By Aurélio Campilho, Mohamed Kamel
The quantity set LNCS 4141, and LNCS 4142 represent the refereed court cases of the 3rd overseas convention on snapshot research and popularity, ICIAR 2006, held in Póvoa de Varzim, Portugal in September 2006.
The seventy one revised complete papers and ninety two revised poster papers offered including 2 invited lectures have been conscientiously reviewed and chosen from 389 submissions. The papers are equipped in topical sections on photo recovery and enhancement, snapshot segmentation, snapshot and video processing and research, snapshot and video coding and encryption, photograph retrieval and indexing, movement research, and monitoring within the first quantity. the second one quantity comprises topical sections on trend popularity for photograph research, computing device imaginative and prescient, biometrics, form and matching, biomedical photograph research, mind imaging, distant sensing snapshot processing, and applications.
Read or Download Image Analysis and Recognition: Third International Conference, ICIAR 2006, Póvoa de Varzim, Portugal, September 18-20, 2006, Proceedings, Part I (Lecture ... Vision, Pattern Recognition, and Graphics) PDF
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Extra resources for Image Analysis and Recognition: Third International Conference, ICIAR 2006, Póvoa de Varzim, Portugal, September 18-20, 2006, Proceedings, Part I (Lecture ... Vision, Pattern Recognition, and Graphics)
On Optimizing Dissimilarity-Based Classification Using Prototype Reduction Schemes Sang-Woon Kim1, and B. John Oommen2, 1 2 Dept. ca Abstract. The aim of this paper is to present a strategy by which a new philosophy for pattern classiﬁcation, namely that pertaining to Dissimilarity-Based Classiﬁers (DBCs), can be eﬃciently implemented. This methodology, proposed by Duin1 and his co-authors (see , , , , ), is a way of deﬁning classiﬁers between the classes, and is not based on the feature measurements of the individual patterns, but rather on a suitable dissimilarity measure between them.
This was done by performing experiments on a number of data sets. The sample vectors of each data set are divided into two subsets of equal size, and used for training and validation, alternately. The training set was used for computing the prototypes and the respective covariance matrices, and the test set was used for evaluating the quality of the corresponding classiﬁer. In our experiments, the three artiﬁcial data sets “Random”, “Non normal 2”, and “Non linear 2” were generated with diﬀerent sizes of testing and training sets of cardinality 400, 1,000, and 1,000 respectively.
3 Dissimilarity Measures Used in DBCs Fundamental to DBCs is the measure used to quantify the dissimilarity between two vectors9 . The work in  reports extensive experiments conducted using various dissimilarity measures (see Table 2 of ). A list of these measures where we quantify the dissimilarity between v and w ∈ Rq , is given below: 1. 2. 3. 4. 5. City Block Norm : D1 = qi=1 |vi − wi |. Euclidean Norm : DE (orD2 ) = (v − w)T (v − w). Max Norm : Dmax = M axi |vi − wi |. 1/p q Lp or Minkowski Norm : Dp = ( i=1 |vi − wi |p ) , p ≥ 1, p = 2.
Image Analysis and Recognition: Third International Conference, ICIAR 2006, Póvoa de Varzim, Portugal, September 18-20, 2006, Proceedings, Part I (Lecture ... Vision, Pattern Recognition, and Graphics) by Aurélio Campilho, Mohamed Kamel