An Optimization Clustering Algorithm Based on Texture Feature Fusion for Color Image Segmentation

We introduce a multi-feature optimization clustering algorithm for color image segmentation. The local binary pattern, the mean of the min-max difference, and the color components are combined as feature vectors to describe the magnitude change of grey value and the contrastive information of neighbor pixels. In clustering stage, it gets the initial clustering center and avoids getting into local optimization by adding mutation operator of genetic algorithm to particle swarm optimization. Compared with well-known methods, the proposed method has an overall better segmentation performance and can segment image more accurately by evaluating the ratio of misclassification.© 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Segmentation of color images by chromaticity features using self-organizing maps

Usually, the segmentation of color images is performed using cluster-based methods and the RGB space to represent the colors. The drawback with these methods is the a priori knowledge of the number of groups, or colors, in the image; besides, the RGB space issensitive to the intensity of the colors. Humans can identify different sections within a scene by the chromaticity of its colors of, as this is the feature humans employ to tell them apart. In this paper, we propose to emulate the human perception of color by training a self-organizing map (SOM) with samples of chromaticity of different colors. The image to process is mapped to the HSV space because in this space the chromaticity is decoupled from the intensity, while in the RGB space this is not possible. Our proposal does not require knowing a priori the number of colors within a scene, and non-uniform illumination does not significantly affect the image segmentation. We present experimental results using some images from the Berkeley segmentation database by employing SOMs with different sizes, which are segmented successfully using only chromaticity features.Usualmente, la segmentación de imágenes de color se realiza empleando métodos de agrupamiento y el espacio RGB para representar los colores. El problema con los métodos de agrupamiento es que se requiere conocer previamente la cantidad de grupos, o colores, en la imagen; además de que el espacio RGB es sensible a la intensidad de colores. Los humanos podemos identificar diferentes secciones de una escena solo por la cromaticidad de los colores, ya que representa la característica que nos permite diferenciarlos entre sí. En este artículo se propone emular la percepción humana del color al entrenar un mapa auto-organizado (MAO) con muestras de cromaticidad de diferentes colores. La imagen a procesar es transformada al espacio HSV porque en tal espacio la cromaticidad es separada de la intensidad, mientras que en el espacio RGB no es posible. Nuestra propuesta no requiere conocer previamente la cantidad de colores que hay en una escena, y la iluminación no uniforme no afecta significativamente la segmentación de la imagen. Presentamos resultados experimentales utilizando algunas imágenes de la base de segmentación de Berkeley empleando MAOs de diferentes tamaños, las cuales son segmentadas exitosamente empleando únicamente características de cromaticidad

A comprehensive review of fruit and vegetable classification techniques

Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-of-the-art has been developed for limited classes and small datasets. The problem is of a multi-dimensional nature and offers significantly hyperdimensional features, which is one of the major challenges with current machine learning approaches. Substantial research has been conducted for the design and analysis of classifiers for hyperdimensional features which require significant computational power to optimise with such features. In recent years numerous machine learning techniques for example, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Trees, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) have been exploited with many different feature description methods for fruit and vegetable classification in many real-life applications. This paper presents a critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable

Segmentación de Imágenes de Color Inspirado en la Percepción Humana del Color

En esta tesis se presentan los resultados obtenidos en la segmentación de imágenes por características de color, utilizando solo la información cromática de los colores; en donde se entrenan redes neuronales no supervisadas para reconocer cromaticidades de diferentes colores y después ser utilizados para procesar imágenes digitales.Usualmente la segmentación de imágenes se realiza considerando las características de textura y/o geométricas. Sin embargo, la segmentación de imágenes utilizando las características de color no es tan común. Los trabajos que abordan la segmentación de imágenes por sus características de color emplean o se basan en métodos nos supervisados o técnicas de agrupamiento, principalmente fuzzy C-means. Los resultados que se reportan son buenos, la desventaja con dichas técnicas es que se requiere definir previamente la cantidad de grupos que en se desean agrupar los colores, pero esto puede limitar la cantidad de colores que existen en la imagen; por otra parte, el procesamiento de los algoritmos no pueden generalizarse para cualquier imagen ya que solo procesan los colores de cada imagen, si estos grupos se intentan emplear para segmentar imágenes diferentes, es muy probable que no funcionarán correctamente. En este trabajo se propone imitar la percepción humana del reconocimiento del color empleando redes neuronales artificiales de tipo competitivas. Los seres humanos reconocen los colores primero por su cromaticidad y después por su intensidad; por otra parte, los seres humanos pueden reconocer áreas o secciones de imágenes dependiendo solamente de la cromaticidad de las partes que conforman la imagen. De aquí que, se propone entrenar una red neuronal que reconozca la cromaticidad de los colores. Una ventaja que tendrá la red neuronal con respecto a los métodos de agrupamiento es que no tendrá que ser entrenada por cada imagen, es decir, una vez entrenada la red neuronal a reconocer la cromaticidad del color, esta puede ser aplicada a cualquier imagen sin volver a ser entrenada.Beca CONACyT para realizar estudios de maestría, con el número de registro 634201

Investigation of Microstructural and Carbon Deposition Effects in SOFC Anodes Through Modelling and Experiments

The investigation of the SOFC anode microstructural properties affected by microstructural parameters and degradation is the focus of this research. Imaging and image processing techniques are developed to achieve quantification of the anode microstructural information. The analytical and Computational Fluid Dynamics based modelling of the microstructure including the degradation effects developed in this work will enable the microstructure optimisation for achieving performance enhancements

Segmentação de imagens coloridas por árvores bayesianas adaptativas

A segmentação de imagens consiste em urna tarefa de fundamental importância para diferentes aplicações em visão computacional, tais como por exemplo, o reconhecimento e o rastreamento de objetos, a segmentação de tomores/lesões em aplicações médicas, podendo também servir de auxílio em sistemas de reconhecimento facial. Embora exista uma extensa literatora abordando o problema de segmentação de imagens, tal tópico ainda continua em aberto para pesquisa. Particularmente, a tarefa de segmentar imagens coloridas é desafiadora devido as diversas inomogeneidades de cor, texturas e formas presentes nas feições descritivas das imagens. Este trabalho apresenta um novo método de clustering para abordar o problema da segmentação de imagens coloridas. Nós desenvolvemos uma abordagem Bayesiana para procura de máximos de densidade em urna distribuição discreta de dados, e representamos os dados de forma hierárquica originando clusters adaptativos a cada nível da hierarquia. Nós aplicamos o método de clustering proposto no problema de segmentação de imagens coloridas, aproveitando sua estrutura hierárquica, baseada em propriedades de árvores direcionadas, para representar hierarquicamente uma imagem colorida. Os experimentos realizados revelaram que o método de clustering proposto, aplicado ao problema de segmentação de imagens coloridas, obteve para a medida de performance Probabilistic Rand lndex (PRI) o valor de 0.8148 e para a medida Global Consistency Error (GCE) o valor 0.1701, superando um total de vinte e um métodos previamente propostos na literatura para o banco de dados BSD300. Comparações visuais confirmaram a competitividade da nossa abordagem em relação aos demais métodos testados. Estes resultados enfatizam a potencialidade do nosso método de clustering para abordar outras aplicações no domínio de Visão Computacional e Reconhecimento de Padrões.Image segmentation is an essential task for several computer vision applications, such as object recognition, tracking and image retrieval. Although extensively studied in the literature, the problem of image segmentation remains an open topic of research. Particularly, the task of segmenting color images is challenging due to the inhomogeneities in the color regions encountered in natural scenes, often caused by the shapes of surfaces and their interactions with the illumination sources (e.g. causing shading and highlights) This work presents a novel non-supervised classification method. We develop a Bayesian framework for seeking modes on the underlying discrete distribution of data and we represent data hierarchically originating adaptive clusters at each levei of hierarchy. We apply the prnposal clustering technique for tackling the problem of color irnage segmentation, taking advantage of its hierarchical structure based on hierarchy properties of directed trees for representing fine to coarse leveis of details in an image. The experiments herein conducted revealed that the proposed clustering method applied to the color image segmentation problem, achieved for the Probabilistic Rand Index (PRI) performance measure the value of 0.8148 and for the Global Consistency Error (GCE) the value of 0.1701, outperforming twenty-three methods previously proposed in the literature for the BSD300 dataset. Visual comparison confirmed the competitiveness of our approach towards state-of-art methods publicly available in the literature. These results emphasize the great potential of our proposed clustering technique for tackling other applications in computer vision and pattem recognition