Showing papers in "Pattern Recognition in 1998"

TL;DR: This paper provides a review of shape analysis methods, which play an important role in systems for object recognition, matching, registration, and analysis.

TL;DR: A fundamental open problem in computer vision—determining pose and correspondence between two sets of points in space—is solved with a novel, fast, robust and easily implementable algorithm using a combination of optimization techniques.

TL;DR: A survey of the most significant techniques, used in the last few years, concerning the coded structured light methods employed to get 3D information, and how each imaged region of the projected pattern carries the needed information to solve the correspondence problem.

TL;DR: A procedure to qualitatively measure the saliency of a feature towards a classification problem based on the plot of the intra-class and inter-class distance distributions and determines that the edge direction-based features have the most discriminative power for the classification problem of interest here.

TL;DR: Considering the substantial amount of time and effort needed for a manual retrieval from a large image database, an automatic shape-based retrieval technique can significantly simplify the retrieval task.

TL;DR: A complete Optical Character Recognition (OCR) system for printed Bangla, the fourth most popular script in the world, is presented and extension of the work to Devnagari, the third most popular Script in the World, is discussed.

TL;DR: In this article, the authors present the state of Arabic character recognition research throughout the last two decades and present the main objective of this paper is to present the current state of the research.

TL;DR: It is argued that modelling person-specific probability densities in a generic face space using mixture models provides a technique applicable to all four face recognition tasks.

TL;DR: The concept of fuzzy c -partition and the maximum fuzzy entropy principle are used to select threshold values for gray-level images and the resulting images can preserve the main features of the components of the original images very well.

TL;DR: It is argued, that edge-like retinal images of faces are initially screened “at a glance” without the involvement of high-level cognitive functions thus delivering high speed and reducing computational complexity.

TL;DR: Experimental results demonstrate that the proposed approach can efficiently detect human facial features and satisfactorily deal with the problems caused by bad lighting condition, skew face orientation, and even facial expression.

TL;DR: Experimental results show that the artificial neural-network classifier achieved the error rate of 2.34% for a test set of 7000 characters, therefore, the Gabor-filter-based method should be considered in recognition of handwritten numeric characters.

TL;DR: The results of the simulation show that the proposed segmentation algorithm is independent of the choice of color coordinates, the shape of clusters, and the type of images.

TL;DR: The modified box-counting method (MBCM), developed as a methodic procedure to set sequence and range, is a new, powerful tool, very simple to use, allowing accurate estimation of Df.

TL;DR: Bagging (bootstrapping and aggregating) is studied for linear classifiers and it is shown experimentally that bagging might improve the performance of the classifier only for very unstable situations.

TL;DR: This work proposes a new text location algorithm that is suitable in a number of applications, including conversion of newspaper advertisements from paper documents to their electronic versions, World Wide Web search, color image indexing and video indexing, and emphasize on extracting important text with large size and high contrast.

TL;DR: Polygonal approximation and classification of concave points on object boundaries into different classes based on its angle and lengths of 2-vertex lines substantially enhanced the robustness of the algorithm.

TL;DR: This work introduces a new segmentation algorithm, guided in part by the global characteristics of the handwriting, which finds the successive segmentation points by evaluating a cost function at each point along the baseline.

TL;DR: This paper focuses on the formulation, development, and evaluation of an autonomous segmentation algorithm which can segment targets in a wide class of highly degraded images.

TL;DR: The PICASSO system is presented, which supports image indexing and retrieval based on colors, and exploits a pyramidal representation of images to allow effective retrieval through specific queries as well as imprecise ones.

TL;DR: A fast recursive 2D entropic thresholding algorithm is proposed by rewriting the formula for calculation of entropy in recurrence form, a lengthy calculation is saved and the computation complexity of 2DEntropic thresholds is reduced to O(L2).

TL;DR: A new approach for the automatic evaluation of document page segmentation algorithms that is region-based: segmentation quality is assessed by comparing the segmentation output, described as a set of regions, to the corresponding ground-truth.

TL;DR: Two ways of introducing spatial information in Dempster–Shafer evidence theory are examined: in the definition of the monosource mass functions, and, during data fusion.

TL;DR: The enhanced color images by the proposed GA approach are better than that by any of the three existing approaches for comparison, which shows the feasibility of the proposed approach.

TL;DR: It is shown that the proposed edge detector has the desirable properties that a good edge detector should have and comparative study reveals its superiority over other morphologic edge detectors.

TL;DR: A machine-printed and handwritten text classification method to automatically identify the identity of texts segmented from a document image to facilitate later optical character recognition task.

TL;DR: A new registration algorithm, adapted to the space-encoding range finder, making use of the eigenvectors of the weighted covariance matrix of the 3-D coordinates of data points to estimate the transformation parameters among multiple range views.

TL;DR: A co-operative strategy within a multi-resolution color image segmentation, which attempt to extract the meaningful information (regions and boundaries), then fuse these two approaches in order to achieve an accurate, robust and suitable segmentation.

TL;DR: A new shape-similarity metric in the eigenshape space for object/image retrieval from a visual database via query-by-example is proposed, which is rotation-, translation- and scale-invariant, and can handle mild deformations of objects.

TL;DR: A new framework for the recognition of handwritten characters using a truly 2-D model: hidden Markov mesh random field (HMMRF) that can provide a better description of the 1-D nature of characters and a new formulation of parameter estimation for character recognition.