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Publication Metadata only ATMOSPHERIC TURBULENCE MITIGATION USING FEATURE BASED OPTICAL FLOW(IEEE, 2014) Caliskan, Tufan; Arica, Nafiz; Deniz Harp Okulu Komutanligi; Bahcesehir UniversityIn this paper, we propose a fast and effective feature based registration technique in removing the distortions caused by atmospheric turbulence. We utilize optical flow method and combine the advantages of previous approaches based on space-invariant deconvolution and lucky frame idea. After an optical flow based registration of degraded image sequence, a patch-wise multi-frame reconstruction technique is applied to fuse the registered images. Blind-deconvolution technique is implemented to deblur the fused image to obtain a single high quality image. The experiments performed on common datasets show that the proposed method produces higher quality images more efficiently than the available methods.Publication Metadata only Atmospheric Turbulence Mitigation Using Optical Flow(IEEE COMPUTER SOC, 2014) Caliskan, Tufan; Arica, Nafiz; Deniz Harp Okulu Komutanligi; Bahcesehir UniversityAtmospheric turbulence causes blurring and geometrical distortion in images acquired from a long distance. In this paper, we propose a fast and effective feature based registration technique in removing the distortions caused by atmospheric turbulence. We utilize optical flow method and combine the advantages of previous approaches based on space-invariant deconvolution and lucky frame idea. After an optical flow based registration of degraded image sequence, a patch-wise multi-frame reconstruction technique is applied to fuse the registered images. Finally, a blind-deconvolution technique is implemented to deblur the fused image to obtain a single high quality image. The experiments performed on common datasets show that the proposed method produces higher quality images more efficiently than the available methods.Publication Metadata only Automatically Inserting Ads into Images(IEEE, 2018) Citak, Erol; Eseoglu, Mustafa Furkan; Celik, Ibrahim Omer; Disanli, Onur; Kutluk, Sezer; Arica, Nafiz; Huawei Technologies; Bahcesehir UniversityIn this study a new method is proposed for inserting advertisement visuals into images automatically and without disturbing the image content. In this method important areas are determined using deep learning based object, face and text detection, edge and saliency maps are obtained, and these information are used for the identification of the best location for inserting the advertisement visual. In order to select the best available advertisement visual from an advertisement pool shape and color features are utilized.Publication Metadata only Hierarchical Image Representation Using Deep Network(SPRINGER INTERNATIONAL PUBLISHING AG, 2015) Ergul, Emrah; Erturk, Sarp; Arica, Nafiz; Murino, V; Puppo, E; Kocaeli University; Bahcesehir UniversityIn this paper, we propose a new method for features learning from unlabeled data. Basically, we simulate k-means algorithm in deep network architecture to achieve hierarchical Bag-of-Words (BoW) representations. We first learn visual words in each layer which are used to produce BoW feature vectors in the current input space. We transform the raw input data into new feature spaces in a convolutional manner such that more abstract visual words are extracted at each layer by implementing Expectation-Maximization (EM) algorithm. The network parameters are optimized as we keep the visual words fixed in the Expectation step while the visual words are updated with the current parameters of the network in the Maximization step. Besides, we embed spatial information into BoW representation by learning different networks and visual words for each quadrant regions. We compare the proposed algorithm with the similar approaches in the literature using a challenging 10-class-dataset, CIFAR-10.Publication Metadata only Risk Classification For Breast Cancer Diagnosis Using HER2 Testing(IEEE, 2016) Aktan, Pinar Ece; Hatipoglu, Gokhan; Arica, Nafiz; Bahcesehir UniversityTrastuzumab is one of the effective treatment for breast cancer. It is an antibody against the protein product of the human epidermal growth factor receptor type 2 (HER2) gene. In HER-2 testing protein levels are determined by IHC staining. For a proper treatment IHC stains are evaluated together with ISH stains. The widespread approach done by the experts is to determine membranes manually, yet it leads subjective interpretation by pathologist and it increases their workload. In this paper, we propose a new method for HER-2 testing based on image analysis algorithms on membrane regions. After, maximally stable extremal regions algorithm is employed for detecting the membrane regions, these areas of those regions are analyzed quantitatively for IHC classification. In the next step, complete/incomplete membrane regions are determined for IHC 2+ classification. The experiments conducted on the IHC stains taken from the clinic patients. Analysis resulted with the successful classification rates.Publication Metadata only Object Tracking by Combining Tracking-by-Detection and Marginal Particle Filter(IEEE, 2016) Maras, Bahri; Arica, Nafiz; Ertuzun, Aysin Baytan; Bogazici University; Bahcesehir UniversityIn this paper, we propose a new algorithm based on tracking-by-detection approach for tracking of the objects having non-linear dynamic motion. For this purpose, the tracking-by-detection method depending on the Gauss kernel using the circulant matrix theory and the Fourier transform is employed together with the marginal particle filter method. Marginal particle filter uses the scores derived from the Gauss kernel at the measurement update phase of the filter to weight the particles propagated around the target that has been tracked. While updating the state variables by marginal particle filter, the object coordinates, the correction values belonging to these coordinates and the dimensions of the image window surrounding the object is estimated. The proposed method is tested on the video sequences which include the object having a high non-linear motion, and it was observed that marginal particle filter enhanced the performance of the track-by-detection method in an important scale.Publication Metadata only Facial Emotion Classification Using Deep Embedding with Triplet Loss Function(IEEE, 2017) Bircanoglu, Cenk; Arica, Nafiz; Bahcesehir UniversityIn this paper, a deep embedding method using triplet loss function is proposed for classification of the emotions in face images. The originality of proposed method lies in the loss function, different from the other deep learning-based facial emotion classification approaches. The input face images are embedded into a lower dimensional feature space using a multi layer convolutional neural network. In this embedding process the loss function is calculated by taking triple samples in the training dataset. For each sample in batches, two samples are selected in such a way that one of them is from the same class and the other one from the different class. The loss function aims to close the samples belonging to the same class to each other in the Euclidean space and to move away from the samples in the different class. In the performance analysis of proposed method, two popular deep architectures, namely AlexNet and VGG are used on two datasets called CIFE and GaMo. The experiments show that the proposed method outperforms the other popular loss functions and the available studies on this particular datasets.Publication Metadata only Auto-encoder based Cognitive Analysis of Questionnary(IEEE, 2018) Akay, Simge; Korkmaz, Hande; Arica, Nafiz; Bahcesehir University; Bahcesehir UniversityIn this study we propose a new method for Likert scale questionnary data analysis using auto-encoders. The proposed method extracts the patterns, which maximally activate the neurons of the hidden layer in the auto-encoder, trained by the questionnary data. These patterns are thought to be cognitive patterns that are influenced by participants filling the questionnary. In the experiments, we employ a questionnary designed to measure the confidence level of a blog author on the web. The cognitive patterns obtained in the auto-encoder are considered as the components that form the general approaches of the participants. In two cognitive patterns drawn from the questionnary, it is observed that the blogger's expertise, integrity, benevolance are evaluated in decreasing or increasing order by the participants. It has also been observed that the proposed method can be used to correct the unintentional mistakes in questionnary answers.Publication Metadata only Face Frontalization Enhanced by Deep Learning(IEEE, 2017) Celik, Anil; Arica, Nafiz; Bahcesehir UniversityIn this study, a new approach based on 3-D models and deep learning to frontalize face images is proposed. Specifically designed for facial expression analysis, the proposed approach aims to reduce possible negative effects that a posed face image can generate, by normalizing the face region. In the first phase, the face image is semi-frontalized, with a pre-established 3-D reference model based approach. Then, missing regions on semi-frontalized images due to geometric transformation are reconstructed with the help of a denoising stacked autoencoder network. In this phase, missing regions created by line of sight are learned, with a deep architecture, using numerous images. When examined, it can be said that, faces acquired with the proposed approach, are objectively better than the faces acquired with a deep learning or 3-D based method alone. Therefore, it is assumed that the proposed approach can be used in face based computer vision methods as a beneficial pre-processing step.Publication Metadata only AN HIERARCHICAL APPROACH FOR HUMAN COMPUTER INTERACTION USING EYELID MOVEMENTS(IEEE, 2014) Celik, Anil; Arica, Nafiz; Bahcesehir University; Bahcesehir UniversityThis work proposes a method to achieve real-time Human-Computer interaction with the movements of the eyelids in low-resolution video. Classification of left and right eye states as closed or open are performed by an hierarchical approach of tracking by detection. After the initial detection of the face area, an efficient face tracking algorithm is used to reduce the search space for detection of eye region. By seperating the eye region into two overlapping pieces, left and right eyes are detected and classified as closed or open. The proposed method is robust against mimics and multiple faces in the frame, while being unaffected by the negative effects of aliasing and resizing. Thus, people who suffer from medical conditions limiting their physical movement capabilities can interact with computers with their eyelid movements.