Browsing by Author "Tasdemir, Sena Busra Yengec"

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Evaluation of Dominant and Non-Dominant Hand Movements For Volleyball Action Modelling
(ASSOC COMPUTING MACHINERY, 1515 BROADWAY, NEW YORK, NY 10036-9998 USA, 2019) Haider, Fasih; Salim, Fahim A.; Tasdemir, Sena Busra Yengec; Naghashi, Vahid; Tengiz, Izem; Cengiz, Kubra; Postma, Dees B. W.; van Delden, Robby; Reidsma, Dennis; van Beijnum, Bert-Jan; Luz, Saturnino; 0000-0002-5150-3359; 0000-0002-7503-573X; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
In this paper, we assess the use of Inertial Measurement Units (IMU) in recognising different volleyball-specific actions. Analysis of the results suggests that all sensors in the IMU (i.e. magnetometer, accelerometer, barometer and gyroscope) contribute unique information in the classification of volleyball-specific actions. We demonstrate that while the accelerometer feature set provides the best Unweighted Average Recall (UAR) overall, "decision fusion" of the accelerometer with the magnetometer improves UAR slightly from 85.86% to 86.9%. Interestingly, it is also demonstrated that the non-dominant hand provides better UAR than the dominant hand. These results are even more marked with "decision fusion".
ROI Detection in Mammogram Images using Wavelet-Based Haralick and HOG Features
(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2018) Tasdemir, Sena Busra Yengec; Tasdemir, Kasim; Aydin, Zafer; 0000-0003-4542-2728; AGÜ, Mühendislik Fakültesi, Mühendislik Bilimleri Bölümü
Digital mammography is a widespread medical imaging technique that is used for early detection and diagnosis of breast cancer. Detecting the region of interest (ROI) helps to locate the abnormal areas, which may be analyzed further by a radiologist or a CAD system. In this paper, a new classification method is proposed for ROI detection in mammography images. Features are extracted using Wavelet transform, Haralick and HOG descriptors. To reduce the number of dimensions and eliminate irrelevant features, a wrapper-based feature selection method is implemented. Several feature extraction methods and machine learning classifiers are compared by performing a leave-one-image-out cross-validation experiment on a difficult dataset. The proposed feature extraction method provides the best accuracy of 87.5% and the second-best area under curve (AUC) score of 84% when employed in a random forest classifier.
A Searching and Automatic Video Tagging Tool for Events of Interest during Volleyball Training Sessions
(ASSOC COMPUTING MACHINERY, 1515 BROADWAY, NEW YORK, NY 10036-9998 USA, 2019) Salim, Fahim A; Postma, Dees B. W.; van Delden, Robby; Reidsma, Dennis; van Beijnum, Bert-Jan; Haider, Fasih; Luz, Saturnino; Tasdemir, Sena Busra Yengec; Naghashi, Vahid; Tengiz, Izem; Cengiz, Kubra; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
Quick and easy access to performance data during matches and training sessions is important for both players and coaches. While there are many video tagging systems available, these systems require manual effort. This paper proposes a system architecture that automatically supplements video recording by detecting events of interests in volleyball matches and training sessions to provide tailored and interactive multi modal feedback.