Effect of Different Window Size on Median Filter Performance with Variable Noise Densities

Rasheed, Asmaa Hameed; Hussein, Haneen Mohammed

doi:10.5120/ijca2017915732

Cited by 5 publications

(1 citation statement)

References 5 publications

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“…A median filter of size 3 × 3 has been glided through the grid of sensor readings with a stride length of 1. A 3 × 3 filter is suitable to filter out moderate density noise without blurring the output [46] and stride 1 ensures minimal information loss. For each filter operation, the filter outputs the median value of the 9 nodes under consideration.…”

Section: B Filteringmentioning

confidence: 99%

A Crowd-Based Efficient Fault-Proof Localization System for IoT and MCS

2021

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In this paper, an efficient and fault-proof active node selection approach for localization tasks in Internet of Things (IoT) and Mobile Crowd Sensing (MCS) systems is proposed. The proposed approach is resilient to the presence of anomalous nodes. Localization is the process of fusing data readings from multiple sensing nodes with the aim of finding the location of a specific target, such as radiation source, forest fires and noisy areas. Current active node selection systems for localization tasks perform algorithms like greedy and genetic methods over the whole Area of Interest (AoI). As such, a system that considers anomalous data is required to detect anomalies and perform localization over a large number of active nodes, which usually takes multiple iterations and is computationally costly. To overcome this, we propose a resilient localization approach which a) uses the median filter based image filtering technique to level out anomalous readings, b) uses the filtered readings to reduce the AoI to be around the target location without being influenced by anomalous nodes, c) detects and eliminates anomalies in the new AoI based on the deviation between filtered readings and original readings, and d) selects remaining nodes in new AoI for localization. As a result, there is a huge reduction in the complexity of active node selection and thus reduction in time taken by the system to perform the task of source localization. The efficacy of the proposed system is evaluated for radiation source localization tasks using simulated radiation dataset, by performing experiments for several test scenarios. The results demonstrate that the system is able to perform localization tasks in significantly reduced time and therefore generate near real-time results while also maintaining low localization error.

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Section: B Filteringmentioning

confidence: 99%

A Crowd-Based Efficient Fault-Proof Localization System for IoT and MCS

2021

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Performance Improvement of a Pressure Sensor Using Fast Median Algorithm

Park

Lee

2022

Int. J. Precis. Eng. Manuf.

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Non Destructive Testing For Detection abnormal Object in The X-Ray Images

Fadel

Al-Hameed

Ahmed

2020

J. Phys.: Conf. Ser.

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The clarification of weaknesses or defects detection in radiographs image is very important especially in medical science and non-destructive testing centers. The pressing need appears to develop the capabilities that serve image processing in several aspects to achieve the desired results and obtain useful information as possible from images. Therefore, it is very important to know the appropriate methods for image processing, especially with poor context radiographs images to improve their clarity and facilitate the interpretation process. In addition to providing important visual information that difficulty of obtaining from its sources noise-free. This research aims to detect non- normal objects that exist in the X-ray image efficiently through the adoption of digital image processing methods. By using the median filter addition to the morphological processes and obtaining satisfactory results. Where the accuracy of the proposed model reached 86.25 and the proposed model was applied to 80 casting images.

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Effect of Different Window Size on Median Filter Performance with Variable Noise Densities

Cited by 5 publications

References 5 publications

A Crowd-Based Efficient Fault-Proof Localization System for IoT and MCS

A Crowd-Based Efficient Fault-Proof Localization System for IoT and MCS

Performance Improvement of a Pressure Sensor Using Fast Median Algorithm

Non Destructive Testing For Detection abnormal Object in The X-Ray Images

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