scholarly journals TreeBASIS Feature Descriptor and Its Hardware Implementation

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Spencer Fowers ◽  
Alok Desai ◽  
Dah-Jye Lee ◽  
Dan Ventura ◽  
James Archibald
Keyword(s):  
Real Time ◽  
Hardware Implementation ◽  
Hamming Distance ◽  
Computation Time ◽  
Floating Point ◽  
Feature Descriptor ◽  
Test Set ◽  
Vocabulary Tree ◽  
Excellent Candidate ◽  
Speed And Accuracy

This paper presents a novel feature descriptor called TreeBASIS that provides improvements in descriptor size, computation time, matching speed, and accuracy. This new descriptor uses a binary vocabulary tree that is computed using basis dictionary images and a test set of feature region images. To facilitate real-time implementation, a feature region image is binary quantized and the resulting quantized vector is passed into the BASIS vocabulary tree. A Hamming distance is then computed between the feature region image and theeffectively descriptive basis dictionary imageat a node to determine the branch taken and the path the feature region image takes is saved as a descriptor. The TreeBASIS feature descriptor is an excellent candidate for hardware implementation because of its reduced descriptor size and the fact that descriptors can be created and features matched without the use of floating point operations. The TreeBASIS descriptor is more computationally and space efficient than other descriptors such as BASIS, SIFT, and SURF. Moreover, it can be computed entirely in hardware without the support of a CPU for additional software-based computations. Experimental results and a hardware implementation show that the TreeBASIS descriptor compares well with other descriptors for frame-to-frame homography computation while requiring fewer hardware resources.

scholarly journals Iris Matching Step Implementation in FPGA

2019 ◽  
Vol 2 (1) ◽  
pp. 26-36
Author(s):  
Aumama M. Farhan ◽  
M. F. Al-Gailani
Keyword(s):  
Execution Time ◽  
Iris Recognition ◽  
Hardware Implementation ◽  
Hamming Distance ◽  
Computation Time ◽  
Recognition System ◽  
Hardware Platform ◽  
System Generator ◽  
Large Memory ◽  
Powerful Strategy

Iris recognition system is broadly being utilized as it has distinctive patterns that gives it a powerful strategy to distinguish between persons for identification purposes. However, this system in this implementation requires large memory capacity and high computation time. These factors make us in a challenge to find a way to run this algorithm in a hardware platform. The hardware implementation features reduce the execution time by exploiting the parallelism and pipeline. The present work addresses this issue when reducing execution time by implementing the matching step using hamming distance algorithm on the target device FPGA KINTEX 7 using Xilinx system generator. The obtained result demonstrates that the execution time has been accelerated to 1.32 ns, which is almost at least four times faster than existing works

scholarly journals A 4K-Capable FPGA Implementation of Single Image Haze Removal Using Hazy Particle Maps

2019 ◽  
Vol 9 (17) ◽  
pp. 3443 ◽  
Author(s):  
Dat Ngo ◽  
Gi-Dong Lee ◽  
Bongsoon Kang
Keyword(s):  
Real Time ◽  
Hardware Implementation ◽  
Dynamic Range ◽  
Median Filter ◽  
Hardware Verification ◽  
Floating Point ◽  
Single Image ◽  
Haze Removal ◽  
Narrow Dynamic Range ◽  
Computation Error

This paper presents a fast and compact hardware implementation using an efficient haze removal algorithm. The algorithm employs a modified hybrid median filter to estimate the hazy particle map, which is subsequently subtracted from the hazy image to recover the haze-free image. Adaptive tone remapping is also used to improve the narrow dynamic range due to haze removal. The computation error of the proposed hardware architecture is minimized compared with the floating-point algorithm. To ensure real-time hardware operation, the proposed architecture utilizes the modified hybrid median filter using the well-known Batcher’s parallel sort. Hardware verification confirmed that high-resolution video standards were processed in real time for haze removal.

scholarly journals Numerical algorithms for high-performance computational science

2020 ◽  
Vol 378 (2166) ◽  
pp. 20190066 ◽  
Author(s):  
Jack Dongarra ◽  
Laura Grigori ◽  
Nicholas J. Higham
Keyword(s):  
High Performance ◽  
Numerical Algorithms ◽  
Computational Science ◽  
Floating Point ◽  
Important Criterion ◽  
Data Movement ◽  
Floating Point Arithmetic ◽  
High Performance Computers ◽  
Point Arithmetic ◽  
Speed And Accuracy

A number of features of today’s high-performance computers make it challenging to exploit these machines fully for computational science. These include increasing core counts but stagnant clock frequencies; the high cost of data movement; use of accelerators (GPUs, FPGAs, coprocessors), making architectures increasingly heterogeneous; and multi- ple precisions of floating-point arithmetic, including half-precision. Moreover, as well as maximizing speed and accuracy, minimizing energy consumption is an important criterion. New generations of algorithms are needed to tackle these challenges. We discuss some approaches that we can take to develop numerical algorithms for high-performance computational science, with a view to exploiting the next generation of supercomputers. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

scholarly journals HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time

2018 ◽  
Vol 7 (12) ◽  
pp. 467 ◽  
Author(s):  
Mengyu Ma ◽  
Ye Wu ◽  
Wenze Luo ◽  
Luo Chen ◽  
Jun Li ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
High Performance ◽  
Large Scale ◽  
Computation Time ◽  
Buffer Analysis ◽  
Data Volume ◽  
Time Buffer ◽  
Real World Datasets ◽  
Spatial Indexes

Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-oriented methods expand rapidly with increasing data volume. In this paper, we introduce HiBuffer, a visualization-oriented model for real-time buffer analysis. An efficient buffer generation method is proposed which introduces spatial indexes and a corresponding query strategy. Buffer results are organized into a tile-pyramid structure to enable stepless zooming. Moreover, a fully optimized hybrid parallel processing architecture is proposed for the real-time buffer analysis of large-scale spatial data. Experiments using real-world datasets show that our approach can reduce computation time by up to several orders of magnitude while preserving superior visualization effects. Additional experiments were conducted to analyze the influence of spatial data density, buffer radius, and request rate on HiBuffer performance, and the results demonstrate the adaptability and stability of HiBuffer. The parallel scalability of HiBuffer was also tested, showing that HiBuffer achieves high performance of parallel acceleration. Experimental results verify that HiBuffer is capable of handling 10-million-scale data.

scholarly journals Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xing Hu ◽  
Shiqiang Hu ◽  
Xiaoyu Zhang ◽  
Huanlong Zhang ◽  
Lingkun Luo
Keyword(s):  
Anomaly Detection ◽  
Nearest Neighbor ◽  
Computation Time ◽  
Compact Representation ◽  
Feature Descriptor ◽  
Neighbor Distance ◽  
Low Level ◽  
Feature Descriptors ◽  
Nearest Neighbor Distance ◽  
Crowded Scenes

We propose a novel local nearest neighbor distance (LNND) descriptor for anomaly detection in crowded scenes. Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages. First, LNND descriptor efficiently incorporates spatial and temporal contextual information around the video event that is important for detecting anomalous interaction among multiple events, while most existing feature descriptors only contain the information of single event. Second, LNND descriptor is a compact representation and its dimensionality is typically much lower than the low-level feature descriptor. Therefore, not only the computation time and storage requirement can be accordingly saved by using LNND descriptor for the anomaly detection method with offline training fashion, but also the negative aspects caused by using high-dimensional feature descriptor can be avoided. We validate the effectiveness of LNND descriptor by conducting extensive experiments on different benchmark datasets. Experimental results show the promising performance of LNND-based method against the state-of-the-art methods. It is worthwhile to notice that the LNND-based approach requires less intermediate processing steps without any subsequent processing such as smoothing but achieves comparable event better performance.

Fast Motion Estimation Algorithm Based on Real Time Monitoring

2011 ◽  
Vol 383-390 ◽  
pp. 5028-5033
Author(s):  
Xue Mei Xu ◽  
Qin Mo ◽  
Lan Ni ◽  
Qiao Yun Guo ◽  
An Li
Keyword(s):  
Motion Estimation ◽  
Real Time ◽  
Computation Time ◽  
Estimation Algorithm ◽  
Video Encoding ◽  
Experimental Result ◽  
Fast Motion Estimation ◽  
Motion Activity ◽  
Search Patterns ◽  
Motion Estimation Algorithm

In the video encoding system, motion estimation plays an important role at the front-end of encoder, which can eliminate inter redundancy efficiently and improve encoding efficiency. However, traditional motion estimation algorithm can’t be used in real-time application like video monitoring due to its computational complexity. In order to improve real-time efficiency, an improved motion estimation algorithm is proposed in this paper. The essential ideas consist of early termination rules, prediction of initial search point, and determination of motion type. Furthermore, our algorithm adopts different search patterns for certain motion activity. Experimental result shows that the improved algorithm reduces the computation time significantly while maintaining the image quality, and satisfies real time requirement in monitoring system.

scholarly journals Space-time clustering-based method to optimize shareability in real-time ride-sharing

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262499
Author(s):  
Negin Alisoltani ◽  
Mostafa Ameli ◽  
Mahdi Zargayouna ◽  
Ludovic Leclercq
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Computation Time ◽  
Clustering Method ◽  
Matching Problem ◽  
Solution Quality ◽  
Mobility Service ◽  
Ride Sharing ◽  
Large Scale Problems ◽  
Spatio Temporal

Real-time ride-sharing has become popular in recent years. However, the underlying optimization problem for this service is highly complex. One of the most critical challenges when solving the problem is solution quality and computation time, especially in large-scale problems where the number of received requests is huge. In this paper, we rely on an exact solving method to ensure the quality of the solution, while using AI-based techniques to limit the number of requests that we feed to the solver. More precisely, we propose a clustering method based on a new shareability function to put the most shareable trips inside separate clusters. Previous studies only consider Spatio-temporal dependencies to do clustering on the mobility service requests, which is not efficient in finding the shareable trips. Here, we define the shareability function to consider all the different sharing states for each pair of trips. Each cluster is then managed with a proposed heuristic framework in order to solve the matching problem inside each cluster. As the method favors sharing, we present the number of sharing constraints to allow the service to choose the number of shared trips. To validate our proposal, we employ the proposed method on the network of Lyon city in France, with half-million requests in the morning peak from 6 to 10 AM. The results demonstrate that the algorithm can provide high-quality solutions in a short time for large-scale problems. The proposed clustering method can also be used for different mobility service problems such as car-sharing, bike-sharing, etc.

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