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Automated classification algorithms have been applied to breast cancer diagnosis in order to improve the diagnostic accuracy and turnover time. However, classification accuracy, sensitivity and specificity could still be improved further. Moreover, reducing computational cost is another challenge as the number of images to be analyzed is typically large. In this paper, a novel Pixel N-gram approach...
Various statistical methods such as co-occurrence matrix, local binary patterns and spectral approaches such as Gabor filters have been used for generating global features for image classification. However, global image features fail to distinguish between local variations within an image. Bag-of-visual-words (BoVW) model do capture local variations in an image, but typically do not consider spatial...
Image processing includes many various procedures and one of the mostly used is edge detection. Sometimes is desirable to improve precision of edge detector to sub-pixel range. In our paper we deal with precise localization of edge which is moving during the exposure time. For such the edges we tested three edge detectors with sub-pixel precision in 1-D images: algorithm that uses function erf to...
In order to minimize the processing time and maintain the color palette, simplify the processing of, our research category the available colors into primary color the colors from the photographic processing by Error Diffusion, the research will use the color space HSL as color model to build the error diffusion application which designed for available and limited colors to making fine arts such as...
Image segmentation is a fundamental problem in computer vision. Recently, ensemble learning receives more and more attention for its robustness, novelty and stability. Generally there are two problems in ensemble learning. One is the generation of the individuals of ensemble. The other is the consensus function of the individuals. We focus on the second problem. A new consensus function is proposed...
In this paper, we validate whether the network modularity can emerge, and the evolution performance can be improved by varying the environment or evolution process under a more freeform artificial evolution. Previous studies have demonstrated that the modular structure naturally arisen as a response of the variations on environment and selection process, however, since the models they used were relatively...
In the pixel based multiscale Markov random field(MRF) model, a sequence of MRF was hierarchically defined on the multiple spatial resolutions which might suffer from the deficiency of modeling the large range of interaction. In order to overcome such a problem, we attempt to introduce the region based multiscale MRF model, in which hierarchical MRF model is defined over multiresolution image segmented...
In this paper, we propose a region-based MRF model with optimized initial regions (RMRF-OIR) for image segmentation. In the RMRF-OIR, a modified mean shift is introduced to get the optimized initial over segmented regions. Then, a region-based MRF is used to model these initial regions on the region adjacency graph. Finally, the segmentation results will be obtained by using a region merging scheme...
Computer simulation for improving the liquid crystal display optical performance includes the one-dimensional (1-D) simulation, the two-dimensional (2-D) or sometimes three-dimensional (3-D) simulation. This paper describes the liquid crystal display optics and computer simulation methods.
By learning the various character image samples, the automatic and synchronistic generation of new Chinese calligraphy styles is a key problem in the computer artistic simulating. A curve analogy method based on FSVM is proposed which can generate new calligraphy styles with the user's constraining styles'parameters. Firstly, the input character image samples are transferred into a hierarchical stroke...
Biological vision systems use saliency-based visual attention mechanisms to limit higher-level vision processing on the most visually-salient subsets of an input image. Among several computational models that capture the visual-saliency in biological system, an information theoretic AIM(Attention based on Information Maximization) algorithm has been demonstrated to predict human gaze patterns better...
For natural image segmentation, due to features from a single image are hard to describe the complex scene information, this paper presents a new method based on the fusion model evaluation index PRI to fuse color histogram features in 3 color spaces, RGB, XYZ, LUV, and texture features. We experiment on images from Berkeley segmentation databases and compare the quantitative and qualitative experimental...
We define a new reduced model to represent coloured images. We propose to use two components for a full definition of a colour instead of three. To that end we take advantage of the geometrical structure of the HCL conical colour space and approximate its circular base by a spiral. We thus write chroma as a function of hue. The resulting spiral is therefore defined by one parameter only. This parameter...
In the field of detection and monitoring of dynamic objects in quasi-static scenes, background subtraction techniques where background is modeled at pixel-level, although showing very significant limitations, are extensively used. In this work we propose a novel approach to background modeling that operates at region-level in a wavelet based multi-resolution framework. Based on a segmentation of the...
In this paper, we present a generic watermarking framework that employs perceptual modeling to embed a watermark in such a way that it cannot be perceived. Our approach consists in building computational models which take into account the most common properties of the HVS (Human Visual System) that can be exploited for watermarking. We describe two ways a perceptual model can be incorporated into...
Stochastic image modeling based on conventional Markov random fields is extensively discussed in the literature. A new stochastic image model based on Markov random fields is introduced in this paper which overcomes the shortcomings of the conventional models easing the computation of the joint density function of images. As an application, this model is used to generate texture patterns. The lower...
This study is a comparison between two image segmentation's methods; the first method is based on normal brain's tissue recognition then tumor extraction using thresholding method. The second method is classification based on EM segmentation which is used for both brain recognition and tumor extraction. The goal of these methods is to detect, segment, extract, classify and measure properties of the...
The surface splatting technique has several advantages in rendering models consisting of points, such as speed and quality of rendered images. After projecting all the splats on the plane of the image, it is necessary to reconstruct surfaces from several fragments generated in the previous step. Several heuristics make use of implicit information about the surface to determine which fragments constitute...
A hybrid model was developed to predict the zero-quantized discrete cosine transform (ZQDCT) coefficients for intra blocks in our previous work. However, the complicated overhead computations seriously degrade its performance in complexity reduction. This paper proposes a new prediction algorithm with less overhead operations. First, each N × N pixel block at the input of transform is resized to a...
Stereoscopic video is an important manner for 3-D video applications, and robust stereoscopic video transmission has posed a technical challenge for stereoscopic video coding. In this paper, an auto-regressive (AR) model based error concealment scheme is proposed for stereoscopic video coding to address the challenging problem. The proposed error concealment scheme includes a temporal AR model for...
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