SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Parameterized Models of Image Motion
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Robust Real-Time Face Detection
International Journal of Computer Vision
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Identifying Individuals in Video by Combining "Generative" and Discriminative Head Models
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
High speed obstacle avoidance using monocular vision and reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Peekaboom: a game for locating objects in images
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Cover trees for nearest neighbor
ICML '06 Proceedings of the 23rd international conference on Machine learning
Toward Category-Level Object Recognition (Lecture Notes in Computer Science)
Toward Category-Level Object Recognition (Lecture Notes in Computer Science)
Robotic Grasping of Novel Objects using Vision
International Journal of Robotics Research
Efficient training of artificial neural networks for autonomous navigation
Neural Computation
Learning methods for generic object recognition with invariance to pose and lighting
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
The 2005 PASCAL visual object classes challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
A local basis representation for estimating human pose from cluttered images
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Object Recognition in 3D Point Clouds Using Web Data and Domain Adaptation
International Journal of Robotics Research
Robotic object detection: learning to improve the classifiers using sparse graphs for path planning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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When building an application that requires object class recognition, having enough data to learn from is critical for good performance, and can easily determine the success or failure of the system. However, it is typically extremely labor-intensive to collect data, as the process usually involves acquiring the image, then manual cropping and hand-labeling. Preparing large training sets for object recognition has already become one of the main bottlenecks for such emerging applications as mobile robotics and object recognition on the web. This paper focuses on a novel and practical solution to the dataset collection problem. Our method is based on using a green screen to rapidly collect example images; we then use a probabilistic model to rapidly synthesize a much larger training set that attempts to capture desired invariants in the object's foreground and background. We demonstrate this procedure on our own mobile robotics platform, where we achieve 135x savings in the time/effort needed to obtain a training set. Our data collection method is agnostic to the learning algorithm being used, and applies to any of a large class of standard object recognition methods. Given these results, we suggest that this method become a standard protocol for developing scalable object recognition systems. Further, we used our data to build reliable classifiers that enabled our robot to visually recognize an object in an office environment, and thereby fetch an object from an office in response to a verbal request.