Subband coding and image compression using CNN

Moreira-Tamayo, Oscar; Gyvez, José Pineda de

doi:10.1002/(sici)1097-007x(199901/02)27:1<135::aid-cta45>3.0.co;2-a

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…Some of the image processing algorithms that have been implemented for CNNs include contrast enhancement, edge detection, text extraction, skeletonisation and smoothing [22,29]. Global image operations performed by CNNs include compression, quantisation and adaptation to illumination conditions [10,20].…”

Section: Cnn-based Image Processingmentioning

confidence: 99%

CNN-based language and interpreter for image processing on GPUs

Dolan

DeSouza

2011

International Journal of Parallel, Emergent and Distributed Sys

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The inherent massive parallelism of cellular neural networks (CNNs) makes them an ideal computational platform for kernel-based algorithms and image processing. General-purpose graphics processing units (GPUs) provide similar massive parallelism, but it can be difficult to design algorithms to make optimal use of the hardware. In this paper, we present a programming environment based on CNNs that can run on GPUs, multi-core CPUs or simply simulated in software. The platform offers a simplified view of massively parallel computation which remains universal and reasonably efficient. An image processing library with visualisation software has been developed using the abstraction to showcase the flexibility and power of cellular computation on GPUs. A simple virtual machine and language is presented to manipulate images using the library for single-core, multi-core and GPU backends.

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Section: Cnn-based Image Processingmentioning

confidence: 99%

CNN-based language and interpreter for image processing on GPUs

Dolan

DeSouza

2011

International Journal of Parallel, Emergent and Distributed Sys

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“…The swarm computer inherits many applications from the generalized cellular automata and cellular neural network such as image processing, image compression, and optimization [17], [15], [16], [28], [27]. In particular, the medium alone is sufficient for computing space invariant image processing operations and, like the cellular neural network, global operations are possible via the propagation of waves through the medium.…”

Section: A Image Processingmentioning

confidence: 99%

The Swarm Computer, an analog cellular-swarm hybrid architecture

Dolan

DeSouza

Caputo

2010

2010 10th International Conference on Hybrid Intelligent Systems

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The "killer apps" of cellular and swarm computing are image processing and optimization, respectively; however, applying these platforms to general-purpose computing remains impractical. Designing systems within the restrictive framework of cellular automata is extremely difficult, though often very efficient and scalable. On the other hand, swarm networks are very powerful but difficult to implement in hardware. Here we introduce a hybrid model, the Swarm Computer, which is both practical to program and efficient to implement. Applications in astrophysics and image processing are considered.

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“…the chain can't move any further. The statistical properties (mean and standard deviation computed on 5x5 neighbourhood) are obtained applying templates suggested in [20] on the edgeness image. "Mean" and "standard deviation" images, just obtained, are then combined through a weighted sum, as shown in fig.…”

Section: Proposed Strategymentioning

confidence: 99%

An edge-based Segmentation Technique for 2D still-image with Cellular Neural Networks

lannizzotto

Rosa

Lanzafame

2005 IEEE Conference on Emerging Technologies and Factory Automation

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When strong CPU power consumption constraints must be met, and high computation speed is mandatory (real-time processing), it can be preferable to adopt custom hardware for some computationally intensive image processing tasks. An alternative approach to the conventional ones is provided by the Cellular Neural Network (CNN) paradigm. CNNs have been extensively used in image processing applications: in the past, we developed a still image segmentation technique based on an active contour obtained via single-layer CNNs. This technique suffered from sensitivity to noise as most of edge-based methods: noise may create meaningless false edges or determine "edge fragmentation". The aim of this paper is to re-formulate the algorithm previously proposed in order to step-over the cited weakness. The new formulation is introduced and motivated and experimental results are presented. Finally, a competitionbased approach for a parameterless version of the presented algorithm is proposed and discussed as an ongoing work.

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Subband coding and image compression using CNN

Cited by 8 publications

References 9 publications

CNN-based language and interpreter for image processing on GPUs

CNN-based language and interpreter for image processing on GPUs

The Swarm Computer, an analog cellular-swarm hybrid architecture

An edge-based Segmentation Technique for 2D still-image with Cellular Neural Networks

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