Current Issue : April - June Volume : 2013 Issue Number : 2 Articles : 5 Articles
Image quality is a vital criterion that guides the technical development of digital cameras. Traditionally, the image\r\nquality of digital cameras has been measured using test-targets and/or subjective tests. Subjective tests should be\r\nperformed using natural images. It is difficult to establish the relationship between the results of artificial test\r\ntargets and subjective data, however, because of the different test image types. We propose a framework for\r\nobjective image quality metrics applied to natural images captured by digital cameras. The framework uses\r\nreference images captured by a high-quality reference camera to find image areas with appropriate structural\r\nenergy for the quality attribute. In this study, the framework was set to measure sharpness. Based on the results,\r\nthe mean performance for predicting subjective sharpness was clearly higher than that of the state-of-the-art\r\nalgorithm and test-target sharpness metrics....
We propose a novel external force for active contours, which we call neighborhood-extending and noise-smoothing\r\ngradient vector flow (NNGVF). The proposed NNGVF snake expresses the gradient vector flow (GVF) as a\r\nconvolution with a neighborhood-extending Laplacian operator augmented by a noise-smoothing mask. We find\r\nthat the NNGVF snake provides better segmentation than the GVF snake in terms of noise resistance, weak edge\r\npreservation, and an enlarged capture range. The NNGVF snake accomplishes this with a reduced computational\r\ncost while maintaining other desirable properties of the GVF snake, such as initialization insensitivity and good\r\nconvergences at concavities. We demonstrate the advantages of NNGVF on synthetic and real images....
As satellite images are widely used in a large number of applications in recent years, content-based image retrieval\r\ntechnique has become important tools for image exploration and information mining; however, their performances\r\nare limited by the semantic gap between low-level features and high-level concepts. To narrow this semantic gap,\r\na region-level semantic mining approach is proposed in this article. Because it is easier for users to understand\r\nimage content by region, images are segmented into several parts using an improved segmentation algorithm,\r\neach with homogeneous spectral and textural characteristics, and then a uniform region-based representation for\r\neach image is built. Once the probabilistic relationship among image, region, and hidden semantic is constructed,\r\nthe Expectation Maximization method can be applied to mine the hidden semantic. We implement this approach\r\non a dataset consisting of thousands of satellite images and obtain a high retrieval precision, as demonstrated\r\nthrough experiments....
As screenshots of copyrighted video content are spreading through the Internet without any regulation, cases of\r\ncopyright infringement have been observed. Further, it is difficult to use existing forensic techniques for\r\ndetermining whether or not a given image was captured from a screen. Thus, we propose a screenshot\r\nidentification scheme using the trace of screen capture. Since most television systems and camcorders use\r\ninterlaced scanning, many screenshots are taken from interlaced videos. Consequently, these screenshots contain\r\nthe trace of interlaced videos, combing artifacts. In this study, we identify a screenshot using the characteristics of\r\ncombing artifacts that appear to be shaped like horizontal jagged noise and can be found around the edges. To\r\nidentify a screenshot, the edge areas are extracted using the gray level co-occurrence matrix (GLCM). Then, the\r\namount of combing artifacts is calculated in the extracted edge areas by using the similarity ratio (SR), the ratio of\r\nthe horizontal noise to the vertical noise. By analyzing the directional inequality of noise components, the\r\nproposed scheme identifies the source of an input image. In the experiments conducted, the identification\r\naccuracy is measured in various environments. The results prove that the proposed identification scheme is stable\r\nand performs well....
On the basis of the Scale Invariant Feature Transform (SIFT) feature, we research the distance measure in the\r\nprocess of image resizing. Through extracting SIFT features from the original image and the resized one,\r\nrespectively, we match the SIFT features between two images, and calculate the distance for SIFT feature vectors to\r\nevaluate the degree of similarity between the original and the resized image. On the basis of the Euclidean\r\ndistance measure, an effective image resizing algorithm combining Seam Carving with Scaling is proposed. We first\r\nresize an image using Seam Carving, and calculate the similarity distance between the original image and its\r\nresized one. Before the salient object and content are damaged obviously, we stop Seam Carving and transfer\r\nresidual task to Scaling. Experiments show that our algorithm is able to avoid the damage and distortion of image\r\ncontent and preserve both the local structure and the global visual effect of the image graciously....
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