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Contemporary media entail colour-coded information. While an average viewer takes colour images for granted, individuals with colour vision deficiency have difficulties in discriminating certain colour combinations and, consequently, have difficulties in perceiving image features. Well-established simulation tools allow us to see the image from their perspective that goes beyond the stereotypical...
We propose a new method for fitting an ellipse to a point sequence extracted from an image. This method can fit an ellipse if a point sequence consists of elliptic arcs and non-elliptic arcs such as line segments. Assuming that input points are spatially connected, we iteratively select inlier points and fit an ellipse to them by computing curvatures of the residual graph. By using simulated data...
We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes...
Multi-atlas segmentation techniques typically comprise generation of multiple candidate labels that are then combined at a final label fusion stage. Label fusion strategies usually leverage information contained in these training labels but ignore local neuroanatomical information. Here, we address this limitation by explicitly incorporating local information at the label fusion stage. The proposed...
Conventional multi-atlas-based segmentation demands pairwise full-fledged registration between each atlas image and the target image, which leads to high computational cost and poses great challenge in the new era of big data. On the other hand, only the most relevant atlases should contribute to final label fusion. In this work, we introduce a two-stage fusion set selection method by first trimming...
In this paper, we present a robust image segmentation technique based on the Geodesic Convex Active Contour (GCAC) and the Chan-Vese (CV) model. The proposed algorithm overcomes the drawbacks of existing interactive image segmentation techniques which are heavily dependent upon the initial user input. Here, we propose to start with a Geodesic based contour before using the Chan-Vese model. Contrary...
In this work, we employ a pair wise Markov Random Field (MRF) and a Conditional Random Field (CRF) for bi-level image segmentation and denoising. For both tasks, the Ising pair wise model and the Iterative Conditional Mode (ICM) inference method are implemented, assuming the parameters of the unary and pair wise potentials are known. Experimental results demonstrate the effectiveness of the proposed...
Writer identification from musical scores is a challenging task. A few pieces of work on writer identification in musical sheets have been published in the literature but to the best of our knowledge all these work were performed after removal of staff lines from the musical scores. In this paper we propose a symbol-independent writer identification framework using HMM in music score without removing...
In this paper, we propose a symbolic approach for classification of traffic videos based on their content. We propose to represent a traffic video by an interval valued features. Unlike the conventional methods, the interval valued feature representation is able to preserve the variations existing among the extracted features of a traffic video. Based on the proposed symbolic representation, we present...
Infinite hidden conditional random fields has been proposed for human behavior analysis which is a non-parametric discriminative model as the extension of HCRF. However, it only model one dimensional temporal relationship by using a chain structure imposed on latent state variables, and would involve huge number of parameters as the number of state increases. In order to solve the 2D object segmentation...
Automated assessment of hepatic fat fraction is clinically important. A robust and precise segmentation would enable accurate, objective and consistent measurement of liver fat fraction for disease quantification, therapy monitoring and drug development. However, segmenting the liver in clinical trials is a challenging task due to the variability of liver anatomy as well as the diverse sources the...
Image segmentation is a challenging task that has several applications in domains like medical imaging and surveillance. Among the various approaches proposed for this task, unsupervised methods have the advantage of being able to segment images without any assistance from the user. However, such methods often suffer from long runtimes and tend to be sensitive to the choice of parameters. Because...
The present study investigates a method for the attribution of scribal hands, inspired by traditional palaeography in being based on comparison of letter shapes. The system was developed for and evaluated on early medieval Caroline minuscule manuscripts. The generation of a prediction for a page image involves writing identification, letter segmentation, and letter classification. The system then...
Kinect Fusion is able to build a 3D reconstruction in real time and provide a 3D model. Kinect Fusion uses Iterative Closest Point (ICP) algorithm for point cloud alignment from the each camera frame and estimates each camera pose. However, ICP algorithm has its limits and the camera poses lack in accuracy. We propose an alignment method which is not only based on point cloud but also line segments...
In this paper, we propose an approach based on 2D vessel model to segment the vessel lumen in three-dimensional coronary computed tomographic angiography (CCTA) images. The 2D parametric intensity model is introduced first to simulate the intensity distribution of vessel lumen with different size in the longitudinal images. Then the Levenberg-Marquardt method is applied to fit the model within a series...
This paper proposes a novel unsupervised cosegmentation method which automatically segments the common objects in multiple images. It designs a simple superpixel matching algorithm to explore the inter-image similarity. It then constructs the object mask for each image using the matched superpixels. This object mask is a convex hull potentially containing the common objects and some backgrounds. Finally,...
Computer vision systems are being introduced in pre-screening of cervical cytopathology slides to identify samples that require study by cytopathologists. These systems work on the principle of imaging and analysis of cytology features in general and nuclear features in particular. Thus accurate localization and segmentation of the nuclei is crucial for the systems. Though several methods have been...
In this paper, we propose a novel automatic and effective method for the vessel segmentation based on Hessian matrix. First, to obtain vessel structure more reliably, we select 25 frames of well-contrast angiograms automatically for further vessel segmentation. Second, we define an adaptive feature transform function using the gray value and the scale to improve the feature response. First, we enhance...
We address the problem of semantic segmentation: classifying each pixel in an image according to the semantic class it belongs to (e.g. dog, road, car). Most existing methods train from fully supervised images, where each pixel is annotated by a class label. To reduce the annotation effort, recently a few weakly supervised approaches emerged. These require only image labels indicating which classes...
A patient-specific left atrium (LA) model extracted from intra-operative C-arm CT plays an important role in planning for transcatheter left atrial fibrillation ablation. Overlaying the LA model onto 2D fluoroscopic images provides valuable visual guidance during the intervention. However, automatic segmentation of the LA, together with the left atrial appendage (LAA) and the pulmonary vein (PV) trunks,...
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