The development of game theory in the early 1940’s by John von Neumann was a reaction against the then dominant view that problems in economic theory can be formulated using standard methods from optimization theory. Indeed, most real – world economic problems typically involve conflicting interactions among decision-making agents that cannot be adequately captured by a single (global) objective function, thereby requiring a different, more sophisticated treatment. Accordingly, the main point made by game theorists is to shift the emphasis from optimality criteria to equilibrium conditions.

Game Theory in Pattern Recognition and Machine Learning: graph transduction

As it provides an abstract theoretically-founded framework to elegantly model complex scenarios, game theory has found a variety of applications not only in economics and, more generally, social sciences but also in different fields of engineering and information technologies. In particular, in the past there have been various attempts aimed at formulating problems in computer vision, pattern recognition and machine learning from a game-theoretic perspective and, with the recent development of algorithmic game theory, the interest in these communities around game-theoretic models and algorithms is growing at a fast pace.

The goal of these three lectures is to offer an introduction to the basic concepts of game theory and to provide an overview of the work we’re currently doing in my group on the use of game-theoretic models in pattern recognition, computer vision, and machine learning.

I shall assume no pre-existing knowledge of game theory by the audience, thereby making the lectures self-contained and understandable by a non-expert.

The three lectures will be structured as follows:

• Lecture 1: Introduction to the basic concepts of game theory
• Lecture 2: Evolutionary games and data clustering
• Lecture 3: Contextual pattern recognition and graph transduction

The lectures are based on two (broader) tutorials I gave at ICPR 2010 and CVPR 2011 (with A. Torsello).

Stefano Mattoccia, of DEIS will held a lecture at MICC entitled “Stereo vision algorithms for dense 3D reconstruction: introduction and recent developments”.

The lecturer Stefano Mattoccia, DEIS, University of Bologna

The stereo vision enables the 3D reconstruction of scenes observed by two or more cameras. In this seminar, considering the case of dense 3D reconstructions, the main problems of stereo vision will be introduced and recent developments in this area will be examined with particular reference to algorithms that lend themselves to being mapped on devices with parallel processing capabilities (eg FPGA, GPU).

Marco Pedersoli will present a method that can dramatically accelerate object detection with part based models. The method is based on the observation that the cost of detection is likely to be dominated by the cost of matching each part to the image, and not by the cost of computing the optimal configuration of the parts as commonly assumed. Therefore accelerating detection requires minimizing the number of part-to-image comparisons.

A method for the fast inference of multi-resolution part based models. (a) example detections; (b) scores obtained by matching the lowest resolution part (root filter) at all image locations; (c) scores obtained by matching the intermediate resolution parts, only at location selected based on the response of the root part; (d) scores obtained by matching the high resolution parts, only at locations selected based on the intermediate resolution scores

To this end we propose a multiple-resolutions hierarchical part based model and a corresponding coarse-to-fine inference procedure that recursively eliminates from the search space unpromising part placements. The method yields a ten-fold speedup over the standard dynamic programming approach and is complementary to the cascade-of-parts approach. Compared to the latter, our
method does not have parameters to be determined empirically, which simplifies its use during the training of the model. Most importantly, the two techniques can be combined to obtain a very significant speedup, of two orders of magnitude in some cases.

We evaluate our method extensively on the PASCAL VOC and INRIA datasets, demonstrating a very high increase in the detection speed with little degradation of the accuracy.

Prof. Angelo Marcelli will present a principled approach to automatic cursive handwriting reading. The approach is based upon handwriting generation models, and according to them assumes that handwriting is a learned complex motoric task which is accomplished by sequencing simpler movement called stroke.

Automatic reading of cursive handwriting

As learning proceeds in human, so does fluency, which results in producing similar sequence of strokes in correspondence of sequence of letters. Such invariants represents therefore the basic drawing units to which an interpretation can be associated. Reading is then achieved by detecting the invariants used to produce the word to be recognized, associating to them their interpretations, and eventually concatenating the interpretations along the ink of the word, without explicit segmentation in characters, as in case of analytical approaches, or using dictionary of possible words, as in case of holistic methods.

He will conclude with a list of key issues to be addressed while pursuing the proposed approach and the steps we have undertaken along this path.