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This paper addresses the problem of recognizing free-form 3D objects in point clouds. Compared to traditional approaches based on point descriptors, which depend on local information around points, we propose a novel method that creates a global model description based on oriented point pair features and matches that model locally using a fast voting(More)
We present a method for detecting 3D objects using multi-modalities. While it is generic, we demonstrate it on the combination of an image and a dense depth map which give complementary object information. It works in real-time, under heavy clutter, does not require a time consuming training stage, and can handle untextured objects. It is based on an(More)
We present a method for real-time 3D object instance detection that does not require a time-consuming training stage, and can handle untextured objects. At its core, our approach is a novel image representation for template matching designed to be robust to small image transformations. This robustness is based on spread image gradient orientations and(More)
We present a method for real-time 3D object detection that does not require a time consuming training stage, and can handle untextured objects. At its core, is a novel template representation that is designed to be robust to small image transformations. This robustness based on dominant gradient orientations lets us test only a small subset of all possible(More)
Augmented reality (AR) is a technology in which a user's view of the real world is enhanced or augmented with additional information generated from a computer model. In order to have a working AR system, the see-through display system must be calibrated so that the graphics is properly rendered. The optical see-through systems present an additional(More)
Augmented reality (AR) is a technology in which a user's view of the real world is enhanced or augmented with additional information generated from a computer model. In order to have a working AR system, the see-through display system must be calibrated so that the graphics is properly rendered. The optical see-through systems present an additional(More)
In this paper, we introduce a novel and efficient approach to dense image registration, which does not require a derivative of the employed cost function. In such a context, the registration problem is formulated using a discrete Markov random field objective function. First, towards dimensionality reduction on the variables we assume that the dense(More)
UNLABELLED Attenuation correction (AC) of whole-body PET data in combined PET/MRI tomographs is expected to be a technical challenge. In this study, a potential solution based on a segmented attenuation map is proposed and evaluated in clinical PET/CT cases. METHODS Segmentation of the attenuation map into 4 classes (background, lungs, fat, and soft(More)
The standard approach to multi-modal registration is to apply sophisticated similarity metrics such as mutual information. The disadvantage of these metrics, in comparison to measuring the intensity difference with, e.g. L1 or L2 distance, is the increase in computational complexity and consequently the increase in runtime of the registration. An(More)