Image Feature Extraction Using OD-Monotone Functions

Edge detection is a basic technique used as a preliminary step for, e.g., object extraction and recognition in image processing. Many of the methods for edge detection can be fit in the breakdown structure by Bezdek, in which one of the key parts is feature extraction. This work presents a method to extract edge features from a grayscale image using the so-called ordered directionally monotone functions. For this purpose we introduce some concepts about directional monotonicity and present two construction methods for feature extraction operators. The proposed technique is competitive with the existing methods in the literature. Furthermore, if we combine the features obtained by different methods using penalty functions, the results are equal or better results than state-of-the-art methods.

[1]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Humberto Bustince,et al.  A gravitational approach to edge detection based on triangular norms , 2010, Pattern Recognit..

[3]  R. Mesiar,et al.  Aggregation Functions (Encyclopedia of Mathematics and its Applications) , 2009 .

[4]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[5]  R. Mesiar,et al.  Aggregation operators: properties, classes and construction methods , 2002 .

[6]  James C. Bezdek,et al.  A geometric approach to edge detection , 1998, IEEE Trans. Fuzzy Syst..

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

[8]  Allan D. Jepson,et al.  Benchmarking Image Segmentation Algorithms , 2009, International Journal of Computer Vision.

[9]  Tim Wilkin,et al.  Weakly Monotonic Averaging Functions , 2015, Int. J. Intell. Syst..

[10]  Manuel González Hidalgo,et al.  On the Choice of the Pair Conjunction–Implication Into the Fuzzy Morphological Edge Detector , 2015, IEEE Transactions on Fuzzy Systems.

[11]  Humberto Bustince,et al.  Ordered Directionally Monotone Functions: Justification and Application , 2018, IEEE Transactions on Fuzzy Systems.

[12]  M. Forero-Vargas,et al.  Fuzzy Thresholding and Histogram Analysis , 2003 .

[13]  Humberto Bustince,et al.  A framework for edge detection based on relief functions , 2014, Inf. Sci..

[14]  Humberto Bustince,et al.  Directional monotonicity of fusion functions , 2015, Eur. J. Oper. Res..

[15]  Jitendra Malik,et al.  An empirical approach to grouping and segmentation , 2002 .

[16]  Tomaso A. Poggio,et al.  On Edge Detection , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Joost van de Weijer,et al.  Local Mode Filtering , 2001, CVPR.

[18]  Humberto Bustince,et al.  A Practical Guide to Averaging Functions , 2015, Studies in Fuzziness and Soft Computing.

[19]  Humberto Bustince,et al.  Quantitative error measures for edge detection , 2013, Pattern Recognit..

[20]  Hidenori Itoh,et al.  Image Filtering, Edge Detection, and Edge Tracing Using Fuzzy Reasoning , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Humberto Bustince,et al.  On the definition of penalty functions in data aggregation , 2017, Fuzzy Sets Syst..

[22]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Gleb Beliakov,et al.  Aggregation Functions: A Guide for Practitioners , 2007, Studies in Fuzziness and Soft Computing.

[24]  Rafael Muñoz-Salinas,et al.  A novel method to look for the hysteresis thresholds for the Canny edge detector , 2011, Pattern Recognit..

[25]  C. López Molina The breakdown structure of edge detection: analysis of individuall components and revisit of the overall structure , 2012 .