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III. SPECIFIC LEARNING OBJECTIVES:

by Hours Credit
"... The Professional Education Seminar is a capstone experience focusing on classroom dynamics, creating positive learning environments, reflective teaching and professionalism. A complete portfolio will be required. ..."
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The Professional Education Seminar is a capstone experience focusing on classroom dynamics, creating positive learning environments, reflective teaching and professionalism. A complete portfolio will be required.

Probabilistic Visual Learning for Object Representation

by Baback Moghaddam, Alex Pentland , 1996
"... We present an unsupervised technique for visual learning which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixture-of ..."
Abstract - Cited by 699 (15 self) - Add to MetaCart
-of-Gaussians model (for multimodal distributions). These probability densities are then used to formulate a maximum-likelihood estimation framework for visual search and target detection for automatic object recognition and coding. Our learning technique is applied to the probabilistic visual modeling, detection

Object class recognition by unsupervised scale-invariant learning

by R. Fergus, P. Perona, A. Zisserman - In CVPR , 2003
"... We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. A probabilistic representation is used for all aspects of the object: shape, appearance, occlusion and ..."
Abstract - Cited by 1127 (50 self) - Add to MetaCart
We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. A probabilistic representation is used for all aspects of the object: shape, appearance, occlusion

Rapid object detection using a boosted cascade of simple features

by Paul Viola, Michael Jones - ACCEPTED CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION 2001 , 2001
"... This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the " ..."
Abstract - Cited by 3283 (9 self) - Add to MetaCart
This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called

Pictorial Structures for Object Recognition

by Pedro F. Felzenszwalb, Daniel P. Huttenlocher - IJCV , 2003
"... In this paper we present a statistical framework for modeling the appearance of objects. Our work is motivated by the pictorial structure models introduced by Fischler and Elschlager. The basic idea is to model an object by a collection of parts arranged in a deformable configuration. The appearance ..."
Abstract - Cited by 816 (15 self) - Add to MetaCart
of detecting an object in an image as well as the problem of learning an object model from training examples, and present efficient algorithms for both these problems. We demonstrate the techniques by learning models that represent faces and human bodies and using the resulting models to locate

The reviewing of object files: Object specific integration of information

by Daniel Kahneman, Anne Treisman, J. Gibbs - Cognitive Psychology , 1992
"... A series of experiments explored a form of object-specific priming. In all experiments a preview field containing two or more letters is followed by a target letter that is to be named. The displays are designed to produce a perceptual interpretation of the target as a new state of an object that pr ..."
Abstract - Cited by 462 (4 self) - Add to MetaCart
A series of experiments explored a form of object-specific priming. In all experiments a preview field containing two or more letters is followed by a target letter that is to be named. The displays are designed to produce a perceptual interpretation of the target as a new state of an object

Robust Real-time Object Detection

by Paul Viola, Michael Jones - International Journal of Computer Vision , 2001
"... This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image ” which allows the features ..."
Abstract - Cited by 1184 (4 self) - Add to MetaCart
This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image ” which allows the features

The PASCAL Visual Object Classes (VOC) Challenge

by M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, A. Zisserman - INTERNATIONAL JOURNAL OF COMPUTER VISION
"... ... and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection. ..."
Abstract - Cited by 629 (20 self) - Add to MetaCart
... and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection

Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories

by Li Fei-fei , 2004
"... Abstract — Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been te ..."
Abstract - Cited by 784 (16 self) - Add to MetaCart
Abstract — Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been

Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary

by Pinar Duygulu, Kobus Barnard, Nando de Freitas, David Forsyth , 2002
"... We describe a model of object recognition as machine translation. In this model, ..."
Abstract - Cited by 613 (34 self) - Add to MetaCart
We describe a model of object recognition as machine translation. In this model,
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