A main component to design fish recognition and classification system architecture

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A main component to design fish recognition and classification system architecture

11 Introduction

In the previous chapter the architecture and the approaches of object recognition and classification system were shown in details Moreover the features of shape characters of fish that will be used for classification stage are provided Therefore this chapter focuses on some background of literature approaches on related work and concepts in the field of object recognition and classification In particular a main component to design fish recognition and classification system architecture is used it will show these experiments history of development in several cases

The following literature review is divided into four main sections The fish recognition and classification first aspect is covered The second aspect relates with image segmentation techniques to segment underwater images are presented Investigates most of the feature extraction and selection by shape representation and description the third aspect is applied Finally the classifiers technique for object recognition and classification in aspect of support vector machine is reported

12 Fish Recognition and Classification

Recently there were many researchers who attempted to design and apply the interaction between an underwater environment and learning techniques to develop the recognition and classification system in order to classify the fish Therefore Castignolles et al 1994 used offline method detection with static thresholds to segment the images that recorded by Svideo tapes and enhance image contrast by using background lighting Furthermore to recognize the species a Bayes classifier was tested after extract twelve geometrical features from fish images However this method needs control on the light of background determine the value of threshold and multiple imaging Moreover where fish are lined up close to each other the applications tend to be impractical for the realtime

The momentinvariant for features extraction is fast and very easy to implement Therefore Zion et al 1999 stated the features extraction from dead fish tails by using momentinvariants in order to identify of species Moreover the image area is used to estimate fish mass Furthermore the accuracy of 99 93 and 93 respectively for grey mullet St Peters fish and carp is got for identification of fish species Therefore later Zion et al 2000 tested this method with live fish swimming in clean water The accuracies were 100 91 and 91 respectively for fish species identification However the features of the tail in the image which were extracted by the momentinvariant are strongly affects by the water opaqueness and fish motion This method needs clear environments and all features appear clearly

An automatic system to select the desirable features for recognition and classification object is needed Therefore Chan et al 1999 developed a 3D point distribution model PDM in order to extract the lateral length measurement automatically from the images without an extensive user interaction to locate individual landmark points on the fish by using an ntuple classifier as a tool to initiate the model Moreover the WISARD architecture is used as a LookUp Table LUT which holds information about the pattern that the classifier tries to recognize in order to assess the performance and usefulness of the ntuple classifier in the application of fish recognition However this method needs to fix the predefined threshold value amount of prior knowledge for the fish and the bigger training set

Determine the landmarks as tips of snout or fins for fish are very important to recognize the fish Therefore Cardin Friedland 1999 stated the morphometric analysis by biometric interpretation of fish homologous landmarks as tips of snout or fins for fish stock discrimination However they did not refer to algorithms for determining landmarks and the external points are not satisfactory because their locations are subjective

From other aspect Cardin 2000 reviewed the landmarks of shape by using morph

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