Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a Sparse Representation for Object Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Visual Object Recognition with Supervised Learning
IEEE Intelligent Systems
Recognition of Planar Object Classes
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Face recognition: component-based versus global approaches
Computer Vision and Image Understanding - Special issue on Face recognition
Data Mining
Observing Sara: a case study of a blind person's interactions with technology
Proceedings of the 9th international ACM SIGACCESS conference on Computers and accessibility
A blind person's interactions with technology
Communications of the ACM - A Blind Person's Interaction with Technology
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Over the course of the first few months of life, our brains accomplish a remarkable feat. They are able to interpret complex visual images so that instead of being just disconnected collections of colors and textures, they become meaningful sets of distinct objects. Exactly how this is accomplished is poorly understood. We approach this problem from both experimental and computational perspectives. On the experimental side, we have launched a new humanitarian and scientific initiative in India, called 'Project Prakash'. This project involves a systematic study of the development of object-perception skills in children following recovery from congenital blindness. Here, we provide an overview of Project Prakash and also describe a specific study related to the development of face-perception skills following sight recovery. Based in part on the results of these experiments, we then develop a computational framework for addressing the problem of object concept discovery. Our model seeks to find repeated instances of a pattern in multiple training images. The source of complexity lies in the non-normalized nature of the inputs: the pattern is unconstrained in terms of where it can appear in the images, the background is complex and constitutes the overwhelming majority of the image, and the pattern can change significantly from one training instance to another. For the purpose of demonstration, we focus on human faces as the pattern of interest, and describe the sequence of steps through which the model is able to extract a face concept from non-normalized example images. Additionally, we test the model's robustness to degradations in the inputs. This is important to assess the model's congruence with developmental processes in human infancy, or following treatment for extended congenital blindness, when visual acuity is significantly compromised.