Yazar "Bastan, Muhammet" seçeneğine göre listele

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    Camera Aided Navigation for the Visually Impaired
    (Ieee, 2016) Isik, Kadir; Taskin, Erol; Unal, M. Fatih; Yilmaz, Ozgur; Bastan, Muhammet
    One of the most challenging problems a visually impaired person experiences is outdoor navigation. We present an Android application to be used by the visually impaired for GPS and compass based navigation, and a camera based visual navigation for accurate guidance which uses visual matching on previously saved visual landmarks. The system has an ergonomic and intuitive touch based user interface suitable for visually impaired.
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    HandVR: a hand-gesture-based interface to a video retrieval system
    (Springer London Ltd, 2015) Genc, Serkan; Bastan, Muhammet; Gudukbay, Ugur; Atalay, Volkan; Ulusoy, Ozgur
    Using one's hands in human-computer interaction increases both the effectiveness of computer usage and the speed of interaction. One way of accomplishing this goal is to utilize computer vision techniques to develop hand-gesture-based interfaces. A video database system is one application where a hand-gesture-based interface is useful, because it provides a way to specify certain queries more easily. We present a hand-gesture-based interface for a video database system to specify motion and spatiotemporal object queries. We use a regular, low-cost camera to monitor the movements and configurations of the user's hands and translate them to video queries. We conducted a user study to compare our gesture-based interface with a mouse-based interface on various types of video queries. The users evaluated the two interfaces in terms of different usability parameters, including the ease of learning, ease of use, ease of remembering (memory), naturalness, comfortable use, satisfaction, and enjoyment. The user study showed that querying video databases is a promising application area for hand-gesture-based interfaces, especially for queries involving motion and spatiotemporal relations.
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    Mobile Image Search Using Multi-Query Images
    (Ieee, 2015) Calisir, Fatih; Bastan, Muhammet; Gudukbay, Ugur; Ulusoy, Ozgur
    Recent advances in mobile device technology have turned the mobile phones into powerfull devices with high resolution cameras and fast processing capabilities. Having more user interaction potential compared to regular PCs, mobile devices with cameras can enable richer content-based object image queries: the user can capture multiple images of the query object from different viewing angles and at different scales, thereby providing much more information about the object to improve the retrieval accuracy. The goal of this paper is to improve the mobile image retrieval performance using multiple query images. To this end, we use the well-known bag-of-visual-words approach to represent the images, and employ early and late fusion strategies to utilize the information in multiple query images. With extensive experiments on an object image dataset with a single object per image, we show that multi-image queries result in higher average precision performance than single image queries.
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    Mobile multi-view object image search
    (Springer, 2017) Calisir, Fatih; Bastan, Muhammet; Ulusoy, Ozgur; Gudukbay, Ugur
    High user interaction capability of mobile devices can help improve the accuracy of mobile visual search systems. At query time, it is possible to capture multiple views of an object from different viewing angles and at different scales with the mobile device camera to obtain richer information about the object compared to a single view and hence return more accurate results. Motivated by this, we propose a new multi-view visual query model on multi-view object image databases for mobile visual search. Multi-view images of objects acquired by the mobile clients are processed and local features are sent to a server, which combines the query image representations with early/late fusion methods and returns the query results. We performed a comprehensive analysis of early and late fusion approaches using various similarity functions, on an existing single view and a new multi-view object image database. The experimental results show that multi-view search provides significantly better retrieval accuracy compared to traditional single view search.
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    Mobile Reader: Turkish Scene Text Reader for the Visually Impaired
    (Ieee, 2016) Kandemir, Hilal; Canturk, Busra; Bastan, Muhammet
    Reading text is one of the crucial needs of the visually impaired people. In this work, we developed a mobile system (Mobile Reader) that can read aloud Turkish scene and book text. Images acquired from the camera are processed in realtime to detect text regions, recognize the text and synthesize speech in Turkish. A fast gradient-based text detection algorithm is employed to enable real-time operation, and character recognition is achieved via the open source Tesseract OCR engine. The performance of the system (running time and accuracy) is measured on a scene text image dataset on a mobile device.
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    Multi-view object detection in dual-energy X-ray images
    (Springer, 2015) Bastan, Muhammet
    Automatic inspection of X-ray scans at security checkpoints can improve the public security. X-ray images are different from photographic images. They are transparent. They contain much less texture. They may be highly cluttered. Objects may undergo in- and out-of-plane rotations. On the other hand, scale and illumination change is less of an issue. More importantly, X-ray imaging provides extra information which are usually not available in regular images: dual-energy imaging, which provides material information about the objects; and multi-view imaging, which provides multiple images of objects from different viewing angles. Such peculiarities of X-ray images should be leveraged for high-performance object recognition systems to be deployed on X-ray scanners. To this end, we first present an extensive evaluation of standard local features for object detection on a large X-ray image dataset in a structured learning framework. Then, we propose two dense sampling methods as keypoint detector for textureless objects and extend the SPIN color descriptor to utilize the material information. Finally, we propose a multi-view branch-and-bound search algorithm for multi-view object detection. Through extensive experiments on three object categories, we show that object detection performance on X-ray images improves substantially with the help of extended features and multiple views.
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    Object Recognition in Multi-View Dual Energy X-ray Images
    (B M V A Press, 2013) Bastan, Muhammet; Byeon, Wonmin; Breuel, Thomas M.
    Object recognition in X-ray images is an interesting application of machine vision that can help reduce the workload of human operators of X-ray scanners at security checkpoints. In this paper, we first present a comprehensive evaluation of image classification and object detection in X-ray images using standard local features in a BoW framework with (structural) SVMs. Then, we extend the features to utilize the extra information available in dual energy X-ray images. Finally, we propose a multi-view branch-and-bound algorithm for multi-view object detection. Through extensive experiments on three object categories, we show that the classification and detection performance substantially improves with the extended features and multiple views.
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    Performance Analysis of a Software Developed with and without Design Patterns: A Case Study
    (Ieee, 2015) Kazan, Emre; Bastan, Muhammet; Canturk, Mehmet
    This study investigates performance difference between a new bandrol tracking system designed using Model-View-Controller, Model-View-Presenter and Proxy patterns and the bandrol tracking system which TRT (Turkish Radio Television Corporation) currently uses and which was designed without using the aforementioned design patterns. The study concludes that the use of design patterns increases the testability of software under development and also decreases the development effort.
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    Turkish OCR on Mobile and Scanned Document Images
    (Ieee, 2015) Karasu, Kurtulus; Bastan, Muhammet
    Optical character recognition (OCR) systems have been widely used to convert documents into digital form. There are lots of both commercial and open source OCR systems available, but a benchmark on Turkish OCR is nonexistent. In this work, we first prepared two publicly available datasets for Turkish OCR, consisting of scanned document images and mobile camera captured document images. Then, we evaluated the Turkish OCR performance of three popular open source OCR systems (Tesseract, CuneiForm, GOCR) on the datasets. Tesseract outperformed the other two on both datasets.