A New Sub-pixel Edge Detection Method of Color Images

Xiao, Feng; Guo, Li; Guo, Lina

doi:10.11591/telkomnika.v12i4.4921

Cited by 3 publications

(2 citation statements)

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“…RGB color images were unable to provide details information in order to identify the important edges or features of the object. Besides, the RGB image was easy to be exposed to the noise variations [25]. Thus, the conversion into the grayscale image was important in reducing complexity from 3 dimensional (3D) pixel value into 2 dimensional (2D) pixel value [26].…”

Section: Results and Analysismentioning

confidence: 99%

Shape Defect Detection using Local Standard Deviation and Rule-Based Classifier for Bottle Quality Inspection

Saad

Rahma

Abdullah

et al. 2017

IJEECS

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This paper presents shape analysis using Local Standard Deviation (LSD) technique to detect shape defect of the bottle for product quality inspection. The proposed analysis framework includes segmentation, feature extraction, and classification. The shape of the bottle was segmented using LSD technique in order to obtain higher enhancement at the low contrast area and low enhancement at the high contrast area. <span lang="EN-MY">The contrast gain that was applied in Adaptive Contrast Enhancement (ACE) algorithm, was presented inversely proportional to LSD in order to detect and eliminate background noise at the bottle edge. After the segmentation process, the parameters of the bottle shape such as height, width, area, and extent were extracted and applied in classification stage. The rule-based classifier was used to classify the shape of the bottle either good or defect. The offline experimental results exhibit superior segmentation on performance with 100% accuracy for 100 sample images. This shows that the LSD could be an effective technique to monitor the product quality.</span>

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Section: Results and Analysismentioning

confidence: 99%

Shape Defect Detection using Local Standard Deviation and Rule-Based Classifier for Bottle Quality Inspection

Saad

Rahma

Abdullah

et al. 2017

IJEECS

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“…Clustering is a subdivision of unsupervised learning processes [1]. It is a method for classifying data in an image and to find clusters with the most likeness in the identical cluster and most unlikeness among diverse clusters [2].…”

Section: Introductionmentioning

confidence: 99%

An Edge Exposure using Caliber Fuzzy C-means With Canny Algorithm

Jeyaraman

Janakiraman

2017

IJEECS

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<p>Edge exposure or edge detection is an important and classical study of the medical field and computer vision. Caliber Fuzzy C-means (CFCM) clustering Algorithm for edge detection depends on the selection of initial cluster center value. This endeavor to put in order a collection of pixels into a cluster, such that a pixel within the cluster must be more comparable to every other pixel. Using CFCM techniques first cluster the BSDS image, next the clustered image is given as an input to the basic canny edge detection algorithm. The application of new parameters with fewer operations for CFCM is fruitful. According to the calculation, a result acquired by using CFCM clustering function divides the image into four clusters in common. The proposed method is evidently robust into the modification of fuzzy c-means and canny algorithm. The convergence of this algorithm is very speedy compare to the entire edge detection algorithms. The consequences of this proposed algorithm make enhanced edge detection and better result than any other traditional image edge detection techniques.</p>

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Reduced-reference Video Quality Metric Using Spatio-temporal Activity Information

2018

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Monitoring and maintaining acceptab le Quality of Experience is of great importance to video service providers. Perceived visual quality of transmitted video via wireless networks can b e degraded b y transmission errors. This paper presents a reduced-reference video quality metric of very low complexity and overhead that makes use of frame b ased spatial (SI) and temporal (TI) activity levels to monitor the effect of channel errors on video transmitted over error prone networks. The performance of the metric is evaluated relative to that of a numb er of full and reduced reference metrics. The proposed metric outperforms some of the most popular full reference metrics whilst requiring very little overhead.

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A New Sub-pixel Edge Detection Method of Color Images

Cited by 3 publications

References 0 publications

Shape Defect Detection using Local Standard Deviation and Rule-Based Classifier for Bottle Quality Inspection

Shape Defect Detection using Local Standard Deviation and Rule-Based Classifier for Bottle Quality Inspection

An Edge Exposure using Caliber Fuzzy C-means With Canny Algorithm

Reduced-reference Video Quality Metric Using Spatio-temporal Activity Information

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