Clustering techniques such as K-means and Forgy as well as their improved version ISODATA group data around one seed point for each cluster. It is well known that these methods do not work well if the shape of the clu...
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Clustering techniques such as K-means and Forgy as well as their improved version ISODATA group data around one seed point for each cluster. It is well known that these methods do not work well if the shape of the cluster is elongated or nonconvex. We argue that for a elongated or nonconvex shaped cluster, more than one seed is needed, In this paper a multiseed clustering algorithm is proposed. A density based representative point selection algorithm is used to choose the initial seed points. To assign several seed points to one cluster, a minimal spanning tree guided novel technique is proposed. Also, a border point detection algorithm is proposed for the detection of shape of the cluster. This border in turn signifies whether the cluster is elongated or not. Experimental results show the efficiency of this clustering technique.
In this paper we propose a neural network model to synthesise texture images. The model is based on a continuous Hopfield-like network where each pixel of the image is occupied by a neuron that is eight-connected to i...
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In this paper we propose a neural network model to synthesise texture images. The model is based on a continuous Hopfield-like network where each pixel of the image is occupied by a neuron that is eight-connected to its neighbours. A state of the neuron denotes a certain grey level of the corresponding pixel. The firing of the neuron changes its state, and hence the grey level of the corresponding pixel. Different two-tone and grey-tone texture images can be synthesised by manipulating the connection weights and by varying the algorithm iteration number. For grey-tone texture synthesis, a Markov chain principle has been employed to decide on the multiple state transition of a neuron. The model can be employed for texture propagation with the advantage that it allows propagation without showing any blocky effect.
This paper presents an application of backpropagation neural network for the detection of linear structures in remote-sensing images. The purpose of the approach is two-fold. First, to exploit the advantages of a neur...
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This paper presents an application of backpropagation neural network for the detection of linear structures in remote-sensing images. The purpose of the approach is two-fold. First, to exploit the advantages of a neural network classifier over the tranditional ones. Second, to avoid the strategic phases of enhancement and thresholding. Once the network is learnt, the classification scheme is real-time. Two critical issues in the present approach an the selection of the network architecture and the rate of convergence of learning. Solutions to these two problems are proposed. Experimental results on IRS and SPOT images are presented. Satisfactory classification results have been obtained using the network.
An OCR system is proposed that can read two Indian language scripts: Bangla and Devnagari (Hindi), the most popular ones in the Indian subcontinent. These scripts, having the same origin in ancient Brahmi script, have...
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An OCR system is proposed that can read two Indian language scripts: Bangla and Devnagari (Hindi), the most popular ones in the Indian subcontinent. These scripts, having the same origin in ancient Brahmi script, have many features in common and hence a single system can be modeled to recognize them. In the proposed model, document digitization, skew detection, text line segmentation and zone separation, word and character segmentation, character grouping into basic, modifier and compound character category are done for both scripts by the same set of algorithms. The feature sets and classification tree as well as the knowledge base required for error correction (such as lexicon) differ for Bangla and Devnagari. The system shows a good performance for single font scripts printed on clear documents.
In a multi-lingual country like India, a document may contain more than one script forms. For such a document it is necessary to separate different script forms before feeding them to OCRs of individual script. In thi...
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In a multi-lingual country like India, a document may contain more than one script forms. For such a document it is necessary to separate different script forms before feeding them to OCRs of individual script. In this paper an automatic word segmentation approach is described which can separate Roman, Bangla and Devnagari scripts present in a single document. The approach has a tree structure where at first Roman script words are separated using the 'headline' feature. The headline is common in Bangla and Devnagari but absent in Roman. Next, Bangla and Devnagari words are separated using some finer characteristics of the character set although recognition of individual character is avoided. At present, the system has an overall accuracy of 96.09%.
An NLP system for Indian languages should have a lexical subsystem that is driven by a morphological analyzer. Such an analyzer should be able to parse a word into its constituent morphemes and obtain lexical projecti...
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An NLP system for Indian languages should have a lexical subsystem that is driven by a morphological analyzer. Such an analyzer should be able to parse a word into its constituent morphemes and obtain lexical projection of the word as a unification of the projections of the constituent morphemes. Lexical projections considered here are f-structures of the Lexical Functional Grammar (LFG). A formalism has been proposed, by which the lexicon writer may specify the lexicon in four levels. The specifications are compiled into a stored lexical knowledge base on one hand and a formulation of derivational morphology called Augmented Finite State Automata (AFSA) on the other to achieve a compact lexical representation. The aspects of AFSA, especially its power of morphological parsing of words in a computationally attractive manner, has been discussed. An additional utility of the AFSA, in the form of spelling error corrector, has also been discussed. Bangla, or Bengali is considered as a case study. Implementation notes based on object-oriented programming principles has been provided.
Given a set of points in multi-dimensional space, we propose a new definition for the neighbors of an arbitrary point P. The definition tries to capture the idea that the neighbors should be as near to P and as symmet...
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Given a set of points in multi-dimensional space, we propose a new definition for the neighbors of an arbitrary point P. The definition tries to capture the idea that the neighbors should be as near to P and as symmetrically placed around P as possible. In contrast, the conventional nearest neighborhood considers only nearness as the criterion for neighborhood. We propose an iterative procedure to compute the neighbors where the first neighbor is the nearest neighbor. The second and other neighbors are chosen so that at any stage the distance between the centroid of the neighbors and P is as small as possible. The centroid criterion takes care of symmetrical placement of the neighbors. One can use median instead of centroid to define the neighbors. The new definition is free from any user-specified parameter and can be used for pattern classification, clustering and low-level description of dot patterns.
This paper deals with an OCR error detection and correction technique for a highly inflectional language script like Bangla (a major Indian language). This is the first report of its kind. Using two separate lexicons ...
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In this paper we describe a texture segmentation approach without feature computation based on a multilayer perceptron network (MLP). Thus, the users need not bother about the selection and then computation of feature...
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We present a semi-automatic method for extracting the 3D boundary of the cells in a compact tissue cross-section photographed by a confocal microscope. The confocal microscope provides pictures at different depths of ...
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