Why my photos look sideways or upside down? Detecting Canonical Orientation of Images using Convolutional Neural Networks

Image orientation detection requires high-level scene understanding. Humans use object recognition and contextual scene information to correctly orient images. In literature, the problem of image orientation detection is mostly confronted by using low-level vision features, while some approaches incorporate few easily detectable semantic cues to gain minor improvements. The vast amount of semantic content in images makes orientation detection challenging, and therefore there is a large semantic gap between existing methods and human behavior. Also, existing methods in literature report highly discrepant detection rates, which is mainly due to large differences in datasets and limited variety of test images used for evaluation. In this work, for the first time, we leverage the power of deep learning and adapt pre-trained convolutional neural networks using largest training dataset to-date for the image orientation detection task. An extensive evaluation of our model on different public datasets shows that it remarkably generalizes to correctly orient a large set of unconstrained images; it also significantly outperforms the state-of-the-art and achieves accuracy very close to that of humans

Why my photos look sideways or upside down? Detecting Canonical Orientation of Images using Convolutional Neural Networks

Image orientation detection requires high-level scene understanding. Humans use object recognition and contextual scene information to correctly orient images. In literature, the problem of image orientation detection is mostly confronted by using low-level vision features, while some approaches incorporate few easily detectable semantic cues to gain minor improvements. The vast amount of semantic content in images makes orientation detection challenging, and therefore there is a large semantic gap between existing methods and human behavior. Also, existing methods in literature report highly discrepant detection rates, which is mainly due to large differences in datasets and limited variety of test images used for evaluation. In this work, for the first time, we leverage the power of deep learning and adapt pre-trained convolutional neural networks using largest training dataset to-date for the image orientation detection task. An extensive evaluation of our model on different public datasets shows that it remarkably generalizes to correctly orient a large set of unconstrained images; it also significantly outperforms the state-of-the-art and achieves accuracy very close to that of humans

Automatic Image Orientation Determination with Natural Image Statistics

In this paper, we propose a new method for automatically determining image orientations. This method is based on a set of natural image statistics collected from a multi-scale multi-orientation image decomposition (e.g., wavelets). From these statistics, a two-stage hierarchal classification with multiple binary SVM classifiers is employed to de- termine image orientation. The proposed method is evaluated and compared to existing methods with experiments performed on 18040 natural images, where it showed promising performance

Captcha test

Tato diplomová práce pojednává o problematice CAPTCHA testů, jež nejsou založeny na rozpoznávání zdeformovaného textu. Cílem je provést rešerši těchto testů a na jejím základě navrhnout a implementovat nový CAPTCHA test. Výstupem práce jsou dva CAPTCHA testy, z nichž jeden je veřejně dostupný přes API pro použití v internetových aplikacích třetích stran.This diploma thesis discuss CAPTCHA tests which are not based on deformed text recognition. Purpose is to propose and implement new CAPTCHA test based on a research of tests mentioned above. As a result there are two CAPTCHA tests, one of them publicly accessible through API for use in third party internet applications.Katedra softwarových technologiíDiplomová práce je zaměřena na problematiku rozlišení člověka od počítače. Cílem práce bylo vytvořit inovativní CAPTCHA test, který nebude založen na konvenčním způsobu ověření člověka na základě rozpoznání zdeformovaného textu. Dle vedoucího je práce zpracována na vysoké úrovni, po obsahové stránce je vyvážená a jednotlivé kapitoly jsou vhodně členěny na pododdíly s přiměřenou mírou detailu. Dle oponenta lze vyzdvihnout u diplomové práce, že student zvolil originální téma, při jehož realizaci využil mnoho technologií (Phyton, Django, OpenCV,....) jež jdou nad rámec výuky v magisterském studiu. Práce byla zpracována na vynikající úrovni, proto komise navrhuje diplomovou práci "Captcha test" na ocenění "Cena rektora 2. stupně"