Computer Engineering / Bilgisayar Mühendisliği
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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.Conference Object Citation - WoS: 7Citation - Scopus: 6Semantic Pose Verification for Outdoor Visual Localization With Self-Supervised Contrastive Learning(IEEE, 2022) Guerrero, Jose J.; Orhan, Semih; Baştanlar, YalınAny city-scale visual localization system has to overcome long-term appearance changes, such as varying illumination conditions or seasonal changes between query and database images. Since semantic content is more robust to such changes, we exploit semantic information to improve visual localization. In our scenario, the database consists of gnomonic views generated from panoramic images (e.g. Google Street View) and query images are collected with a standard field-of-view camera at a different time. To improve localization, we check the semantic similarity between query and database images, which is not trivial since the position and viewpoint of the cameras do not exactly match. To learn similarity, we propose training a CNN in a self-supervised fashion with contrastive learning on a dataset of semantically segmented images. With experiments we showed that this semantic similarity estimation approach works better than measuring the similarity at pixel-level. Finally, we used the semantic similarity scores to verify the retrievals obtained by a state-of-the-art visual localization method and observed that contrastive learning-based pose verification increases top-1 recall value to 0.90 which corresponds to a 2% improvement.Conference Object Zamanda ortalaması alınmış ikili önplan imgeleri kullanarak taşıt sınıflandırması(IEEE, 2015) Karaimer, Hakkı Can; Baştanlar, YalınWe describe a shape-based method for classification of vehicles from omnidirectional videos. Different from similar approaches, the binary images of vehicles obtained by background subtraction in a sequence of frames are averaged over time. We show with experiments that using the average shape of the object results in a more accurate classification than using a single frame. The vehicle types we classify are motorcycle, car and van. We created an omnidirectional video dataset and repeated experiments with shuffled train-test sets to ensure randomization.Conference Object Citation - WoS: 2Citation - Scopus: 8İnsansız Araçlar için Anlamsal Bölütleme ile İmge Tabanlı Konumlandırma(Institute of Electrical and Electronics Engineers Inc., 2019) Çınaroğlu, İbrahim; Baştanlar, YalınBilgisayarlı Görü alanındaki popülerliğini koruyan araştırma konularından birisi insansız araçlarda yer tespiti ve konumlandırmadır. Araçların konumlandırılmasında kullanılan GPS sistemlerinin bazı durumlarda faal olamadığı bilinen bir gerçektir ve bu yetersizlik imge tabanlı konumlandırma çalışmalarına hız vermiştir. Bizim çalışmamızda, araç içinden elde edilmiş Malaga şehir merkezi görüntülerinden oluşan bir veri tabanı kullanılarak imge tabanlı konumlandırma yapılmıştır. İlk olarak, anlamsal (semantik) bölütleme sonucunda elde edilen bir anlamsal betimleyici oluşturulmuş ve yaklaşık en yakın komşuluk araması tekniği de kullanılarak bir konumlandırma yapılmıştır. Ardından bu yöntemin başarısı, literatürde sıkça kullanılan yerel betimleyici tabanlı yöntemin başarısıyla kıyaslanmıştır. Ayrıca, bu iki yöntemin birleştirilmesi ile elde edilen melez bir yöntem önerilmiştir. Önerilen melez imge-tabanlı konumlandırmanın, sadece yerel betimleyici ve sadece anlamsal betimleyici kullanan yöntemden daha başarılı olduğu, dolayısıyla yerel betimleyici tabanlı yöntemlerin anlamsal betimleyiciler ile desteklenmesinin başarıyı artırdığı, deneysel sonuçlarla gösterilmiştir.Conference Object Citation - WoS: 4Citation - Scopus: 13Classification and Tracking of Traffic Scene Objects With Hybrid Camera Systems(Institute of Electrical and Electronics Engineers Inc., 2018) Barış, İpek; Baştanlar, YalınIn a hybrid camera system combining an omnidirectional and a Pan-Tilt-Zoom (PTZ) camera, the omnidirectional camera provides 360 degree horizontal field-of-view, whereas the PTZ camera provides high resolution at a certain direction. This results in a wide field-of-view and high resolution camera system. In this paper, we exploit this hybrid system for real-time object classification and tracking for traffic scenes. The omnidirectional camera detects the moving objects and performs an initial classification using shape-based features. Concurrently, the PTZ camera classifies the objects using high resolution frames and Histogram of Oriented Gradients (HOG) features. PTZ camera also performs high-resolution tracking for the objects classified as the target class by the omnidirectional camera. The object types we worked on are pedestrian, motorcycle, car and van. Extensive experiments were conducted to compare the classification accuracy of the hybrid system with single camera alternatives.Conference Object Tümyönlü ve Ptz Kameralar ile Taşıt Sınıflandırması(Institute of Electrical and Electronics Engineers Inc., 2016) Barış, İpek; Baştanlar, YalınÇalışmamızda trafik sahneleri üzerindeki araçların tespit edilip sınıflandırması için bir tümyönlü bir de PTZ (pantilt-zoom) kamera kullanan bir yöntem önerilmiştir. Önerilen yöntem, tümyönlü kamerada arkaplan çıkarımı sonrası saptanan nesnenin konumuna göre PTZ kamerayı uygun açıya yönlendirmekte ve PTZ kamerada yapılan ikincil tespit sonrası çıkarılan öznitelikler ile araç sınıflandırılmaktadır. Sınıflandırma başarısı ayrıca sadece tümyönlü kamerada yapılan sınıflandırma ile karşılaştırılmıştır. Üzerine çalışılan nesne tipleri motorsiklet, araba, dolmuş ve yayadır.Book Part Citation - WoS: 299Citation - Scopus: 406Introduction To Machine Learning(Humana Press, 2014) Baştanlar, Yalın; Özuysal, MustafaThe machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.Article Reduced egomotion estimation drift using omnidirectional views(Centre de Visio per Computador, 2014) Baştanlar, YalınEstimation of camera motion from a given image sequence is a common task for multi-view 3D computer vision applications. Salient features (lines, corners etc.) in the images are used to estimate the motion of the camera, also called egomotion. This estimation suffers from an error built-up as the length of the image sequence increases and this causes a drift in the estimated position. In this letter, this phenomenon is demonstrated and an approach to improve the estimation accuracy is proposed. The main idea of the proposed method is using an omnidirectional camera (360° horizontal field of view) in addition to a conventional (perspective) camera. Taking advantage of the correspondences between the omnidirectional and perspective images, the accuracy of camera position estimates can be improved. In our work, we adopt the sequential structure-from-motion approach which starts with estimating the motion between first two views and more views are added one by one. We automatically match points between omnidirectional and perspective views. Point correspondences are used for the estimation of epipolar geometry, followed by the reconstruction of 3D points with iterative linear triangulation. In addition, we calibrate our cameras using sphere camera model which covers both omnidirectional and perspective cameras. This enables us to treat the cameras in the same way at any step of structure-from-motion. We performed simulated and real image experiments to compare the estimation accuracy when only perspective views are used and when an omnidirectional view is added. Results show that the proposed idea of adding omnidirectional views reduces the drift in egomotion estimation.Conference Object Citation - WoS: 16Citation - Scopus: 18Combining Shape-Based and Gradient-Based Classifiers for Vehicle Classification(Institute of Electrical and Electronics Engineers Inc., 2015) Karaimer, Hakkı Can; Çınaroğlu, İbrahim; Baştanlar, YalınIn this paper, we present our work on vehicle classification with omnidirectional cameras. In particular, we investigate whether the combined use of shape-based and gradient-based classifiers outperforms the individual classifiers or not. For shape-based classification, we extract features from the silhouettes in the omnidirectional video frames, which are obtained after background subtraction. Classification is performed with kNN (k Nearest Neighbors) method, which has been commonly used in shape-based vehicle classification studies in the past. For gradient-based classification, we employ HOG (Histogram of Oriented Gradients) features. Instead of searching a whole video frame, we extract the features in the region located by the foreground silhouette. We use SVM (Support Vector Machines) as the classifier since HOG+SVM is a commonly used pair in visual object detection. The vehicle types that we worked on are motorcycle, car and van (minibus). In experiments, we first analyze the performances of shape-based and HOG-based classifiers separately. Then, we analyze the performance of the combined classifier where the two classifiers are fused at decision level. Results show that the combined classifier is superior to the individual classifiers. © 2015 IEEE.Conference Object Citation - Scopus: 5Detection and Classification of Vehicles From Omnidirectional Videos Using Temporal Average of Silhouettes(INSTICC, 2015) Karaimer, Hakkı Can; Baştanlar, YalınThis paper describes an approach to detect and classify vehicles in omnidirectional videos. The proposed classification method is based on the shape (silhouette) of the detected moving object obtained by background subtraction. Different from other shape based classification techniques, we exploit the information available in multiple frames of the video. The silhouettes extracted from a sequence of frames are combined to create an 'average' silhouette. This approach eliminates most of the wrong decisions which are caused by a poorly extracted silhouette from a single video frame. The vehicle types that we worked on are motorcycle, car (sedan) and van (minibus). The features extracted from the silhouettes are convexity, elongation, rectangularity, and Hu moments. The decision boundaries in the feature space are determined using a training set, whereas the performance of the proposed classification is measured with a test set. To ensure randomization, the procedure is repeated with the whole dataset split differently into training and testing samples. The results indicate that the proposed method of using average silhouettes performs better than using the silhouettes in a single frame.