A survey of curve and surface methods in CAGD
Computer Aided Geometric Design
Curve and surface constructions using rational B-splines
Computer-Aided Design
Mathematical elements for computer graphics (2nd ed.)
Mathematical elements for computer graphics (2nd ed.)
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Application of Affine-Invariant Fourier Descriptors to Recognition of 3-D Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer graphics: principles and practice (2nd ed.)
Computer graphics: principles and practice (2nd ed.)
A novel algorithm for color constancy
International Journal of Computer Vision
3-D Shape Recovery Using Distributed Aspect Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Geometric invariance in computer vision
Geometric invariance in computer vision
Computing the Perspective Projection Aspect Graph of Solids of Revolution
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric invariants and object recognition
International Journal of Computer Vision
Wavelets and subband coding
The Illumination-Invariant Recognition of 3D Objects Using Local Color Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelet-Based Affine Invariant Representation: A Tool for Recognizing Planar Objects in 3D Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of 2D Object Contours Using the Wavelet Transform Zero-Crossing Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Illumination-Invariant Matching of Deterministic Local Structure in Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Planar Objects Using Invariant Image Features
Recognizing Planar Objects Using Invariant Image Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Thermophysical Algebraic Invariants from Infrared Imagery for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the Second Joint European - US Workshop on Applications of Invariance in Computer Vision
Illumination and geometry invariant recognition of texture in color images
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
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IEEE Transactions on Image Processing
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Dyadic Wavelet Affine Invariant Function for 2D Shape Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Affine invariants for object recognition using the wavelet transform
Pattern Recognition Letters
A System to Navigate a Robot into a Ship Structure
ICVS '01 Proceedings of the Second International Workshop on Computer Vision Systems
Highly Discriminative Invariant FEatures for Image Matching
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Using cross-ratios to model curve data for aircraft recognition
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affine Invariant Multiscale Wavelet-Based Shape Matching Algorithm
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
An Affine-Invariant Tool for Retrieving Images from Homogeneous Databases
Multimedia Tools and Applications
Description and recognition of object contours using arc length and tangent orientation
Pattern Recognition Letters
Wavelet Approximation-Based Affine Invariant Shape Representation Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image retrieval and perceptual similarity
ACM Transactions on Applied Perception (TAP)
Integral Invariants for Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Retrieval of images of man-made structures based on projective invariance
Pattern Recognition
Synthesized affine invariant function for 2D shape recognition
Pattern Recognition
Object recognition using wavelets, L-G graphs and synthesis of regions
Pattern Recognition
An image retrieval system based on identifying objects of interest
Integrated Computer-Aided Engineering
Foundations and Trends® in Computer Graphics and Vision
On Signature Invariants for Effective Motion Trajectory Recognition
International Journal of Robotics Research
Foundations and Trends in Robotics
A framework of context-aware object recognition for smart home
ICOST'07 Proceedings of the 5th international conference on Smart homes and health telematics
3D scene retrieval and recognition with Depth Gradient Images
Pattern Recognition Letters
Undoing the affine transformation using blind source separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Efficient multiscale shape-based representation and retrieval
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
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We propose invariant formulations that can potentially be combined into a single system. In particular, we describe a framework for computing invariant features which are insensitive to rigid motion, affine transform, changes of parameterization and scene illumination, perspective transform, and view point change. This is unlike most current research on image invariants which concentrates on either geometric or illumination invariants exclusively. The formulations are widely applicable to many popular basis representations, such as wavelets, short-time Fourier analysis, and splines. Exploiting formulations that examine information about shape and color at different resolution levels, the new approach is neither strictly global nor local. It enables a quasi-localized, hierarchical shape analysis which is rarely found in other known invariant techniques, such as global invariants. Furthermore, it does not require estimating high-order derivatives in computing invariants (unlike local invariants), whence is more robust. We provide results of numerous experiments on both synthetic and real data to demonstrate the validity and flexibility of the proposed framework.