Automated salamander recognition using deep neural networks and feature extraction

This paper presents a study conducted to recognize salamanders by using their unique body markings based on images. The detection and matching of unique patterns in a salamander’s body can be complex due variability in individual animals size, shape, orientation and also influence from the external enviornment. While traditional methods require time intensive manual image corrections of the salamanders to achieve accurate recognition, in this work we propose a fully automatic techinque for straigthening. We also propose a matching technique based on the corrected images. The convolutional neural network ResNet50 and dense scale-invariant feature transform (DSIFT) are used for belly pattern localization, and matching for salamander recognition

Automated Salamander Recognition Using Deep Neural Networks and Feature Extraction

Hensikten med dette prosjektet er å utvikle et automatisert verktøy og metode som kan identifisere salamandere ved å bruke deres individspesifikke bukmønstre. Vi har undersøkt en rekke med forskjellige eksisterende metoder for og oppnå dette. Mens de tradisjonelle metodene krever tidskrevende manuel retting av salamandrene, foreslår vi et system som gjør dette helautomatisk. Vi undersøker en rekke forskjellige metoder for å oppnå dette. I tillegg oppnådde vi en god gjenkjenningsrate. Den endelige versjonen bruker det foldbare nevrale nettverket ResNet50 og kubisk kurve interpolasjon for bukmønster lokalisering, og sammenlikner bildenes tette scale-invariant feature transform (DSIFT) for gjenkjenning

Automated salamander recognition using deep neural networks and feature extraction

This paper presents a study conducted to recognize salamanders by using their unique body markings based on images. The detection and matching of unique patterns in a salamander’s body can be complex due variability in individual animals size, shape, orientation and also influence from the external enviornment. While traditional methods require time intensive manual image corrections of the salamanders to achieve accurate recognition, in this work we propose a fully automatic techinque for straigthening. We also propose a matching technique based on the corrected images. The convolutional neural network ResNet50 and dense scale-invariant feature transform (DSIFT) are used for belly pattern localization, and matching for salamander recognition