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Example-Based Object Detection in Images by Components
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Computer and Robot Vision
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Unsupervised Learning of Models for Recognition
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Face Detection With Information-Based Maximum Discrimination
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Recognition invariance obtained by extended and invariant features
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MILES: Multiple-Instance Learning via Embedded Instance Selection
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Shape Representation and Classification Using the Poisson Equation
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Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition
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Pattern Recognition
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Distinctiveness of faces: A computational approach
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Classification in an informative sample subspace
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Flexible Spatial Configuration of Local Image Features
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Learning semantic object parts for object categorization
Image and Vision Computing
Incremental and robust learning of subspace representations
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Efficient Learning of Relational Object Class Models
International Journal of Computer Vision
A cascade of boosted generative and discriminative classifiers for vehicle detection
EURASIP Journal on Advances in Signal Processing
Semantic image classification using statistical local spatial relations model
Multimedia Tools and Applications
Combinatorial and statistical methods for part selection for object recognition
International Journal of Computer Mathematics - Computer Vision and Pattern Recognition
Class information predicts activation by object fragments in human object areas
Journal of Cognitive Neuroscience
People detection in image and video data
VNBA '08 Proceedings of the 1st ACM workshop on Vision networks for behavior analysis
Shape Matching by Segmentation Averaging
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Shape Based Detection and Top-Down Delineation Using Image Segments
International Journal of Computer Vision
Evolving Local Descriptor Operators through Genetic Programming
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Image and Vision Computing
Evolutionary learning of local descriptor operators for object recognition
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Object Class Recognition Using SNoW with a Part Vocabulary
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
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Protocols from perceptual observations
Artificial Intelligence - Special volume on connecting language to the world
Object recognition with uncertain geometry and uncertain part detection
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Radon representation-based feature descriptor for texture classification
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Global registration of overlapping images using accumulative image features
Pattern Recognition Letters
Hierarchical appearance-based classifiers for qualitative spatial localization
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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Flexible spatial models for grouping local image features
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Long term carefully learning for person detection application to intelligent surveillance system
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Computer Vision and Image Understanding
Located hidden random fields: learning discriminative parts for object detection
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MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
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ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Attentive object detection using an information theoretic saliency measure
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IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
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Engineering Applications of Artificial Intelligence
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We present an approach for learning to detect objects in still gray images, that is based on a sparse, part-based representation of objects. A vocabulary of information-rich object parts is automatically constructed from a set of sample images of the object class of interest. Images are then represented using parts from this vocabulary, along with spatial relations observed among them. Based on this representation, a feature-efficient learning algorithm is used to learn to detect instances of the object class. The framework developed can be applied to any object with distinguishable parts in a relatively fixed spatial configuration. We report experiments on images of side views of cars. Our experiments show that the method achieves high detection accuracy on a difficult test set of real-world images, and is highly robust to partial occlusion and background variation.In addition, we discuss and offer solutions to several methodological issues that are significant for the research community to be able to evaluate object detection approaches.