Diagnostic ultrasound is a useful and noninvasive method in clinical medicine. Although due to its qualitative, sub- jective and experience-based nature, ultrasound image interpretation can be influenced by image cond...
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Diagnostic ultrasound is a useful and noninvasive method in clinical medicine. Although due to its qualitative, sub- jective and experience-based nature, ultrasound image interpretation can be influenced by image conditions such as scanning frequency and machine settings. In this paper, a novel method is proposed to extract the liver features using the joint features of fractal dimension and the entropies of texture edge co-occurrence matrix based on ultrasound images, which is not sensitive to changes in emission frequency and gain. Then, Fisher linear classifier and support vector machine are employed to test a group of 99 in-vivo liver fibrosis images from 18 patients, as well as other 273 liver images from 18 normal human volunteers.
A novel segmentation algorithm for natural color image is proposed. Fibonacci Lattice-based Sampling is used to get the color labels of image so as to take advantage of the traditional approaches developed for gray-sc...
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A universal approach to camera calibration based on features of some representative lines on traffic ground is presented. It uses only a set of three parallel edges with known intervals and one of their intersected li...
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A universal approach to camera calibration based on features of some representative lines on traffic ground is presented. It uses only a set of three parallel edges with known intervals and one of their intersected lines with known slope to gain the focal length and orientation parameters of a camera. A set of equations that computes related camera parameters has been derived from geometric properties of the calibration pattern. With accurate analytical implementation, precision of the approach is only decided by accuracy of the calibration target selecting. Final experimental results have showed its validity by a snapshot from real automatic visual traffic surveillance （AVTS） scencs.
Locality Preserving Projections (LPP) is a linear projective map that arises by solving a variational problem that optimally preserves the neighborhood structure of the data set. Though LPP has been applied in many do...
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Iris-based biometric recognition outperforms other biometric methods in terms of accuracy. In this paper an iris normalization model for iris recognition is proposed, which combines linear and non-linear methods to un...
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Rectification for airborne linear images is an indispensable preprocessing step. This paper presents in detail a two-step rectification algorithm. The first step is to establish the model of direct georeference positi...
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Rectification for airborne linear images is an indispensable preprocessing step. This paper presents in detail a two-step rectification algorithm. The first step is to establish the model of direct georeference position using the data provided by the Po- sitioning and Orientation System (POS) and obtain the mathematical relationships between the image points and ground reference points. The second step is to apply polynomial distortion model and Bilinear Interpolation to get the final precise rectified images. In this step, a reference image is required and some ground control points (GCPs) are selected. Experiments showed that the final rectified images are satisfactory, and that our two-step rectification algorithm is very effective.
Weighted Color Co-occurrence Matrix (WCCM) is introduced as a novel feature for image retrieval. When indexing images with WCCM feature, the similarities of diagonal elements and non-diagonal elements are weighted res...
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To over come the drawbacks existing in current measurement methods for detecting and controlling colors in printing process, a new medal for color separation and dot recognition is proposed from a view of digital imag...
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To over come the drawbacks existing in current measurement methods for detecting and controlling colors in printing process, a new medal for color separation and dot recognition is proposed from a view of digital imageprocessing and patter recognition. In this model, firstly data samples are collected from some color patches by the Fuzzy C-Means （FCM） method; then a classifier based on the Cerebellar Model Articulation Controller （CMAC） is constructed which is used to recognize color pattern of each pixel in a microscopic halftone image. The principle of color separation and the algorithm model are introduced and the experiments show the effectiveness of the CMAC-based classifier as opposed to the BP network.
The feature contrast model (FCM), which is the simplest form of the matching function in Tversky's set-theoretic similarity, is a famous similarity model in psychological society. Although FCM can be employed to e...
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The feature contrast model (FCM), which is the simplest form of the matching function in Tversky's set-theoretic similarity, is a famous similarity model in psychological society. Although FCM can be employed to explain the similarity with both semantic and perceptual features, it is very difficult for FCM to measure natural image similarity with semantic features because of the requirement that all features must be binary and the complex mechanism that semantic features are transformed into binary features. The fuzzy feature contrast model (FFCM) is an extension of FCM, which replaces the complex feature representation mechanism with a proper fuzzy membership function. By this fuzzy logic, visual features, in the FFCM, can be represented as multidimensional points instead of expansible feature set and used to measure visual similarity between two images. Based on the analysis of the distinction between two feature structures (i.e., the expansible feature set and multidimensional vector), we propose a ratio model, which expresses similarity between two images as a ratio of the measures of semantic features set to that of multidimensional visual features. Experiments results, over real-world image collections, show that our model addresses the distinction between semantic and visual feature structures to some extension. In particular, our model is suit for the case that semantic features are implicitly obtained from interaction with users and the visual features are transparent for users, for example, the relevance feedback in interactive image retrieval.
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