GPU accelerated parallel Iris segmentation

A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the person. Iris recognition systems are the most definitive biometric system since complex random iris patterns are unique to each individual and do not change with time. Iris Recognition is basically divided into three steps, namely, Iris Segmentation or Localization, Feature Extraction and Template Matching. To get a performance gain for the entire system it becomes vital to improve performance of each individual process. Localization of the iris borders in an eye image can be considered as a vital step in the iris recognition process due to high processing required. The Iris Segmentation algorithms are currently implemented on general purpose sequential processing systems, such as common Central Processing Units (CPUs). In this thesis, an attempt has been made to present a more straight and parallel processing alternative using the graphics processing unit (GPU), which originally was used exclusively for visualization purposes, and has evolved into an extremely powerful coprocessor, offering an opportunity to increase speed and potentially intensify the resulting system performance. To realize a speedup in Iris Segmentation, NVIDIA’s Compute Unified Device Architecture (CUDA) programming model has been used. Iris Localization is achieved by implementing Hough Circular Transform on edge image obtained by using Canny edge detection technique. Parallelism is employed in Hough Transformation step

GPU Accelerated Parallel Iris Localization

Iris recognition is quite a computation intensive task with huge amounts of pixel processing. After the image acquisition of the eye, Iris recognition is basically divided into Iris localization, Feature Extraction and Matching steps. Each of these tasks involves a lot of processing. It thus becomes essential to improve the performance of each step to gain an overall increase in performance. The localization step is of utmost importance since it nds out the essential region over which further steps of Iris Recognition are to be performed. It thus decreases the amount of computation that will be needed in the subsequent steps. In this thesis an effort has been made to improve the performance of Iris localization by the use of parallel computing techniques. Recently the General Purpose Graphics Processing Units(GPUs) have come to be very popular in solving complex computational tasks. In order to achieve a speedup in the localization step, the Compute Unifed Device Architecture(CUDA) platform released by NVIDIA corporation has been used. Hough Transform for circles has been used to perform the localization step since it has the ability to handle noisy data very effciently. The edge image has been obtained using the popular canny edge detector and it serves as the input for the Hough Transformation step. Since the image data as well as the edge detecting mechanism may not be perfect, the Hough transform method carries out a voting mechanism over the image objects, in order to deal with imperfections like noisy data. Parallelism is employed in the Hough transformation step, when for each possible value of the radius a large number of circles have to be generated in the parameter space, and this task is taken over by parallel blocks and threads, which substantially improves the computation time required to identify the circular contours in the image space

Iris localization using parallel computing

In this thesis, we have proposed a parallel iris localization technique by implementing canny edge detection in parallel on Graphical Processing Units(GPU) with the help of Compute Unified Device Architecture(CUDA) plateform. The output of canny edge detector which is binary image transfer from GPU/Device to CPU/Host and it is given to serial circular hough transform as input that locate the iris region from image. In this thesis, we follow the Wilde’s approach of iris recognition in which he used the edge detector, and Circular Hough Transform for detecting iris region from an eye image.We processed canny edge detection part of iris localization on GPU in parallel manner and Hough transform serially on CPU. In edge detection, we processed a number of pixels in parallel that execute on cores of GPU in block and thread manner, that reduces the execution time. The outcome of canny edge detector given to serial hough transform that locate the iris region from image. Then we compare the execution time of our parallel technique with existing serial one. In our case, execution time is reduced by 10 to 12 percent in comparison of serial approach. We use the 96 core NVidia GeForce GT 630 GPU for implementation

An accelerated shape based segmentation approach adopting the pattern search optimizer

AbstractAll known solutions of the shape based segmentation problem are slower than real-time application requirements. In this paper, the problem is formulated as a global optimization problem for an energy objective function with several constraints. This formulation allows the use of the global optimization solvers as a solution. However, this solution will be slow as it requires the evaluation of the objective function for several thousand times. The objective function computation is one of the critical factors that affect the time needed to reach a solution. The authors implemented two accelerated parallel versions of the solution that integrates the objective function and the pattern search solver. The first uses a GPU accelerated implementation of the objective function and the second uses a CPU parallel version which is executed on several processors/cores. The results of the proposed solution show that the GPU version has substantial speed compared to other approaches

Paralleizing AwSpPCA for robust facial recognition using CUDA

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2014.Cataloged from PDF version of thesis report.This paper was conducted to analyze the performance benefits of parallelizing the Adaptive Weighted Sub-patterned Principle Component Analysis (Aw SP PCA) algorithm, given that the algorithm is implemented so as to retain the accuracy from its serialized version. The serialized execution of this algorithm is analyzed first and then compared against its parallel implementation, both compiled and run on the same computer. Throughout this paper, the methodology is to undergo a step by step procedure which can clearly outline and describe the problems faced when trying to parallelize this algorithm. It will also describe where, how and why parallelizing procedures were used. The results of the research have shown that while not all parts of the algorithm can be implemented in parallel in the first place, some of the sections that can be parallelized does not necessarily yield a considerable amount of benefits. Also, it was seen that not all sections scale well with problem size, meaning that some portions of the algorithm can be left in its serialized state without much loss in time. The sections which can be parallelized were discussed in detail. Some changes were also made to certain variables to ensure the best accuracy possible. Finally, through analysis and experimentation, a speedup of 2.76 was achieved, with a recognition accuracy of 92.6%.Syed Amer ZawadAshfaque AliB. Computer Science and Engineerin

Doctor of Philosophy

dissertationCine phase contrast (PC) magnetic resonance imaging (MRI) is a useful imaging technique that allows for the quantitative measurement of in-vivo blood velocities over the cardiac cycle. Velocity information can be used to diagnose and learn more about the mechanisms of cardio-vascular disease. Compared to other velocity measuring techniques, PC MRI provides high-resolution 2D and 3D spatial velocity information. Unfortunately, as with many other MRI techniques, PC MRI su ers from long acquisition times which places constraints on temporal and spatial resolution. This dissertation outlines the use of temporally constrained reconstruction (TCR) of radial PC data in order to signi cantly reduce the acquisition time so that higher temporal and spatial resolutions can be achieved. A golden angle-based acquisition scheme and a novel self-gating method were used in order to allow for exible selection of temporal resolution and to ameliorate the di culties associated with external electrocardiogram (ECG) gating. Finally, image reconstruction times for TCR are signi cantly reduced by implementation on a high-performance computer cluster. The TCR algorithm is executed in parallel across multiple GPUs achieving a 50 second reconstruction time for a very large cardiac perfusion data set

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to present their current research, and to discuss topics with other students in order to look for synergies and common research topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big data management, training, contributing to glue disparate researchers working across different areas and provide a meeting ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in research topics such as sustainable software solutions (applications and system software stack), data management, energy efficiency, and resilience.European Cooperation in Science and Technology. COS