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Providing accurate image-guidance for soft-tissue interventions remains a complex task. Most of the time, preoperative models and planning data are no more valid during the surgical process due to motions and deformations of the organ of interest. In this paper, two core components of a computer-assisted system for liver surgery are presented. One is an ultrasound segmentation techniques that allows...
This paper proposes a hybrid model for deformable template which combines alignable and non-alignable sketches. These sketches are subject to slight or considerable translations in different images. For slight translations, Wu et al proposed active basis model to capture them, where each sketch is allowed to shift in position and orientation. For larger translations of sketches, assumed that they...
This paper introduces a clever way of computing inner products between images in order to drastically reduce the computational complexity of fitting appearance models to images. This speedup is possible since computing the hessian matrix for the parameter updates becomes several orders of magnitude faster which in turn has enormous impact on applications. Contrary to previous work within the area,...
This paper describes a method to localize 3D objects, which is the extension of the segment-based object recognition method to use on a STL CAD model. Models for localization are automatically generated using contour generators, which are estimated by occluding contours of projected images of the CAD model from multiple viewing directions and depth images computed with a graphics accelerator. In addition,...
Document clustering techniques have been applied in several areas, with the Web as one of the most recent and influent. Both general-purpose and text-oriented techniques exist and can be used to cluster a collection of documents in many ways. In this work we propose an online, single-pass document clustering model that can be combined with a variety of text-oriented similarity measures. An experimental...
In this paper, a novel statistical indoor activity recognition algorithm is introduced. While conditional random fields (CRFs) have prominent properties to this task, no optimal performance is obtained due to the fact that the performance is optimized for offline estimation. Furthermore, no previous researches provide efficient training process to optimize classifiers in on-site recognition perspective...
We describe an ensemble approach to learning salient regions from data partitioned according to the distributed processing requirements of large-scale simulations. The volume of the data is such that classifiers can train only on data local to a given partition. Classes will likely be missing from some, or even most, partitions. We combine a fast ensemble learning algorithm with scaled probabilistic...
This paper presents a novel color face recognition approach based on 2DPCA. A matrix-representation model, which encodes the color information directly, is proposed to describe the color face image. The matrix-representation model defines the pixel in color face image as the basic unit, the color information of the pixel as the basic component, and then represents the color face image efficiently...
Recently, researchers are paying more attention to 3D model classification due to its useful applications in multimedia, computer graphics, and so on. Although there exist a number of approaches to classify 3D models, few of them consider the prior knowledge during the process of 3D model classification. In this paper, we propose a new framework called knowledge based cluster ensemble which incorporates...
This paper addresses the inference of probabilistic classification models using weakly supervised learning. In contrast to previous work, the use of proportion-based training data is investigated in combination to non-linear classification models. An application to fisheries acoustics and fish school classification is considered and experiments are reported for synthetic and real datasets.
This article presents a preceding car rear view tracking algorithm which utilizes a particle filter and belief function data fusion. Most of tracking applications resort to only one source of information, making the system dependent on the source reliability. To achieve more robust and longer tracking, multiple source data fusion is a solution. Belief functions are a powerful tool for data fusion...
Wide area surveillance requires high-resolution images of the object of interest derived possibly from only low-resolution video of the whole scene. We propose a combined tracking and resolution enhancement approach that increases the resolution of the object of interest during tracking. The key idea is the use of an object-specific 3D mesh model with which we are able to track non-planar objects...
This paper proposes a technique for simplifying a given Gaussian mixture model, i.e. reformulating the density in a more parcimonious manner, if possible (less Gaussian components in the mixture). Numerous applications requiring aggregation of models from various sources, or index structures over sets of mixture models for fast access, may benefit from the technique. Variational Bayesian estimation...
We propose a method to improve the results of image search engines on the Internet to satisfy users who desire to see relevant images in the first few pages. The method re-ranks the results of text based systems by incorporating visual similarity of the resulting images. We observe that, together with many unrelated ones, results of text based systems include a subset of correct images, and this set...
Complex simulations can generate very large amounts of data stored disjointedly across many local disks. Learning from this data can be problematic due to the difficulty of obtaining labels for the data. We present an algorithm for the application of semi-supervised learning on disjoint data generated by complex simulations. Our semi-supervised technique shows a statistically significant accuracy...
Regional scores (e.g. strain, perfusion) of the Left Ventricle (LV) functionality are playing an increasing role in the diagnosis of cardiac diseases. A main limitation is the lack of normality models for complementary scores oriented to assessment of the LV integrity. This paper introduces an original framework based on a parametrization of the LV domain, which allows comparison across subjects of...
In this work we present a new string similarity feature, the sparse spatial sample (SSS). An SSS is a set of short substrings at specific spatial displacements contained in the original string. Using this feature we induce the SSS kernel (SSSK) which measures the agreement in the SSS content between pairs of strings. The SSSK yields better prediction performance at substantially reduced computational...
A new variational Maximum A Posteriori (MAP) contextual modeling approach is presented that minimizes the product of two ratios: (a) the ratio of the model distribution to the distribution of currently estimated foreground pixels; (b) the ratio of the background distribution to the model distribution for all estimated background pixels. This approach provides robust discrimination to identify the...
In this paper, we propose a novel framework of object categorization, namely layered object categorization, which takes advantage of hierarchical category information and performs object categorization at different levels. The proposed hierarchical structure of object categories is built bottom-up and top-down simultaneously accordingly to cognitive rules. First, part-based models are learnt to evaluate...
In this paper we present a method of combining stereo and shape-from-shading information, taking account of the local reliability of each shape estimate. Local estimates of disparity and orientation are modelled using Gaussian distributions. A Gaussian-Markov random field is used to represent the disparity-map, taking into account interactions between disparity measurements and surface orientation,...
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