Applications of a fast parallel algorithm for the extraction and interpretation of optical flow

by Hilary Buxton

Publisher: Queen Mary College, Department of Computer Science and Statistics in London

Written in English
Published: Pages: 10 Downloads: 527
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Edition Notes

StatementHilary Buxton and Nick Williams.
SeriesReport -- No. 419
ContributionsWilliams, Nick., Queen Mary College. Department of Computer Science and Statistics.
The Physical Object
Number of Pages10
ID Numbers
Open LibraryOL13934515M

In Europe or Canada, contact your local application specialist. For More Information For more information on general flow cytometry, review the following: • Givan AL. Flow Cytometry: First Principles. New York, NY: Wiley-Liss; (ISBN ). • Melamed MR. Flow Cytometry and Sorting. New York, NY: Wiley-Liss; (ISBN Parallel processing involves utilizing several factors, such as parallel architectures, parallel algorithms, parallel programming lan­ guages and performance analysis, which are strongly interrelated. In general, four steps are involved in performing a computational problem in parallel. Parallel processing involves utilizing several factors, such as parallel architectures, parallel algorithms, parallel programming lan­ guages and performance analysis, which are strongly interrelated. In general, four steps are involved in performing a computational problem in s: 3. Abstract. A phase-difference-based algorithm for disparity and optical flow estimation is implemented on a TI-C based parallel DSP system. The module performs real-time computation of disparity maps on images of size x pixels and computation of optical flows on images of size 64 x 64 pixels.

Completely revised and updated, this text provides an easy-to-read guide to the concept of mass spectrometry and demonstrates its potential and limitations. Written by internationally recognised experts and utilising real life examples of analyses and applications, the book presents real cases of qualitative and quantitative applications of mass spectrometry. Vision Interpretation Detect Identify Classify Algorithm Development Application Development Files Software Hardware Access Code and Applications. 7 Key Products for Computer Vision – Optical flow – Template matching – Background estimation using Gaussian mixture models. This four volume set LNCS , , and constitutes the refereed proceedings of the 15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP , held in Zhangjiajie, China, in November The revised full papers presented together with In this paper, we shall present fast distributed parallel algorithms for training deep neural network model based on CNN on parallel and distributed environment with GPUs for various number of models in order to extract most similar weather map from CNN.

This book is a celebration of Lamport's work on concurrency, interwoven in four-and-a-half decades of an evolving industry: from the introduction of the first personal computer to an era when parallel and distributed multiprocessors are abundant. This book chapter introduces the parallel scan as a computation primitive. The book is hard to find, but you can freely download the chapter from the author's Web site. Alan Gibbons and Wojciech Rytter, Efficient Parallel Algorithms, Cambridge University Press, COVID Resources. Reliable information about the coronavirus (COVID) is available from the World Health Organization (current situation, international travel).Numerous and frequently-updated resource results are available from this ’s WebJunction has pulled together information and resources to assist library staff as they consider how to handle coronavirus.

Applications of a fast parallel algorithm for the extraction and interpretation of optical flow by Hilary Buxton Download PDF EPUB FB2

Abstract: Optical flow estimation is a fundamental task of many computer vision applications. In this paper, we propose a fast simple algorithm to compute optical flow based on the 3-D gradient in video sequences.

Although the algorithm does not provide highly accurate results, it is computationally simple and fast, and the output is applicable for many by: Efficient parallel algorithms for many problems, including polynomial and matrix computations, sorting, and string matching, are presented.

The sorting and string-matching algorithms are particularly noteworthy. Almost all these algorithms are within a polylog factor of the optical-computing ~VLSIO. lower bounds derived by Barakat.

Both the tangential and normal components of the flow can be computed reliably where the image Hessian is well-conditioned. A fast algorithm to propagate flow along contours from such locations is proposed. Experimental results for an intrinsically parallel algorithm for computing the flow along zero-crossing contours are by:   This paper describes a new parallel algorithm to compute the optical flow of a video sequence.

A previous sequential algorithm has been distributed over a cluster. It has been implemented in a cluster with 8 nodes connected by means of a Gigabit by: 5. These applications are mainly based on motion tracking algorithms such as optical flow computation and image features extraction and tracking (SIFT [6] and SURF [1] descriptors).

the flow along the path which need too much message passing. We tested our parallel algorithm on several types of networks used in the first DIMACS Implementation Challenge, and found that the parallel algorithm has very good acceleration ratio as to sequential algorithm for most types of sparse networks, even beyond our expectation.

Optical flow and tracking - Introduction - Optical flow & KLT tracker - Motion segmentation Forsyth, Ponce “Computer vision: a modern approach”: Chap Sec - Chap Sec Szeliski, “Computer Vision: algorithms and applications" - Chapter 8, Sec.

Parallel Algorithm Appl)Journal description. Parallel Algorithms and Applications aims to publish high quality scientific papers arising from original research and development from the. Parallel Algorithms and Parallel Architectures 13 Relating Parallel Algorithm and Parallel Architecture 14 Implementation of Algorithms: A Two-Sided Problem 14 Measuring Benefi ts of Parallel Computing 15 Amdahl’s Law for Multiprocessor Systems 19 Gustafson–Barsis’s Law 21 Applications of Parallel Computing ow algorithms while sparse optical ow algorithms estimate the displacement for a selected number of pix-els in the image [1].

Dense OF algorithms, such as the Horn-Schunck method [13], calculate the displacement at each pixel by using global constraints. These methods completely avoid feature extraction but are less robust to noise.

1. Introduction. Many problems in low-level computer vision can be formulated as labeling problems using Markov Random Fields (MRFs). Among the examples are image segmentation, image restoration, dense stereo estimation and shape estimation.When the number of labels is equal to 2, the problem can sometimes be formulated as a maximum flow or a minimum cut problem.

Optical flow is a critical component of video editing applications, e.g. for tasks such as object tracking, segmen-tation, and selection. In this paper, we propose an optical flow algorithm called SimpleFlow whose running times increase sublinearly in the number of pixels.

Central to our approach is a probabilistic representation of the mo. Most recent works in optical flow extraction focus on the accuracy and neglect the time complexity.

However, in real-life visual applications, such as tracking, activity detection and recognition, the time complexity is critical. We propose a solution with very low time complexity and competitive accuracy for the computation of dense optical flow.

It consists of three parts: 1) inverse search. Special Issue "Emerging Algorithms and Applications in Vision Sensors System based on Artificial Intelligence" an optical monitoring system is designed and realized to assess the amount of fouling material remaining in process tanks, and to predict the required cleaning time.

constant memory, fast math compiler options, and asynchronous. Basic to this is the concept of optical flow, and the chapter demonstrates how foci of expansion and contraction arise for rigid objects moving relative to the camera. It also outlines the limitations of the optical flow model and develops the focus of expansion concept to deal with problems of collision avoidance and time-to-adjacency analysis.

(). PARALLEL ALGORITHMS AND APPLICATIONS. Parallel Algorithms and Applications: Vol. 3, No.pp. The invention belongs to the parallel processor technical field and relates to a GPU-based acceleration method of an image feature extraction algorithm.

According to the GPU-based acceleration method of the invention, fine-granularity parallel implementation of existing main image feature extraction algorithms is performed on GPUs, and optimized acceleration can be performed according to the. On Performance Analysis of Optical Flow Algorithms 3 ranking with the intent to draw attention to the fact that the performance of an algorithm consists of a set of criteria (or requirements) that can vary with the needs of di erent applications and types of data.

As we will discuss, we want to. It is, however, possible to compute suitable 'optical flows' that are qualitatively similar to the velocity field in most cases. We describe a simple, parallel algorithm that computes an optical flow from sequences of real images, which is consistent with human psychophysics.

Seepage flow through embankment dams and their sub-base is a crucial safety concern that can initiate internal erosion of the structure. The thermometric method of seepage monitoring employs the study of heat transfer characteristics in the soils, as the temperature distribution in earth-filled structures can be influenced by the presence of seepage.

Thus, continuous temperature measurements. A parallel fast direct solver with applications. High-Performance Computing and Networking, () The NRL layered ocean model. On Poisson solvers and semi-direct methods for computing area based optical flow. IEEE Transactions on Pattern Analysis and Machine IntelligenceA fast algorithm for simulation of a.

Parallel Algorithms and Applications Volume 6, NumberFrancisco Almeida and Felix García and Daniel Gonzalez and Casiano Rodríguez A Parallel Algorithm for the Integer Knapsack Problem for Pipeline Networks Reid Baldwin and Moon Jung Chung and Yunmo Chung Overlapping Window Algorithm for Computing GVT in Time Warp.

The treatment of complex multidisciplinary problems occurring in all application areas was discussed. (2) Algorithms: Design, analysis and implementation of generic parallel algorithms, including their scalability, in particular to a large number of processors (MPP), portability and adaptability.

Sparse optical flow algorithms, such as the Lucas-Kanade approach, provide more robustness to noise than dense optical flow algorithms and are the preferred approach in many scenarios. Sparse optical flow algorithms estimate the displacement for a selected number of pixels in the image.

These pixels can be chosen randomly. Parallel bicontinuous flows, which include stratified and core-annular flow, have applications in liquid–liquid extraction in microchannels.

The flow regime has a significant impact on interphase mass transfer. Either stratified flow or core-annular flow can result in better extraction, depending on the physical properties of the fluids and solute and the operating conditions.

A fast asynchronous algorithm for linear feature extraction on IBM SP-2 pp. Implementation of low level image processing algorithms on a reconfigurable perception system pp. Parallel algorithm for object recognition and its implementation on a MIMD machine pp. Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do.

Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of.

Regular data-flow • An application will often have regular data-flow at a higher level, e.g. a simple linear pipeline, where each stage or task in the pipeline executes in parallel.

–Signal processing (wireless, radio, radar, ODFM, UMTS, real-time beam former), graphics pipelines, multimedia compression and decompression algorithms. @article{osti_, title = {Parallel algorithms for optical digital computers}, author = {Huang, A}, abstractNote = {Conventional computers suffer from several communication bottlenecks which fundamentally limit their performance.

These bottlenecks are characterised by an address-dependent sequential transfer of information which arises from the need to time-multiplex information over a.

Fast Optical Flow using Dense Inverse Search Till Kroeger1 Radu Timofte1 Dengxin Dai1 Luc Van Gool1,2 1Computer Vision Laboratory, D-ITET, ETH Zurich 2VISICS / iMinds, ESAT, K.U. Leuven fkroegert, timofter, dai, [email protected] Abstract Most recent works in optical flow extraction focus on the accuracy and neglect the time complexity.

Although Gaussian elimination with partial pivoting is a robust algorithm to solve unsymmetric sparse linear systems of equations, it is difficult to implement efficiently on parallel machines because of its dynamic and somewhat unpredictable way of generating work and intermediate results at run time.

In this paper, we present an efficient parallel algorithm that overcomes this difficulty.Pixel-Level Granularity: Low Level Vision.

The starting point of the Low-level module of the platform is an improved FPGA-based implementation [14,15], which is briefly explained in this optical flow Multichannel Gradient Model (McGM), designed by Johnston [16–20], was chosen to implement the Low-level vision system in VLSI due its robustness and bio-inspiration.Roy I, Srivastava A, Grimm M and Aluru S Parallel interval stabbing on the automata processor Proceedings of the Sixth Workshop on Irregular Applications: Architectures and Algorithms, () Peñafiel H Access path selection in SQL database optimizers Proceedings of the 11th Latin-American Conference on Pattern Languages of Programming, ().