Detecting irregularity in videos using spatiotemporal volumes.

Li, Yun.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 68-72).Abstracts in English and Chinese.Abstract --- p.I摘要 --- p.IIIAcknowledgments --- p.IVList of Contents --- p.VIList of Figures --- p.VIIChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Visual Detection --- p.2Chapter 1.2 --- Irregularity Detection --- p.4Chapter Chapter 2 --- System Overview --- p.7Chapter 2.1 --- Definition of Irregularity --- p.7Chapter 2.2 --- Contributions --- p.8Chapter 2.3 --- Review of previous work --- p.9Chapter 2.3.1 --- Model-based Methods --- p.9Chapter 2.3.2 --- Statistical Methods --- p.11Chapter 2.4 --- System Outline --- p.14Chapter Chapter 3 --- Background Subtraction --- p.16Chapter 3.1 --- Related Work --- p.17Chapter 3.2 --- Adaptive Mixture Model --- p.18Chapter 3.2.1 --- Online Model Update --- p.20Chapter 3.2.2 --- Background Model Estimation --- p.22Chapter 3.2.3 --- Foreground Segmentation --- p.24Chapter Chapter 4 --- Feature Extraction --- p.28Chapter 4.1 --- Various Feature Descriptors --- p.29Chapter 4.2 --- Histogram of Oriented Gradients --- p.30Chapter 4.2.1 --- Feature Descriptor --- p.31Chapter 4.2.2 --- Feature Merits --- p.33Chapter 4.3 --- Subspace Analysis --- p.35Chapter 4.3.1 --- Principal Component Analysis --- p.35Chapter 4.3.2 --- Subspace Projection --- p.37Chapter Chapter 5 --- Bayesian Probabilistic Inference --- p.39Chapter 5.1 --- Estimation of PDFs --- p.40Chapter 5.1.1 --- K-Means Clustering --- p.40Chapter 5.1.2 --- Kernel Density Estimation --- p.42Chapter 5.2 --- MAP Estimation --- p.44Chapter 5.2.1 --- ML Estimation & MAP Estimation --- p.44Chapter 5.2.2 --- Detection through MAP --- p.46Chapter 5.3 --- Efficient Implementation --- p.47Chapter 5.3.1 --- K-D Trees --- p.48Chapter 5.3.2 --- Nearest Neighbor (NN) Algorithm --- p.49Chapter Chapter 6 --- Experiments and Conclusion --- p.51Chapter 6.1 --- Experiments --- p.51Chapter 6.1.1 --- Outdoor Video Surveillance - Exp. 1 --- p.52Chapter 6.1.2 --- Outdoor Video Surveillance - Exp. 2 --- p.54Chapter 6.1.3 --- Outdoor Video Surveillance - Exp. 3 --- p.56Chapter 6.1.4 --- Classroom Monitoring - Exp.4 --- p.61Chapter 6.2 --- Algorithm Evaluation --- p.64Chapter 6.3 --- Conclusion --- p.66Bibliography --- p.6

Tracking dynamic regions of texture and shape

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 137-142).The tracking of visual phenomena is a problem of fundamental importance in computer vision. Tracks are used in many contexts, including object recognition, classification, camera calibration, and scene understanding. However, the use of such data is limited by the types of objects we are able to track and the environments in which we can track them. Objects whose shape or appearance can change in complex ways are difficult to track as it is difficult to represent or predict the appearance of such objects. Furthermore, other elements of the scene may interact with the tracked object, changing its appearance, or hiding part or all of it from view. In this thesis, we address the problem of tracking deformable, dynamically textured regions under challenging conditions involving visual clutter, distractions, and multiple and prolonged occlusion. We introduce a model of appearance capable of compactly representing regions undergoing nonuniform, nonrepeating changes to both its textured appearance and shape. We describe methods of maintaining such a model and show how it enables efficient and effective occlusion reasoning. By treating the visual appearance as a dynamically changing textured region, we show how such a model enables the tracking of groups of people. By tracking groups of people instead of each individual independently, we are able to track in environments where it would otherwise be difficult, or impossible. We demonstrate the utility of the model by tracking many regions under diverse conditions, including indoor and outdoor scenes, near-field and far-field camera positions, through occlusion and through complex interactions with other visual elements, and by tracking such varied phenomena as meteorological data, seismic imagery, and groups of people.by Joshua Migdal.Ph.D

Αυτόματες Διαπραγματεύσεις Υπολογιστικά Νοημόνων Οντοτήτων σε Ηλεκτρονικές Αγορές

Οι αυτόματες διαπραγματεύσεις που διεξάγονται στα πλαίσια των Ηλεκτρονικών Αγορών αποτελούν ερευνητικό αντικείμενο με αρκετό ενδιαφέρον τα τελευταία χρόνια. Το σενάριο που απεικονίζει πραγματικές καταστάσεις υποδεικνύει πως οι οντότητες λειτουργούν κάτω από πλήρη άγνοια για τα χαρακτηριστικά των υπολοίπων. Αυτό σημαίνει πως οι συμπεριφορές που πρόκειται να αναπτυχθούν πρέπει να ενσωματώνουν μηχανισμούς διαχείρισης της αβεβαιότητας που δημιουργεί η άγνοια αυτή αλλά και έξυπνες μεθόδους για τη μοντελοποίηση της κάθε πτυχής του εξεταζόμενου σεναρίου. Στην παρούσα διατριβή υιοθετούμε τεχνικές υπολογιστικής νοημοσύνης ώστε να προτείνουμε αποδοτικούς μηχανισμούς καθορισμού της συμπεριφοράς των οντοτήτων που συμμετέχουν σε διαπραγματεύσεις. Καλύπτουμε όλο το φάσμα μιας αγοράς προτείνοντας μεθόδους για τον καθορισμό των βασικών παραμέτρων αλλά και μοντέλα λήψης αποφάσεων σε κάθε γύρο των διαπραγματεύσεων. Λαμβάνουμε υπόψιν μας την αβεβαιότητα στις ενέργειες των οντοτήτων ταυτόχρονα με το στόχο της μεγιστοποίησης του επιδιωκόμενου κέρδους. Προτείνουμε μοντέλα λήψης απόφασης τα οποία βασίζονται σε διαφορετικές πτυχές του σεναρίου μιας διαπραγμάτευσης ώστε να αναδείξουμε ποιο από αυτά είναι το βέλτιστο για να υιοθετηθεί. Μελετήσαμε τη συμπεριφορά των αγοραστών όπως επίσης και των πωλητών. Οι προτεινόμενοι μηχανισμοί λήψης αποφάσεων για κάθε ένα από τους δύο βασίζονται στην Ασαφή Λογική, τη Θεωρία Παιγνίων, τη Θεωρία του Σμήνους και τη Θεωρία Βέλτιστης παύσης.Automated negotiations consist an interesting research domain for many years. A scenario, mostly depicting real life negotiations, defines that entities act under no knowledge on the characteristics of the rest of them. This means that their behavior should incorporate mechanisms for handling uncertainty created by the lack of knowledge as well as intelligent methods for modelling every aspect of the discussed scenario. In this PhD Thesis, we adopt computational intelligence techniques in order to propose efficient mechanisms for the definition of the behavior of entities participating in Electronic Markets. We cover the entire framework defined in a marketplace by proposing methodologies for the definition of basic parameters together with decision making models at every step of each negotiation. We take into consideration the uncertainty in such scenarios together with profit maximization. We propose decision making models that are based on different aspects of the discussed scenario in order to reveal the optimal one. We study buyers’ as well as sellers’ behavior. The proposed decision making mechanisms, for every part (buyers and sellers), are based on Fuzzy Logic, Game Theory, Swarm Intelligence and Optimal Stopping Theory