Possibilistic clustering for shape recognition

Clustering methods have been used extensively in computer vision and pattern recognition. Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering in that total commitment of a vector to a given class is not required at each iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from Bezdek's Fuzzy C-Means (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Unfortunately, the memberships resulting from FCM and its derivatives do not correspond to the intuitive concept of degree of belonging, and moreover, the algorithms have considerable trouble in noisy environments. Recently, we cast the clustering problem into the framework of possibility theory. Our approach was radically different from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We constructed an appropriate objective function whose minimum will characterize a good possibilistic partition of the data, and we derived the membership and prototype update equations from necessary conditions for minimization of our criterion function. In this paper, we show the ability of this approach to detect linear and quartic curves in the presence of considerable noise

Fuzzy Set Methods for Object Recognition in Space Applications

Progress on the following four tasks is described: (1) fuzzy set based decision methodologies; (2) membership calculation; (3) clustering methods (including derivation of pose estimation parameters), and (4) acquisition of images and testing of algorithms

Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 2

Papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake, held 1-3 Jun. 1992 at the Lyndon B. Johnson Space Center in Houston, Texas are included. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making

Robust techniques and applications in fuzzy clustering

This dissertation addresses issues central to frizzy classification. The issue of sensitivity to noise and outliers of least squares minimization based clustering techniques, such as Fuzzy c-Means (FCM) and its variants is addressed. In this work, two novel and robust clustering schemes are presented and analyzed in detail. They approach the problem of robustness from different perspectives. The first scheme scales down the FCM memberships of data points based on the distance of the points from the cluster centers. Scaling done on outliers reduces their membership in true clusters. This scheme, known as the Mega-clustering, defines a conceptual mega-cluster which is a collective cluster of all data points but views outliers and good points differently (as opposed to the concept of Dave\u27s Noise cluster). The scheme is presented and validated with experiments and similarities with Noise Clustering (NC) are also presented. The other scheme is based on the feasible solution algorithm that implements the Least Trimmed Squares (LTS) estimator. The LTS estimator is known to be resistant to noise and has a high breakdown point. The feasible solution approach also guarantees convergence of the solution set to a global optima. Experiments show the practicability of the proposed schemes in terms of computational requirements and in the attractiveness of their simplistic frameworks. The issue of validation of clustering results has often received less attention than clustering itself. Fuzzy and non-fuzzy cluster validation schemes are reviewed and a novel methodology for cluster validity using a test for random position hypothesis is developed. The random position hypothesis is tested against an alternative clustered hypothesis on every cluster produced by the partitioning algorithm. The Hopkins statistic is used as a basis to accept or reject the random position hypothesis, which is also the null hypothesis in this case. The Hopkins statistic is known to be a fair estimator of randomness in a data set. The concept is borrowed from the clustering tendency domain and its applicability to validating clusters is shown here. A unique feature selection procedure for use with large molecular conformational datasets with high dimensionality is also developed. The intelligent feature extraction scheme not only helps in reducing dimensionality of the feature space but also helps in eliminating contentious issues such as the ones associated with labeling of symmetric atoms in the molecule. The feature vector is converted to a proximity matrix, and is used as an input to the relational fuzzy clustering (FRC) algorithm with very promising results. Results are also validated using several cluster validity measures from literature. Another application of fuzzy clustering considered here is image segmentation. Image analysis on extremely noisy images is carried out as a precursor to the development of an automated real time condition state monitoring system for underground pipelines. A two-stage FCM with intelligent feature selection is implemented as the segmentation procedure and results on a test image are presented. A conceptual framework for automated condition state assessment is also developed

The synthesis of multisensor non-destructive testing of civil engineering structural elements with the use of clustering methods

In the thesis, clustering-based image fusion of multi-sensor non-destructive (NDT) data is studied. Several hard and fuzzy clustering algorithms are analysed and implemented both at the pixel and feature level fusion. Image fusion of ground penetrating radar (GPR) and infrared\ud thermography (IRT) data is applied on concrete specimens with inbuilt artificial defects, as well as on masonry specimens where defects such as plaster delamination and structural cracking were generated through a shear test. We show that on concrete, the GK clustering algorithm exhibits the best performance since it is not limited to the detection of spherical clusters as are the FCM and PFCM algorithms. We also prove that clustering-based fusion outperforms supervised fusion, especially in situations with very limited knowledge about the material properties\ud and depths of the defects. Complementary use of GPR and IRT on multi-leaf masonry walls enabled the detection of the walls’ morphology, texture, as well as plaster delamination\ud and structural cracking. For improved detection of the latter two, we propose using data fusion at the pixel level for data segmentation. In addition to defect detection, the effect of moisture is analysed on masonry using GPR, ultrasonic and complex resistivity tomographies. Within the\ud thesis, clustering is also successfully applied in a case study where a multi-sensor NDT data set was automatically collected by a self-navigating mobile robot system. Besides, the classification of spectroscopic spatial data from concrete is taken under consideration. In both applications, clustering is used for unsupervised segmentation of data

A Novel Probability-based Data Clustering Application for Detecting Elongated Clusters with Application to the Line Detection Problem

Ένα σημαντικό πρόβλημα που απαντάται σε διάφορα πεδία εφαρμογών, όπως η ανάλυση γεωχωρικών δεδομένων (geospatial data analysis), η συμπίεση εικόνας (image compression) και η εξαγωγή δρόμων (road extraction), μεταξύ άλλων παραδειγμάτων, είναι αυτό της ανίχνευσης ευθείων γρμμών ή ευθυγράμμων τμημάτων σε μια δεδομένη εικόνα. Στη διατριβή αυτή, προτείνεται μια νέα προσέγγιση στο παραπάνω πρόβλημα, η οποία βασίζεται στην πιθανοτική ομαδοποίηση (probabilistic clustering). Πιο συγκεκριμένα, ορίζεται μια νέα κατανομή πυκνότητας πιθανότητας η οποία αποτελεί παραλλαγή της Γκαουσσιανής κατανομής πιθανοτήτων (Gaussian probability distribution), στην οποία το κέντρο της δεν είναι πλέον σημείο, αλλά ένα ευθύγραμμο τμήμα, με στόχο τη μοντελοποίηση ευθυγράμμων τμημάτων. Στη συνέχεια, το σύνολο των δεδομένων σημείων θεωρείται ότι προέρχεται από μία κατανομή που εκφράζεται ως ένα σταθμισμένο άθροισμα (μίξη)επιμέρους κατανομών και ο στόχος είναι ο προσδιορισμός αυτών των κατανομών, κάθε μία από τις οποίες μοντελοποιεί και μια (γραμμική) συστάδα (linear cluster). Προτείνεται ένας αγόριθμος, ο οποίος ονομάζεται Αγλόριθμος Πιθανοτικής Συσταδοποίησης Ευθυγράμμων Τμημάτων (Probabilistic Line Segment Clustering algorithm – PLSC) και ακολουθεί τη λογική της Αποδόμησης Μίξης (Mixture Decomposition). Η διαδικασία εύρεσης βέλτιστων τοποθετήσεων των ευθυγράμμων τμημάτων (κέντρων των κατανομών πιθανοτήτων) φέρεται εις πέρας μέσω μιας επαναληπτικής διαδικασίας παρόμοιας του αλγορίθμου Αναμενόμενης Τιμής Βελτιστοποίησης (Expectation Maximization), κατά την οποία, τα τμήματα μετακινούνται σταδιακά με σκοπό να ταιριάξουν στις γραμμικές ομάδες που σχηματίζονται από τα δεδομένα, βάσει ενός ευρετικού κανόνα (heuristic rule). Ο αλγόριθμος δεν απαιτεί εκ των προτέρων γνώση του αριθμού των συστάδων. Αντί αυτού, ξεκινά κάνοντας μiα υπερεκτίμηση του πλήθους τους και σταδιακά τις μειώνει μέσω κατάλληλων μηχανισμών απαλοιφής και συνένωσης. Με σκοπό την τεκμηρίωση της αξίας της προτεινώμενης μεθόδου, διεξήχθησαν αρκετά πειράματα, τα αποτελέσματα των οποίων δείχνουν ότι η τρέχουσα μέθοδος είναι ικανή να αναγνωρίσει σε πολύ ικανοποιητικό βαθμό συστάδες τόσο σε απλούστερες όσο και σε πολυπλοκότερες περιπτώσεις. Ο αλγόριθμος μπορεί να αποδώσει παρόμοια και, σε μερικές περιπτώσεις, καλύτερα απολέσματα συγκρινόμενος με ένα επιλεγμένο πλήθος σχετικών δημοσιευμένων μεθόδων.Line detection is the process of identifying straight lines or line segments in a given image. Potential applications are commonly found in a variety of fields, such as analysis of geospatial data, image compression and road extraction, to name a few. In this dissertation an approach to the above problem based on probabilistic clustering is explored. A variation of the Gaussian probability distribution centered around a line segment is defined accordingly for the two dimensional space in order to model the line segments in the image under study and an algorithm, called Probabilistic Line Segment Clustering (PLSC) that follows the Mixture Decomposition approach is proposed. The process of finding the optimal positioning of the line segments is carried out by an iterative ExpectationMaximizationlike procedure in which the segments are gradually moved in order to fit the actual edges of the image using a heuristic rule. In order to find the appropriate number of segments/clusters, the algorithm starts with an overestimation of it and progressively reduces it via appropriate elimination and unification mechanisms. Toward supporting the value of the proposed method, experimental results have been carried out and discussed in which it is shown that the current method is able to appropriately identify clusters in multiple scenarios. The algorithm can perform mostly comparably and in some cases, even favorably with regard to a selection of relevant published methods

Robust fuzzyclustering for object recognition and classification of relational data

Prototype based fuzzy clustering algorithms have unique ability to partition the data while detecting multiple clusters simultaneously. However since real data is often contaminated with noise, the clustering methods need to be made robust to be useful in practice. This dissertation focuses on robust detection of multiple clusters from noisy range images for object recognition. Dave\u27s noise clustering (NC) method has been shown to make prototype-based fuzzy clustering techniques robust. In this work, NC is generalized and the new NC membership is shown to be a product of fuzzy c-means (FCM) membership and robust M-estimator weight (or possibilistic membership). Thus the generalized NC approach is shown to have the partitioning ability of FCM and robustness of M-estimators. Since the NC (or FCM) algorithms are based on fixed-point iteration technique, they suffer from the problem of initializations. To overcome this problem, the sampling based robust LMS algorithm is considered by extending it to fuzzy c-LMS algorithm for detecting multiple clusters. The concept of repeated evidence has been incorporated to increase the speed of the new approach. The main problem with the LMS approach is the need for ordering the distance data. To eliminate this problem, a novel sampling based robust algorithm is proposed following the NC principle, called the NLS method, that directly searches for clusters in the maximum density region of the range data without requiring the specification of number of clusters. The NC concept is also introduced to several fuzzy methods for robust classification of relational data for pattern recognition. This is also extended to non-Euclidean relational data. The resulting algorithms are used for object recognition from range images as well as for identification of bottleneck parts while creating desegregated cells of machine/ components in cellular manufacturing and group technology (GT) applications

Early detection of health changes in the elderly using in-home multi-sensor data streams

The rapid aging of the population worldwide requires increased attention from health care providers and the entire society. For the elderly to live independently, many health issues related to old age, such as frailty and risk of falling, need increased attention and monitoring. When monitoring daily routines for older adults, it is desirable to detect the early signs of health changes before serious health events, such as hospitalizations, happen, so that timely and adequate preventive care may be provided. By deploying multi-sensor systems in homes of the elderly, we can track trajectories of daily behaviors in a feature space defined using the sensor data. In this work, we investigate a methodology for learning data distribution from streaming data and tracking the evolution of the behavior trajectories over long periods (years) using high dimensional streaming clustering and provide very early indicators of changes in health. If we assume that habitual behaviors correspond to clusters in feature space and diseases produce a change in behavior, albeit not highly specific, tracking trajectory deviations can provide hints of early illness. Retrospectively, we visualize the streaming clustering results and track how the behavior clusters evolve in feature space with the help of two dimension-reduction algorithms, Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE). Moreover, our tracking algorithm in the original high dimensional feature space generates early health warning alerts if a negative trend is detected in the behavior trajectory. We validated our algorithm on synthetic data, real-world data and tested it on a pilot dataset of four TigerPlace residents monitored with a collection of motion, bed, and depth sensors over ten years. We used the TigerPlace electronic health records (EHR) to understand the residents' behavior patterns and to evaluate and explain the health warnings generated by our algorithm. The results obtained on the TigerPlace dataset show that most of the warnings produced by our algorithm can be linked to health events documented in the EHR, providing strong support for a prospective deployment of the approach.Includes bibliographical references