Introduction to Grey system theory
The Journal of Grey System
Detection and estimation of circular arc segments
Pattern Recognition Letters
Boundary-based corner detection using eigenvalues of covariance matrices
Pattern Recognition Letters
Pattern Recognition Letters
Corner Detection and Interpretation on Planar Curves Using Fuzzy Reasoning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant 2D object recognition using the wavelet modulus maxima
Pattern Recognition Letters
Affine invariants for object recognition using the wavelet transform
Pattern Recognition Letters
Simple Gabor feature space for invariant object recognition
Pattern Recognition Letters
The application of DBF neural networks for object recognition
Information Sciences—Informatics and Computer Science: An International Journal
Pattern recognition using higher-order local autocorrelation coefficients
Pattern Recognition Letters
Complete invariants for robust face recognition
Pattern Recognition
A Corner-Finding Algorithm for Chain-Coded Curves
IEEE Transactions on Computers
Expert Systems with Applications: An International Journal
Applying particle swarm optimization algorithm to roundness measurement
Expert Systems with Applications: An International Journal
Angle Detection on Digital Curves
IEEE Transactions on Computers
Letter: GreyART network for data clustering
Neurocomputing
Learning Performance Assessment Approach Using Web-Based Learning Portfolios for E-learning Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A self-organizing CMAC network with gray credit assignment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Expert Systems with Applications: An International Journal
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Computer vision has been extensively adopted in industry for the last two decades. It enhances productivity and quality management, and is flexibility, efficient, fast, inexpensive, reliable and robust. This study presents a new translation, rotation and scaling-free object recognition method for 2D objects. The proposed method comprises two parts: KRA feature extractor and GRA classifier. The KRA feature extractor employs K-curvature, re-sampling, and autocorrelation transformation to extract unique features of objects, and then gray relational analysis (GRA) classifies the extracted invariant features. The boundary of the digital object was first represented as the form of the K-curvature over a given region of support, and was then re-sampled and transformed with autocorrelation function. After that, the extracted features own the unique property that is invariant to translation, rotation and scaling. To verify and validate the proposed method, 50 synthetic and 50 real objects were digitized as standard patterns, and 10 extra images of each object (test images) which were taken at different positions, orientations and scales, were acquired and compared with the standard patterns. The experimental results reveal that the proposed method with either GRA or MD methods is effective and reliable for part recognition.