Bildiri Koleksiyonu
https://hdl.handle.net/11421/19937
2024-03-28T11:09:36ZPatch warping based face frontalization [Yama çarpitma tabanli yüz önleştirme]
https://hdl.handle.net/11421/20788
Patch warping based face frontalization [Yama çarpitma tabanli yüz önleştirme]
Erdem, M. E.; Topal, C.
Face frontalization increases accuracies of face and gesture recognition applications. In this paper, we propose a 2D patch warping based face frontalization method which that has a simple but efficient flow due to its lower computation cost. We partition the human face into 23 nearly planar regions that are constituted by 68 landmark points to form a frontal face model and used for warping process. Planar places warped by using homography unlike other affine transform based methods. Warping rectangle regions with homography preserve global structure of face as well as it decreased the computational cost of frontalization as againts situations that work with a lot of triangular region like Delaunay triangulation. In order to test recognition performance, every test sample frontalized with respect to average face model computed as the average of all train samples. Test sets created by the pose angles of samples, tested separately to measure the contribution of proposed method to recognition and we compare the proposed method to another state of art frontalization method in literature
Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas; 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- -- 137780
2018-01-01T00:00:00ZReal-time detection and mitigation of distributed denial of service (DDoS) attacks in software defined networking (SDN)
https://hdl.handle.net/11421/20786
Real-time detection and mitigation of distributed denial of service (DDoS) attacks in software defined networking (SDN)
Lawal, Babatunde Hafis; At, Nuray
The emergence of Software Defined Network (SDN) and its promises in networking technology has gotten every stakeholder excited. However, it is believed that every technological development comes with its own challenges of which the most prominent in this case is security. This paper presents a real time detection of the distributed denial of service (DDoS) attacks on the SDN and a control method based on the sFlow mitigation technology. sFlow analyses samples of packets collected from the network traffic and generates handling rules to be sent to the controller in case of an attack detection. The implementation was done by emulating the network in Mininet which runs on a Virtual Machine (VM) and it was shown that the proposed method effectively detects and mitigates DDoS attacks
Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas; 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- -- 137780
2018-01-01T00:00:00ZLogo recognition via fusion of spatial and spectral features [Uzamsal ve spektral öznitelik birleşimi ile logo tanima]
https://hdl.handle.net/11421/20787
Logo recognition via fusion of spatial and spectral features [Uzamsal ve spektral öznitelik birleşimi ile logo tanima]
Shakir, S.; Gacav, Caner; Topal, C.
Machine vision based logo and trademark recognition, is one of the most efficient and widely used method to measure brand awareness on internet and social media. Similarity of logos geometric structure, difference pose and lighting conditions are the leading factors that makes the recognition task tedious. For this reason, different image descriptors have been used to extract the same information under various conditions. In this work, we examine fusion of image descriptors which obtained by extracting data from spectral and spatial domains independently. thereby features extracted from various domains targeted to form non-overlapping distinctive feature vectors. As spectral and spatial features we used GIST and FHOG descriptors. Experimental results held on the latest dataset Logos-32plus. Quantitative evaluation shows that our method have higher accuracy rates against the state of the art method
Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas; 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- -- 137780
2018-01-01T00:00:00ZDataset augmentation for accurate object detection [Yüksek dogrulukta nesne tespiti için veri kümesi artirma]
https://hdl.handle.net/11421/20785
Dataset augmentation for accurate object detection [Yüksek dogrulukta nesne tespiti için veri kümesi artirma]
Uysal, M. C.; Karapinar, T.; Benligiray, Burak; Topal, Cihan
Hand detection has many important applications in human-computer interaction. But hand detection is a difficult problem because hand image can vary greatly in images. Vision based hand interfaces require fast and extremely robust hand detection. Large data sets are needed in the process of creating classifiers to detect. This study proposes an alternative method for creating positive images that the classifier needs. This method, which is to be presented, is aimed at obtaining a large number of positive images autonomously from a certain number of hand images, instead of annotating positive images under human supervision. Therefore, less time have been spent and a wider set of data has been achieved
Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas; 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- -- 137780
2018-01-01T00:00:00Z