Browsing by Subject "Image resolution"

Now showing 1 - 3 of 3

Open Access
Identification of relative protein bands in Polyacrylamide Gel Electrophoresis (PAGE) using multiresolution snake algorithm
(IEEE, 1998-10) Gürcan, Metin Nafi; Koyutürk, Mehmet; Yıldız, H. Serkan; Çetin Atalay, Rengül; Çetin, A. Enis
Polyacrylamide Gel Electrophoresis (PAGE) is one of the most widely used techniques in protein research. In the protein purification process, it is important to determine the efficiency of each purification step in terms of percentage of protein of interest found in the protein mixture. This study provides a rapid and reliable way to determine this percentage. The region of interest containing the protein is detected using the snake algorithm. The iterative snake algorithm is implemented in a multiresolutional framework. The snake is initialized on a low resolution image. Then, the final position of the snake at low resolution is used as the initial position in the higher resolution image. Finally, tile area of the protein is estimated as the area enclosed by the final position of the snake.
Open Access
Multi-resolution segmentation and shape analysis for remote sensing image classification
(IEEE, 2005-06) Aksoy, Selim; Akçay H. Gökhan
We present an approach for classification of remotely sensed imagery using spatial information extracted from multi-resolution approximations. The wavelet transform is used to obtain multiple representations of an image at different resolutions to capture different details inherently found in different structures. Then, pixels at each resolution are grouped into contiguous regions using clustering and mathematical morphology-based segmentation algorithms. The resulting regions are modeled using the statistical summaries of their spectral, textural and shape properties. These models are used to cluster the regions, and the cluster memberships assigned to each region in multiple resolution levels are used to classify the corresponding pixels into land cover/land use categories. Final classification is done using decision tree classifiers. Experiments with two ground truth data sets show the effectiveness of the proposed approach over traditional techniques that do not make strong use of region-based spatial information. © 2005 IEEE.
Open Access
Super-resolution using multiple quantized images
(IEEE, 2010) Özçelikkale, Ayça; Akar, G. B.; Özaktas, Haldun M.
In this paper, we study the effect of limited amplitude resolution (pixel depth) in super-resolution problem. The problem we address differs from the standard super-resolution problem in that amplitude resolution is considered as important as spatial resolution. We study the trade-off between the pixel depth and spatial resolution of low resolution (LR) images in order to obtain the best visual quality in the reconstructed high resolution (HR) image. The proposed framework reveals great flexibility in terms of pixel depth and number of LR images in super-resolution problem, and demonstrates that it is possible to obtain target visual qualities with different measurement scenarios including images with different amplitude and spatial resolutions.