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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-10272020-142319


Tipo di tesi
Tesi di laurea magistrale
Autore
NUZZO, FEDERICO
URN
etd-10272020-142319
Titolo
KIDD: a CNN based Keypoint Detector and Descriptor
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
EMBEDDED COMPUTING SYSTEMS
Relatori
relatore Dott. Tecchia, Franco
Parole chiave
  • computer vision
  • convolutional neural networks
  • deep features
  • keypoint detection and description
  • machine learning
Data inizio appello
20/11/2020
Consultabilità
Tesi non consultabile
Riassunto
This Thesis investigates using convolutional neural networks as a keypoint detector and descriptor for Computer Vision tasks, and proposes an original identification scheme named KIDD. To realize the system, State of the Art solutions have been examined, a framework has been designed and developed, three different test datasets were created and different models were trained on each, all able to predict the existence of a keypoint in a certain patch. Non-maximal suppression was applied in order to retrieve a balanced number of keypoints, and deep features were extracted and used as descriptors for keypoint matching between similar images. All models have been evaluated on every other dataset, and tests regarding the number of found keypoints and their match have been analyzed for a certain number of image pairs and compared to existing STAR methods.
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