A time-efficient image processing algorithm for multicore/manycore parallel computing

Asaduzzaman, Abu; Martínez, A. Ruiz; Sepehri, Aras

doi:10.1109/secon.2015.7132924

Cited by 14 publications

(5 citation statements)

References 4 publications

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“…A study by Asaduzzaman et al 34 implements a test algorithm that loads the image pixel's bytes in a one‐dimensional CUDA array and then applies various filters (such as grayscale filter, invert filter, etc.) using GPU.…”

Section: Performance Centric CV For Centralized‐computingmentioning

confidence: 99%

A survey on parallel computing for traditional computer vision

Jaiswal

Kumar

2021

Concurrency and Computation

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Summary The applications of computer vision (CV) are continuously increasing along with the enormous demand for real‐time data processing. This visual data processing is done with various compute‐intensive image/video processing algorithms that may belong to traditional approaches or deep learning approaches. This article aims to provide a survey of state‐of‐the‐art hardware platforms and software frameworks for parallel implementation of traditional CV applications. The article discusses various options for hardware platforms for centralized‐computing architecture and edge‐computing architecture, and various software frameworks that can be used to leverage the hardware. This discussion is based on a systematic survey of studies/works that show the use of various hardware platforms and software frameworks in order to achieve real‐time processing for CV algorithms. Based on the survey, some possible future directions are also discussed.

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Section: Performance Centric CV For Centralized‐computingmentioning

confidence: 99%

A survey on parallel computing for traditional computer vision

Jaiswal

Kumar

2021

Concurrency and Computation

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“…Traditional computer vision and image processing techniques are computationally intensive and therefore may not be feasible to execute them on distributed computing platforms [4]. Therefore, it is not possible to scale computing nodes on such systems to improve service time.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven MPC for a Fog-Cloud Platform With AI-Inferencing in Mobile-Robotics

Vinod,

Singh,

Saikrishna

2023

IEEE Access

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One of the most appealing challenges, especially in mobile robotics, is the real-time inferencing of the vision data offloaded from a mobile robot for its enhanced navigation performance. Leveraging the benefits of an on-premise and an integrated fog-cloud computing platform for processing vision data is a viable solution. This paper addresses the mentioned challenge via a data-driven control approach for the development of an autonomic fog-cloud computing platform suitable for processing the offloaded vision data within a prescribed time bound called the service time. The approach comprises developing a data-driven linear parameter varying (LPV) framework for modeling and design of a model predictive controller (MPC) for the fog and the cloud platforms independently. A heuristic algorithm performs the distribution of the mobile robot vision data (MRVD) between the fog and the cloud platforms. The LPV model for the fog and the cloud platforms are developed by characterizing the MRVD as the variable frame rate and resolution of the objects (under active consideration in a given frame) acquired during navigation. We validate the developed theory for a mobile robot while navigating in the application environment such as the warehouse. The experimental results presented for object detection under service time bounds show the efficacy of the proposed approach.

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“…Parallel capabilities of modern processors and graphics cards allow to process several images simultaneously [4], [5], which can increase the number of processed images per second significantly. However, it does not necessarily mean the processing results are provided in a shorter time, see Fig.…”

Section: Introductionmentioning

confidence: 99%

Low-latency image processing for vision-based navigation systems

Čížek

Faigl

Diar

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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This paper concerns a problem of the latency reduction in the vision-based mobile robot navigation, which is considered as the crucial system property to determine a control command based on visual data in practical deployments of mobile robots. The problem is addressed by a processor centric FPGA-based System-on-Chip design allowing power and computationally efficient on-line image processing. The proposed architecture is considered in an autonomous visionbased navigation with a teach-and-repeat algorithm based on detection and tracking of image salient points. The architecture has been evaluated and compared with a CPU-based solution on different platforms and the results indicate that the proposed FPGA-based implementation outperforms pure CPU solutions in the overall latency, speed, and power consumption.

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A time-efficient image processing algorithm for multicore/manycore parallel computing

Cited by 14 publications

References 4 publications

A survey on parallel computing for traditional computer vision

A survey on parallel computing for traditional computer vision

Data-Driven MPC for a Fog-Cloud Platform With AI-Inferencing in Mobile-Robotics

Low-latency image processing for vision-based navigation systems

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