AbstractThis study investigates the use of texture, i.e. the grey level variation in digital images, as a basis for identification of strongylid eggs. Texture features were defined by algorithms applied to digital images of eggs from the bovine parasitic nematodes, Ostertagia ostertagi, Cooperia oncophora, and Oesophagostotnum radiatum. The resulting data served to establish classification criteria by linear discrimination analysis, and the criteria were subsequently evaluated by crossvalidations. From 25 texture features, ten features were selected by their significant discriminatory powers. Using a classification criterion based on these ten texture features, an average of 91.2% of eggs from the three species were correctly classified. All O. radiatum eggs were correctly classified, 11.8% of O. ostertagi and C. oncophora were reciprocally misclassified, and 2.9% of O. ostertagi were identified as O. radiatum. When the ten texture features were used singly an average of 51.2 to 37.9% of the species could be classified correctly. When texture was used together with the shape and size features, a higher percentage of eggs were correctly classified compared with the classification based on either texture, or shape and size. Hence, all O. radiatum were correctly classified as well as 88.3% of O. ostertagi and 91.2% of C. oncophora, resulting in an average of 93.1% correctly classified eggs. The rapid and accurate measurements of texture features may serve as a basis for identification or enhance performance of classification criteria based on egg shape/size.
Localization of Copy-Move Forgery in Digital Images through Differential Excitation Texture Features
