Shape analysis using morphological processing and genetic algorithms

Nicoletti

2015

ICA

This paper proposes a computational modeling for image filtering processes based on the Cartesian Genetic Programming (CGP) methodology, suitable for hardware devices. A computational system named ALIF-CGP (Automatic Learning of Image Filters Using Cartesian Genetic Programming) was designed as a simulator for automatically constructing a sequence of operators, mainly morphological and logical, which can filter a particular shape of image. ALIF-CGP is a convenient option for executing the non-trivial task, usually manually done by human experts, of selecting the sequence of nonlinear operators to be used in morphological filters. ALIF-CGP has already a built-in pool of morphological and logical operators, which can be used by default. The user, however, has the flexibility of choosing only those operators which are of interest or then, conveniently introduce new ones. The system expects as input a pair of images (input-target). The flexibility given by the CGP-based computational modeling used by ALIF-CGP as well as its efficiency and satisfactory results, obtained in various image processing case studies, recommend its use when developing a hardware implementation for the purposes of image filtering. A few case studies using ALIF-CGP are presented and comparatively analyzed in relation to previous results available in the literature.The manual process employed by humans, when dealing with digital image processing, is usually very slow due to the trial and error approach commonly used. The ad-hoc nature of the problem invariably turns the search for the best sequence of image operators into a complex task and its solution unsuitable for reuse.Many available research works published over the past years have focused their efforts trying to reduce the complexity involved in designing new image operators that would be able to perform various computational tasks [8,9,49,50,65]. Particularly, computational tools based on the mathematical morphology formalism are considered the most effective approach when applied in practical and theoretical problems from areas such as image analysis and image processing. Developments in these areas have great importance and impact in subjacent areas such as robotic vision, visual inspection, medicine, analysis of textures, among many others.

Section: Introductionmentioning

confidence: 71%

Automatic learning of image filters using Cartesian genetic programming

Paris

Nicoletti

2015

ICA

“…Although several approaches have been used for the automatic programming of the process, as a side effect to overcome difficulties, invariably a few limitations are introduced, such as: (a) a limited number of available operators to choose from; (b) the process only allows the use of regular structuring elements with mathematical morphology. A few research proposals where non-regular structuring elements have been employed, however, can be found [5,32]. The proposal described in this paper uses the convolution operator in conjunction with morphological and logical operators enlarging, in this way, the scope of its use.…”

Section: Linear and Nonlinear Operatorsmentioning

confidence: 99%

“…In this context, there is an urgent need to automate the process of determining the sequence of operators to be applied. The automation process can be performed by several methods; see, for instance, those described in [7,8,34] and, particularly, those involving evolutionary procedures, as the ones proposed in [5,[9][10][11]20,40,41].…”

Section: Introductionmentioning

confidence: 99%

A genetic programming based system for the automatic construction of image filters

Roda

Kato

et al. 2013

ICA

The manual selection of linear and nonlinear operators for producing image filters is not a trivial task in practice, so new proposals that can automatically improve and speed up the process can be of great help. This paper presents a new proposal for constructing image filters using an evolutionary programming approach, which has been implemented as the IFbyGP software. IFbyGP employs a variation of the Genetic Programming algorithm (GP) and can be applied to binary and gray level image processing. A solution to an image processing problem is represented by IFbyGP as a set of morphological, convolution and logical operators. The method has a wide range of applications, encompassing pattern recognition, emulation filters, edge detection, and image segmentation. The algorithm works with a training set consisting of input images, goal images, and a basic set of instructions supplied by the user, which would be suitable for a given application. By making the choice of operators and operands involved in the process more flexible, IFbyGP searches for the most efficient operator sequence for a given image processing application. Results obtained so far are encouraging and they stress the feasibility of the proposal implemented by IFbyGP. Also, the basic language used by IFbyGP makes its solutions suitable to be directly used for hardware control, in a context of evolutionary hardware. Although the proposal implemented by IFbyGP is general enough for dealing with binary, gray level and color images, only applications using the first two are considered in this paper; as it will become clear in the text, IFbyGP aims at the direct use of induced sequences of operations by hardware devices. Several application examples discussing and comparing IFbyGP results with those obtained by other methods available in the literature are presented and discussed.

“…Some expert knowledge is necessary to properly select the structuring element and the morphological operators to solve a certain problem [14]. In the literature, there are several approaches using automatic programming to overcome these difficulties [15][16][17][18][19][20][21][22]; however, they present several drawbacks as a limited number of operators, only regular forms of structuring elements and only morphological instructions, to name just a few.…”

Section: Introductionmentioning

confidence: 99%

A Genetic Programming Approach to Reconfigure a Morphological Image Processing Architecture

International Journal of Reconfigurable Computing

Saito

Roda

2011

Mathematical morphology supplies powerful tools for low-level image analysis. Many applications in computer vision require dedicated hardware for real-time execution. The design of morphological operators for a given application is not a trivial one. Genetic programming is a branch of evolutionary computing, and it is consolidating as a promising method for applications of digital image processing. The main objective of genetic programming is to discover how computers can learn to solve problems without being programmed for that. In this paper, the development of an original reconfigurable architecture using logical, arithmetic, and morphological instructions generated automatically by a genetic programming approach is presented. The developed architecture is based on FPGAs and has among the possible applications, automatic image filtering, pattern recognition and emulation of unknown filter. Binary, gray, and color image practical applications using the developed architecture are presented and the results are compared with similar techniques found in the literature.