A 3-D Contour Segmentation Scheme Based on Curvature and Torsion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Descriptors for 3D Object Recognition and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
The non-existence of general-case view-invariants
Geometric invariance in computer vision
Geometric invariants and object recognition
International Journal of Computer Vision
Invariant signatures for planar shape recognition under partial occlusion
CVGIP: Image Understanding
Smoothing and matching of 3-D space curves
International Journal of Computer Vision
Robot programming by demonstration (RPD): supporting the induction by human interaction
Machine Learning - Special issue on robot learning
Differential and Numerically Invariant Signature Curves Applied to Object Recognition
International Journal of Computer Vision
Geometric and Illumination Invariants for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerically Invariant Signature Curves
International Journal of Computer Vision
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
View-Invariant Representation and Recognition of Actions
International Journal of Computer Vision
Affine invariants for object recognition using the wavelet transform
Pattern Recognition Letters
On Using Functions to Describe the Shape
Journal of Mathematical Imaging and Vision
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Real Time Face and Object Tracking as a Component of a Perceptual User Interface
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
A Complete and Stable Set of Affine-Invariant Fourier Descriptors
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Multiscale Fourier Descriptor for Shape Classification
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Recognizing Hand Gesture using Fourier Descriptors
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Extraction and Temporal Segmentation of Multiple Motion Trajectories in Human Motion
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
Elastic Translation Invariant Matching of Trajectories
Machine Learning
View Invariance for Human Action Recognition
International Journal of Computer Vision
Feature-Based Sequence-to-Sequence Matching
International Journal of Computer Vision
Integral Invariants for Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elastic partial matching of time series
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
IEEE Transactions on Robotics
Wavelet descriptor of planar curves: theory and applications
IEEE Transactions on Image Processing
Affine-invariant B-spline moments for curve matching
IEEE Transactions on Image Processing
Invariant matching and identification of curves using B-splines curve representation
IEEE Transactions on Image Processing
Motion trajectory reproduction from generalized signature description
Pattern Recognition
Probabilistic cluster signature for modeling motion classes
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Kernel-based sparse representation for gesture recognition
Pattern Recognition
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Motion trajectory can be an informative and descriptive clue that is suitable for the characterization of motion. Studying motion trajectory for effective motion description and recognition is important in many applications. For instance, motion trajectory can play an important role in the representation, recognition and learning of most long-term human or robot actions, behaviors and activities. However, effective trajectory descriptors are lacking and most reported work just uses motion trajectory in its raw data form. In this paper, we propose a novel motion trajectory signature descriptor and study its rich descriptive invariants which benefit effective motion trajectory recognition. These invariants are key measures of the flexibility and effectiveness of a descriptor. Substantial descriptive invariants can be deduced from the proposed trajectory signature, which is attributed to the computational locality of the signature components. We first present the signature definition and its robust implementation. Then the signature's invariants are elaborated. A non-linear inter-signature matching algorithm is developed to measure the signature's similarity for trajectory recognition. Experiments are conducted to recognize human sign language, in which both synthetic and real data are used to verify the signature's invariants, and to illustrate the effectiveness in the signature recognition.