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Mobilna aplikacija za razpoznavanje objektov iz več pogledov
ID Račič, Matej (Author), ID Kristan, Matej (Mentor) More about this mentor... This link opens in a new window

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MD5: 25CC52BD0EE150E4ACF7DF8655157346
PID: 20.500.12556/rul/b75e5f31-3811-4a72-8b45-94334931f2eb

Abstract
Diplomsko delo opisuje problem klasifikacije objektov na sliki s pomočjo mobilne aplikacije. Podrobneje smo preizkusili implementacijo Inception, ki z uporabo okolja Tensorflow \cite{tensor} omogoča enostavno spremljanje in optimizacijo učenega procesa klasifikatorja. Zajeli smo specialno zbirko za realistično objektivno analizo delovanja aplikacije. Predstavili smo dobre prakse v procesu učenja in vpliv parametrov, kot sta število iteracij in učnih primerov. Vso znanje smo uspešno uporabili in klasifikator prilagodili razpoznavanju na izbrani domeni za doseganje želenih rezultatov in uspešno klasifikacijo objektov iz različnih pogledov. S pomočjo okolja Android Studio smo tudi generirani razpoznavalnik prenesli na mobilno napravo.

Language:Slovenian
Keywords:mobilne naprave, razpoznavanje objektov, nevronske mreže
Work type:Bachelor thesis/paper
Organization:FRI - Faculty of Computer and Information Science
Year:2017
PID:20.500.12556/RUL-95928 This link opens in a new window
Publication date in RUL:25.09.2017
Views:1670
Downloads:428
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Secondary language

Language:English
Title:A mobile application for multiview object recognition
Abstract:
The thesis deals with the problem of multiview object recognition with a mobile application. To achieve this, we have used the neural network architecture called Inception, that uses the Tensorflow environment which enables simple modifications of the learning process. We used a specific collection of images, to help us objectively and realistically evaluate how well the object recognition works. We have presented good practices and we have applied that knowledge in the development of our object recognition. We have also examined various effects of different parameters such as number of iterations and training samples. We have combined it all and modified the model for recognising our own selected categories and maximising the results of the object recognition. Using Android Studio we have transferred the generated model to a mobile device.

Keywords:mobile devices, object recognition, neural networks

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