Edge detection in digital images using fuzzy numbers

The aim of this paper is to introduce a new methodology for micro-pattern analysis in digital images. The gray-level pixels' structure in an image neighborhood describes a spatial specific context. Edge, line, spot, blob, corner or texture can be described by this structure. The gray-level values of the image pixel are interpreted as a fuzzy set, and each pixel gray-level as a fuzzy number. A membership function can be defined to describe the membership degree of the central pixel to the others in an image neighborhood. We have called this method the Local Fuzzy Pattern (LFP). If a sigmoid membership function is used, the proposed methodology describes the texture very well, and if a symmetrical triangular membership function is applied, the LFP is better for edge's detection. The results were compared to the Local Binary Pattern (LBP), for texture classification getting the better hit-rate. Our proposed formulation for the LFP is a generalization of previously published techniques, such as Texture Unit, LBP, FUNED, and Census Transform.

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“…The LFP-t is the same for the FUNED [13] [14] approach. It was proposed recently for edge detection.…”

Section: Resultsmentioning

confidence: 99%

“…The Fuzzy Number Edge Detector (FU-NED) [14] can be obtained by using the triangular symmetric membership function shown in Equation 2. Through Equation 1, it is possible to derive some previously published approaches for micro-pattern analysis.…”

Section: Methodsmentioning

confidence: 99%

Local Fuzzy Pattern: A New Way for Micro-pattern Analysis

Vieira¹,

Chierici²,

Ferraz³

et al. 2012

Lecture Notes in Computer Science

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“…A membership function is used to describe the membership degree of the central pixel to a particular neighborhood. Moreover, the LFP methodology is a generalization of previously published techniques such as the LBP, the Texture Unit [6], the Fuzzy Number Edge Detector (FUNED) [4] and the Census Transform [16]. One evolution of the LFP is the Local Mapped Pattern (LMP) [5], where the authors consider the sum of the differences of each gray-level of a given neighborhood to the central pixel as a local pattern that can be mapped to a histogram bin using a mapping function.…”

Section: Introductionmentioning

confidence: 99%

Feature description based on center-symmetric local mapped patterns

Ferraz

Pereira

Gonzaga

2014

Proceedings of the 29th Annual ACM Symposium on Applied Computing

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Local feature description has gained a lot of interest in many applications, such as texture recognition, image retrieval and face recognition. This paper presents a novel method for local feature description based on gray-level difference mapping, called Center-Symmetric Local Mapped Pattern (CS-LMP). The proposed descriptor is invariant to image scale, rotation, illumination and partial viewpoint changes. Furthermore, this descriptor more effectively captures the nuances of the image pixels. The training set is composed of rotated and scaled images, with changes in illumination and view points. The test set is composed of rotated and scaled images. In our experiments, the descriptor is compared to the Center-Symmetric Local Binary Pattern (CS-LBP). The results show that our descriptor performs favorably compared to the CS-LBP.

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