Yazar "Albayrak, Songul" seçeneğine göre listele

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    Classification with Emotional Faces via a Robust Sparse Classifier
    (IEEE, 2012) Sonmez, Elena Battini; Sankur, Bulent; Albayrak, Songul
    We consider the problem of emotion recognition in faces as well as subject identification in the presence of emotional facial expressions. We propose alternative solutions for this identification and recognition problems using the idea of sparsity, in terms of Sparse Representation based Classifier (SRC) paradigm. In both cases, the problem is formulated as finding the most parsimonious set of representatives from a training set, which will best reconstruct the test image. For emotion classification, we considered the six fundamental states and the SRC performance was compared with that of the Active Appearance Model (AAM) algorithm [1]. For face recognition displaying various emotions, in order to test the robustness of SRC, we considered gallery faces of subjects having one or more expression variety while the probe faces had a different expression. We experimented with both the whole faces or faces observed with multiple blocks. The SRC algorithm, while not demanding any training, performed surprisingly well in both emotion identification across subjects and subject identification across emotions.
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    Critical parameters of the sparse representation-based classifier
    (Inst Engineering Technology-Iet, 2013) Sonmez, Elena Battini; Albayrak, Songul
    In recent years, the growing attention in the study of the compressive sensing (CS) theory suggested a novel classification algorithm called sparse representation-based classifier (SRC), which obtained promising results by casting classification as a sparse representation problem. Whereas SRC has been applied to different fields of applications and several variations of it have been proposed, less attention has been given to its critical parameters, that is, measurements correlated to its performance. This work underlines the differences between CS and SRC, it gives a mathematical definition of five measurements possible correlated to the performance of SRC and identifies three of them as critical parameters. The knowledge of the critical parameters is necessary to fuse multiple scores of SRC classifiers allowing for classification. The authors addressed the problem of two-dimensional face classification: using the Extended Yale B dataset to monitor the critical parameters and the Extended Cohn-Kanade database to test the robustness of SRC with emotional faces. Finally, the authors increased the initial performance of the holistic SRC with a block-based SRC, which uses one critical parameter for automatic selection of the most successful blocks.
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    Face Classification via Sparse Approximation
    (Springer-Verlag Berlin, 2011) Sonmez, Elena Battini; Sankur, Bulent; Albayrak, Songul
    We address the problem of 2D face classification under adverse conditions. Faces are difficult to recognize since they are highly variable due to such factors as illumination, expression, pose, occlusion and resolution. We investigate the potential of a method where the face recognition problem is cast as a sparse approximation. The sparse approximation provides a significant amount of robustness beneficial in mitigating various adverse effects. The study is conducted experimentally using the Extended Yale Face B database and the results are compared against the Fisher classifier benchmark.
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    Object Detection in Shelf Images with YOLO
    (IEEE, 2019) Melek, Ceren Gulra; Sonmez, Elena Battini; Albayrak, Songul
    Object detection in shelf images can solve many problems in retails sales such as monitoring the number of products on the shelves, completing the missing products and matching the planogram continuously. This study aims to detect object in shelf images with deep learning algorithms. Firstly, object detection algorithms and datasets are examined in the literature. Then, experimental study is performed using Coca Cola images obtained from Imagenet and Grocery dataset with YOLO (You Only Look Once) algorithm. Results of the study are discussed from different sides such as number of classes, threshold values and numder of iteration.
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    A Survey of Product Recognition in Shelf Images
    (IEEE, 2017) Melek, Ceren Gulra; Sonmez, Elena Battini; Albayrak, Songul
    Nowadays, merchandising is one of the significant method which allows to increase the sales. Therefore, activities such as monitoring the number of products on the shelves, completing the missing products and matching the planogram continuously have become important. An autonomous system is needed to automate operations such as product or brand recognition, stock tracking and planogram matching. In the literature, it is seen that many studies have been carried out in order to address this issue. This survey classifies and compares all existing works with the aim to guide researchers working on merchandising.