On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Indexing: Efficient 3-D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
CVGIP: Image Understanding
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Determining Pose of 3D Objects With Curved Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-based object recognition and localization in 3D-space, using a single video image
Computer Vision and Image Understanding
Optimal pose estimation in two and tree dimensions
Computer Vision and Image Understanding
Evaluation of Interest Point Detectors
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Multiple view geometry in computer vision
Saliency, Scale and Image Description
International Journal of Computer Vision
Recognizing Planar Objects Using Invariant Image Features
Recognizing Planar Objects Using Invariant Image Features
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Artificial Vision for Mobile Robots: Stereo Vision and Multisensory Perception
Artificial Vision for Mobile Robots: Stereo Vision and Multisensory Perception
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IEEE Computational Science & Engineering
Multidimensional Indexing for Recognizing Visual Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
About Moment Normalization and Complex Moment Descriptors
Proceedings of the 4th International Conference on Pattern Recognition
Vision-guided mobile robot navigation using neural networks and topological models of the environment
A differential geometric approach to computer vision and its applications in control
A differential geometric approach to computer vision and its applications in control
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Moment invariants for recognition under changing viewpoint and illumination
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Detecting and matching interest points in relative scale
Machine Graphics & Vision International Journal
Scale-invariant shape features for recognition of object categories
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Beyond keypoints: novel techniques for content-based image matching and retrieval
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
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This paper proposes an efficient method to locate a three-dimensional object in cluttered environment. Model of the object is represented in a reference scale by the local features extracted from several reference images. A PCA-based hashing technique is introduced for accessing the database of reference features efficiently. Localization is performed in an estimated relative scale. Firstly, a pair of stereo images is captured simultaneously by calibrated cameras. Then the object is identified in both images by extracting features and matching them with reference features, clustering the matched features with generalized Hough transformation, and verifying clusters with spatial relations between the features. After the identification process, knowledge-based correspondences of features belonging to the object present in the stereo images are used for the estimation of the 3D position. The localization method is robust to different kinds of geometric and photometric transformations in addition to cluttering, partial occlusions and background changes. As both the model representation and localization are single-scale processes, the method is efficient in memory usage and computing time. The proposed relative scale method has been implemented and experiments have been carried out on a set of objects. The method results very good accuracy and takes only a few seconds for object localization by our primary implementation. An application of the relative scale method for exploration of an object in cluttered environment is demonstrated. The proposed method could be useful for many other practical applications.