Edge detection based on type-1 fuzzy logic and guided smoothening

Edge detection is an important phenomenon in computer vision. Edge detection is helpful in contour detection and thus helpful in obtaining the important information. Edge detection process heavily depends on chosen technique. Soft computing techniques are considered as powerful edge detection methods due to their adaptability. This paper presents a fuzzy logic based edge detection method where the quality of edges is controlled using sharpening guided filter and noise due to the sharpening is controlled using Gaussian filter. The accuracy of the method is judged using a variety of statistical measures. It has been found that by proper selecting the smoothening parameters a significant improvement in the detected edges can be obtained.

[1]  Mark Johnston,et al.  A novel particle swarm optimisation approach to detecting continuous, thin and smooth edges in noisy images , 2013, Inf. Sci..

[2]  Alan L. Yuille,et al.  Adversarial Examples for Edge Detection: They Exist, and They Transfer , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[3]  Begol,et al.  Improving Digital Image Edge Detection by Fuzzy Systems , 2012 .

[4]  Gholamreza Akbarizadeh,et al.  Optimized fuzzy cellular automata for synthetic aperture radar image edge detection , 2018 .

[5]  Mohammad Zahedinejad,et al.  Novel Edge Detection Using BP Neural Network Based on Threshold Binarization , 2009, 2009 Second International Conference on Computer and Electrical Engineering.

[6]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Ayman A. Aly,et al.  Edge Detection in Digital Images Using Fuzzy Logic Technique , 2009 .

[8]  Joost van de Weijer,et al.  Color in Computer Vision: Fundamentals and Applications , 2012 .

[9]  Long Chen,et al.  Noise robust image edge detection based upon the automatic anisotropic Gaussian kernels , 2017, Pattern Recognit..

[10]  Plamen P. Angelov,et al.  Autonomous visual self-localization in completely unknown environment , 2007, 2012 IEEE Conference on Evolving and Adaptive Intelligent Systems.

[11]  David Malah,et al.  A study of edge detection algorithms , 1982, Comput. Graph. Image Process..

[12]  Robert M. Haralick,et al.  Optimal matching problem in detection and recognition performance evaluation , 2002, Pattern Recognit..

[13]  Uwe Stilla,et al.  Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection , 2016, ISPRS Journal of Photogrammetry and Remote Sensing.

[14]  Slawomir Wesolkowski,et al.  Comparison of color image edge detectors in multiple color spaces , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[15]  W. Eric L. Grimson,et al.  Edge-based rich representation for vehicle classification , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[16]  Jian Ma,et al.  Edge Detection by Adaptive Neuro-Fuzzy Inference System , 2009, 2009 2nd International Congress on Image and Signal Processing.

[17]  Jian Sun,et al.  Guided Image Filtering , 2010, ECCV.

[18]  Plamen P. Angelov,et al.  An approach to automatic real‐time novelty detection, object identification, and tracking in video streams based on recursive density estimation and evolving Takagi–Sugeno fuzzy systems , 2011, Int. J. Intell. Syst..

[19]  Jadwiga Rogowska,et al.  Overview and fundamentals of medical image segmentation , 2000 .

[20]  Kyeong-Deok Moon,et al.  A novel approach for detecting the horizon using a convolutional neural network and multi-scale edge detection , 2018, Multidimens. Syst. Signal Process..

[21]  Shashank Mathur,et al.  APPLICATION OF FUZZY LOGIC ON IMAGE EDGE DETECTION , 2008 .

[22]  Jaideva C. Goswami,et al.  Fundamentals of wavelets , 1999 .

[23]  Plamen P. Angelov,et al.  On line learning fuzzy rule-based system structure from data streams , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[24]  Shengmei Zhao,et al.  Edge detection based on single-pixel imaging. , 2018, Optics express.

[25]  Frank Y. Shih,et al.  Image Processing and Pattern Recognition: Fundamentals and Techniques , 2010 .

[26]  Plamen Angelov,et al.  A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition , 2017 .

[27]  Oscar Castillo,et al.  Optimization of interval type-2 fuzzy systems for image edge detection , 2016, Appl. Soft Comput..

[28]  Nitesh V. Chawla,et al.  Editorial: special issue on learning from imbalanced data sets , 2004, SKDD.

[29]  Humberto Bustince,et al.  Baddeley’s Delta metric for local contrast computation in hyperspectral imagery , 2017, Progress in Artificial Intelligence.

[30]  Djemel Ziou,et al.  Image Quality Metrics: PSNR vs. SSIM , 2010, 2010 20th International Conference on Pattern Recognition.

[31]  Andrew K. Chan,et al.  Fundamentals of Wavelets: Theory, Algorithms, and Applications , 2011 .

[32]  Jian Sun,et al.  Guided Image Filtering , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Marin van Heel,et al.  Similarity measures between images , 1987 .

[34]  Wesley E. Snyder,et al.  Fundamentals of Computer Vision , 2017 .

[35]  Yu Liu,et al.  Learning Relaxed Deep Supervision for Better Edge Detection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Hamid Hassanpour,et al.  Edge Detection Techniques: Evaluations and Comparison , 2008 .