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Image Processing Using FPGAs



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Autore: Bailey Donald Visualizza persona
Titolo: Image Processing Using FPGAs Visualizza cluster
Pubblicazione: MDPI - Multidisciplinary Digital Publishing Institute, 2019
Descrizione fisica: 1 electronic resource (204 p.)
Soggetto non controllato: nuclei detection
System-on-Chip
FPGA
K-Means
hardware acceleration
image analysis
perceptual coding
line buffer
heterogeneous computing
window filters
processor architectures
hardware accelerators
stream processing
embedded systems
image processing pipeline
image processing
generalized Laplacian of Gaussian filter
background estimation
real-time systems
FPGA implementation
hardware architecture
compression
image borders
memory
zig-zag scan
histopathology
just-noticeable difference (JND)
memory management
downsampling
image segmentation
feature extraction
design
mean Shift clustering
high-throughput
segmentation
streaming architecture
power
D-SWIM
hardware/software co-design
high-level synthesis
contrast masking
texture detection
pipeline
field programmable gate array (FPGA)
JPEG-LS
low-latency
connected components analysis
luminance masking
field programmable gate arrays (FPGA)
Sommario/riassunto: This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs.
Titolo autorizzato: Image Processing Using FPGAs  Visualizza cluster
ISBN: 3-03897-919-8
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910346693003321
Lo trovi qui: Univ. Federico II
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