Book ChapterDOI
Learning a Sparse Representation for Object Detection
Shivani Agarwal,Dan Roth +1 more
- pp 113-130
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TLDR
An approach for learning to detect objects in still gray images, that is based on a sparse, part-based representation of objects, that achieves high detection accuracy on a difficult test set of real-world images, and is highly robust to partial occlusion and background variation.Abstract:
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.read more
Citations
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Book
Computer Vision: Algorithms and Applications
TL;DR: Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images and takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene.
Proceedings ArticleDOI
PCA-SIFT: a more distinctive representation for local image descriptors
Yan Ke,Rahul Sukthankar +1 more
TL;DR: This paper examines (and improves upon) the local image descriptor used by SIFT, and demonstrates that the PCA-based local descriptors are more distinctive, more robust to image deformations, and more compact than the standard SIFT representation.
Journal ArticleDOI
Pedestrian Detection: An Evaluation of the State of the Art
TL;DR: An extensive evaluation of the state of the art in a unified framework of monocular pedestrian detection using sixteen pretrained state-of-the-art detectors across six data sets and proposes a refined per-frame evaluation methodology.
Proceedings ArticleDOI
Object class recognition by unsupervised scale-invariant learning
TL;DR: The flexible nature of the model is demonstrated by excellent results over a range of datasets including geometrically constrained classes (e.g. faces, cars) and flexible objects (such as animals).
Journal ArticleDOI
Fast Feature Pyramids for Object Detection
TL;DR: For a broad family of features, this work finds that features computed at octave-spaced scale intervals are sufficient to approximate features on a finely-sampled pyramid, and this approximation yields considerable speedups with negligible loss in detection accuracy.
References
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Proceedings ArticleDOI
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Book
Computer and Robot Vision
TL;DR: This two-volume set is an authoritative, comprehensive, modern work on computer vision that covers all of the different areas of vision with a balanced and unified approach.
Proceedings ArticleDOI
Training support vector machines: an application to face detection
TL;DR: A decomposition algorithm that guarantees global optimality, and can be used to train SVM's over very large data sets is presented, and the feasibility of the approach on a face detection problem that involves a data set of 50,000 data points is demonstrated.