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Remote Sensing is a technique to obtain data from objects without physical contact using sensors. If it is performed by hyperspectral sensors, then is possible to have a large number of bands that allows many applications. In this case, a large amount of data needs to be processed and analyzed, and therefore the practical use of these images have challenges with storage, transport and processing time...
This paper proposes a spectral-spatial classification scheme for the classification of remotely sensed images, based on a new version of the recently proposed Genetic Sequential Image Segmentation (GeneSIS). GeneSIS segments the image in an iterative manner, whereby at each iteration a single object is extracted via a genetic algorithm-based object extraction method. In the previous version of GeneSIS,...
We describe how a task in computer vision can be effectively resolved by employing Genetic Algorithm. This paper focuses on the problem of semantic segmentation of digital images. We propose to use an improved genetic algorithm for the learning parameters of weak classifiers in a boosting learning set up. We propose a new encoding and genetic operators in accordance with this problem. Beside that,...
Hidden Markov Random Field (HMRF) model and Finite Mixture Model (FMM) parameter estimation algorithm provides an interesting framework for image segmentation task, hence a technique that capitalizes on the benefits of both algorithms would achieve better performance. In this regard, we propose a new segmentation algorithm which combines with HMRF model and FMM parameter estimation algorithm. Firstly,...
In this article, a variant quantum inspired genetic algorithm for the determination of the optimal threshold of gray-level images is presented. The proposed algorithm initiates with a population of randomly superposed trial solutions in the form of quantum bits. Subsequently, some deterministic nonlinear point transformations are applied on these solutions to generate randomly interfered solutions...
Support Vector Machine (SVM) is a useful technique for data classification with successful applications in different fields of bioinformatics, image segmentation, data mining, etc. A key problem of these methods is how to choose an optimal kernel and how to optimize its parameters in the learning process of SVM. The objective of this study is to propose a Genetic Algorithm approach for parameter optimization...
In this paper, we propose a novel multi-label image annotation for image retrieval based on annotated keywords. For multi-label image annotation, a bi-coded genetic algorithm is employed to select optimal feature subsets and corresponding optimal weights for every one vs. one SVM classifiers. After an unlabelled image is segmented into several regions with image segmentation algorithm, pre-trained...
Traditional Fuzzy c-means (FCM) algorithm is commonly used in unsupervised learning. However, there are some limitations. Cluster number should be determined and the cluster center should be initialized before classification. A new algorithm is proposed in the paper. The best cluster number is obtained by analyzing cluster validity function and the cluster center is initialized by HCM. The data set...
In this paper, linking with the basic principle of FCM algorithm, on the basis of theory research, a method of the cluster analysis that FCM and the genetic algorithm are combined together is proposed. Firstly, the approximate optimal solution obtained by the genetic algorithm is taken as the original value of the FCM algorithm, then carrying on the local search to obtain the global optimal solution,...
Potato quality control has improved in the last years thanks to automation techniques like machine vision, mainly making the classification task between different quality degrees faster, safer and less subjective. We present a system that classifies potatoes depending on their external defects and diseases. Firstly, some image processing techniques are used to segment and analyze the potatoes. Then,...
Natural computing (NC) is a novel approach to solve real life problems inspired in the life itself. A diversity of algorithms had been proposed such as evolutionary techniques, genetic algorithms and particle swarm optimization (PSO). These approaches, together with fuzzy and neural networks, give powerful tools for researchers in a diversity of problems of optimization, classification, data analysis...
Image segmentation is an important processing step in many image, video and computer vision applications. Artificial Immune Systems (AIS) is a diverse area of research that attempts to bridge the divide between immunological and engineering. In this paper, we present a threshold method based on granular immune algorithm (GIA) for image segmentation, which includes granular hierarchy and immunological...
In this paper, we describe a segmentation method for brain MR images using an ant colony optimization (ACO) algorithm. This is a relatively new meta-heuristic algorithm and a successful paradigm of all the algorithms which take advantage of the insectpsilas behavior. It has been applied to solve many optimization problems with good discretion, parallel, robustness and positive feedback. As an advanced...
An improved genetic K-means clustering algorithm is proposed and is applied to image segmentation. According to the characteristics of the image, the feature vector of the pixel is properly chosen and the weight factors of the feature vector are adjusted, which enhances the segmentation precision. The selection of conventional genetic algorithm and the modification of mutation operations improve the...
The K-means algorithm is one of the widely used clustering algorithms in the image classification systems. However, the K-Means algorithm is easily trapped into the local optimal solutions. Several optimization techniques have been proposed to solve this problem such as genetic algorithms, simulated annealing and swarm intelligence. In this paper, we develop hybrid techniques using different particle...
This paper proposes a computer aided decision support system for an automated diagnosis and classification of breast tumor using mammogram. The proposed method differentiates two breast diseases namely benign masses and malignant tumors. From the preprocessed mammogram image, texture and shape features are extracted. The optimal features can be extracted by using a feature selection scheme based on...
This paper proposes a method of dynamic fuzzy clustering analysis based on improved ant colony algorithm. This method makes use of the great ability of ant colony algorithm for disposing local convergence, which overcomes sensitivity to initialization of fuzzy clustering method (FCM) and fixes on the numbers of clustering as well as the centers of clustering dynamically. This paper improves the traditional...
A license plate location method based on projection and genetic algorithm was proposed in this paper. Projection located the left and right edges of license plate. Genetic algorithm searched license plate region with second order moment as objective function which described texture in license plate region quantitatively. Four sub-nets were designed to classify Chinese characters, alphabet characters,...
On one hand, there is the problem to solve which is the image segmentation. It is a low-level processing task which consists in partitioning an image into homogeneous regions. Segmentation can be seen as a combinatorial optimization problem. In fact, considering the huge amount of information that an image carries, it is impossible to find the best segmentation. On the other hand, the reduced individual...
Rough set theory offers a novel approach to manage uncertainty that has been used for the discovery of data dependencies, importance of features, patterns in sample data, feature space dimensionality reduction, and the classification of objects. Consequently, rough sets have been successfully employed for various image processing tasks including image segmentation, enhancement and classification....
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