Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Foundations and Trends® in Computer Graphics and Vision
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selecting features for object detection using an AdaBoost-compatible evaluation function
Pattern Recognition Letters
Object detection by global contour shape
Pattern Recognition
Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection
International Journal of Computer Vision
Multi-stage Contour Based Detection of Deformable Objects
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Tracking with Dynamic Hidden-State Shape Models
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Contour Grouping with Partial Shape Similarity
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
International Journal of Computer Vision
Contour Grouping Based on Contour-Skeleton Duality
International Journal of Computer Vision
Shape Based Detection and Top-Down Delineation Using Image Segments
International Journal of Computer Vision
Shape-Based Object Localization for Descriptive Classification
International Journal of Computer Vision
Extraction of Windows in Facade Using Kernel on Graph of Contours
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Object Recognition Based on Efficient Sub-window Search
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Object Detection and Localization in Clutter Range Images Using Edge Features
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Graph-based robust shape matching for robotic application
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
From Images to Shape Models for Object Detection
International Journal of Computer Vision
An Approach to the Parameterization of Structure for Fast Categorization
International Journal of Computer Vision
Real-time shape retrieval for robotics using skip Tri-grams
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Shape detection from line drawings with local neighborhood structure
Pattern Recognition
Automatic facial expression recognition using boosted discriminatory classifiers
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Robust color contour object detection invariant to shadows
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Contour based object detection using part bundles
Computer Vision and Image Understanding
Windows and facades retrieval using similarity on graph of contours
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Using partial edge contour matches for efficient object category localization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Voting by grouping dependent parts
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
A unified contour-pixel model for figure-ground segmentation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Object category classification using occluding contours
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
Generic object class detection using boosted configurations of oriented edges
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Inference and Learning with Hierarchical Shape Models
International Journal of Computer Vision
Analyzing Ancient Maya Glyph Collections with Contextual Shape Descriptors
International Journal of Computer Vision
Skeleton Search: Category-Specific Object Recognition and Segmentation Using a Skeletal Shape Model
International Journal of Computer Vision
Searching the past: an improved shape descriptor to retrieve maya hieroglyphs
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Located hidden random fields: learning discriminative parts for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Accurate Object Recognition with Shape Masks
International Journal of Computer Vision
A boundary-fragment-model for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Learning class-specific edges for object detection and segmentation
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
A variational statistical framework for object detection
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Supervised scale-invariant segmentation (and detection)
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Shape-Based Object Detection via Boundary Structure Segmentation
International Journal of Computer Vision
Evolutionary Hough Games for coherent object detection
Computer Vision and Image Understanding
Object class detection: A survey
ACM Computing Surveys (CSUR)
Fast detection of multiple textureless 3-D objects
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
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We present a novel categorical object detection scheme that uses only local contour-based features. A two-stage, partially supervised learning architecture is proposed: a rudimentary detector is learned from a very small set of segmented images and applied to a larger training set of un-segmented images; the second stage bootstraps these detections to learn an improved classifier while explicitly training against clutter. The detectors are learned with a boosting algorithm which creates a location-sensitive classifier using a discriminative set of features from a randomly chosen dictionary of contour fragments. We present results that are very competitive with other state-of-the-art object detection schemes and show robustness to object articulations, clutter, and occlusion. Our major contributions are the application of boosted local contour-based features for object detection in a partially supervised learning framework, and an efficient new boosting procedure for simultaneously selecting features and estimating per-feature parameters.