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Classification is an important task in Hyperspectral data analysis. Hyperspectral images show strong correlations across spatial and spectral neighbors. Theoretically, classifier designed with a joint spectral and spatial correlations can improve classification performance than classifier which only utilize one of the correlations. Gaussian Processes(GPs) have been used for Hyperspectral imagery classification...
The genomic revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. As proteins are integral components of cell function, it is critical to understand their properties such as structure and localization. Knowledge of a protein's subcellular distribution can contribute to a complete understanding of its function. Processing of subcellular...
To improve the precision of remote sensing image classification, hybrid multi-classifier combination method is proposed. Taking the characteristic of abstract level and measurement level into consideration, the optimal sub-classifier, bagging algorithm and the most large confidence algorithm are combined. By using this model, respective advantages of different sub-classifiers are gathered. This method...
In this paper, an objective assessment model based on three-component weighted structural similarity is presented for stereoscopic image. Since different distortions affect details of stereoscopic image differently, the image is classified into three kinds of regions including edge, smooth and texture regions so that different weights are associated to different regions to assess distorted stereoscopic...
Object tracking in video sequences has been extensively studied in computer vision. Although promising results have been achieved, often the proposed solutions are tailored for particular objects, structured to specific conditions or constrained by tight guidelines. In real cases it is difficult to recognize these situations automatically because a large number of parameters must be tuned. Factors...
For a Driving Assistance System, which is designed to improve safety on the roads, knowledge about the type of lane border markings and other painted road objects is required. Furthermore, information about the position of the painted objects can be used by other systems to create a correct perception of the road. This paper describes a Lab VIEW based system for detection, measurement and classification...
In this work, we propose novel image descriptors for identifying head poses in low resolution images. The key novelty of our method is to exploit two types of non-local metric for estimating head poses: non-local intensity difference feature (iDF) and non-local color difference feature (cDF). Unlike the existing methods that one pixel can only represent one head pose information, our proposed features...
Scene-context plays an important role in scene analysis and object recognition. Among various sources of scene-context, we focus on scene-context scale, which means the effective region size of local context to classify an image pixel in a scene. This paper presents semantic segmentation and object recognition using scene-context scale. The scene-context scale can be estimated by the entropy of the...
In this paper, we propose a JND (Just Notice Distortion)-loss less image compression scheme that can improve the compression performance for JPEG-LS, while maintaining the image perceptual quality simultaneously. JND-loss less can be easily achieved by setting the quantization step size (QSS) to be double the JND value. However, dynamic JNDs make the coding of varying QSSs difficult and the JND estimated...
Research topic covered was the identification of a characteristic method of authentication based on biometric iris reading to achieve a solution to secure communications. Biometric identification solution based on iris reading was combined with conventional authentication methods to achieve more secure communications and computers better protected. The paper presents three iris classification techniques:...
Due to the prevalence of digital cameras, it is easy to retrieve digital images from the Internet. With the rapid development of digital image processing, databases, and Internet technologies, how to efficiently manage a large amount of digital images is very important. In this paper, we proposed a novel approach for automatic image annotation. We extract color, texture, and shape features from a...
Luminance edges are classified into reflectance edges and illumination edges. Most of the edge detection papers published focus on reflectance edges detections, whereas only few of them deal with illumination edges. In this paper, a new approach to detect illumination edges in grayscale images is proposed. Differing from the conventional illumination edge detection techniques, which were mostly based...
M-FISH (Multiplex Fluorescent In Situ Hybridization) is a multichannel chromosome imaging technique that allows the color discrimination of human chromosomes. Although M-FISH facilitates the visual detection of chromosome rearrangements, the success of this technique largely depends on the accuracy of the pixel-by-pixel classification. In this study, we present a semi unsupervised method for M-FISH...
Data from medical imaging system need to be analysed for diagnostics and clinical purposes. In a computerized system, the analysis normally involves classification process to determine disease and its condition. In an earlier work based on a database of 315 fundus images (FINDeRS), it is found that the foveal avascular zone (FAZ) enlargement strongly correlates with diabetic retinopathy (DR) progression...
Super resolution mapping is a set of techniques to increase the spatial resolution of a land cover map obtained by soft classification methods. Linear spectral unmixing have been developed to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the instantaneous field of view represented by the pixel. The use...
Remotely sensed hyperspectral imagery plays an important role in land cover classification by supplying the user with additional spectral data as compared to high-resolution color imagery. The web application described in this paper enables users to test their classification algorithms without the risk of bias by withholding the majority of the true classification data and only providing a small section...
Remotely sensed spectral imagery is used in many disciplines, including environmental monitoring, agricultural health, defense and security applications, astronomy, medical imaging, and food quality assessment. The basic tasks performed within any of these fields are target or anomaly detection, classification or clustering, change detection, and physical parameter estimation. Hyperspectral image...
Hyperspectral data consists of three dimensional images; the third dimension is the spectral signature of each pixel. The question always arises: which bands should be used and how should they be weighted. In this paper, a band selection algorithm based on the Fisher's Linear Discriminant classifier (FLD) is implemented. An initial segmentation of the image into two different classes (gas and background)...
It has been shown that the probability to develop breast cancer is strongly correlated with the appearance of tissue in mammographic images. This appearance incorporates both greylevel and tissue pattern aspects and models of local texture information, which incorporate both greylevel and spatial aspects, can as such be related to mammographic risk assessment. Here we represent texture by the variation...
The problem of object detection in image and video has been treated by a large number of researchers. Many design factors degrade the reliability of the problem solutions, such as manual modeling of the object, manual features selection, handcrafting architecture, and learning algorithm selection. Here, a generalized object detection and localization system is presented. It has the ability to learn...
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