Publications
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Deep learning fusion of RGB and depth images for pedestrian detection Ghent University
In this paper, we propose an effective method based on the Faster-RCNN structureto combine RGB and depth images for pedestrian detection. During the training stage,we generate a semantic segmentation map from the depth image and use it to refine theconvolutional features extracted from the RGB images. In addition, we acquire moreaccurate region proposals by exploring the perspective projection with the help of depthinformation. Experimental ...
Learning Solutions to Partial Differential Equations using LS-SVM KU Leuven
© 2015 Elsevier B.V. This paper proposes an approach based on Least Squares Support Vector Machines (LS-SVMs) for solving second order partial differential equations (PDEs) with variable coefficients. Contrary to most existing techniques, the proposed method provides a closed form approximate solution. The optimal representation of the solution is obtained in the primal-dual setting. The model is built by incorporating the initial/boundary ...
How to carve up the world: Learning and collaboration for structure recommendation KU Leuven
Structuring is one of the fundamental activities needed to understand data. Human structuring activity lies behind many of the datasets found on the internet that contain grouped instances, such as file or email folders, tags and bookmarks, ontologies and linked data. Understanding the dynamics of large-scale structuring activities is a key prerequisite for theories of individual behaviour in collaborative settings as well as for applications ...
SLAC: Statistical total lesion metabolic activity computation by fuzzy unsupervised learning of PET images KU Leuven
Accurate lesion metabolic response estimation is imperative for efficient tumor staging and follow-up studies. Positron emission tomography (PET) successfully images the lesion metabolic activity. Nonetheless, on course of accurate delineation, chances are high to end up with activity underestimation as, due to the limited resolution, the PET images suffer from partial volume effects. Recently, PET images were modeled as a fuzzy mixture to ...
Learning about objects in the meeting rooms from people trajectories Ghent University
In ambient intelligence object recognition is an important step towards behaviour analysis and the understanding interactions between people and the environment. Existing methods focus on a detailed analysis of image content using colour, shape, texture and motion analysis (direct recognition). In this paper we present a method for recognizing furniture, i.e. chairs, tables and the walking area in a meeting room using the estimated trajectories ...
Skin Cancer Classification Using Inception Network and Transfer Learning Vrije Universiteit Brussel
Medical data classification is typically a challenging task due to imbalance between classes. In this paper, we propose an approach to classify dermatoscopic images from HAM10000 (Human Against Machine with 10000 training images) dataset, consisting of seven imbalanced types of skin lesions, with good precision and low resources requirements. Classification is done by using a pretrained convolutional neural network. We evaluate the accuracy ...