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This paper presents the parallel multicore Sobel edge algorithm which parallelizes the traditional sequential Sobel edge detection algorithm on a parallel multicore platform. The current advancement of multicore architecture can be utilized by the parallel programming paradigm when focuses on the thread operations. The CPUs/cores provide more proce...
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... The key point of paralleling classical sequential algorithms is the search of the efficiency and such efficiency is strongly linked to the development of new parallel hardware architectures which allow a realistic implementation of the theoretical advantages of the parallel processes. Different hardware architectures (clusters, grids, FPGA,...) propose [94,95,112,149]. One of the most used architectures in the papers presented above has been the Compute Unified Device Architecture, CUDA TM . ...
Membrane computing is a well-known research area in computer science inspired by the organization and behavior of live cells and tissues. Their computational devices, called P systems, work in parallel and distributed mode and the information is encoded by multisets in a localized manner. All these features make P systems appropriate for dealing with digital images. In this paper, some of the open research lines in the area are presented, focusing on segmentation problems, skeletonization and algebraic-topological aspects of the images. An extensive bibliography about the application of membrane computing to the study of digital images is also provided.
... Abdul Khaled et al. [2] studied the parallel implementation of the Sobel edge algorithm on two multicore processors (a dual-core and a quad-core processor). An image is split into many parts and processed by various threads using Message Passing Interface (MPI). ...
... The speedup indicates how much faster a parallel implementation is, compared to the sequential one. The speedup is found using the following equation [2]: Comparing Tables 1 and 2, it can be concluded that using a number of threads equal to the number of cores results in speedup equal to the number of cores, and that the speedup increases above the number of cores when the number of threads is double the number of cores. ...
... The speedup of the vertical partitioning technique using 4 logical threads on 2 Physical cores with respect to the sequential algorithm that has a speedup of 1. Fig. 8. The speedup of the rectangular block partitioning technique using 4 logical threads on 2 physical cores with respect to the sequential algorithm that has a speedup of 1. Table 6 shows the key characteristics and the results of the speedup of the proposed parallel implementation compared to reference [2]. The test images used herehave resolutions of 720*576 and 1024*1024 pixels. ...
Current computers are multi-core, with more than one physical core in one microprocessor chip. Many applications in digital image processing are parallel in nature. Therefore, multi-core processors can be exploited to perform such computations in parallel. In this paper, the standard OpenMP threading library is used to speed-up the edge detection operation on multicore processors. Different partitioning methods of the input image are tested and their effect on the performance of the parallel implementation of the Sobel Edge Detection algorithm is analyzed. It is shown that the horizontal partitioning of the image leads to better performance than vertical partitioning or two-dimensional block partitioning. Various numbers of blocks of the image are tested. It is shown that a number of blocks equal to 0.25 the size of the cache line and a number of threads double the number of physical core give the best performance of the parallel Sobel algorithm.
... For example, on a Beowulf cluster the standard message-passing interface MPI was applied to parallelize the Sobel algorithm using the inter-communication between the nodes of the system [4]. On multi-core processors, a shared-memory system, an algorithm for implementing Sobel operator using MPI library to distribute data and collect results between the cores of the CPU was implemented in [3]. This work using message passing to communicate through shared memory. ...
Performance of applications executed on multi-core processors is not boosted by just dividing the work among a team of threads and assigning them blindly to the CPU cores. Factors such as data access patterns in memory, the way of allocating the threads to the physical cores, and how the data are partitioned among the threads significantly affect performance. In this paper, we target the acceleration of the Sobel image gradient computing which is important in segmenting images for further processing in computer vision and image analysis applications. We present a multi-threaded algorithm using the standard OpenMP threading library to parallelize the computations using two Intel multi-core processors. The effect of the parallelization factors on the performance of the proposed algorithm are evaluated using different image resolutions to draw accurate conclusions. Our results showed a maximum attained speedup closer to the number of physical cores in the CPU, which is the maximum theoretical value.
... Image edge detection is one of the hot issues in the field of image processing, the current classic methods of image edge detection include Roberts operator [1], Sobel operator [2], Prewitt operator [3], Kirsch operator [4], Log operator [5], Canny operator [6], and so on. These methods are all utilizing the maximum value of image gradient or the value of zero-crossing point in second derivative to detect edge, and then the convolution is performed by differential operator templates. ...
An edge detection algorithm based on improved Rotating Kernel Transformation, IRKT edge detection method (IRKTE), is proposed in this paper. The algorithm adopt the line detection approach RKT, and defines a new model of edge detection according to the direction difference between edge and smooth regions. Simultaneously, an accurate edge location approach based on edge normal direction is presented to overcome the wide width caused by a large scale kernel in IRKT. Furthermore, the improvement of previous IRKT with weight edge detection (IRKTEW) is proposed to improve the ability to resist the noise effectively. A series of experiments are carried out through the picture libraries with ground truth and the performance is analyzed with ROC curves. The experimental results show that the proposed method can effectively detect the edge under the strong noises, and the performance of edge detection is improved with the proposed approach.
... The evaluation of the performance scaling is measured with respect to speedup, performance improvement and efficiency with reference to the time taken for both serial and parallel processing (Haron et al., 2010; Khalid et al., 2011). Speedup measures how much a parallel algorithm is faster than a corresponding serial algorithm. ...
Task-intensive applications are synonymous with today's computing era. Large amount of computing resource is required in order to process such applications. Therefore, running task-intensive applications via off-the-shelf personal computer (PC) will lead to long execution time. Parallel processing via MapReduce generates more computing resources by connecting multiple PC to execute a common task. Despite this, MapReduce parallel processing is mostly used for data-intensive applications. This research explores how MapReduce parallel processing scales the performance of task-intensive applications. Travelling salesman problem (TSP) via brute-force approach is used as the case study for the experiment. The experiment is conducted by using 1, 2, 3, and 4 node configurations. Parallel processing criteria such as performance improvement, speedup, and efficiency are discussed for performance benchmarks. It is discovered that MapReduce parallel processing allows good performance scaling for task-intensive applications.
... To decrease the Canny's complexity, edge detection can also be implemented using only Sobel edge detector [51][52][53][54]. In [53], the trade-offs involved in choosing one memory system over another at different memory systems on an FPGA are investigated. ...
In this paper, a new methodology for speeding up edge and line detection algorithms is presented, achieving improved performance over the state of the art software library OpenCV (speedup from 1.35 up to 2.22) and other conventional implementations, in both general and embedded processors, by reducing the number of load/store and arithmetic instructions, the number of data cache accesses and data cache misses in memory hierarchy and the algorithm memory size. This is achieved by fully exploiting the combination of the software and hardware parameters which are considered simultaneously as one problem and not separately. Furthermore, the edge and line detection algorithms have been simplified for a computer vision application in a Virtex-5 Xilinx FPGA using Microblaze soft processor (detection and measurement of flow fronts in a microfluid device); it achieves speedup up to 660 times in comparison with conventional software implementations.
... The usage of Multicore to speed up image processing task is cheaper solution compare to GPU [18]. Image processing task such as edge detection algorithms are suitable and prove to be successful and economical to be implemented on the desktop personal computers [18]. ...
... The usage of Multicore to speed up image processing task is cheaper solution compare to GPU [18]. Image processing task such as edge detection algorithms are suitable and prove to be successful and economical to be implemented on the desktop personal computers [18]. Clustering algorithm is very practical to be implemented on multicores platform [19]. ...
... In other hand, Abdul Khalid, et al. (2011) [8], presented the parallel multicore Sobel edge algorithm which parallelizes the traditional sequential Sobel edge detection algorithm on a parallel multicore platform. They used the MPI where the algorithm is implemented on various thread [8], and they depended on data decomposition, and they reached to good method not only for image processing by depending on threads. ...
... In other hand, Abdul Khalid, et al. (2011) [8], presented the parallel multicore Sobel edge algorithm which parallelizes the traditional sequential Sobel edge detection algorithm on a parallel multicore platform. They used the MPI where the algorithm is implemented on various thread [8], and they depended on data decomposition, and they reached to good method not only for image processing by depending on threads. ...
... • The performance improvement measures the relative improvement that the parallel algorithm has over the sequential. This performance is measured as following formula [8]: ...
The size of images in image processing considered a critical point in processing the images, so we must process the large size of images in small time es-pecially in medical applications.In this paper, we present a new design of parallelizing Sobel edge de-tection algorithm in order to decrease the computa-tion time. The parallel algorithm is implemented using MPI library and decentralized architecture. The new methodology depending on domain and function decomposition to improve the results. Ex-perimental is done in one machine and the results demonstrate that the new design gives a good re-sults.
Researchers entering into a new research area are interested in knowing the current research trends, popular publications and influential (popular) researchers in that area in order to initiate their research. In this work, we attempt to determine the influential researcher for a specific topic. The active participation of the researchers in both the academic and social network activities signifies the researchers' influence level across time. The content and frequency of social interaction to a researcher reflects his or her influence. In our system, appropriate time-based social and academic features are selected using entropy based feature selection approach of rough set theory. A three layer model comprising semantically related concepts, researcher and social relations is developed based on the appropriate (influential) features. The researchers' topic trajectories are identified and recommended using Spreading activation algorithm. To cope up with the scalable academic network, map reduce paradigm has been employed in the spreading activation algorithm.
