Abstract
In this paper we conduct a case study of ¯sh species classi-
fication based on shape and texture. We consider three fish species: cod,
haddock, and whiting. We derive shape and texture features from an appearance model of a set of training data. The fish in the training images
were manual outlined, and a few features including the eye and backbone
contour were also annotated. From these annotations an optimal MDL
curve correspondence and a subsequent image registration were derived.
We have analyzed a series of shape and texture and combined shape and
texture modes of variation for their ability to discriminate between the
fish types, as well as conducted a preliminary classfication. In a linear
discrimant analysis based on the two best combined modes of variation
we obtain a resubstitution rate of 76 %
Original language | English |
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Book series | Lecture Notes in Computer Science |
Volume | 5575 |
Pages (from-to) | 745-749 |
ISSN | 0302-9743 |
DOIs | |
Publication status | Published - 2009 |
Event | 16th Scandinavian Conference on Image Analysis (SCIA) - Oslo, Norway Duration: 15 Jun 2009 → 18 Jun 2009 http://www.scia2009.org/ |
Conference
Conference | 16th Scandinavian Conference on Image Analysis (SCIA) |
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Country/Territory | Norway |
City | Oslo |
Period | 15/06/2009 → 18/06/2009 |
Internet address |