Workshop in Visual Object Recognition

Useful Terms

Object Recognition: Object Detection + Object Identification.
Object Detection: The recognition of a learned object class. Example: finding cars in a street scene.
Object Identification: An individual instance of an object is recognized. Example: identification of a specific person face.
Image Classification: Image detection applied to the whole image. Example: "is this an image of a tiger?".

Introduction

"Vision is a process that produces from images of the external world a description that is useful to the viewer and not cluttered with irrelevant information." (Marr and Nishihara).

In the past few years, significant progress has been made in the development of new technologies and in their use as the core of several real-time vis ion systems for detecting specific classes of objects, including people, faces and cars, within complex images. In our workshop we will explore several of the more successful image representations and supervised learning algorithms that enable computers to start tackling the challenging task of image understanding.

Workshop Topics

We will cover a variety of object recognition topics including:

Vision as an inverse problem: The different types of object recognition problems. Pose and illumination variations and invariant representations. The problem of clutter in images.
Local image representations: A comprehensive study of modern image representations such as SIFT, C2, HOG, gray level patches, etc.
Statistical models: Generative and discriminative models. Basic graphical models. Off-the-shelf discriminative classifiers (SVM, boosting). Avoiding over-fitting and learning from few training examples.
Learned image representations: Classical and Modern dimensionality reduction algorithms; Basic feature selection techniques.
Combined image segmentation and recognition: The connection between the problems and proposed solutions, including: Jigsaw models, backprojection-based, OBJCUT.
Video analysis: From images to video; representing motion; combining detection with tracking.

We will examine many real-world applications such as: face identification, vehicle detection and behavior analysis.

Schedule

We shall meet every Friday 11:30-14:30. Please refer to the detailed schedule (MS excel format).

Class Presentations

Please send me your presentations after you present them in class and correct them, (1) as a pdf file (you may want to use PDFcreator) and (2) as .jpg file of the first slide (size should be 307x230 pixels, you may want to use irfanview). Please make sure to include in each slide that you borrow explicit credit to the creator of that slide. Please put this credit on the lower right corner.

Contact Me

Feel free to contact me.