Characterization of Texture in Images by Using a Cellular Automata Approach

Leguizamón, Saturnino; Espínola, Moisés; Ayala, Rosa; Iribarne, Luís; Menenti, Massimo

doi:10.1007/978-3-642-16324-1_61

Cited by 9 publications

(5 citation statements)

References 7 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another promising related approach in the last years has been cellular automata (CA). In [26], the authors develop a direct CA modeling by binarizing the original image and iteratively applying well established transition functions over the CA lattice. More recently, an elaborated strategy has been proposed in [35] where a physical model (corrosion) was used to formulate transition rules of a CA model.…”

Section: Related Workmentioning

confidence: 99%

Reorganizing local image features with chaotic maps: an application to texture recognition

Florindo¹

2020

Preprint

View full text Add to dashboard Cite

Despite the recent success of convolutional neural networks in texture recognition, model-based descriptors are still competitive, especially when we do not have access to large amounts of annotated data for training and the interpretation of the model is an important issue. Among the model-based approaches, fractal geometry has been one of the most popular, especially in biological applications. Nevertheless, fractals are part of a much broader family of models, which are the non-linear operators, studied in chaos theory. In this context, we propose here a chaos-based local descriptor for texture recognition. More specifically, we map the image into the three-dimensional Euclidean space, iterate a chaotic map over this three-dimensional structure and convert it back to the original image. From such chaos-transformed image at each iteration we collect local descriptors (here we use local binary patters) and those descriptors compose the feature representation of the texture. The performance of our method was verified on the classification of benchmark databases and in the identification of Brazilian plant species based on the texture of the leaf surface. The achieved results confirmed our expectation of a competitive performance, even when compared with some learning-based modern approaches in the literature.

show abstract

Section: Related Workmentioning

confidence: 99%

Reorganizing local image features with chaotic maps: an application to texture recognition

Florindo¹

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Regarding all these investigations, it is surprising that we can hardly find CA-based approaches combining image processing and pattern recognition for image classification. The literature has presented solutions based on binarization of the image followed by modeling of a CA with classical transition functions (Leguizamón et al, 2010). The study done by Silva et al Silva et al (da Silva et al, 2015) demonstrates how a CA model can be helpful for the recognition of images, especially textures.…”

Section: Related Workmentioning

confidence: 99%

“…Whereas the literature has presented applications of CA models in image processing (Wongthanavasu and Tangvoraphonkchai, 2007;Gao and Yang, 2014;Rosin, 2006Rosin, , 2010Leguizamón et al, 2010;Gu and Sun, 2018) or general applications of pattern recognition (Chandramouli and Izquierdo, 2006;Guo et al, 2010a), the use of CAs to provide image descriptors is still a topic little explored, at least in explicit terms.…”

Section: Introductionmentioning

confidence: 99%

A cellular automata approach to local patterns for texture recognition

Florindo¹,

Metze²

2020

Preprint

View full text Add to dashboard Cite

Texture recognition is one of the most important tasks in computer vision and, despite the recent success of learning-based approaches, there is still need for model-based solutions. This is especially the case when the amount of data available for training is not sufficiently large, a common situation in several applied areas, or when computational resources are limited. In this context, here we propose a method for texture descriptors that combines the representation power of complex objects by cellular automata with the known effectiveness of local descriptors in texture analysis. The method formulates a new transition function for the automaton inspired on local binary descriptors. It counterbalances the new state of each cell with the previous state, in this way introducing an idea of "controlled deterministic chaos". The descriptors are obtained from the distribution of cell states. The proposed descriptors are applied to the classification of texture images both on benchmark data sets and a real-world problem, i.e., that of identifying plant species based on the texture of their leaf surfaces. Our proposal outperforms other classical and state-of-the-art approaches, especially in the real-world problem, thus revealing its potential to be applied in numerous practical tasks involving texture recognition at some stage.

show abstract

“…One limitation of texture extraction is the existence of unreliable classification results near the edges of classes. In the paper [21] a characterization of the texture of images by using cellular automata approach has been explained.…”

Section: Contextual Classification Through Texture Extractionmentioning

confidence: 99%

Supervised Techniques and Approaches for Satellite Image Classification

Nair¹,

Bindhu²

2016

IJCA

View full text Add to dashboard Cite

Remote Sensing is a multidisciplinary technique for image acquisition and measurement of information. Remote sensing analysis paved way for satellite image classification which facilitates the image interpretation of large amount of data. Satellite Images covers large geographical span and results in the exploitation of huge information which includes classifying into different sectors. Different classification algorithms exist for image classification, but with the wide range of applications an algorithm with improved performance in terms of accuracy is required. Here in this paper we analyze different methods of supervised classification, different post classification techniques, spectral contextual classification and provide a comparative study on their efficiency.

show abstract

Characterization of Texture in Images by Using a Cellular Automata Approach

Cited by 9 publications

References 7 publications

Reorganizing local image features with chaotic maps: an application to texture recognition

Reorganizing local image features with chaotic maps: an application to texture recognition

A cellular automata approach to local patterns for texture recognition

Supervised Techniques and Approaches for Satellite Image Classification

Contact Info

Product

Resources

About