Four half-day tutorials will be given on Monday, September 10.
We'll schedule the tutorials taking into account the requests, placing two of them in the morning and two in the afternoon.


Advances of statistical learning and applications to computer vision

Francesca Odone
Ernesto De Vito

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Abstract: The goal of this tutorial is to provide a comprehensive introduction to a large class of statistical learning algorithms in the supervised setting with applications to a variety of computer vision problems. As for the theoretical aspects, having as a guide regularized least squares, we will introduce a new class of algorithms de ned in terms of lter functions on the kernel matrix. We will give some examples and we discuss the theoretical and computational properties. Finally we will briefly present algorithms that enforce the sparsity of the solution by means of l1 constraints. As for the applications to computer vision we will suggest applications to some filter algorithms that are simpler to implement and to tune than other kernel methods (such as SVMs). We will also discuss how methods that enforce sparsity can be used for feature selection, and compare this approach to state-of-the-art feature selection (e.g., Adaboost) and dimensionality reduction methods (e.g., PCA) , on the well known face detection framework. We plan to balance theory and application aspects.
Video Surveillance and Monitoring

Mubarak Shah

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Abstract: Recently, computer vision has gradually been making the transition away from understanding single images to analyzing image sequences, or video understanding. Video understanding deals with understanding video sequences, e.g., recognition of gestures, activities, and facial expressions. The main shift in the classic paradigm has been from the recognition of static objects in the scene to motion-based recognition of actions and events. Since most videos are about people, this work has mainly focused on analysis of human motion. In particular, there has been a significant interest in the automated visual surveillance systems. Such systems have the advantage of providing continuous active warning capabilities and are especially useful in the areas of law enforcement, national defense, border control and airport security. The main steps in video understanding are: detection of objects of interest in video (e.g. people, vehicles), tracking of those objects from frame to frame, and recognition of their activities and behavior. In this tutorial, I will present our work in object detection, tracking and human activity recognition.
Content-Based Image and Video Retrieval

Theo Gevers
Nicu Sebe
Arnold Smeulders

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Abstract: The growing capacity of computers, the abundance of digital cameras and the increased connectivity of the world all point to large digital multimedia archives. They include images and videos from the World Wide Web, museum objects, flowers, trademarks, and views from everyday life. The faster they grow, the more prominently needed is the efficient access to the content of the images and videos. In this short course, we will give a survey of the most recent developments on image and video search engines. First, the important step of feature extraction will be discussed in detail such as color, shape and texture information, particularly paying attention to discriminatory power and invariance. Then, we focus on the concepts of indexing and genre classification as intermediate step to sort the data. We pay attention to (interactive) ways to perform browsing and retrieval by means of information visualization and relevance feedback. Methods are being discussed to localize the retrieved objects in images.
Computer vision and image analysis of master drawings and paintings

David G. Stork

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Abstract: This one-day tutorial will apply methods from image processing, computer vision and image analysis to problems in the history and understanding of master paintings. Some of these analysis techniques are built upon methods used in forensic image analysis of photographs but are tailored to specific contingencies of painting. Questions addressed include: How do we judge the sizes and positions of objects depicted and the geometry of structures such as architecture? Was the image created using a mechanical or optical aid? What were the sources of illumination and their color? What form of perspective was used? What is revealed by shadows and reflections depicted within a painting? Some of the analysis techniques require nothing more than a tutored and perceptive eye; others merely a ruler and pencil; yet others require advanced statistical estimation procedures and computer analysis. This course is based almost entirely on the analysis of images, not the physical or chemical analysis of pigments and media, the purview of traditional art conservators.

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