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image Feature Matching and Its Parallelization Using OpenMP

Image Feature Matching and Its Parallelization Using OpenMP

引用

International Symposium on Collaborative Technologies and systems

作者： Nupur Kohli Teng-Sheng Moh Department of Computer Science San Jose State University San Jose CA USA

Parallel Computing has been gaining interest nowadays due to physical constraints preventing frequency scaling. Therefore, in order to achieve high performance on multicore systems, programmers need to focus on parallelizing their programs. Although there are many available parallelized APIs written by experts that should improve coding, they do not automatically guarantee good performance. This paper focuses on understanding factors that help to achieve better performance. This paper specifically focuses on SIFT based feature matching for various image collections. SIFT works by extracting information from images and then later compares this information for feature matching. Since SIFT performs CPU intensive computations, it requires parallelization in order to be feasible. This paper focuses on exploring a shared memory model based API known as Open multi-processing (OpenMP) to accelerate existing serial SIFT based image matching. In this paper, we were able to achieve a speedup of ~2× by using various OpenMP features. The paper later discusses factors like scalability and speedup and how multi-threading impacts them. We also discovered the importance of different levels of parallelism and their effects on performance.

关键词： Parallel processing Multicore processing Synchronization Feature extraction Computational modeling Computer vision Approximation algorithms

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A Fast Filter Tracker against Serious Occlusion

A Fast Filter Tracker against Serious Occlusion

引用

IEEE International conference on Bioinformatics and Biomedicine

作者： Libo Zhang Tiejian Luo Yihan Sun Lin Yang University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509016129

Many tracking algorithms applied in medical image processing, such as observing the movement of cells, have a great improvement in accuracy and robustness. However, it is difficult to deal with the large area occlusion and complete occlusion. In this paper, we propose a fast scale adaptive tracking algorithm based on correlation filtering. Except tracking the change of the target scale quickly, our method can also deal with the problem of large area occlusion and the complete disappearance of the target. Compared with the outstanding scale adaptive tracking method, the proposed method demonstrates higher performances in terms of the accuracy of tracking the target and the real-time performance.

关键词： Biomedical imaging Real-time systems Robustness Visualization Biomedical imaging real-time systems Visualization Occlusion Robustness Adaptive tracking Medical image processing Targets

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Hardware-in-the-loop simulation platform for image-based object tracking method using small UAV

Hardware-in-the-loop simulation platform for image-based obj...

引用

conference on Digital Avionics systems (DASC)

作者： Seung-hyeon Cheon Seok-wun Ha Yong-ho Moon Dept. of Aerospace Engineering Gyeongsang National University Jinju Rep. of Korea Dept. of Aerospace and Software Engineering/ERI Gyeongsang National University Jinju Rep. of Korea

ISBN: (纸本)9781509025244

In this paper, a hardware-in-the loop simulation (HILS) platform is presented for verifying the image-based object tracking method adopted in the small Unmanned Aerial Vehicle (sUAV). The platform is constructed by image processing module, scene generation module, and flight control module. In the image processing module, the motion of target object is measured by using the speeded-up robust features (SURF) algorithm and the feature matching technique. And then, control command is provided to allow the target object to be tracked by sUAV automatically. The JMAVSIM software developed by PX4 dev-team is used in the proposed platform to simulate the flight of sUAV and provide virtual scene and flight data. Pixhawk based on PX4 firmware which is a popular flight control computer is used as flight control module in the proposed platform. Experimental results show that the object tracking method based on sUAV is effectively tested and evaluated in the proposed HILS platform.

关键词： Object tracking Aerospace control Feature extraction Target tracking Object recognition Software algorithms

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A SYSTEM FOR HIGH PRECISION GLASS-TO-GLASS DELAY MEASUREMENTS IN VIDEO COMMUNICATION

A SYSTEM FOR HIGH PRECISION GLASS-TO-GLASS DELAY MEASUREMENT...

引用

IEEE International conference on image processing

作者： Christoph Bachhuber Eckehard Steinbach Technical University of Munich Chair of Media Technology

ISBN: (纸本)9781467399623

Ultra low delay video transmission is becoming increasingly important. Video-based applications with ultra low delay requirements range from teleoperation scenarios such as controlling drones or telesurgery to autonomous control of dynamic processes using computer vision algorithms applied on real-time video. To evaluate the performance of the video transmission chain in such systems, it is important to be able to precisely measure the glass-to-glass (G2G) delay of the transmitted video. In this paper, we present a low-complexity system that takes a series of pairwise independent measurements of G2G delay and derives performance metrics such as mean delay or minimum delay etc. from the data. The precision is in the sub-millisecond range, mainly limited by the sampling rate of the measurement system. In our implementation, we achieve a G2G measurement precision of 0.5 milliseconds with a sampling rate of 2kHz.

关键词： Video signal processing Glass-to-glass delay measurement Video delay distribution Video signal processing sampling rate G2G measurement system telesurgery Precision autonomous control delay mean delay Video transmission transient processes Performance metrics images computer vision Complex systems

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Real-time combustion state identification via image processing: A dynamic data-driven approach

Real-time combustion state identification via image processi...

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American Control conference

作者： Michael Hauser Yue Li Jihang Li Asok Ray Mechanical & Nuclear Engineering Department Pennsylvania State University University Park 16802 USA

ISBN: (纸本)9781467386838

This paper proposes a framework for analyzing video of physical processes as a paradigm of dynamic data-driven application systems (DDDAS). The algorithms were tested on a combustion system under fuel lean and ultra-lean conditions. The main challenge here is to develop feature extraction and information compression algorithms with low computational complexity such that they can be applied to real-time analysis of video captured by a high-speed camera. In the proposed method, image frames of the video is compressed into a sequence of image features. Then, these image features are mapped to a sequence of symbols by partitioning of the feature space. Finally, a special class of probabilistic finite state automata (PFSA), called D-Markov machines, are constructed from the symbol strings to extract pertinent features representing the embedded dynamic characteristics of the physical process. This paper compares the performance and efficiency of three image feature extraction algorithms: Histogram of Oriented Gradients, Gabor Wavelets, and Fractal Dimension. The k-means clustering algorithm has been used for feature space partitioning. The proposed algorithm has been validated on experimental data in a laboratory environment combustor with a single fuel-injector.

关键词： Feature extraction Combustion Heuristic algorithms Principal component analysis Probabilistic logic Automata Probabilistic logic Feature extraction Heuristic algorithms Automata Combustion principal components analysis Eigenvector Videotapes combustion system image feature Physical Processes image sequences SYMBOL STRINGS data driven images

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A Randomised Primal-Dual Algorithm for Distributed Radio-Interferometric Imaging

A Randomised Primal-Dual Algorithm for Distributed Radio-Int...

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European Signal processing conference

作者： Alexandru Onose Rafael E. Carrillo Jason D. McEwen Yves Wiaux Institute of Sensors Signals and Systems Heriot-Watt University Signal Processing Laboratory (LTS5) Ecole Polytechnique Federale de Lausanne Mullard Space Science Laboratory University College London

ISBN: (纸本)9781509018918

Next generation radio telescopes, like the Square Kilometre Array, will acquire an unprecedented amount of data for radio astronomy. The development of fast, parallelisable or distributed algorithms for handling such large-scale data sets is of prime importance. Motivated by this, we investigate herein a convex optimisation algorithmic structure, based on primal-dual forward-backward iterations, for solving the radio interferometric imaging problem. It can encompass any convex prior of interest. It allows for the distributed processing of the measured data and introduces further flexibility by employing a probabilistic approach for the selection of the data blocks used at a given iteration. We study the reconstruction performance with respect to the data distribution and we propose the use of nonuniform probabilities for the randomised updates. Our simulations show the feasibility of the randomisation given a limited computing infrastructure as well as important computational advantages when compared to state-of-the-art algorithmic structures.

关键词： Primal-dual algorithm image processing Radio interferometry radiowave interferometry image processing data distribution RADIO TELESCOPES RANDOMISING Distributed processing distributed algorithms Randomisation Square kilometre Dataset iterative methods

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GPGPU based concurrent classification using trained model of handwritten digits

GPGPU based concurrent classification using trained model of...

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International conference on Open Source systems and Technologies (ICOSST)

作者： Syed Tahir Hussain Rizvi Gianpiero Cabodi Arslan Arif Muhammad Yaqoob Javed Muhammad Majid Gulzar Politecnico di Torino Torino Piemonte IT Politecnico Di Torino Turin Italy University of Science and Technology of China Hefei China University of Central Punjab Lahore Pakistan

ISBN: (纸本)9781509055876

In this paper, General Purpose Graphical processing Unit (GPGPU) based concurrent implementation of handwritten digit classifier is presented. Different styles of handwriting make it difficult to recognize a pattern but using neural network, it is not a difficult task to perform. Different softwares like torch and MATLAB provide the support of multiple training algorithms to train a network. By choosing an appropriate training algorithm for a specific application, speed of training can be increased. Furthermore, using computational power of GPUs, training and classification speed of neural network can be significantly improved. In this work, Modified National Institute of Standards and Technology (MNIST) database of handwritten digits is used to train the network. Accuracy and training time of digit classifier is evaluated for different algorithms and then concurrent training is performed by exploiting power of GPU. Trained parameters are imported and used for the concurrent classification with Compute Unified Device Architecture (CUDA) computing language which can be useful in numerous practical applications. Finally, the results of sequential and concurrent operations of training and classification are compared.

关键词： Training Graphics processing units Neurons image processing MATLAB Biological neural networks

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Single image haze removal algorithm using pixel-based airlight constraints

Single image haze removal algorithm using pixel-based airlig...

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International conference on Automation and Computing (ICAC)

作者： Zhenwei Gao Yongqiang Bai Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing China

Fast single image dehazing has been a challenging problem in many fields, such as computer vision and real-time applications. The existing image dehazing algorithms cannot achieve a trade-off between the dehazing performance and the computational complexity. The proposed approach first applies the mean filter twice to estimate airlight, which include pixel-based dark channel and bright channel constraints. And then the relationship between channel values of the restored image and atmospheric light is qualitatively analyzed to give the optimum estimate of atmospheric light. Using the airlight and atmospheric light, we can easily restore the scene radiance via the atmospheric scattering model. Compared with others, the main advantage of the proposed approach is its high speed and significant visibility improvement even in the sky and white areas. This speed allows the enhanced haze image to be applied in real-time processing applications. A comparative study and quantitative evaluation are proposed with a few other state of the art algorithms which demonstrates that similar or better quality results are obtained.

关键词： Atmospheric modeling image restoration Real-time systems Channel estimation Scattering Filtering Computational modeling

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Sparse regularization methods in ultrafast ultrasound imaging

Sparse regularization methods in ultrafast ultrasound imagin...

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European Signal processing conference (EUSIPCO)

作者： Adrien Besson Rafael E. Carrillo Miaomiao Zhang Denis Friboulet Olivier Bernard Yves Wiaux Jean-Philippe Thiran Institute of Sensors Signals and Systems Heriot-Watt University Edinburgh UK Signal Processing Laboratory (LTS5) Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland CREATIS University of Lyon1 Villeurbanne France Department of Radiology University of Lausanne (UNIL) Lausanne Switzerland

Ultrafast ultrasound (US) imaging based on plane wave (PW) insonification is a widely used modality nowadays. Two main types of approaches have been proposed for image reconstruction either based on classical delay-and-sum (DAS) or on Fourier reconstruction. Using a single PW, these methods lead to a lower image quality than DAS with multi-focused beams. In this paper we review recent beamforming approaches based on sparse regularization methods. The imaging problem, either spatial-based (DAS) or Fourier-based, is formulated as a linear inverse problem and convex optimization algorithms coupled with sparsity priors are used to solve the ill-posed problem. We describe two applications of the framework namely the sparse inversion of the beamforming problem and the compressed beamforming in which the framework is combined with compressed sensing. Based on numerical simulations and experimental studies, we show the advantage of the proposed methods in terms of image quality compared to classical methods.

关键词： Array signal processing Mathematical model Imaging image quality image reconstruction Interpolation Inverse problems

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Sensor Fusion for Occupancy Detection and Activity Recognition using Time-of-Flight Sensors

Sensor Fusion for Occupancy Detection and Activity Recogniti...

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International conference on Information Fusion

作者： Tianna-Kaye Woodstock Richard J. Radke Arthur C. Sanderson Center for Lighting Enabled Systems and Applications (LESA) Troy NY 12180 USA

ISBN: (纸本)9781509020126

New technologies in lighting enable the design of illumination systems that autonomously meet the needs of occupants. In recent years, lighting design for both commercial and residential spaces has advanced beyond task performance, to a broader range of occupant needs including economic, energy and environmental constraints, architectural integration, human health, productivity, interpersonal communication, and aesthetic quality. Such systems require distributed sensing and control systems with sensory feedback to detect the lighting conditions in the space. Occupancy sensing and activity recognition are core components of this distributed sensor-based control system, and multisensor fusion of integrated sensors is viewed as a key attribute. This paper describes an expanded view of multisensor technologies, signal processing and pattern recognition algorithms that are being developed and evaluated for occupancy detection and activity recognition. This paper presents a multisensor testbed system that incorporates a sparse array of time-of-flight range sensors with pattern recognition algorithms for geometric form and motion detection of human occupants. Three levels of analysis are implemented: (1) Occupant detection and tracking, (2) Occupant pose classification (sitting, standing, and walking), (3) Occupant activity sequence recognition. These algorithms are dependent on statistical training of human pose and motion and implemented with a Bayesian formulation for detection, classification, and recognition. Evaluation of resulting performance in the testbed conference room demonstrates pose and activity recognition accuracy of greater than 97% for single occupants.

关键词： Lighting Thyristors Sensor arrays image color analysis Aerospace electronics avionics Lighting image colour analysis Thyristors Sensor arrays Sensor fusion Activity recognition control systems Vehicle Occupancy LIGHTING systems Perspective drawings Geometric forms integrated sensor TIME OF FLIGHT

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