Elektrik Elektronik Mühendisliği Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/411

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A Hybrid Framework for Matching Printing Design Files to Product Photos
(2020) Akagunduz, Erdem; Kaplan, Alper; 233834
We propose a real-time image matching framework, which is hybrid in the sense that it uses both hand - crafted features and deep features obtained from a well -tuned deep convolutional network. The matching problem, which we concentrate on, is specific to a certain application, that is, printing design to product photo matching. Printing designs are any kind of template image files, created using a design tool, thus are perfect image signals. For this purpose, we create an image set that includes printing design and corresponding product photo pairs with collaboration of an actual printing facility. Using this image set, we benchmark various hand-crafted (SIFT, SURF, GIST, HoG) and deep features for matching performance. Various segmentation algorithms including deep learning based segmentation methods are applied to select feature regions. Results show that SIFT features selected from deep segmented regions achieves up to 96% product photo to design file matching success in our dataset. We propose a framework in which deep learning is utilized with highest contribution, but without disabling real-time operation using an ordinary desktop computer.
Comparison of Single Channel Indices for U-Net Based Segmentation of Vegetation in Satellite Images
(SPIE, 2020) Ülkü, İrem; Barmpoutis, P.; Stathaki, T.; Akagündüz, Erdem; 233834
Hyper-spectral satellite imagery, consisting of multiple visible or infrared bands, is extremely dense and weighty for deep operations. Regarding problems related to vegetation as, more specifically, tree segmentation, it is difficult to train deep architectures due to lack of large-scale satellite imagery. In this paper, we compare the success of different single channel indices, which are constructed from multiple bands, for the purpose of tree segmentation in a deep convolutional neural network (CNN) architecture. The utilized indices are either hand-crafted such as excess green index (ExG) and normalized difference vegetation index (NDVI) or reconstructed from the visible bands using feature space transformation methods such as principle component analysis (PCA). For comparison, these features are fed to an identical CNN architecture, which is a standard U-Net-based symmetric encoder-decoder design with hierarchical skip connections and the segmentation success for each single index is recorded. Experimental results show that single bands, which are constructed from the vegetation indices and space transformations, can achieve similar segmentation performances as compared to that of the original multi-channel case
Citation - WoS: 5
Citation - Scopus: 7
Comparison of single channel indices for U-Net based segmentation of vegetation in satellite images
(Spie-int Soc Optical Engineering, 2020) Ulku, Irem; Barmpoutis, Panagiotis; Stathaki, Tania; Akagunduz, Erdem; 233834
Hyper-spectral satellite imagery, consisting of multiple visible or infrared bands, is extremely dense and weighty for deep operations. Regarding problems related to vegetation as, more specifically, tree segmentation, it is difficult to train deep architectures due to lack of large-scale satellite imagery. In this paper, we compare the success of different single channel indices, which are constructed from multiple bands, for the purpose of tree segmentation in a deep convolutional neural network ( CNN) architecture. The utilized indices are either hand-crafted such as excess green index (ExG) and normalized difference vegetation index (NDVI) or reconstructed from the visible bands using feature space transformation methods such as principle component analysis (PCA). For comparison, these features are fed to an identical CNN architecture, which is a standard U-Net-based symmetric encoder-decoder design with hierarchical skip connections and the segmentation success for each single index is recorded. Experimental results show that single bands, which are constructed from the vegetation indices and space transformations, can achieve similar segmentation performances as compared to that of the original multi-channel case.
Defining Image Memorability Using the Visual Memory Schema
(2020) Akagündüz, Erdem; Bors, Adrian G.; Evans, Karla K.; 233834
Memorability of an image is a characteristic determined by the human observers' ability to remember images they have seen. Yet recent work on image memorability defines it as an intrinsic property that can be obtained independent of the observer. The current study aims to enhance our understanding and prediction of image memorability, improving upon existing approaches by incorporating the properties of cumulative human annotations. We propose a new concept called the Visual Memory Schema (VMS) referring to an organization of image components human observers share when encoding and recognizing images. The concept of VMS is operationalised by asking human observers to define memorable regions of images they were asked to remember during an episodic memory test. We then statistically assess the consistency of VMSs across observers for either correctly or incorrectly recognised images. The associations of the VMSs with eye fixations and saliency are analysed separately as well. Lastly, we adapt various deep learning architectures for the reconstruction and prediction of memorable regions in images and analyse the results when using transfer learning at the outputs of different convolutional network layers.
Citation - WoS: 3
Citation - Scopus: 3
Filter design for small target detection on infrared imagery using normalized-cross-correlation layer
(Tubitak Scientific & Technological Research Council Turkey, 2020) Demir, H. Seckin; Akagunduz, Erdem; 233834
In this paper, we introduce a machine learning approach to the problem of infrared small target detection filter design. For this purpose, similar to a convolutional layer of a neural network, the normalized-cross-correlational (NCC) layer, which we utilize for designing a target detection/recognition filter bank, is proposed. By employing the NCC layer in a neural network structure, we introduce a framework, in which supervised training is used to calculate the optimal filter shape and the optimum number of filters required for a specific target detection/recognition task on infrared images. We also propose the mean-absolute-deviation NCC (MAD-NCC) layer, an efficient implementation of the proposed NCC layer, designed especially for FPGA systems, in which square root operations are avoided for real-time computation. As a case study we work on dim-target detection on midwave infrared imagery and obtain the filters that can discriminate a dim target from various types of background clutter, specific to our operational concept.

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