Implementation of some cluster validity methods for fuzzy cluster analysis

Cluster analysis is an important tool in the exploration of large collections of data, revealing patterns and significant correlations in the data. The fuzzy approach to the clustering problem enhances the modeling capability as the results are expressed in soft clusters (instead of crisp clusters), where the data points may have partial memberships in several clusters. In this paper we will discuss about the most used fuzzy cluster analysis techniques and we will address an important issue: finding the optimal number of clusters. This problem is known as the cluster validity problem and is one of the most challenging aspects of fuzzy and classical cluster analysis. We will describe several methods and we will combine and compare them on several synthetic data sets

Estimating Degradation Model Parameters from Character Images

This paper discusses the use of character images to determine the parameters of an image degradation model. The acute angles in character images provide information used to find the model parameters. Three experiments are conducted to evaluate the use of characters. In the first experiment, large quantities of corners from character images are used to investigate how their contribution affects the mean and the standard deviation of the parameter estimators. In the second experiment, we focus on the relationship between the angles of the corners used in estimation and the estimation results. In the last experiment, we examine how likely the text in a common page would offer a reasonable estimation result compared to the results from experiments 1 and 2

Perceptual color clustering for color image segmentation based on CIEDE2000 color distance

In this paper, a novel technique for color clustering with application to color image segmentation is presented. Clustering is performed by applying the k-means algorithm in the L*a*b* color space. Nevertheless, Euclidean distance is not the metric chosen to measure distances, but CIEDE2000 color difference formula is applied instead. K-means algorithm performs iteratively the two following steps: assigning each pixel to the nearest centroid and updating the centroids so that the empirical quantization error is minimized. In this approach, in the first step, pixels are assigned to the nearest centroid according to the CIEDE2000 color distance. The minimization of the empirical quantization error when using CIEDE2000 involves finding an absolute minimum in a non-linear equation and, therefore, an analytical solution cannot be obtained. As a consequence, a heuristic method to update the centroids is proposed. The proposed algorithm has been compared with the traditional k-means clustering algorithm in the L*a*b* color space with the Euclidean distance. The Borsotti parameter was computed for 28 color images. The new version proposed outperformed the traditional one in all cases

Estimating Scanning Characteristics from Corners in Bilevel Images

Degradations that occur during scanning can cause errors in Optical Character Recognition (OCR). Scans made in bilevel mode (no grey scale) from high contrast source patterns are the input to the estimation processes. Two scanner system parameters are estimated from bilevel scans using models of the scanning process and bilevel source patterns. The scanner\u27s point spread function (PSF) width and the binarization threshold are estimated by using corner features in the scanned images. These estimation algorithms were tested in simulation and with scanned test patterns. The resulting estimates are close in value to what is expected based on grey-level analysis. The results of estimation are used to produce synthetically scanned characters that in most cases bear a strong resemblance to the characters scanned on the scanner at the same settings as the test pattern used for estimation

Speedup of Optical Scanner Characterization Subsystem

Small image deformations such as those introduced by optical scanners significantly reduce the accuracy rate of optical character recognition (OCR) software. Characterization of the scanner used in the OCR process may diminish the impact on recognition rates. Theoretical methods have been developed to characterize a scanner based on the bi-level image resulting from scanning a high contrast document. Two bottlenecks in the naïve implementation of these algorithms have been identified, and techniques were developed to improve the execution time of the software. The algorithms are described and analyzed. Since approximations are used in one of the techniques, the error of the approximations is examined

Geometric Reasoning With a Virtual Workforce (Crowdsourcing for CAD/CAM)

This paper reports the initial results of employing a commercial Crowdsourcing (aka Micro-outsourcing) service to provide geometric analysis of complex 3D models of mechanical components. Although Crowdsourcing sites (which distribute browser based tasks to potentially large numbers of anonymous workers on the Internet) are well established for image analysis and text manipulation there is little academic work on the effectiveness or limitations of the approach. The work reported here describes the initial results of using Crowdsourcing to determine the 'best' canonical, or characteristic, views of complex 3D models of engineering components. The results suggest that the approach is a cheap, fast and effective method of solving what is a computationally difficult problem

Meta-optimizations for Cluster Analysis

This dissertation thesis deals with advances in the automation of cluster analysis.This dissertation thesis deals with advances in the automation of cluster analysis

Advances in Stereo Vision

Stereopsis is a vision process whose geometrical foundation has been known for a long time, ever since the experiments by Wheatstone, in the 19th century. Nevertheless, its inner workings in biological organisms, as well as its emulation by computer systems, have proven elusive, and stereo vision remains a very active and challenging area of research nowadays. In this volume we have attempted to present a limited but relevant sample of the work being carried out in stereo vision, covering significant aspects both from the applied and from the theoretical standpoints

Degradation Specific OCR

Optical Character Recognition (OCR) is the mechanical or electronic translation of scanned images of handwritten, typewritten, or printed text into machine-encoded text. OCR has many applications, such as enabling a text document in a physical form to be editable, or enabling computer searching on a computer of a text that was initially in printed form. OCR engines are widely used to digitize text documents so that they can be digitally stored for remote access, mainly for websites. This facilitates the availability of these invaluable resources instantly, no matter the geographical location of the end user. Huge OCR misclassification errors can occur when an OCR engine is used to digitize a document that is degraded. The degradation may be due to varied reasons, including aging of the paper, incomplete printed characters, and blots of ink on the original document. In this thesis, the degradation due to scanning text documents was considered. To improve the OCR performance, it is vital to train the classifier on a large training set that has significant data points similar to the degraded real-life characters. In this thesis, characters with varying degrees of blurring and binarization thresholds were generated and they were used to calculate Edge Spread degradation parameters. These parameters were then used to divide the training data set of the OCR engine into more homogeneous sets. The resulting classification accuracy by training on these smaller sets was analyzed. The training data set consisted of 100,000 data points of 300 DPI, 12 point Sans Serif font lowercase characters ‘c and ‘e’. These characters were generated with random values of threshold and blur width with random Gaussian noise added. To group the similar degraded characters together, clustering was performed using the Isodata clustering algoirithm. The two edge-spread parameters, one calculated on isolated edges named DC, one calculated on edges in close proximity accounting for interference effects, named MDC, were estimated to fit the cluster boundaries. These values were then used to divide the training data and a Bayesian classifier was used for recognition. It was verified that MDC is slightly better than DC as a division parameter. A choice of either 2 or 3 partitions was found to be the best choice for dataset division. An experimental way to estimate the best boundary to divide the data set was determined and tests were conducted that verified it. Both crisp and fuzzy approaches for classifier training and testing were implemented and various combinations were tried with the crisp training and fuzzy testing being the best approach, giving a 98.08% classification rate for the data set divided into 2 partitions and 98.93% classification rate for the data set divided into 3 partitions in comparison to 94.08% for the classification of the data set with no divisions