A Step Towards Unification of Syntactic and Statistical Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special memorial issue for Professor King-Sun Fu
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Mathematics of Generalization: Proceedings: SFI-CNLS Workshop on Formal Approaches to Supervised Learning (1992: Santa Fe, N. M.)
A note on core research issues for statistical pattern recognition
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Trainable Similarity Measure for Image Classification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Estimation of Dependences Based on Empirical Data: Empirical Inference Science (Information Science and Statistics)
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Building Road-Sign Classifiers Using a Trainable Similarity Measure
IEEE Transactions on Intelligent Transportation Systems
Recognition of Protruding Objects in Highly Structured Surroundings by Structural Inference
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
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Statistical inference of sensor-based measurements is intensively studied in pattern recognition. It is usually based on feature representations of the objects to be recognized. Such representations, however, neglect the object structure. Structural pattern recognition, on the contrary, focusses on encoding the object structure. As general procedures are still weakly developed, such object descriptions are often application dependent. This hampers the usage of a general learning approach. This paper aims to summarize the problems and possibilities of general structural inference approaches for the family of sensor-based measurements: images, spectra and time signals, assuming a continuity between measurement samples. In particular it will be discussed when probabilistic assumptions are needed, leading to a statistically-based inference of the structure, and when a pure, non-probabilistic structural inference scheme may be possible.