Comparisions of Robert, Prewitt, Sobel operator based edge detection methods for real time uses on FPGA

Chaple, Girish; Daruwala, Rohin; Gofane, Manoj S.

doi:10.1109/ictsd.2015.7095920

Cited by 112 publications

(36 citation statements)

References 4 publications

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“…The authors employed mean squared error (MSE) [25], peak signal-to-noise ratio (PSNR) [61] and structural similarity index (SSIM) methods were used for comparison of the performance of those edge detection operators in a panel defect detection study. They are common methods of image processing analysis.…”

Section: Validation Testsmentioning

confidence: 99%

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Evaluation of Classical Operators and Fuzzy Logic Algorithms for Edge Detection of Panels at Exterior Cladding of Buildings

et al. 2019

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The automated process of construction defect detection using non-contact methods provides vital information for quality control and updating building information modelling. The external cladding in modular construction should be regularly controlled in terms of the quality of panels and proper installation because its appearance is very important for clients. However, there are limited computational methods for examining the installation issues of external cladding remotely in an automated manner. These issues could be the incorrect sitting of a panel, unequal joints in an elevation, scratches or cracks on the face of a panel or dimensions of different elements of external cladding. This paper aims to present seven algorithms to detect panel edges and statistically compare their performance through application on two scenarios of buildings in construction sites. Two different scenarios are selected, where the building façades are available to the public, and a sample of 100 images is taken using a state-of-the-art 3D camera for edge detection analysis. The experimentation results are validated by using a series of computational error and accuracy analyses and statistical methods including Mean Square Error, Peak Signal to Noise Ratio and Structural Similarity Index. The performance of an image processing algorithm depends on the quality of images and the algorithm utilised. The results show better performance of the fuzzy logic algorithm because it detects clear edges for installed panels. The applications of classical operators including Sobel, Canny, LoG, Prewitt and Roberts algorithms give similar results and show similarities in terms of the average of errors and accuracy. In addition, the results show that the minor difference of the average of the error and accuracy indices for Sobel, Canny, LoG, Prewitt and Roberts methods between both scenarios are not statistically significant, while the difference in the average of the error and accuracy indices for RGB-Sobel and Fuzzy methods between both scenarios are statistically significant. The accuracy of the algorithms can be improved by removing unwanted items such as vegetation and clouds in the sky. The evaluated algorithms assist practitioners to analyse their images collected day to day from construction sites, and to update building information modelling and the project digital drawings. Future work may need to focus on the combination of the evaluated algorithms using new data sets including colour edge detection for automatic defect identification using RGB and 360-degree images.

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Section: Validation Testsmentioning

confidence: 99%

“…Prewitt and Sobel are more difficult than Roberts, but are still the basic algorithms. Roberts, Prewitt and Sobel can all be used for real time image processing in industry [25][26][27][28]. LoG and Canny are much difficult than those three, but still commonly used in different aspects [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Classical Operators and Fuzzy Logic Algorithms for Edge Detection of Panels at Exterior Cladding of Buildings

et al. 2019

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“…( , ) seg x y represent the segmentation image; X S symbols the horizontal component of Sobel edge detection graph; Y S symbols the vertical edge detection graph; S symbols the Sobel edge detection graph of the whole image, as in [10]. X S is shown as:…”

Section: The Riverbank Line Detectionmentioning

confidence: 99%

Real-time Riverbank Line Detection for USV System

Feng

Xiong

Xiao

et al. 2019

2019 IEEE International Conference on Mechatronics and Automation (ICMA)

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Accurate and quick riverbank line detection plays an import role in extracting the region of interest (ROI) for an unmanned surface vehicle (USV). Because of the riverbank line can be used for USV obstacle detection, visual navigation, motion state estimation. Different from the sea line detection, the riverbank line detection is usually affected by water waves, reflection and inverted image, so the background of land river riverbank line detection are more complex and diversified. In this paper, a robust morphological riverbank line method to a different environment is presented. This method includes following steps: First, image pre-processing (Resize, Gaussian blue, Map RGB colour to HSV space); Then, the value channel was operated by the morphological gradient to highlight the edge; subsequently, segmentation of watershed algorithm; finally extracting the riverbank line in classical Sobel operator. Experiment result proves that our morphological approach can boost efficiency and performance significantly in multiple and complex environments. Index Terms -Unmanned surface vehicle; HSV color space;Morphology; Riverbank line detection.

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“…One mask works in the vertical direction and the other mask is in the horizontal direction of image pixels. These kernels of the SF can be used separately to obtain isolated estimations of the gradient in horizontal and vertical directions, Gx and Gy, respectively [20]. The two kernels can be joined together to find the value of the gradient at each pixel and the direction of the gradient as it can be seen in Figure 1.…”

Section: Sobel Filtermentioning

confidence: 99%

Performance of Face Recognition System Using Gradient Laplacian Operators and New Features Extraction Method Based on Linear Regression Slope

Alazzawi

Uçan

Bayat

2018

Mathematical Problems in Engineering

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Recent research proves that face recognition systems can achieve high-quality results even in non-ideal environments. Edge detection techniques and feature extraction methods are popular mechanisms used in face recognition systems. Edge detection can be used to construct the face map in the image efficiently, in which feature extraction techniques generate the most suitable features that can identify human faces. In this study, we present a new and efficient face recognition system that uses various gradient-and Laplacian-based operators with a new feature extraction method. Different edge detection operators are exploited to obtain the best image edges. The new and robust method based on the slope of the linear regression, called SLP, uses the estimated face lines in its feature extraction step. Artificial neural network (ANN) is used as a classifier. To determine the best scheme that gives the best performance, we test combinations of various techniques such as (Sobel filter (SF), SLP/principal component analysis (PCA), ANN), (Prewitt filter(PF), SLP/PCA, ANN), (Roberts filter (RF), SLP/PCA, ANN), (zero cross filter (ZF), SLP/PCA, ANN), (Laplacian of Gaussian filter (LG), SLP/PCA, ANN), and (Canny filter(CF), SLP/PCA, ANN). The BIO ID dataset is used in the training and testing phases for the proposed face recognition system combinations. Experimental results indicate that the proposed schemes achieve satisfactory results with high-accuracy classification. Notably, the combinations of (SF, SLP, ANN) and (ZF, SLP, ANN) gain the best results and outperform all the other algorithm combinations.

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Comparisions of Robert, Prewitt, Sobel operator based edge detection methods for real time uses on FPGA

Cited by 112 publications

References 4 publications

Evaluation of Classical Operators and Fuzzy Logic Algorithms for Edge Detection of Panels at Exterior Cladding of Buildings

Evaluation of Classical Operators and Fuzzy Logic Algorithms for Edge Detection of Panels at Exterior Cladding of Buildings

Real-time Riverbank Line Detection for USV System

Performance of Face Recognition System Using Gradient Laplacian Operators and New Features Extraction Method Based on Linear Regression Slope

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