Medical image enhancement by using cellular neural networks

Gacsádi, A.; Grava, Cristian; Grava, Adriana

doi:10.1109/cic.2005.1588231

Cited by 13 publications

(13 citation statements)

References 11 publications

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“…Due to their locally connections, the field areas occupied on the chip by the connection wire is minimized so that these networks could be implemented in the present VLSI technology [8]. Cells that are not directly connected together may affect each other indirectly because of the propagation effects of the continuous-time dynamics of cellular neural networks.…”

Section: Cellular Neural Networkmentioning

confidence: 99%

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Vision based algorithm for path planning of a mobile robot by using cellular neural networks

Gavrilut

Gacsádi

Grava

et al. 2006

2006 IEEE International Conference on Automation, Quality and Testing, Robotics

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The paper presents a new vision based algorithm for mobile robots path planning in an environment with obstacles. Cellular Neural Networks (CNNs) processing techniques are used here for real time motion planning to reach a fixed target. The CNN methods have been considered a solution for image processing in autonomous mobile robots guidance. The choice of CNNs for the visual processing is based on the possibility of their hardware implementation in large networks on a single VLSI chip (Cellular Neural Networks -Universal Machine, CNN-UM [2], [11]).

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Section: Cellular Neural Networkmentioning

confidence: 99%

“…The basic circuit units named cells or artificial neurons are connected only to its neighbor units. The basic cellular neural network [1], [8] has a twodimensional rectangular structure composed from identical, nonlinear analog circuits (cells) arranged, for example, in M rows and N columns (see Figure 1). …”

Section: Cellular Neural Networkmentioning

confidence: 99%

Vision based algorithm for path planning of a mobile robot by using cellular neural networks

Gavrilut

Gacsádi

Grava

et al. 2006

2006 IEEE International Conference on Automation, Quality and Testing, Robotics

Self Cite

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“…Mάtyάs Brendel et al [23] addressed the contrast enhancement problem by proposing a set of linear templates, which however do not provide good results for all test images due to the high nonlinear nature of the images. A. Gacsάdi et al [24] have designed a set of templates for image enhancement by minimizing the image energy function [23]; . (c) Image enhancement based on approach by A.Gacsάdi et al [24];.…”

Section: Benchmarkingmentioning

confidence: 99%

“…image contrast enhancement [22]- [24], edge detection [25] and segmentation [26], [27] just to name a few. Despite the ideal framework offered by the CNN paradigm to perform parallel and therefore ultrafast image processing there are still some important related issues that still need a better theoretical foundation.…”

Section: Introductionmentioning

confidence: 99%

A novel image processing approach combining a 'coupled nonlinear oscillators'-based paradigm with cellular neural networks for dynamic robust contrast enhancement

Kyamakya

Chedjou

Latif

et al. 2010

2010 12th International Workshop on Cellular Nanoscale Networks and Their Applications (CNNA 2010)

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In this paper, a systematic discussion of both pros and cons of two well-known traditional approaches for image contrast enhancement is conducted. The first approach is based on the CNN paradigm and the second one is based on the coupled nonlinear oscillators' paradigm for image processing. In the later case an extensive bifurcation analysis is carried out and analytical formulas are derived to define the various states of the system. Both equilibrium and oscillatory states of the system are depicted. It is shown that each of these states has a significant impact on the quality of the resulting image contrast enhancement. A benchmarking is considered whereby a comparison is performed between the results obtained by a CNNbased processing, on one side, with those obtained by a 'coupled non linear oscillators' based processing, on the other side. The superiority of the later approach (for contrast enhancement) is demonstrated both analytically and through various experiments. A major drawback of the CNN based image processing is the practical inability to adjust/re-calculate templates in real-time in face of a dynamic scene with input images experiencing visibility and/or lighting related spatio-temporal dynamics. Finally, a novel hybrid approach integrating both schemes in an efficient way is proposed: the 'coupled nonlinear oscillators' based image processing is the main processing scheme that is however realized on top of a CNN processors' framework. The hybrid approach does prove to overcome key practical problems faced by both original approaches.

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“…The image filtering is used to remove weather noise. In our system, we use a partial differential equation (PDE)-based enhancement technique which improves the contrast and reduces the noise [12] at the same time. The CNN template for this PDE-based approach can be found in Eq.…”

Section: Pre-processing Phasementioning

confidence: 99%

Real-time raindrop detection based on cellular neural networks for ADAS

Machot

Ali

Mosa

et al. 2016

J Real-Time Image Proc

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A core aspect of advanced driver assistance systems (ADAS) is to support the driver with information about the current environmental situation of the vehicle. Bad weather conditions such as rain might occlude regions of the windshield or a camera lens and therefore affect the visual perception. Hence, the automated detection of raindrops has a significant importance for video-based ADAS. The detection of raindrops is highly time critical since video pre-processing stages are required to improve the image quality and to provide their results in real-time. This paper presents an approach for real-time raindrops detection which is based on cellular neural networks (CNN) and support vector machines (SVM). The major idea is to prove the possibility of transforming the support vectors into CNN templates. The advantage of CNN is its ultra fast precessing on embedded platforms such as FPGAs and GPUs. The proposed approach is capable to detect raindrops that might negatively affect the vision of the driver. Different classification features were extracted to evaluate and compare the performance between the proposed approach and other approaches.

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Medical image enhancement by using cellular neural networks

Abstract: The

Cited by 13 publications

References 11 publications

Vision based algorithm for path planning of a mobile robot by using cellular neural networks

Vision based algorithm for path planning of a mobile robot by using cellular neural networks

A novel image processing approach combining a 'coupled nonlinear oscillators'-based paradigm with cellular neural networks for dynamic robust contrast enhancement

Real-time raindrop detection based on cellular neural networks for ADAS

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