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We present a convex variational active contour model with shape priors, for spatio-temporal segmentation of the endocardium in 2D B-mode ultrasound sequences, which can be solved by Continuous Cuts. A four component (signal dropout, echocardiographic artifacts, blood and tissue) Rayleigh mixture model is proposed for modeling the inside and outside of the endocardium. The parameters of the mixture...
We develop a new algorithm to segment the hippocampus from MR images. Our method uses a new classifier ensemble algorithm to correct segmentation errors produced by a multi-atlas based segmentation method. Our classifier ensemble algorithm searches for the maximum likelihood solution via gradient ascent optimization. Compared to the additive regression based algorithm, LogitBoost, our algorithm avoids...
In this paper, we propose a new segmentation algorithm that combines a graph-based shape model with image cues based on boosted features. The landmark-based shape model encodes prior constraints through the normalized Euclidean distances between pairs of control points, alleviating the need of a large database for the training. Moreover, the graph topology is deduced from the dataset using manifold...
In this work, we present a nonrigid approach to jointly solving the tasks of 2D-3D pose estimation and 2D image segmentation. In general, most frameworks that couple both pose estimation and segmentation assume that one has exact knowledge of the 3D object. However, under nonideal conditions, this assumption may be violated if only a general class to which a given shape belongs is given (e.g., cars,...
Automatic facial action unit (AU) detection from video is a long-standing problem in computer vision. Two main approaches have been pursued: (1) static modeling - typically posed as a discriminative classification problem in which each video frame is evaluated independently; (2) temporal modeling - frames are segmented into sequences and typically modeled with a variant of dynamic Bayesian networks...
We present a study on grocery detection using our object detection system, ShelfScanner, which seeks to allow a visually impaired user to shop at a grocery store without additional human assistance. ShelfScanner allows online detection of items on a shopping list, in video streams in which some or all items could appear simultaneously. To deal with the scale of the object detection task, the system...
Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales. However, these models do not allow multiple labels to be assigned to a single node. At higher scales in the image, this yields an oversimplified model, since multiple classes can be reasonable expected to appear within...
We introduce a 3D segmentation framework which uses principal shapes. The probabilistic energy function of the method is defined based on intensity, tissue type, and location information of the structures using a multiple atlas method. For intensity information, nonparametric probability density function is used which considers intensity relation of different structures. To find a local minimum of...
Sparse orthonormal transforms (SOT) has recently been proposed as a data compression method that can achieve sparser representations in transform domain. Given initial conditions, the optimization method utilized to generate the dictionary of SOT also achieves the optimal orthonormal transform for hard thresholding. In the context of translation-invariant denoising, one can use this dictionary to...
Conventional binarization methods try to obtain optimal results based on the single image only. They make distinct diversity of binarization quality sometimes even for images of the same documents. Using a binarization evaluation and feedback mechanism, this paper proposed a learning-based binarization method which can improve the binarization of same-type document, especially in the quality stability...
Graph cut algorithms (i.e., min s-t cuts) [3][10][15] are useful in many computer vision applications. In this paper we develop a formulation that allows the addition of side constraints to the min s-t cuts algorithm in order to improve its performance. We apply this formulation to foreground/background segmentation and provide empirical evidence to support its usefulness. From our experiments on...
In this paper, multiobjective optimization is applied to determine the optimal feature weights for a multi-resolution image quality metric. The optimization is conducted with respect to two aims; maximization of quality prediction accuracy and generalisation ability to unknown images. A thresholding of the optimal weights is applied to further reduce computational complexity of the quality metric...
Training a shape prior has been potent scheme for anatomical object segmentations, especially for images with noisy or weak intensity patterns. When the shape representation lives in a high dimensional space, principal component analysis is often used to calculate a low dimensional variation subspace from frequently limited number of training samples. However, the eigenmodes of the sub-space tend...
We introduce a method for fully automatic touch-up of face images by making inferences about the structure of the scene and undesirable textures in the image. A distribution over image segmentations and labelings is computed via a conditional random field; this distribution controls the application of various local image transforms to regions in the image. Parameters governing both the labeling and...
Intravascular ultrasound (IVUS) is a catheter-based medical imaging technique that produces cross-sectional images of blood vessels and is particularly useful for studying atherosclerosis. In this paper, we present a probabilistic approach for the semi-automatic identification of the luminal border on IVUS images. Specifically, we parameterize the lumen contour using a mixture of Gaussian that is...
In this work, we introduce a novel implicit representation of shape which is based on assigning to each pixel a probability that this pixel is inside the shape. This probabilistic representation of shape resolves two important drawbacks of alternative implicit shape representations such as the level set method: Firstly, the space of shapes is convex in the sense that arbitrary convex combinations...
The detection performance on the sintering state of the rotary kiln is mostly dependent on the features used in the recognition process. So an optimization approach of sintering feature parameters based on fuzzy support vector machines (SVM) is proposed. This method firstly uses many feature parameters to describe an image, and then reduce some useless features by portfolio optimization algorithm...
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