Scheduling of low level computer vision algorithms on networks of heterogeneous machines

Nolan, Adam R.; Everding, Bryan; Wee, William G.

doi:10.1109/camp.1995.521059

“…This is crucial as shadows reduce the clarity of the image and extrapolates its dimensions and requires a more complex algorithm to process the image and also reduces the accuracy as rounding off is required if shadows are involved. Also, using clear images makes the processing easier and scheduling [3] them in a processor is done more efficiently for a clear image.…”

Section: Process Resolutionmentioning

confidence: 99%

Machine Vision for General Cameras for Quality Testing and Dimension Calculations

Murali¹

2015

IJMO

1

0

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This paper looks into an economical way to bring machine vision to smartphones and basic camera modules without any major setup using basic image processing techniques. This technique can be used to measure the dimensions of random objects as well as be used to detect particular objects in an image. A single camera module can be used for both purposes i.e. measure objects whose image distance is known and measure dimensions of objects whose image distance needs to be calculated, making the process efficient, economical and portable. We look into a new area where machine vision can aid the general population and be affordable to small scale industries as well. Index Terms-Machine vision, image processing, quality assurance, dimension calculation, portability.12 12Use a standard image as a reference image, we can find the distance of an arbitrary image by scaling it till LHS and RHS are equal. Given the distance. This is an accurate method computer science. He is currently researching in algorithms and mobile computing.Mr. Murali has won awards for his research work at

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“…The communication mode is exclusive and thus, the primary processor can send or receive data to or from only one secondary node at a time. With optimal scheduling, after each node performs its computation, there may not be any idling [25]. However, this assumption is valid only if the load is finely divisible.…”

Section: Optimality Conditionmentioning

confidence: 99%

An optimization based parallel particle filter for multitarget tracking

Sutharsan

¹

,

Sinha

²

,

Kirubarajan

³

et al. 2005

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Particle filters are being used in a number of state estimation applications because of their capability to effectively solve nonlinear and non-Gaussian problems. However, they have high computational requirements and this becomes even more so in the case of multi target tracking, where data association is the bottleneck. In order to perform data association and estimation jointly, typically an augmented state vector, whose dimensions depend on the number of targets, is used in particle filters. As the number of targets increases, the corresponding computational load increases exponentially. In this case, parallelization is a possibility for achieving real-time feasibility in large-scale multitarget tracking applications. In this paper, we present an optimization-based scheduling algorithm that minimizes the total computation time for the bus-connected heterogeneous primary-secondary architecture. This scheduler is capable of selecting the optimal number of processors from a large pool of secondary processors and mapping the particles among the selected ones. A new distributed resampling algorithm suitable for parallel computing is also proposed. Furthermore, a less communication intensive parallel implementation of the particle filter without sacrificing tracking accuracy using an efficient load balancing technique, in which optimal particle migration among secondary processors is ensured, is presented. Simulation results demonstrate the tracking effectiveness of the new parallel particle filter and the speedup achieved using parallelization. 111To my mother, who sacrificed so much for my well-being, and to my brother and sister

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An Optimization-Based Parallel Particle Filter for Multitarget Tracking

Sutharsan

¹

,

Kirubarajan

²

,

Lang

³

et al. 2012

IEEE Trans. Aerosp. Electron. Syst.

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Particle filters are being used in a number of state estimation applications because of their capability to effectively solve nonlinear and non-Gaussian problems. However, they have high computational requirements and this becomes even more so in the case of multi target tracking, where data association is the bottleneck. In order to perform data association and estimation jointly, typically an augmented state vector, whose dimensions depend on the number of targets, is used in particle filters. As the number of targets increases, the corresponding computational load increases exponentially. In this case, parallelization is a possibility for achieving real-time feasibility in large-scale multitarget tracking applications. In this paper, we present an optimization-based scheduling algorithm that minimizes the total computation time for the bus-connected heterogeneous primary-secondary architecture. This scheduler is capable of selecting the optimal number of processors from a large pool of secondary processors and mapping the particles among the selected ones. A new distributed resampling algorithm suitable for parallel computing is also proposed. Furthermore, a less communication intensive parallel implementation of the particle filter without sacrificing tracking accuracy using an efficient load balancing technique, in which optimal particle migration among secondary processors is ensured, is presented. Simulation results demonstrate the tracking effectiveness of the new parallel particle filter and the speedup achieved using parallelization. 111To my mother, who sacrificed so much for my well-being, and to my brother and sister

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Scheduling of low level computer vision algorithms on networks of heterogeneous machines

Cited by 3 publications

References 10 publications

Machine Vision for General Cameras for Quality Testing and Dimension Calculations

Machine Vision for General Cameras for Quality Testing and Dimension Calculations

An optimization based parallel particle filter for multitarget tracking

An Optimization-Based Parallel Particle Filter for Multitarget Tracking

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