Improved Edge Detection using Variable Thresholding Technique and Convolution of Gabor with Gaussian Filters

Medical Field, Robotic vision, Pattern recognition, Hurdle detection, and smart city are examples of areas that require image processing to achieve automation. Detecting an edge is an important stage in any computer vision application. The performance of the edge detecting algorithm is largely affected by the noise present in an image. An Image with a low signal-to-noise ratio (SNR), imposes a challenge to locate its edges. To improve the observable image boundaries, an adaptive filtering technique is proposed in this article. The proposed algorithm uses convolution of Gabor filter with Gaussian (GoG) operator to clean the noise before non-Maxima suppression. Furthermore, using variable hysteresis thresholding can further improve edge locating. The implementation of the algorithm was done by Python and Matlab. The obtained results were compared to a number of reviewed algorithms such as the Canny method, Laplacian of Gaussian, The Marr-Hildreth method, Sobel operator, and the Haar wavelet-based method. Three performance factors were used; PNSR, MSE, and processing time. The simulation result shows that the proposed method has higher PNSR, lower MSE, and shorter processing time when compared to the Canny detector, the Marr-Hildreth, Haar wavelet-based, Laplacian of Gaussian, and the Sobel operator methods. The higher PNSR, lower MSE, and shorter processing time mean improved edge details of the processed image