Innovation in Image Processing with Programmable Logic gates

Submission Deadline: 15 February 2022 (closed)

Guest Editors

Dr. Mahendrakhan K, Hindusthan Institute of Technology, India.
Dr. Paulchamy Balaiyah, Hindusthan Institute of Technology, India.
Dr. Uma Maheshwari, Hindusthan Institute of Technology, India.

Summary

Image processing applications are increasingly being implemented using Field Programmable Gate Arrays (FPGAs). This is especially true in real-time embedded applications, where latency and power are crucial factors. An FPGA built in a smart camera can execute much of the image processing directly as the image is delivered from the sensor, instead of supplying images. Modern system-on-chip (SoC) FPGAs enable an application's architecture to be effectively partitioned between hardware and software to take advantage of both platforms' strengths. Because many image processing algorithms have been optimised for a serial processor, simply moving a software algorithm onto an FPGA typically yields disappointing results. To take advantage of the parallelism and resources available on an FPGA, the method must usually be transformed. This could lead to new image processing algorithms and hardware computational architectures, both at the operation and application levels. The goal of this Special Issue is to showcase new FPGA algorithms, designs, approaches, and applications in the field of image processing.

Because many image processing algorithms have been optimised for a serial processor, simply moving a software algorithm onto an FPGA typically yields disappointing results. To take advantage of the parallelism and resources available on an FPGA, the method must usually be transformed. This could lead to new image processing algorithms and hardware computational architectures, both at the operation and application levels. The goal of this Special Issue is to showcase new FPGA algorithms, designs, approaches, and applications in the field of image processing.


Keywords

Image Processing
Artificial Intelligence
Field programmable Gate Arrays
hardware computational architectures
Field programmable Gate Arrays based smart cameras
Field programmable Gate Arrays based deep learning

Published Papers


  • Open Access

    ARTICLE

    Planetscope Nanosatellites Image Classification Using Machine Learning

    Mohd Anul Haq
    Computer Systems Science and Engineering, Vol.42, No.3, pp. 1031-1046, 2022, DOI:10.32604/csse.2022.023221
    (This article belongs to this Special Issue: Innovation in Image Processing with Programmable Logic gates)
    Abstract To adopt sustainable crop practices in changing climate, understanding the climatic parameters and water requirements with vegetation is crucial on a spatiotemporal scale. The Planetscope (PS) constellation of more than 130 nanosatellites from Planet Labs revolutionize the high-resolution vegetation assessment. PS-derived Normalized Difference Vegetation Index (NDVI) maps are one of the highest resolution data that can transform agricultural practices and management on a large scale. High-resolution PS nanosatellite data was utilized in the current study to monitor agriculture’s spatiotemporal assessment for the Al-Qassim region, Kingdom of Saudi Arabia (KSA). The time series of NDVI was utilized to assess the vegetation… More >

Share Link

WeChat scan