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Feature Selection in Pattern Recognition
IEEE Transactions on Systems Science and Cybernetics, 1970The problem of feature selection in pattern recognition is briefly reviewed. Feature selection techniques discussed include 1) information theoretic approach, 2) direct estimation of error probability, 3) feature-space transformation, and 4) approach of using stochastic automata model.
Fu, K. S. +2 more
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Dynamic Features for Iris Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012The human eye is sensitive to visible light. Increasing illumination on the eye causes the pupil of the eye to contract, while decreasing illumination causes the pupil to dilate. Visible light causes specular reflections inside the iris ring. On the other hand, the human retina is less sensitive to near infra-red (NIR) radiation in the wavelength range
R M, da Costa, A, Gonzaga
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Feature modelling by incremental feature recognition
Computer-Aided Design, 1993Abstract A novel feature-modelling system which implements a hybrid of feature-based design and feature recognition in a single framework is described. During the design process of a part, the user can modify interactively either the solid model or the feature model of the part while the system keeps the other model 3onsistent with the changed one ...
Timo Laakko, Martti Mäntylä
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Integration of Feature Based Design and Feature Recognition
ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium, 1995Abstract Process planning for machined parts typically requires that a part be described through machining features such as holes, slots and pockets. This paper presents a novel feature finder, which automatically generates a part interpretation in terms of machining features, by utilizing information from a variety of sources such as ...
JungHyun Han, Aristides AG Requicha
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Logo Recognition Using CNN Features
2015In this paper we propose a method for logo recognition based on Convolutional Neural Networks, instead of the commonly used keypoint-based approaches. The method involves the selection of candidate subwindows using an unsupervised segmentation algorithm, and the SVM-based classification of such candidate regions using features computed by a CNN.
BIANCO, SIMONE +3 more
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Feature Recognition Techniques
2009Almost all applications of remotely sensed imagery require generic algorithms for image feature extraction and classification to gain the required information. Therefore the GMOSS project defined a work package Feature recognition to serve the application work packages in their need to derive information for their tasks.
Andreas Wimmer +4 more
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Laminae-based feature recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001Motivated by the needs of mould and die manufacturers, this paper presents a novel approach to recognizing shape features on geometric models composed of both simple and complex ruled surfaces. The algorithm described uses a network of adjacent 2D-laminae (i.e., bounded surfaces) derived from a component's CAD model to both locate and create generic ...
Lim, T.C., Corney, J.R., Clark, D.
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Robust features for word recognition
Speech Communication, 1988This paper presents a new approach to automatic recognition of spoken words. After discussing the demands upon appropriate subword units and reporting some experiments in using phone superclasses for word recognition we will develop techniques of robust classification, segmentation, and lexical access, utilizing binary phonetic features as processing ...
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Irrelevant Features in Pattern Recognition
IEEE Transactions on Computers, 1978The concept of irrelevant features in Bayesian models for pattern recognition is introduced, and its mathematical meaning is explained. A technique for computing the conditional probabilities of irrelevant features, if necessary, is described. The effect of irrelevant features on feature selection in sequential classification is discussed and ...
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Feature-Based Tactile Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987Tactile sensing offers powerful capabilities for robotic perception. Through the use of array-force sensors, precisely located surface information about objects in the workspace is available wherever the robot arm may reach. In order to use this information to identify objects and their placement, interpretation processes should employ proprioceptive ...
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