Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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Conference Object Citation - Scopus: 1Monocular Vision-Based Prediction of Cut-In Manoeuvres With Lstm Networks(Springer, 2023) Nalçakan, Yağız; Baştanlar, YalınAdvanced driver assistance and automated driving systems should be capable of predicting and avoiding dangerous situations. In this paper, we first discuss the importance of predicting dangerous lane changes and provide its description as a machine learning problem. After summarizing the previous work, we propose a method to predict potentially dangerous lane changes (cut-ins) of the vehicles in front. We follow a computer vision-based approach that only employs a single in-vehicle RGB camera, and we classify the target vehicle’s maneuver based on the recent video frames. Our algorithm consists of a CNN-based vehicle detection and tracking step and an LSTM-based maneuver classification step. It is computationally efficient compared to other vision-based methods since it exploits a small number of features for the classification step rather than feeding CNNs with RGB frames. We evaluated our approach on a publicly available driving dataset and a lane change detection dataset. We obtained 0.9585 accuracy with the side-aware two-class (cut-in vs. lane-pass) classification model. Experiment results also reveal that our approach outperforms state-of-the-art approaches when used for lane change detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.Book Part Citation - Scopus: 2Dementia Detection With Deep Networks Using Multi-Modal Image Data(CRC Press, 2023) Yiğit, Altuğ; Işık, Zerrin; Baştanlar, YalınNeurodegenerative diseases give rise to irreversible neural damage in the brain. By the time it is diagnosed, the disease may have progressed. Although there is no complete treatment for many types of neurodegenerative diseases, by detecting the disease in its early stages, treatments can be applied to relieve some symptoms or prevent disease progression. Many invasive and non-invasive methods are employed for the diagnosis of dementia. Computer-assisted diagnostic systems make the diagnosis based on volumetric features (structural or functional) or some two-dimensional brain perspectives obtained from a single image modality. This chapter firstly introduces a broad review of multi-modal imaging approaches proposed for dementia diagnosis. Then it presents deep neural networks, which extract structural and functional features from multi-modal imaging data, are employed to diagnose Alzheimer’s and mild cognitive impairments. While MRI scans are safer than most types of scans and provide structural information about the human body, PET scans provide information about functional activities in the brain. Thus, the setup has been designed to make experiments using both MRI and FDG-PET scans. Performances of multi-modal models were compared with single-modal solutions. The multi-modal solution showed superiority over single-modals due to the advantage of focusing on assorted features. © 2023 selection and editorial matter, Jyotismita Chaki; individual chapters, the contributors.Article Citation - WoS: 43Citation - Scopus: 47Semantic Segmentation of Outdoor Panoramic Images(Springer, 2021) Orhan, Semih; Baştanlar, YalınOmnidirectional cameras are capable of providing 360. field-of-view in a single shot. This comprehensive view makes them preferable for many computer vision applications. An omnidirectional view is generally represented as a panoramic image with equirectangular projection, which suffers from distortions. Thus, standard camera approaches should be mathematically modified to be used effectively with panoramic images. In this work, we built a semantic segmentation CNN model that handles distortions in panoramic images using equirectangular convolutions. The proposed model, we call it UNet-equiconv, outperforms an equivalent CNN model with standard convolutions. To the best of our knowledge, ours is the first work on the semantic segmentation of real outdoor panoramic images. Experiment results reveal that using a distortion-aware CNN with equirectangular convolution increases the semantic segmentation performance (4% increase in mIoU). We also released a pixel-level annotated outdoor panoramic image dataset which can be used for various computer vision applications such as autonomous driving and visual localization. Source code of the project and the dataset were made available at the project page (https://github.com/semihorhan/semseg-outdoor-pano). © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.Article Citation - WoS: 3Citation - Scopus: 4Affordable person detection in omnidirectional cameras using radial integral channel features(Springer Verlag, 2019) Demiröz, Barış Evrim; Salah, Albert Ali; Baştanlar, Yalın; Akarun, LaleOmnidirectional cameras cover more ground than perspective cameras, at the expense of resolution. Their comprehensive field of view makes omnidirectional cameras appealing for security and ambient intelligence applications. Person detection is usually a core part of such applications. Conventional methods fail for omnidirectional images due to different image geometry and formation. In this study, we propose a method for person detection in omnidirectional images, which is based on the integral channel features approach. Features are extracted from various channels, such as LUV and gradient magnitude, and classified using boosted decision trees. Features are pixel sums inside annular sectors (doughnut slice shapes) contained by the detection window. We also propose a novel data structure called radial integral image that allows to calculate sums inside annular sectors efficiently. We have shown with experiments that our method outperforms the previous state of the art and uses significantly less computational resources.