Morphological algorithm design for binary images using genetic programming

Quintana, Marcos; Poli, Riccardo; Claridge, Ela

doi:10.1007/s10710-006-7012-3

Cited by 41 publications

(21 citation statements)

References 29 publications

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“…Generalizability to unseen data, sampling requirements, method extensions, and method integration are open topics [6,54,55]. Note that parameters may also define construction processes of lower-level building blocks for image analysis, with mathematical morphology and genetic programming well suited to such designs [56,57].…”

Section: Sample Supervised Segment Generationmentioning

confidence: 99%

On Attribute Thresholding and Data Mapping Functions in a Supervised Connected Component Segmentation Framework

Fourie¹

2015

Remote Sensing

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Search-centric, sample supervised image segmentation has been demonstrated as a viable general approach applicable within the context of remote sensing image analysis. Such an approach casts the controlling parameters of image processing-generating segments-as a multidimensional search problem resolvable via efficient search methods. In this work, this general approach is analyzed in the context of connected component segmentation. A specific formulation of connected component labeling, based on quasi-flat zones, allows for the addition of arbitrary segment attributes to contribute to the nature of the output. This is in addition to core tunable parameters controlling the basic nature of connected components. Additional tunable constituents may also be introduced into such a framework, allowing flexibility in the definition of connected component connectivity, either directly via defining connectivity differently or via additional processes such as data mapping functions. The relative merits of these two additional constituents, namely the addition of tunable attributes and data mapping functions, are contrasted in a general remote sensing image analysis setting. Interestingly, tunable attributes in such a context, conjectured to be safely useful in general settings, were found detrimental under cross-validated conditions. This is in addition to this constituent's requiring substantially greater computing time. Casting connectivity definitions as a searchable component, here via the utilization of data mapping functions, proved more beneficial and robust in this context. The results suggest that further investigations into such a general framework could benefit more from focusing on the aspects of data mapping and modifiable connectivity as opposed to the utility of thresholding various geometric and spectral attributes.

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Section: Sample Supervised Segment Generationmentioning

confidence: 99%

On Attribute Thresholding and Data Mapping Functions in a Supervised Connected Component Segmentation Framework

Fourie¹

2015

Remote Sensing

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“…The direct/difference flag means to choose the morphological operation result directly or the arithmetic difference between it with the input as the current output. The storage flag means whether the result of this gene should be stored or not [2]. The logical operator flag represents which logical operation will be applied to the current gene.…”

Section: Figure 1: Chromosome Definitionmentioning

confidence: 99%

“…As in [2], we also use an artificial data set composed by four features: squares, circles, rings and stars, and use this algorithm to obtain valid objects. This algorithm was trained on a small area of source image, and four operation sequences were produced to extract the four features separately.…”

Section: Experiments On Binary Imagesmentioning

confidence: 99%

“…This algorithm was trained on a small area of source image, and four operation sequences were produced to extract the four features separately. The heavy computation load of Quintana's algorithm needed a cluster with 23 nodes in his experiments [2], while our algorithm finished the work on a PC workstation in a few minutes. Executing these sequences on the whole image, the average fitness values of obtained features were compared with Quintana's in Table 2.…”

Section: Experiments On Binary Imagesmentioning

confidence: 99%

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A novel genetic programming based morphological image analysis algorithm

Wang

Tan

2010

Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation

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This paper gives an applicable genetic programming(GP) approach to solve the binary image analysis and gray scale image enhancement problems. By showing a section of binary image and the corresponding goal image, this algorithm automatically produces a mathematic morphological operation sequence to transform the target into the goal. While the operation sequence is applied to the whole image, the objective of image analysis is achieved. With well-defined chromosome structure and evolution strategy, the effectiveness of evolution is promoted and more complex morphological operations can be composed in a short sequence. In addition, this algorithm is also applied to infrared finger vein gray scale images to enhance the region of interest. Whose effect is examined by an application of identity authentication, and the accuracy of authentication is promoted.

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“…This algorithm is restricted to search for fixed length chromosomes and for limited structuring elements in four patterns. Quintana et al present an approach of morphological binary image analysis based on G-P [31] [32]. Their algorithms are constructed by logic operators and the basic morphological operators -erosion and dilation.…”

Section: Introductionmentioning

confidence: 99%

Morphological image enhancement procedure design by using genetic programming

Wang

Tan

2011

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

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In this paper, we propose a genetic programming algorithm to design the morphological image enhancement procedure. Given a group of morphological operations and logical operations as function set, this algorithm evolves to produce a rational procedure which can enhance the input images. A novel mechanism which combines the ground truth method and feature significance is brought forward to evaluate the performance of images enhanced by generated procedures. In each generation, the best fitted individuals are selected on the basis of fitness values, and some individuals participate in crossover or mutation with a probability. After each generation, this algorithm outputs the best individual. Seven morphological operations and five logical operations are used in this algorithm. Furthermore, the structuring elements of morphological operations are randomly generated and varied in the whole pattern space. These methods promote the expressive ability of generated procedures. Examined by the binary image feature extraction, the procedure generated by this algorithm is more accurate and intelligible than previous work. In the task of gray scale image enhancement, the generated procedure is applied to infrared finger vein images to enhance the region of interest. More accurate features are extracted and the accuracy of authentication is promoted.

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Morphological algorithm design for binary images using genetic programming

Cited by 41 publications

References 29 publications

On Attribute Thresholding and Data Mapping Functions in a Supervised Connected Component Segmentation Framework

On Attribute Thresholding and Data Mapping Functions in a Supervised Connected Component Segmentation Framework

A novel genetic programming based morphological image analysis algorithm

Morphological image enhancement procedure design by using genetic programming

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