Image processing-enhancement, filtering and edge detection using the fuzzy logic approach

Tyan, Ching-Yu; Wang, P.P.

doi:10.1109/fuzzy.1993.327420

Cited by 35 publications

(17 citation statements)

References 6 publications

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“…Generalized logistic functions in the arguments of (12) where is again any norm on and are real positive constants can be locally concave or convex near zero in . is an infinite family of three parameter blending functions whose general shape is affected more by ( , ) than by the norm used in (12). For example, with the two norm and fixed, varying in (12) produces a family of surfaces that range from an extremely "punctured" shape (large values of ) such as is seen in Fig.…”

Section: B Waterfall Functionsmentioning

confidence: 99%

“…Since for any choice of parameters in (12), none of these functions takes the value zero at the origin. This is evident in Fig.…”

Section: (B) (mentioning

confidence: 99%

“…17(a) and (b) is the norm used in (12). The one-norm seems to result in somewhat brighter and thicker edges, and the internal structure of the breast is slightly more well-defined than when the two-norm is used.…”

Section: Tomentioning

confidence: 99%

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A geometric approach to edge detection

Bezdek

Chandrasekhar

Attikouzel³

1998

IEEE Trans. Fuzzy Syst.

109

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Abstract-This paper describes edge detection as a composition of four steps: conditioning, feature extraction, blending, and scaling. We examine the role of geometry in determining good features for edge detection and in setting parameters for functions to blend the features. We find that: 1) statistical features such as the range and standard deviation of window intensities can be as effective as more traditional features such as estimates of digital gradients; 2) blending functions that are roughly concave near the origin of feature space can provide visually better edge images than traditional choices such as the city-block and Euclidean norms; 3) geometric considerations can be used to specify the parameters of generalized logistic functions and Takagi-Sugeno input-output systems that yield a rich variety of edge images; and 4) understanding the geometry of the feature extraction and blending functions is the key to using models based on computational learning algorithms such as neural networks and fuzzy systems for edge detection. Edge images derived from a digitized mammogram are given to illustrate various facets of our approach.

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Section: B Waterfall Functionsmentioning

confidence: 99%

“…Since for any choice of parameters in (12), none of these functions takes the value zero at the origin. This is evident in Fig.…”

Section: (B) (mentioning

confidence: 99%

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A geometric approach to edge detection

Bezdek

Chandrasekhar

Attikouzel³

1998

IEEE Trans. Fuzzy Syst.

109

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“…Nowadays, digital image processing techniques are being used in combination with fuzzy based expert system in many different applications [3,6,9,17]. There have been numerous studies of the use of expert systems in autofocusing [14], auto-exposure, auto-zooming, filtering and edge detection [15] etc. Asgharzadeh and Gaewsky [1] outline the use of a fuzzy rule based systems in controlling video recorders.…”

Section: Expert Systems and Image Enhancementmentioning

confidence: 99%

Analog and digital image enhancement using fuzzy expert systems

El-Osery

Jaṁshidi

2002

Soft Computing

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Image enhancement is a field that is being used in various areas and disciplines. Advances in computers, microcontrollers and DSP boards have opened new horizons to digital image processing, and have opened many avenues to the design and implementation of new innovative techniques. This paper compares image enhancement via the modification of the probability density function of the gray levels with the new techniques that involves the use of knowledge-base (fuzzy expert) systems that are capable of mimicking the behavior of a human expert. A fuzzy expert system based software for image enhancement, called SmartPhotoLab â has been introduced for the above purpose. IntroductionImage processing is a field that dates back to the 1920s, but the limitations due to the speed of the processing units and the available storage space have handicapped its progress and spread. Nowadays, the ever-growing advances in the computer technologies have reduced these limitations to a negligible level. Hence, the interest and the need for digital image processing have increased sharply. Today, image processing has applications in many fields and disciplines. Improvement of pictorial information for human interpretation and processing image information for autonomous machine perception were two main reasons behind the need for image processing [8]. Traditionally, all the image processing techniques have been based purely on mathematical approaches without any incorporation of human experience and knowledge that might have developed after years of working in the field. Recently, new techniques are being developed to mimic human reasoning, and to encode it into computer software programs that in some cases have proven superior results over the conventional techniques [2, 11].Processing of images for human perception is a rather complicated task. The determination of what is a ''good'' image is a subjective matter and differs from one viewer to another based on their taste and experience. On the other hand, image processing for autonomous machine perception is somewhat easier. Machines don't have taste, and the sole goal of image processing is to provide the information needed for the machine to perform a specific task. In either case, the determination of the right technique needs experimentation.This paper's main objective is to present a fuzzy expert system which can enhance digital and analog images. This expert system represents the core of an associated software environment known as SmartPhotoLab â , which can be used to enhance images from all popular formats. The expert system in SmartPhotoLab â is also used in hardware set up to enhance standard 35-mm film analog images.In the first part of this paper concentrates on providing a brief understanding of few of the key elements and methodologies of image processing. The second part will be dedicated to examining one of the conventional techniques used in image enhancement for human interpretation as well as the new intelligent techniques that are based on expert systems. Two example...

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“…Substantial contributions to chemical mapping of III-V compounds have also been given by De Jong and Van Dyck [3], Thoma and Cerva [4] and Walther et al [S]. In contrast to these in the present paper a fuzzy logic approach is introduced, initiated by a paper of Tyan and Wang [6], which also attains near-atomic resolution, given by the size of crystal sub-cells of the material imaged.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy logic approach to image analysis of HREM micrographs of III–V compounds

et al. 1996

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High resolution electron microscopy can within certain limits provide quantitative information on morphology and composition of crystalline materials. In layered structures of III-V semiconductor compounds changes resulting from interdiffusion phenomena across interfaces may result in image contrast changes because of composition sensitive electron scattering. A new approach for extracting the corresponding information from cross-section images of such interfaces is introduced. Local variations of similarity, a measure of comparability with respect to previously defined templates of known material composition on both sides of the interface, are recorded by image analysis. On the basis of the well-worked outfuzzy set theory variations of the underlying chemical composition are evaluated by applying fuzzy logic criteria of inference to image regions of about 1 nm x 1 nm in size.With comparatively little computational effort this new approach can be used to localize regions of significant composition changes, i.e. edge detection, and to determine the composition across the interface region, i.e. chemical mapping, respectively. The method is introduced and its reliability demonstrated by means of simulated contrast patterns of GaAs/GaAs, _XP, as well as of experimental images of AlAs/Al,Ga, _.As.

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Image processing-enhancement, filtering and edge detection using the fuzzy logic approach

Cited by 35 publications

References 6 publications

A geometric approach to edge detection

A geometric approach to edge detection

Analog and digital image enhancement using fuzzy expert systems

A fuzzy logic approach to image analysis of HREM micrographs of III–V compounds

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