In order to visualize the results on real images, we have made a co-simulation of this bloc using the Simulink tool of Matlab.
Face detection in a fixed image without special hypothesis is a difficult problem due to the high... more Face detection in a fixed image without special hypothesis is a difficult problem due to the high variability of the shape to detect. Many techniques of detection and face recognition have been developed in recent years and many of which are very efficient. Among these methods, we find the method of Viola and Jones were studied in this work. The aim of our work is to study this method to implement on the CPU with C / C + +, then acceleration was presented with OpenCV. Subsequently, a second implementation in FPGA was presented
International Journal of Advanced Media and Communication, 2014
Many applications in image processing have high degrees of inherent parallelism and are thus good... more Many applications in image processing have high degrees of inherent parallelism and are thus good candidates for parallel implementation. In fact, programming tools for field programmable gate array (FPGA), SIMD instructions on CPU and a large number of cores on graphic processor unit (GPU) have been developed, but it is still difficult to achieve high performance on these platforms. This paper analyses the distinct features of compute unified device architecture (CUDA) GPU and summarises the general program mode of CUDA. Furthermore, we present three different implementations of Sobel edge detection on CPU, FPGA and GPU. Tested image data are also used in these hardware platforms to compare computational efficiency of CPU, GPU and FPGA.