1,234 research outputs found
Obscurance-based Volume Rendering Framework
IEEE/ EG Symposium on Volume and Point-Based Graphics (2008) H.- C. Hege, D. Laidlaw, R. Pajarola, O. Staadt (Editors)lighting effects in a faster way than global illumination. Its application in volume visualization is of special interest since it permits us to generate a high quality rendering at a low cost. In this paper, we propose an obscurancebased framework that allows us to obtain realistic and illustrative volume visualizations in an interactive manner. Obscurances can include color bleeding effects without additional cost. Moreover, we obtain a saliency map from the gradient of obscurances and we show its application to enhance volume visualization and to select the most salient views.publishedVersio
A false colouring real time visual saliency algorithm for reference resolution in simulated 3-D environments
In this paper we present a novel false colouring visual
saliency algorithm and illustrate how it is used in the Situated Language Interpreter system to resolve natural language references
Saliency-based approaches for multidimensional explainability of deep networks
In deep learning, visualization techniques extract the salient patterns exploited by deep networks to perform a task (e.g. image classification) focusing on single images. These methods allow a better understanding of these complex models, empowering the identification of the most informative parts of the input data. Beyond the deep network understanding, visual saliency is useful for many quantitative reasons and applications, both in the 2D and 3D domains, such as the analysis of the generalization capabilities of a classifier and autonomous navigation. In this thesis, we describe an approach to cope with the interpretability problem of a convolutional neural network and propose our ideas on how to exploit the visualization for applications like image classification and active object recognition. After a brief overview on common visualization methods producing attention/saliency maps, we will address two separate points: firstly, we will describe how visual saliency can be effectively used in the 2D domain (e.g. RGB images) to boost image classification performances: as a matter of fact, visual summaries, i.e. a compact representation of an ensemble of saliency maps, can be used to improve the classification accuracy of a network through summary-driven specializations. Then, we will present a 3D active recognition system that allows to consider different views of a target object, overcoming the single-view hypothesis of classical object recognition, making the classification problem much easier in principle. Here we adopt such attention maps in a quantitative fashion, by building a 3D dense saliency volume which fuses together saliency maps obtained from different viewpoints, obtaining a continuous proxy on which parts of an object are more discriminative for a given classifier. Finally, we will show how to inject this representations in a real world application, so that an agent (e.g. robot) can move knowing the capabilities of its classifier
Towards Data-Driven Large Scale Scientific Visualization and Exploration
Technological advances have enabled us to acquire extremely large
datasets but it remains a challenge to store, process, and extract
information from them. This dissertation builds upon recent advances
in machine learning, visualization, and user interactions to
facilitate exploration of large-scale scientific datasets. First, we
use data-driven approaches to computationally identify regions of
interest in the datasets. Second, we use visual presentation for
effective user comprehension. Third, we provide interactions for
human users to integrate domain knowledge and semantic information
into this exploration process.
Our research shows how to extract, visualize, and explore informative
regions on very large 2D landscape images, 3D volumetric datasets,
high-dimensional volumetric mouse brain datasets with thousands of
spatially-mapped gene expression profiles, and geospatial trajectories
that evolve over time. The contribution of this dissertation include:
(1) We introduce a sliding-window saliency model that discovers
regions of user interest in very large images; (2) We develop visual
segmentation of intensity-gradient histograms to identify meaningful
components from volumetric datasets; (3) We extract boundary surfaces
from a wealth of volumetric gene expression mouse brain profiles to
personalize the reference brain atlas; (4) We show how to efficiently
cluster geospatial trajectories by mapping each sequence of locations
to a high-dimensional point with the kernel distance framework.
We aim to discover patterns, relationships, and anomalies that would
lead to new scientific, engineering, and medical advances. This work
represents one of the first steps toward better visual understanding
of large-scale scientific data by combining machine learning and human
intelligence
Deep visible and thermal image fusion for enhanced pedestrian visibility
Reliable vision in challenging illumination conditions is one of the crucial requirements of future autonomous automotive systems. In the last decade, thermal cameras have become more easily accessible to a larger number of researchers. This has resulted in numerous studies which confirmed the benefits of the thermal cameras in limited visibility conditions. In this paper, we propose a learning-based method for visible and thermal image fusion that focuses on generating fused images with high visual similarity to regular truecolor (red-green-blue or RGB) images, while introducing new informative details in pedestrian regions. The goal is to create natural, intuitive images that would be more informative than a regular RGB camera to a human driver in challenging visibility conditions. The main novelty of this paper is the idea to rely on two types of objective functions for optimization: a similarity metric between the RGB input and the fused output to achieve natural image appearance; and an auxiliary pedestrian detection error to help defining relevant features of the human appearance and blending them into the output. We train a convolutional neural network using image samples from variable conditions (day and night) so that the network learns the appearance of humans in the different modalities and creates more robust results applicable in realistic situations. Our experiments show that the visibility of pedestrians is noticeably improved especially in dark regions and at night. Compared to existing methods we can better learn context and define fusion rules that focus on the pedestrian appearance, while that is not guaranteed with methods that focus on low-level image quality metrics
IMPORTANCE-DRIVEN TRANSFER FUNCTION DESIGN FOR VOLUME VISUALIZATION OF MEDICAL IMAGES
Ph.DDOCTOR OF PHILOSOPH
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