TR-Dizin İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8652
Browse
Browsing TR-Dizin İndeksli Yayınlar Koleksiyonu by Author "Akagunduz, Erdem"
Now showing 1 - 2 of 2
- Results Per Page
- Sort Options
Article Citation - WoS: 3Citation - Scopus: 3Filter Design for Small Target Detection on Infrared Imagery Using Normalized-Cross Layer(Tubitak Scientific & Technological Research Council Turkey, 2020) Demir, H. Seckin; Akagunduz, Erdem; 233834In 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.Article A Hybrid Framework for Matching Printing Design Files To Product Photos(2020) Akagunduz, Erdem; Kaplan, Alper; 233834We 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.
