Automatic Fish Classification: Using Image Processing and Case-Based Reasoning

Abstract

Counting and classifying fish moving upstream in rivers to spawn is a useful way of monitoring the population of different species. Today, there exist some commercial solutions, along with some research that addresses the area. Case-based reasoning is a process that can be used to solve new problems based on previous problems. This thesis studies the possibilities of combining image processing techniques and case-based reasoning to classify species of fish which are similar to each other in both shape, size and color. Methods for image preprocessing are discussed, and tested. Methods for feature extraction and a case-based reasoning prototype are proposed, implemented and tested with promising results.