Browsing by Author "Choupani, Roya"

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Citation Count: Choupani, Roya; Wong, Stephan; Tolun, Mehmet R., "A drift-reduced scheme for hierarchical wavelet coding scalable video transmissions", 2009 First International Conference On Advances In Multimedia, (2009).
A drift-reduced scheme for hierarchical wavelet coding scalable video transmissions
(IEEE, 2009) Choupani, Roya; Wong, Stephan; Tolun, Mehmet R.
Scalable video coding allows for the capability of (partially) decoding a video bitstream when faced with communication deficiencies such as low handwidth or loss of data resulting in lower video quality. As the encoding is usually based on perfectly reconstructed frames, such deficiencies result in differently decoded frames at the decoder than the ones used in the encoder and, therefore, leading to errors being accumulated in the decoder. This is commonly referred to as the drift error. Drift-free scalable video coding methods also suffer from the low performance problem as they do not combine the residue encoding scheme of the current standards such as MPEG-4 and H.264 with scalability characteristics. We propose a scalable video coding method which is based on the motion compensation and residue encoding methods found in current video standards combined with the scalability property of discrete wavelet transform. Our proposed method aims to reduce the drift error while preserving the compression efficiency. Our results show that the drift error has been greatly reduced when a hierarchical structure for frame encoding is introduced.
A Robust Watermarking Scheme Over Quadrant Medical Image in Discrete Wavelet Transform Domain
(IEEE, 2018) Göker, Onur; Nazlı, Nazlı; Erol, Mehmet Murat; Choupani, Roya; 21259
The diffusion of digital content is very fast in today's technology. The velocity of gathering data might cause unlawful distribution of content. The major problem in content authorization is the robustness of methodology. Since frangible methodologies result unauthorized content access quicker, more robust solutions are essential for copyright protection. Watermarking technology is considered as a robust solution for copyright protection and authentication. In watermarking, quality of the image is a challenge. Applying a watermark on a medical image might cause corruption in original image, which leads to misleading content. The proposed copyright protection mechanism includes Quadtree algorithm which finds a region of non-interest to apply watermark on medical image in Discrete Wavelet Domain to provide authentication of the content without altering region of interest. Furthermore, in this paper, the visual quality of watermark implemented medical images and sample values are also discussed with the experimental results as well.
Citation Count: Choupani, Roya; Wong, Stephan; Tolun, Mehmet R. "Adaptive Embedded Zero Tree for Scalable Video Coding", World Congress On Engıneering, Wce 2011, Vol Iı, pp. 1567-1571, (2011).
Adaptive Embedded Zero Tree For Scalable Video Coding
(Int Assoc Engineers-Laeng, 2011) Choupani, Roya; Wong, Stephan; Tolun, Mehmet R.; 1863
Video streaming over the Internet has gained popularity during recent years mainly due to the revival of video-conferencing and video-telephony applications and the proliferation of (video) content providers. However, the heterogeneous, dynamic, and best-effort nature of the Internet cannot always guarantee a certain bandwidth for an application utilizing the Internet. Scalability has been introduced to deal with such issues (up to a certain point) by adapting the video quality with the available bandwidth. In addition, wavelet based scalability combined with representation methods such as embedded zero trees (EZWs) provides the possibility of reconstructing the video even when only the initial part of the streams have been received. EZW prioritizes the wavelet coefficients based on their energy content. Our experiments however, indicate that giving more priority to low frequency content improves the video quality at a specific bit rate. In this paper, we propose a method to improve on the compression rate of the EZW by prioritizing the coefficients by combining each frequency sub-band with its energy content. Initial experimental show that the first two layers of the generated EZW are about 22.6% more concise.
Citation Count: CHOUPANI, R. (2002). Client-server communication in remote control. Yayımlanmamış yüksek lisans tezi. Ankara: Çankaya Üniversitesi Fen Bilimleri Enstitüsü.
Client-server communication in remote control
(2002) Choupani, Roya
The client server communication model has been used in remote controlling of devices. The main feature in this study is that the Internet has been used as the common media to transmit controlling data and receive information. Client server model on the Internet restricts the access to client computers and has the disadvantage of unknown platform on the client side. This problem has been solved by means of platform independent programming and Java applets. Socket interface available in application layer of TCP/IP protocol suit has been used to establish reliable connection between clients and server. Security issues have been dealt with in the server side by checking the IP addresses of requesting clients.
Citation Count: Choupani, Roya. "Distributed Query Processing and Reasoning over Linked Big Data", IEEE International Conference on Semantic Computing 2020, 2020.
Distributed Query Processing and Reasoning over Linked Big Data
(2020) Choupani, Roya; 21259
Citation Count: Karasolak, Mustafa; Choupani, Roya (2019). "Face Photograph Recognition via Generation from Sketches using Convolutional Neural Networks", International Journal of Multimedia and Image Processing, Vol. 9, No. 1, pp. 459-465.
Face Photograph Recognition via Generation from Sketches using Convolutional Neural Networks
(2019) Karasolak, Mustafa; Choupani, Roya; 21259
Face photo-sketch matching is an important problem for law enforcement agencies in terms of identifying suspects. In this study, a new sketch-photo generation and recognition technique is proposed by using residual convolutional neural network architecture. The suggested RCNN architecture consists of 6 convolutions, 6 ReLU, 4 poolings, 2 deconvolution layers. The proposed architecture is trained with face photos and sketches. Sketches are supplied as an input to the RCNN architecture and, generated face photos are obtained as the output. Then, the generated face photos are compared with the photos of the people in the database. Structural Similarity Index (SSIM) is used to measure the pairwise similarity and the photo with the highest index score is matched. CUHK Face Sketch Database containing 188 images is tested. In the experiments, 148, 20, and 20 images are used for training, validation, and testing, respectively. Data augmentation applied to 148 training images produced 444 images. Experimental results show that the success of the training curve is 90.55% and the validation success is 91.1%. True face recognition success from generated face images with SSIM is 93.89% for CUHK Face Sketch database (CUFS) and 84.55% AR database.
Citation Count: Choupani, Roya; Tolun, Mehmet R. (2005). "Hand gesture recognition in variable length sequences", WSEAS Transactions on Information Science and Applications, Vol. 2, No. 9, pp. 1294-1301.
Hand gesture recognition in variable length sequences
(2005) Choupani, Roya; Tolun, Mehmet R.; 1863
Using hand gestures in human computer interaction has been a major challenge during the recent years. Many of the hand gesture recognition systems however, have been based on the recognition of hand postures and estimating the related gesture which is restricted to a few numbers of possible movements. However when dealing with applications such as understanding sign languages which include a large number of classes, an automatic learning method based on matching a sequence of postures with the characterizing feature sequence of each class is necessary. An important characteristic of this method is that each sample sequence of a class may have a variable length and different position of the key features. In this paper a syntactic method has been proposed for classifying the input sequences. An algorithm foe extracting the grammar of the method during training stage is also given.
Citation Count: Choupani, Roya; Wong, Stephan; Tolun, Mehmet. "Hierarchical SNR Scalable Video Coding with Adaptive Quantization for Reduced Drift Error",10th International Conference on Computer Vision Theory and Applications (VISAPP-2015), pp.117-123, 2015.
Hierarchical SNR Scalable Video Coding with Adaptive Quantization for Reduced Drift Error
(2015) Choupani, Roya; Wong, Stephan; Tolun, Mehmet
In video coding, dependencies between frames are being exploited to achieve compression by only coding the differences. This dependency can potentially lead to decoding inaccuracies when there is a communication error, or a deliberate quality reduction due to reduced network or receiver capabilities. The dependency can start at the reference frame and progress through a chain of dependent frames within a group of pictures (GOP) resulting in the so-called drift error. Scalable video coding schemes should deal with such drift errors while maximizing the delivered video quality. In this paper, we present a multi-layer hierarchical structure for scalable video coding capable of reducing the drift error. Moreover, we propose an optimization to adaptively determine the quantization step size for the base and enhancement layers. In addition, we address the trade-off between the drift error and the coding efficiency. The improvements in terms of average PSNR values when one frame in a GOP is lost are 3.70(dB) when only the base layer is delivered, and 4.78(dB) when both the base and the enhancement layers are delivered. The improvements in presence of burst errors are 3.52(dB) when only the base layer is delivered, and 4.50(dB) when both base and enhancement layers are delivered.
Citation Count: Choupani, Roya; Wong, Stephan; Tolun, Mehmet. "Hierarchical SNR Scalable Video Coding with Adaptive Quantization for Reduced Drift Error", 10th International Conference on Computer Vision Theory and Applications, pp. 117-123, 2015.
Hierarchical SNR Scalable Video Coding with Adaptive Quantization for Reduced Drift Error
(2015) Choupani, Roya; Wong, Stephan; Tolun, Mehmet
In video coding, dependencies between frames are being exploited to achieve compression by only coding the differences. This dependency can potentially lead to decoding inaccuracies when there is a communication error, or a deliberate quality reduction due to reduced network or receiver capabilities. The dependency can start at the reference frame and progress through a chain of dependent frames within a group of pictures (GOP) resulting in the so-called drift error. Scalable video coding schemes should deal with such drift errors while maximizing the delivered video quality. In this paper, we present a multi-layer hierarchical structure for scalable video coding capable of reducing the drift error. Moreover, we propose an optimization to adaptively determine the quantization step size for the base and enhancement layers. In addition, we address the trade-off between the drift error and the coding efficiency. The improvements in terms of average PSNR values when one frame in a GOP is lost are 3.70(dB) when only the base layer is delivered, and 4.78(dB) when both the base and the enhancement layers are delivered. The improvements in presence of burst errors are 3.52(dB) when only the base layer is delivered, and 4.50(dB) when both base and enhancement layers are delivered.
Citation Count: Bayrak, Betül; Choupani, Roya; Doğdu, Erdoğan (2020). "Link Prediction in Knowledge Graphs with Numeric Triples Using Clustering", Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, Virtual, Atlanta, 10 December 2020 through 13 December 2020, pp, 4492-4498.
Link Prediction in Knowledge Graphs with Numeric Triples Using Clustering
(2020) Bayrak, Betül; Choupani, Roya; Doğdu, Erdoğan
Knowledge graphs (KG) include large amounts of structured data in many different domains. Knowledge or information is captured by entities and relationships between them in KG. One of the open problems in knowledge graphs area is "link prediction", that is predicting new relationships or links between the given existing entities in KG. A recent approach in graph-based learning problems is "graph embedding", in which graphs are represented as low-dimensional vectors. Then, it is easier to make link predictions using these vector representations. We also use graph embedding for graph representations. A sub-problem of link prediction in KG is the link prediction in the presence of literal values, and specifically numeric values, on the receiving end of links. This is a harder problem because of the numeric literal values taking arbitrary values. For such entries link prediction models cannot work, because numeric entities are not embedded in the vector space. There are several studies in this area, but they are all complex approaches. In this study, we propose a novel approach for link prediction in KG in the presence of numerical values. To overcome the embedding problem of numeric values, we used a clustering approach for clustering these numerical values in a knowledge graph and then used the clusters for performing link prediction. Then we clustered the numerical values to enhance the prediction rates and evaluated our method on a part of Freebase knowledge graph, which includes entities, relations, and numerical literals. Test results show that a considerable increase in link prediction rate can be achieved in comparison to previous studies. © 2020 IEEE.
Citation Count: Mohanned, Hamza Haruna; Sürücü, Selim; Choupani, Roya. "Lung Inflammatory Classification of Diseases using X-ray Images", International Conference on Computer Science and Engineering (UBMK), 15-17 September 2021, Ankara.
Lung Inflammatory Classification of Diseases using X-ray Images
(2021) Mohanned, Hamza Haruna; Sürücü, Selim; Choupani, Roya
Recently, studies in inflammatory diseases categorization become of interest in the research community, especially with the sudden outbreak of the Covid-19 virus. Transfer learning proved to be the state-of-the-art when it comes to image classification problems, or related tasks. These methods achieve good results in this type of applications. Lately, this pre-trained embedding became even popular due to X-ray related studies for early Covid-19 diagnosis. In this study, we investigate the X-ray image classification problem using the transfer learning method. We fine-tuned and trained our model using pre-trained models such as AlexNet, VGG16, DenseNet etc, and a baseline deep neural network. We then evaluated this model in terms of classification evaluation metrics. The study shows that DenseNet achieves high accuracy compared to the other pre-trained and baseline CNN models.
Citation Count: Choupani, Roya; Tolun, Mehmet R. (2007). "Main issues in scalable video coding: A review", Proceedings of the 2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007, 2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007; 25 June 2007 through 28 June 2007pp. 497-505.
Main issues in scalable video coding: A review
(2007) Choupani, Roya; Tolun, Mehmet R.
Video streaming over the Internet has gained popularity during the recent years which is mainly the result of the introduction of videoconferencing and videotele-phony. These in turn have made it possible to bring to life many applications such as transmitting video over the Internet and over telephone lines, surveillance and monitoring, telemedicine (medical consultation and diagnosis at a distance), and computer based training and education. The heterogeneous, dynamic and best-effort structure of the Internet however, can not guarantee any specific bandwidth for a connection. Many video coding standards have tried to deal with this problem by introducing the scalability feature as adapting video streams to the fluctuations in the available bandwidths. In this review, we have discussed the main technical features of more common scalable video coding techniques. The main problems of these methods and their applicability together with the available motion compensated video coding methods are discussed as well.
Citation Count: Choupani, Roya. "Mugshot Matching via Generation from Sketches using Convolutional Neural Networks", Internatıonal Journal of Multimedıa and Image Processing, 2019.
Mugshot Matching via Generation from Sketches using Convolutional Neural Networks
(2019) Choupani, Roya; 21259
Citation Count: Choupani, Roya; Wong, Stephan; Tolun, Mehmet R., "Multiple description coding for SNR scalable video transmission over unreliable networks", Multimedia Tools And Applications, Vol69, No.3, pp.843-858, (2014).
Multiple description coding for SNR scalable video transmission over unreliable networks
(Springer, 2014) Choupani, Roya; Wong, Stephan; Tolun, Mehmet R.
Streaming multimedia data on best-effort networks such as the Internet requires measures against bandwidth fluctuations and frame loss. Multiple Description Coding (MDC) methods are used to overcome the jitter and delay problems arising from frame losses by making the transmitted data more error resilient. Meanwhile, varying characteristics of receiving devices require adaptation of video data. Data transmission in multiple descriptions provides the feasibility of receiving it partially and hence having a scalable and adaptive video. In this paper, a new method based on integrating MDC and signal-to-noise ratio (SNR) scalable video coding algorithms is proposed. Our method introduces a transform on data to permit transmitting them using independent descriptions. Our results indicate that on average 1.71dB reduction in terms of Y-PSNR occurs if only one description is received.
Citation Count: Choupani, Roya; Wong, Stephan; Tolun, Mehmet R. "Multiple Description Scalable Coding for Video Transmission over Unreliable Networks", Embedded Computer Systems: Architectures, Modeling, and Simulation, pp. 58-67, 2009.
Multiple Description Scalable Coding for Video Transmission over Unreliable Networks
(2009) Choupani, Roya; Wong, Stephan; Tolun, Mehmet R.; 1863
Developing real time multimedia applications for best effort networks such as the Internet requires prohibitions against jitter delay and frame loss. This problem is further complicated in wireless networks as the rate of frame corruption or loss is higher in wireless networks while they generally have lower data rates compared to wired networks. On the other hand, variations of the bandwidth and the receiving device characteristics require data rate adaptation capability of the coding method. Multiple Description Coding (MDC) methods are used to solve the jitter delay and frame loss problems by making the transmitted data more error resilient, however, this results in reduced data rate because of the added overhead. MDC methods do not address the bandwidth variation and receiver characteristics differences. In this paper a new method based on integrating MDC and the scalable video coding extension of H.264 standard is proposed. Our method can handle both jitter delay and frame loss, and data rate adaptation problems. Our method utilizes motion compensating scheme and, therefore, is compatible with the current video coding standards such as MPEG-4 and H.264. Based on the simulated network conditions, our method shows promising results and we have achieved up to 36dB for average Y-PSNR.
Citation Count: Choupani, Roya; Wong, Stephan; Tolun, Mehmet (2013). "Optimized Multiple Description Coding for Temporal Video Scalability", Advances in Intelligent Systems and Computing, Vol. 225, No. 1, pp. 167-176.
Optimized Multiple Description Coding for Temporal Video Scalability
(2013) Choupani, Roya; Wong, Stephan; Tolun, Mehmet
The vast application of video streaming over the Internet requires video adaptation to the fluctuations of the available bandwidth, and the rendering capabilities of the receiver device. On the other hand, the available video coding standards are designed for optimum bit rate which makes them susceptible to packet losses. A combination of video adaptation methods and error resilient methods can make the video stream more robust against networking problems. In this paper, an optimization for combining scalable video coding with multiple description coding schemes have been proposed. Our proposed method is capable of creating balanced descriptions with optimum coding efficiency.
Phishing e-mail detection by using deep learning algorithms
(2018) Hassanpour, Reza; Doğdu, Erdoğan; Choupani, Roya; Göker, Onur; 21259
Phishing e-mails are considered as spam e-mails, which aim to collect sensitive personal information about the users via network. Since the main purpose of this behavior is mostly to harm users financially, it is vital to detect these phishing or spam e-mails immediately to prevent unauthorized access to users’ vital information. To detect phishing e-mails, using a quicker and robust classification method is important. Considering the billions of e-mails on the Internet, this classification process is supposed to be done in a limited time to analyze the results. In this work, we present some of the early results on the classification of spam email using deep learning and machine methods. We utilize word2vec to represent emails instead of using the popular keyword or other rule-based methods. Vector representations are then fed into a neural network to create a learning model. We have tested our method on an open dataset and found over 96% accuracy levels with the deep learning classification methods in comparison to the standard machine learning algorithms.
Citation Count: Choupani, Roya; Tolun, Mehmet R. (2006). "Recent challenges in video coding and streaming", International Conference on Signals and Electronic Systems, ICSES'0617 September 2006through 20 September 2006, pp. 157-160.
Recent challenges in video coding and streaming
(2006) Choupani, Roya; Tolun, Mehmet R.; 1863
Video streaming over the Internet has gained popularity during the recent years which is mainly the result of the introduction of video-conferencing and videotelephony. These in turn have made it possible to bring to life many applications such as transmitting video over the Internet and telephone lines, surveillance and monitoring, telemedicine (medical consultation and diagnosis at a distance), and computer based training and education. These applications need a large bandwidth which is not available in all cases. Many video encoding standards have been introduced to deal with video compression and transmission problems. In this study, we have discussed the main technical features of the most important video coding standards in a comparative approach. The appropriateness of these features is application and transmission environment dependant. Manipulating video stream features or video transcoding methods are discussed as well.
Citation Count: Choupani, Roya; Wong, Stephan; Tolun, Mehmet, "Unbalanced multiple description wavelet coding for scalable video transmission", Journal of Electronic Imaging, Vol. 21, No. 4, (2012)
Unbalanced Multiple Description Wavelet Coding for Scalable Video Transmission
(IS&T & SPIE, 2012) Choupani, Roya; Wong, Stephan; Tolun, Mehmet R.; 21259
Scalable video coding and multiple description coding are the two different adaptation schemes for video transmission over heterogeneous and best-effort networks such as the Internet. We propose a new method to encode video for unreliable networks with rate adaptation capability. Our proposed method groups three dimensional discrete wavelet transform coefficients in different descriptions and applies a modified embedded zero tree data for rate adaptation. The proposed method optimizes the bit-rates of the descriptions with respect to the channel bit rates and the maximum acceptable distortion. The experimental results in the presence of one description loss indicate that on average the videos at the rate of 1000 Kbit/s are reconstructed with Y-component of peak signal to noise ratio (Y-PSNR) value of 36.2 dB. The dynamic allocation of descriptions to the network channels is optimized for rate distortion minimization. The improvement in term of Y-PSNR achieved by rate distortion optimization has been between 0.7 and 5.3 dB in different bit rates. (c) 2012 SPIE and IS&T. [DOI: 10.1117/1.JEI.21.4.043006]
Citation Count: Choupani, Roya; Wong, Stephan; Tolun, Mehmet. Using wavelet transform self-similarity for effective multiple description video coding, 2015 10th International Conference on Information, Communications and Signal Processing (ICICS), 2016.
Using wavelet transform self-similarity for effective multiple description video coding
(IEEE, 2016) Choupani, Roya; Wong, Stephan; Tolun, Mehmet
Video streaming over unreliable networks requires preventive measures to avoid quality deterioration in the presence of packet losses. However, these measures result in redundancy in the transmitted data which is utilized to estimate the missing packets lost in the delivered portions. In this paper, we have used the self-similarity property if the discrete wavelet transform (DWT) to minimize the redundancy and improve the fidelity of the delivered video streams in presence of data loss. Our proposed method decomposes the video into multiple descriptions after applying the DWT. The descriptions are organized in such a way that when one of them is lost during transmission, it is estimated using the delivered portions by means of self-similarity between the DWT coefficients. In our experiments, we compare video reconstruction in the presence of data loss in one or two descriptions. Based on the experimental results, we have ascertained that our estimation method for missing coefficients by means of self-similarity is able to improve the video quality by 2.14dB and 7.26dB in case of one description and two descriptions, respectively. Moreover, our proposed method outperforms the state-of-the-art Forward Error Correction (FEC) method in case of higher bit-rates.