Edge Detection: A Collection of Pixel based Approach for Colored Images

Sadiq, Bashir Olaniyi; Sani, S. M.; Garba, S.

doi:10.5120/19825-1667

Cited by 11 publications

(31 citation statements)

References 13 publications

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“…Edge detection is a basic but fundamental step in image prepossessing and analysis. Improper technique in the extraction of edges can lead to the identification of false and broken edges amongst others (Sadiq et al, 2015). In literature, some of the most common edge detection techniques are the Sobel, Prewitt, Laplacian, Canny, Robert edge detectors.…”

Section: Introductionmentioning

confidence: 99%

“…However, using the Laplacian operator, the location accuracy is lower due to the size of the filtering kernel. Therefore, a collection of pixel approach is required to explore the larger area to overcome noise and consider the global structure of edges with a view to reducing false and broken edges in a reasonable time (Sadiq et al, 2015). Nonetheless, the technique presented by the authors in (Sadiq et al, 2015) works best for clean images only.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, a collection of pixel approach is required to explore the larger area to overcome noise and consider the global structure of edges with a view to reducing false and broken edges in a reasonable time (Sadiq et al, 2015). Nonetheless, the technique presented by the authors in (Sadiq et al, 2015) works best for clean images only. In order for their technique to function properly, the authors in (Sadiq et al, 2016) augmented (Sadiq et al, 2015) with a Particle Swarm Optimization (PSO) based median filter with a view to removing the noise present in the image.…”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, the technique presented by the authors in (Sadiq et al, 2015) works best for clean images only. In order for their technique to function properly, the authors in (Sadiq et al, 2016) augmented (Sadiq et al, 2015) with a Particle Swarm Optimization (PSO) based median filter with a view to removing the noise present in the image. These aforementioned techniques of edge detections are based on single value pixel with the exception of the works of Sadiq et al (2015) and Sadiq et al (2016) that used a collection of pixel-based approach.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

On the Accuracy of Edge Detectors in Number Plate Extraction

Sadiq¹,

Ochia²,

Zakariyya³

et al. 2019

BJMC

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Edge detection as a pre-processing stage is a fundamental and important aspect of the number plate extraction system. This is due to the fact that the identification of a particular vehicle is achievable using the number plate because each number plate is unique to a vehicle. As such, the characters of a number plate system that differ in lines and shapes can be extracted using the principle of edge detection. This paper presents a method of number plate extraction using edge detection technique. Edges in number plates are identified with changes in the intensity of pixel values. Therefore, these edges are identified using a single based pixel or collection of pixel-based approach. The efficiency of these approaches of edge detection algorithms in number plate extraction in both noisy and clean environment are experimented. Experimental results are achieved in MATLAB 2017b using the Pratt Figure of Merit (PFOM) as a performance metric.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the Accuracy of Edge Detectors in Number Plate Extraction

Sadiq¹,

Ochia²,

Zakariyya³

et al. 2019

BJMC

View full text Add to dashboard Cite

show abstract

“…Nevertheless, the Canny algorithm also as popular as Sobel algorithm [21]. Both of them are often used to detect the presence of the obstacles.…”

Section: F Obstacle Edge Detection (Right Image)mentioning

confidence: 99%

Grid-Edge-Depth Map Building Employing Sad With Sobel Edge Detector

Rahmani

Harjoko

Priyambodo

2017

International Journal on Smart Sensing and Intelligent Systems

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-This paper presents a new method of a map building which is suitable for a wheeled robot. The 2D map represents the obstacle's position and distance in the environment. The information of the obstacles obtained from a calibrated stereo camera. The stereo images size were 320x240 pixels. Hereafter the images were rectified and the disparity map was built using a Sum of Absolute Difference (SAD) algorithm. The depth map was calculated using disparity map, focal length, and baseline parameter values. In order to detect the obstacles, Sobel edge detection was implemented. The edge detection image was compared and substituted with the depth map which is resulting edge-depth map. The edge-depth map was divided into 25 grids (5 grids horizontal and 5 grids vertical). Finally, the minimum depth of detected obstacles for each grid was calculated. This process was resulting in a grid-edge-depth map (GED map). The proposal has been tested with a mobile robot in 5x3 meters living environment. Finally, experimental results are presented. The average error of feature points in the previous study was 5.40 cm, whereas in this study is 3.82 cm. There has been a decrease in the measurement gap of 29.26% from the previous study.

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Correlated Microstructure Descriptor for Image Retrieval

et al. 2019

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In this paper, a novel feature descriptor named ''correlated microstructure descriptor (CMSD)'' is proposed for image retrieval. CMSD represents high level semantics by identifying microstructures via establishing correlations between texture orientation, color, and intensity features. The proposed CMSD follows a two-staged approach for feature extraction and their integration. Color information is extracted by exclusively quantizing each component of HSV color space. Richer edge orientation information is extracted by using the multi-directional Sobel operator. Local contrast information is obtained by quantizing V component of HSV. Correlated microstructures are then identified by correlations established on the basis of proximity and continuation relations. The identified microstructures are then mapped to color, texture orientation, and intensity features exclusively to obtain micro-color, micro-orientation, and micro-intensity maps, respectively. Proposed three concatenated micro-maps are represented through a 2-D histogram, incorporate information regarding semantic and spatial contents of an image. The experiments are performed on standard datasets, i.e., Corel 1k, Corel 5k, and Corel 10k. The results evaluation depicts the outstanding performance of proposed CMSD as compared to state-of-the-art methods including TCM, GLCM, MTH, MSD, MTSD, and SED. INDEX TERMSContent based image retrieval, correlated low-level visual features, correlated primary visual features, micro-maps, microstructures.

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Edge Detection: A Collection of Pixel based Approach for Colored Images

Cited by 11 publications

References 13 publications

On the Accuracy of Edge Detectors in Number Plate Extraction

On the Accuracy of Edge Detectors in Number Plate Extraction

Grid-Edge-Depth Map Building Employing Sad With Sobel Edge Detector

Correlated Microstructure Descriptor for Image Retrieval

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