Noise induced segmentation of noisy color image
28 Mar 2013-pp 1-5
TL;DR: Noise Induced HSI model based noisy and blurred colour image segmentation technique that uses additive noise to suppress the effect of internal noise present in an image for proper detection of objects from such images is proposed.
Abstract: In this paper we have proposed Noise Induced HSI model based noisy and blurred colour image segmentation technique. This approach uses additive noise to suppress the effect of internal noise present in an image for proper detection of objects from such images. In this algorithm we decompose a given image in Hue, Saturation and Intensity (HSI) components and then apply processing on intensity component of the decomposed image. We measured performance of proposed algorithm in terms of correlation coefficient and number of mismatch pixels. The effectiveness of the proposed algorithm is compared with the different existing techniques. It is observed that the computational complexity of our algorithm is less in comparison with several existing techniques, because it deals only with intensity component of the decomposed image. Furthermore, an additional advantage, our technique of segmentation gives better performance as compared to SSR based segmentation using RGB model, SR-extended, integrated region matching, watershed and marker controlled watershed based segmentation method.
Citations
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13 Aug 2014
TL;DR: This paper presents a systematic noise-enhanced information processing framework to analyze and optimize the performance of engineered systems and discusses the constructive effect of noise in associative memory recall.
Abstract: Noise, traditionally defined as an unwanted signal or disturbance, has been shown to play an important constructive role in many information processing systems and algorithms. This noise enhancement has been observed and employed in many physical, biological, and engineered systems. Indeed stochastic facilitation (SF) has been found critical for certain biological information functions such as detection of weak, subthreshold stimuli or suprathreshold signals through both experimental verification and analytical model simulations. In this paper, we present a systematic noise-enhanced information processing framework to analyze and optimize the performance of engineered systems. System performance is evaluated not only in terms of signal-to-noise ratio but also in terms of other more relevant metrics such as probability of error for signal detection or mean square error for parameter estimation. As an important new instance of SF, we also discuss the constructive effect of noise in associative memory recall. Potential enhancement of image processing systems via the addition of noise is discussed with important applications in biomedical image enhancement, image denoising, and classification.
66 citations
Cites background from "Noise induced segmentation of noisy..."
...A suitable amount of noise has also been shown to improve image segmentation quality [114], [115], watermark/logo recognition, and image resizing detection [116]–[118]....
[...]
TL;DR: The noise always plays a key role in different science and engineering applications, and here, the effect of the addition of external noise (i.e., stochastic resonance (SR) noise) in weak signal detection application is studied and the proposed detection technique is compared with the state-of-the-art techniques.
Abstract: The noise always plays a key role in different science and engineering applications. Here, we study the effect of the addition of external noise (i.e., stochastic resonance (SR) noise) in weak signal detection application. We also explore the conditions of improvability and non-improvability for a particular SR noise. We analyze both symmetric and asymmetric SR noises in our example. With certain equality and inequality constraints, we discuss the penalty function method which is used to design a single objective function. Furthermore, the particle swarm optimization technique has been used to maximize the probability of detection (
$$P_\mathrm{D}$$
) at a constant value of the probability of false alarm (
$$P_\mathrm{FA}$$
). With a numerical example, we have exhibited the performance of the proposed detector. We compare our proposed detection technique with the state-of-the-art techniques, and it is observed that the optimum $$P_\mathrm{D}$$
is comparable at a constant value of $$P_\mathrm{FA}$$
. The proposed detection technique is also used for watermark detection application to show the practicality of the proposed technique.
14 citations
25 Jan 2017-Compel-the International Journal for Computation and Mathematics in Electrical and Electronic Engineering
TL;DR: In this paper, a multi-load available, response reliable and product-friendly method is in urgent need to diagnose the signs of incipient arcing, and a novel algorithm that originates the application of correlativity analysis of wavelet highfrequency component in state discrimination and further in arcing detection.
Abstract: Purpose
A multi-load available, response reliable and product-friendly method is in urgent need to diagnose the signs of incipient arcing. This paper aims to propose a novel algorithm that originates the application of correlativity analysis of wavelet high-frequency component in state discrimination and further in arcing detection.
Design/methodology/approach
The proposed method calculates the correlation coefficient between the extraction by wavelet transform of arcing series current and that of normal, compares it with a predefined threshold and outputs a trip signal when eight qualified arcing half cycles within a period of 0.5 s are detected.
Findings
Typical appliances are selected in laboratory for arc detection to test the method which carries on independently of impedance type. The algorithm could be optimized to identify arcing for different kinds of loads, including resistive, inductive, capacitive and switching power supply loads, with a same correlation coefficient threshold.
Practical implications
The arithmetic operations of the method are addition and multiplication, which contribute to efficient data computation and transmission for micro-processor to undertake. The reference optimal sampling rate recommended for the algorithm helps to reduce the processed data volume and shows its promising prospect for portable product development.
Originality/value
This proposed correlativity analysis of wavelet transform component algorithm could classify the tested signal into two categories, which benefits the discrimination of normal and fault states in condition monitoring. Laboratory tests prove that it works effectively in arc detection for the commonly used impedance types of loads and needs no offline self-learning or training of samples.
9 citations
13 Jul 2020
TL;DR: A noise-enhanced community detection framework that improves communities detected by existing community detection methods and introduces three noise methods to help detect communities better.
Abstract: Community structure plays a significant role in uncovering the structure of a network. While many community detection algorithms have been introduced, improving the quality of detected communities is still an open problem. In many areas of science, adding noise improves system performance and algorithm efficiency, motivating us to also explore the possibility of adding noise to improve community detection algorithms. We propose a noise-enhanced community detection framework that improves communities detected by existing community detection methods. The framework introduces three noise methods to help detect communities better. Theoretical justification and extensive experiments on synthetic and real-world datasets show that our framework helps community detection methods find better communities.
8 citations
Cites background from "Noise induced segmentation of noisy..."
...Adding noise can also improve image segmentation [16], image re-sampling detection [26], and image resizing detection [25]....
[...]
11 May 2021
TL;DR: In this article, a noise-enhanced link prediction framework was proposed to improve the link prediction performance by introducing three noise methods to improve link prediction heuristics, and extensive experiments on synthetic and real-world datasets showed that their framework helps improve current link prediction methods.
Abstract: Link prediction has attracted attention from multiple research areas. Although several – mostly unsupervised – link prediction methods have been proposed, improving them is still under study. In several fields of science, noise is used as an advantage to improve information processing, inspiring us to also investigate noise enhancement in link prediction. In this research, we study link prediction from a data preprocessing point of view by introducing a noise-enhanced link prediction framework that improves the links predicted by current link prediction heuristics. The framework proposes three noise methods to help predict better links. Theoretical explanation and extensive experiments on synthetic and real-world datasets show that our framework helps improve current link prediction methods.
5 citations
References
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TL;DR: A critical review of several definitions of watershed transform and associated sequential algorithms is presented in this paper, where the need to distinguish between definition, algorithm specification and algorithm implementation is pointed out.
Abstract: The watershed transform is the method of choice for image segmentation in the field of mathematical morphology. We present a critical review of several definitions of the watershed transform and the associated sequential algorithms, and discuss various issues which often cause confusion in the literature. The need to distinguish between definition, algorithm specification and algorithm implementation is pointed out. Various examples are given which illustrate differences between watershed transforms based on different definitions and/or implementations. The second part of the paper surveys approaches for parallel implementation of sequential watershed algorithms.
1,439 citations
TL;DR: In this paper, the synchronization of noise-induced transition processes is studied with the help of a simple electronic bistable system and it is shown that stochastic resonance occurs when the Kramers characteristic transition time equals the external forcing period.
683 citations
"Noise induced segmentation of noisy..." refers background in this paper
...SR occurs in physical and biological systems such as ring lasers [1], threshold hysteretic Schmitt triggers circuits [2], and A/D converters [3]....
[...]
TL;DR: The first observation of stochastic resonance in an optical device, the bidirectional ring laser, is reported and the addition of injected noise can lead to an improved signal-to-noise ratio.
Abstract: We report the first observation of stochastic resonance in an optical device, the bidirectional ring laser. The experiment exploits a new technique to modulate periodically the asymmetry between the two counter-rotating lasing modes. The measurements verify that the addition of injected noise can lead to an improved signal-to-noise ratio (relative to that observed with no externally injected noise).
682 citations
"Noise induced segmentation of noisy..." refers background in this paper
...SR occurs in physical and biological systems such as ring lasers [1], threshold hysteretic Schmitt triggers circuits [2], and A/D converters [3]....
[...]
TL;DR: This work shows that stochastic resonance enhances the normal feeding behaviour of paddlefish (Polyodon spathula), which use passive electroreceptors to detect electrical signals from planktonic prey, and demonstrates significant broadening of the spatial range for the detection of plankton when a noisy electric field of optimal amplitude is applied in the water.
Abstract: Stochastic resonance is the phenomenon whereby the addition of an optimal level of noise to a weak information-carrying input to certain nonlinear systems can enhance the information content at their outputs1,2,3,4. Computer analysis of spike trains has been needed to reveal stochastic resonance in the responses of sensory receptors5,6,7 except for one study on human psychophysics8. But is an animal aware of, and can it make use of, the enhanced sensory information from stochastic resonance? Here, we show that stochastic resonance enhances the normal feeding behaviour of paddlefish (Polyodon spathula)9,10, which use passive electroreceptors11,12 to detect electrical signals from planktonic prey13. We demonstrate significant broadening of the spatial range for the detection of plankton when a noisy electric field of optimal amplitude is applied in the water. We also show that swarms of Daphnia plankton are a natural source of electrical noise. Our demonstration of stochastic resonance at the level of a vital animal behaviour, feeding, which has probably evolved for functional success, provides evidence that stochastic resonance in sensory nervous systems is an evolutionary adaptation14.
423 citations
"Noise induced segmentation of noisy..." refers background in this paper
...SR occurs in physical and biological systems such as ring lasers [1], threshold hysteretic Schmitt triggers circuits [2], and A/D converters [3]....
[...]
TL;DR: A locally adaptive thresholding algorithm is proposed, concerning the extraction of targets from a given field of background, by introducing a shape connectivity measure based on co-occurrence statistics for threshold evaluation and a no-target identification procedure for modeling a local-processing paradigm.
Abstract: A locally adaptive thresholding algorithm, concerning the extraction of targets from a given field of background, is proposed. Conventional histogram-based or global-type methods are deficient in detecting small targets of possibly low contrast as well. The present research is notable for solving the mentioned problems by introducing (1) shape connectivity measure based on co-occurrence statistics for threshold evaluation; and (2) no-target identification procedure for modeling a local-processing paradigm. In this manner, thresholds are determined adaptively even in the presence of space-varying noise or clutter. Experiments show that the results are reliable and even outperform those that manual operations can achieve for global thresholding. >
75 citations
"Noise induced segmentation of noisy..." refers background in this paper
...SR also occurs in biological systems such as rat [4]....
[...]