Parallelization and characterization of SIFT on multi-core systems

This paper parallelizes and characterizes an important computer vision application — Scale Invariant Feature Transform (SIFT) both on a Symmetric Multiprocessor (SMP) platform and a large scale Chip Multiprocessor (CMP) simulator. SIFT is an approach for extracting distinctive invariant features from images and has been widely applied. In many computer vision problems, a real-time or even super-real-time processing capability of SIFT is required. To meet the computation demand, we optimize and parallelize SIFT to accelerate its execution on multi-core systems. Our study shows that SIFT can achieve a 9.7x ~ 11x speedup on a 16-core SMP system. Furthermore, Single Instruction Multiple Data (SIMD) and cache-conscious optimization bring another 85 % performance gain at most. But it is still three times slower than the real-time requirement for High-Definition Television (HDTV) image. Then we study the performance of SIFT on a 64-core CMP simulator. The results show that for HDTV image, SIFT can achieve an excellent speedup of 52x and run in real-time finally. Besides the parallelization and optimization work, we also conduct a detailed performance analysis for SIFT on those two platforms. We find that load imbalance significantly limits the scalability and SIFT suffers from intensive burst memory bandwidth requirement on the 16-core SMP system. However, on the 64-core CMP simulator the memory pressure is not high due to the shared last-level cache (LLC) which accommodates tremendous read-write sharing in SIFT. Thus it does not affect the scaling performance. In short, understanding the characterization of SIFT can help identify the program bottlenecks and give us further insights into designing better systems. 1

Accelerating SIFT on Parallel Architectures

SIFT is a widely-used algorithm that extracts features from images; using it to extract information from hundreds of terabytes of aerial and satellite photographs requires parallelization in order to be feasible. We explore accelerating an existing serial SIFT implementation with OpenMP parallelization and GPU execution

Parallel Spatial Pyramid Match Kernel Algorithm for Object Recognition using a Cluster of Computers

This paper parallelizes the spatial pyramid match kernel (SPK) implementation. SPK is one of the most usable kernel methods, along with support vector machine classifier, with high accuracy in object recognition. MATLAB parallel computing toolbox has been used to parallelize SPK. In this implementation, MATLAB Message Passing Interface (MPI) functions and features included in the toolbox help us obtain good performance by two schemes of task-parallelization and dataparallelization models. Parallel SPK algorithm ran over a cluster of computers and achieved less run time. A speedup value equal to 13 is obtained for a configuration with up to 5 Quad processors

Image Feature Extraction Acceleration

Image feature extraction is instrumental for most of the best-performing algorithms in computer vision. However, it is also expensive in terms of computational and memory resources for embedded systems due to the need of dealing with individual pixels at the earliest processing levels. In this regard, conventional system architectures do not take advantage of potential exploitation of parallelism and distributed memory from the very beginning of the processing chain. Raw pixel values provided by the front-end image sensor are squeezed into a high-speed interface with the rest of system components. Only then, after deserializing this massive dataflow, parallelism, if any, is exploited. This chapter introduces a rather different approach from an architectural point of view. We present two Application-Specific Integrated Circuits (ASICs) where the 2-D array of photo-sensitive devices featured by regular imagers is combined with distributed memory supporting concurrent processing. Custom circuitry is added per pixel in order to accelerate image feature extraction right at the focal plane. Specifically, the proposed sensing-processing chips aim at the acceleration of two flagships algorithms within the computer vision community: the Viola-Jones face detection algorithm and the Scale Invariant Feature Transform (SIFT). Experimental results prove the feasibility and benefits of this architectural solution.Ministerio de Economía y Competitividad TEC2012-38921-C02, IPT-2011- 1625-430000, IPC-20111009Junta de Andalucía TIC 2338-2013Xunta de Galicia EM2013/038Office of NavalResearch (USA) N00014141035

Query Extraction Using Filtering Technique over the Stored Data in the Database

Many variety of users approaching server to perform their continuous queries which incorporates the knowledge desires and obtain notified at anytime supported the question that has been printed. To makes this task with efficiency servers ought to keep classification methodology that compares the knowledge in information. we tend to gift a unique question classification and reorganization formula that supports mathematician IF and that we determine totally different reorganization choices for the indexes and demonstrate the importance of question insertion order within the construction of the classification structure. we tend to through an experiment judge completely different reorganization methods and showcase their impact in filtering potency victimization 2 different real-world datasets and each artificial and real question sets. we tend to planned a CF primarily based algorithms for economical filtering performance. It doesn't base on the insertion of queries in information

Parallel processing applied to image mosaic generation

The automatic construction of large mosaics obtained from high resolution digital images is an area of great importance, with applications in different areas. In agriculture, the requirements of cartographic accuracy of mosaics of annual or perennial crops are not so high, but the speed in obtaining them is the most critical factor. The efficiency in decision making is related to the obtaining faster and more accurate information, especially in the control of pests, diseases or fire control. This project proposes a methodology based on SIFT Transform and parallel processing to build mosaics automatically, using high resolution agricultural aerial images. Build mosaics with high resolution images requires high computational effort for processing them. To treat the problem of computational effort, the standard OpenMP of parallel processing was used to accelerate the process and results are presented for a computer with 2, 4 and 8 threads

Characterizing and Optimizing the Performance of the MAESTRO 49-core Processor

As space-based imagery-intelligence systems become increasingly complex, processing units are needed that can process the extra data these systems seek to collect. However, the space environment presents a number of threats, such as ambient or malicious radiation, that can damage and otherwise interfere with electronic systems. There is a need, then, for processors that can tolerate radiation-induced faults, and that also have sufficient computational power to handle the large flow of data they encounter. This research investigates one potential solution: a multi-core processor that is radiation-hardened and designed to provide highly parallelized MIMD execution of applicable workloads. A variety of benchmarking programs are used to explore the capabilities of this processor. Additionally, the source code is modified in an attempt to enhance the processor speed and efficiency; the consequent improvements in performance are documented