Yazar "Ozay, Evrim Korkmaz" seçeneğine göre listele

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    Color to Grayscale Image Conversion Using High Dimensional Model Representation
    (IEEE, 2021) Ceylan, Ayca; Tunga, Ve Burcu; Ozay, Evrim Korkmaz
    Color images are converted to grayscale when texture information is needed more than color information when it is desired to reproduce or when it is used for artistic purposes.The methods used to convert color images to grayscale should both produce perceptually acceptable grayscale results and retain as much information as possible about the original color image. In this study, an alternative color-to-grayscale image conversion algorithm has been developed for decolarization using the High Dimensional Model Representation (HDMR) method. In order to evaluate the efficiency of the proposed algorithm, normalized cross correlation, color contrast preservation ratio, E-score and color content fidelity ratio have been used within the scope of standard objective measures.
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    An efficient feature extraction approach for hyperspectral images using Wavelet High Dimensional Model Representation
    (Taylor & Francis Ltd, 2022) Tuna, Suha; Ozay, Evrim Korkmaz; Tunga, Burcu; Gurvit, Ercan; Tunga, M. Alper
    Hyperspectral (HS) Imagery helps to capture information using specialized sensors to extract detailed data at numerous narrow wavelengths. Hyperspectral imaging provides both spatial and spectral characteristics of regions or objects for subsequent analysis. Unfortunately, various noise sources decrease the interpretability of these images as well as the correlation between neighbouring pixels, hence both reduce the classification performance. This study focuses on developing an ensemble algorithm that enables to denoise the spectral signals while decorrelating the spatio-spectral features concurrently. The developed method is called Wavelet High Dimensional Model (W-HDMR) and combines High Dimensional Model Representation (HDMR) with the Discrete Wavelet Transform (DWT). Through W-HDMR, denoised and decorrelated features are extracted from the HS cubes. HDMR decorrelates each dimension in HS data while DWT denoises the spectral signals. The classification performance of W-HDMR as a new feature extraction technique for HS images is assessed by exploiting a Support Vector Machines algorithm. The results indicate that the proposed W-HDMR method is an efficient feature extraction technique and is considered an adequate tool in the HS classification problem.
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    Face Recognition using Tridiagonal Matrix Enhanced Multivariance Products Representation
    (Amer Inst Physics, 2017) Ozay, Evrim Korkmaz
    This study aims to retrieve face images from a database according to a target face image. For this purpose, Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR) is taken into consideration. TMEMPR is a recursive algorithm based on Enhanced Multivariance Products Representation (EMPR). TMEMPR decomposes a matrix into three components which are a matrix of left support terms, a tridiagonal matrix of weight parameters for each recursion, and a matrix of right support terms, respectively. In this sense, there is an analogy between Singular Value Decomposition (SVD) and TMEMPR. However TMEMPR is a more flexible algorithm since its initial support terms (or vectors) can be chosen as desired. Low computational complexity is another advantage of TMEMPR because the algorithm has been constructed with recursions of certain arithmetic operations without requiring any iteration. The algorithm has been trained and tested with ORL face image database with 400 different grayscale images of 40 different people. TMEMPR's performance has been compared with SVD's performance as a result.
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    Hyperspectral image denoising with enhanced multivariance product representation
    (Springer London Ltd, 2022) Ozay, Evrim Korkmaz; Tunga, Burcu
    Hyperspectral images are used in many different fields due to their ability to capture wide areas and rich spectrality. However, applications on hyperspectral image (HSI) are affected or limited by various types of noise. Therefore, denoising is an important pre-processing technique for HSI analysis. Tensor decomposition-based denoising algorithms are frequently used due to constraints of traditional two-dimensional methods. An alternative tensor decomposition, enhanced multivariance product representation (EMPR) has been derived from high-dimensional model representation (HDMR) for multivariate functions and discretized for tensor-type data sets. In this study, EMPR-based denoising method is proposed for HSI denoising. EMPR is a decomposition method which is easy to compute and does not include a rank problem that exists in the other tensor decomposition methods. The performance of EMPR-based denoising is evaluated by means of simulated and real experiments on different HSI data sets which include different types of noise. The obtained results are compared with the state-of-the-art tensor-based methods.
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    A novel method for multispectral image pansharpening based on high dimensional model representation
    (Pergamon-Elsevier Science Ltd, 2021) Ozay, Evrim Korkmaz; Tunga, Burcu
    Pansharpening methods are used to enhance the spatial resolution of a low resolutional multispectral (MS) image by fusing with a high resolutional panchromatic image (PAN). The main difficulty of pansharpening is avoiding spectral distortion while getting a sharpened MS image with high spatial resolution. Intensity-Hue-Saturation (IHS) based methods are applied to transform from color space to IHS and provide equalization of a PAN component with an MS image to eliminate distortion problems. However, most of the modified IHS methods still cause spectral distortion. To overcome this problem, a novel pansharpening method, based on Adaptive High Dimensional Model Representation is proposed in this article. HDMR is a well-known decomposition method for multivariate functions and data sets. The algorithm we propose includes three stages: the first stage is to obtain HDMR components of the MS image using the HDMR decomposition and then to use scaling factors to optimize the effects of the information the components hold. The second stage requires the calculation of some weighting factors in each band to minimize the spectral distortion. Computing the spatial details obtained from the difference between the PAN image and the Adaptive HDMR expansion of the MS image, and adding the difference to the MS image constitutes the third stage. Our proposed algorithm is easy to implement in pansharpening similar to component substitution (CS) based methods, HDMR terms are calculated once and then used adaptively by employing scaling and weighting factors which are determined through a straightforward methodology. The method also provides greater spectral fidelity than the traditional CS based methods as a result of the scaling factors. The proposed method has been tested on different MS images and compared with state-of-the-art pan sharpening methods. The results are given both in terms of visual quality and numerical assessments.
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    Weighted tridiagonal matrix enhanced multivariance products representation (WTMEMPR) for decomposition of multiway arrays: applications on certain chemical system data sets
    (Springer, 2017) Ozay, Evrim Korkmaz; Demiralp, Metin
    This work focuses on the utilization of a very recently developed decomposition method, weighted tridiagonal matrix enhanced multivariance products representation (WTMEMPR) which can be equivalently used on continuous functions, and, multiway arrays after appropriate unfoldings. This recursive method has been constructed on the Bivariate EMPR and the remainder term of each step therein has been expanded into EMPR from step to step until no remainder term appears in one of the consecutive steps. The resulting expansion can also be expressed in a three factor product representation whose core factor is a tridiagonal matrix. The basic difference and novelty here is the non-constant weight utilization and the applications on certain chemical system data sets to show the efficiency of the WTMEMPR truncation approximants.