11,050 research outputs found
Image Segmentation and Classification of Marine Organisms
To automate the arduous task of identifying and classifying images through their domain expertise, pioneers in the field of machine learning and computer vision invented many algorithms and pre-processing techniques. The process of classification is flexible with many user and domain specific alterations. These techniques are now being used to classify marine organisms to study and monitor their populations. Despite advancements in the field of programming languages and machine learning, image segmentation and classification for unlabeled data still needs improvement. The purpose of this project is to explore the various pre-processing techniques and classification algorithms that help cluster and classify images and hence choose the best parameters for identifying the various marine species present in an image
Gray Image extraction using Fuzzy Logic
Fuzzy systems concern fundamental methodology to represent and process
uncertainty and imprecision in the linguistic information. The fuzzy systems
that use fuzzy rules to represent the domain knowledge of the problem are known
as Fuzzy Rule Base Systems (FRBS). On the other hand image segmentation and
subsequent extraction from a noise-affected background, with the help of
various soft computing methods, are relatively new and quite popular due to
various reasons. These methods include various Artificial Neural Network (ANN)
models (primarily supervised in nature), Genetic Algorithm (GA) based
techniques, intensity histogram based methods etc. providing an extraction
solution working in unsupervised mode happens to be even more interesting
problem. Literature suggests that effort in this respect appears to be quite
rudimentary. In the present article, we propose a fuzzy rule guided novel
technique that is functional devoid of any external intervention during
execution. Experimental results suggest that this approach is an efficient one
in comparison to different other techniques extensively addressed in
literature. In order to justify the supremacy of performance of our proposed
technique in respect of its competitors, we take recourse to effective metrics
like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal to Noise
Ratio (PSNR).Comment: 8 pages, 5 figures, Fuzzy Rule Base, Image Extraction, Fuzzy
Inference System (FIS), Membership Functions, Membership values,Image coding
and Processing, Soft Computing, Computer Vision Accepted and published in
IEEE. arXiv admin note: text overlap with arXiv:1206.363
Machine-Part cell formation through visual decipherable clustering of Self Organizing Map
Machine-part cell formation is used in cellular manufacturing in order to
process a large variety, quality, lower work in process levels, reducing
manufacturing lead-time and customer response time while retaining flexibility
for new products. This paper presents a new and novel approach for obtaining
machine cells and part families. In the cellular manufacturing the fundamental
problem is the formation of part families and machine cells. The present paper
deals with the Self Organising Map (SOM) method an unsupervised learning
algorithm in Artificial Intelligence, and has been used as a visually
decipherable clustering tool of machine-part cell formation. The objective of
the paper is to cluster the binary machine-part matrix through visually
decipherable cluster of SOM color-coding and labelling via the SOM map nodes in
such a way that the part families are processed in that machine cells. The
Umatrix, component plane, principal component projection, scatter plot and
histogram of SOM have been reported in the present work for the successful
visualization of the machine-part cell formation. Computational result with the
proposed algorithm on a set of group technology problems available in the
literature is also presented. The proposed SOM approach produced solutions with
a grouping efficacy that is at least as good as any results earlier reported in
the literature and improved the grouping efficacy for 70% of the problems and
found immensely useful to both industry practitioners and researchers.Comment: 18 pages,3 table, 4 figure
Dealing with non-metric dissimilarities in fuzzy central clustering algorithms
Clustering is the problem of grouping objects on the basis of a similarity measure among them. Relational clustering methods can be employed when a feature-based representation of the objects is not available, and their description is given in terms of pairwise (dis)similarities. This paper focuses on the relational duals of fuzzy central clustering algorithms, and their application in situations when patterns are represented by means of non-metric pairwise dissimilarities. Symmetrization and shift operations have been proposed to transform the dissimilarities among patterns from non-metric to metric. In this paper, we analyze how four popular fuzzy central clustering algorithms are affected by such transformations. The main contributions include the lack of invariance to shift operations, as well as the invariance to symmetrization. Moreover, we highlight the connections between relational duals of central clustering algorithms and central clustering algorithms in kernel-induced spaces. One among the presented algorithms has never been proposed for non-metric relational clustering, and turns out to be very robust to shift operations. (C) 2008 Elsevier Inc. All rights reserved
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