31,466 research outputs found
Fast approximation of visibility dominance using topographic features as targets and the associated uncertainty
An approach to reduce visibility index computation time andmeasure the associated uncertainty in terrain visibility analysesis presented. It is demonstrated that the visibility indexcomputation time in mountainous terrain can be reduced substantially,without any significant information loss, if the lineof sight from each observer on the terrain is drawn only to thefundamental topographic features, i.e., peaks, pits, passes,ridges, and channels. However, the selected sampling of targetsresults in an underestimation of the visibility index ofeach observer. Two simple methods based on iterative comparisonsbetween the real visibility indices and the estimatedvisibility indices have been proposed for a preliminary assessmentof this uncertainty. The method has been demonstratedfor gridded digital elevation models
Feature and viewpoint selection for industrial car assembly
Abstract. Quality assurance programs of today’s car manufacturers show increasing demand for automated visual inspection tasks. A typical example is just-in-time checking of assemblies along production lines. Since high throughput must be achieved, object recognition and pose estimation heavily rely on offline preprocessing stages of available CAD data. In this paper, we propose a complete, universal framework for CAD model feature extraction and entropy index based viewpoint selection that is developed in cooperation with a major german car manufacturer
Surface networks
© Copyright CASA, UCL. The desire to understand and exploit the structure of continuous surfaces is common to researchers in a range of disciplines. Few examples of the varied surfaces forming an integral part of modern subjects include terrain, population density, surface atmospheric pressure, physico-chemical surfaces, computer graphics, and metrological surfaces. The focus of the work here is a group of data structures called Surface Networks, which abstract 2-dimensional surfaces by storing only the most important (also called fundamental, critical or surface-specific) points and lines in the surfaces. Surface networks are intelligent and “natural ” data structures because they store a surface as a framework of “surface ” elements unlike the DEM or TIN data structures. This report presents an overview of the previous works and the ideas being developed by the authors of this report. The research on surface networks has fou
Visualising the structure of architectural open spaces based on shape analysis
This paper proposes the application of some well known two-dimensional
geometrical shape descriptors for the visualisation of the structure of
architectural open spaces. The paper demonstrates the use of visibility
measures such as distance to obstacles and amount of visible space to calculate
shape descriptors such as convexity and skeleton of the open space. The aim of
the paper is to indicate a simple, objective and quantifiable approach to
understand the structure of open spaces otherwise impossible due to the complex
construction of built structures.Comment: 10 pages, 9 figure
Exploring Human Vision Driven Features for Pedestrian Detection
Motivated by the center-surround mechanism in the human visual attention
system, we propose to use average contrast maps for the challenge of pedestrian
detection in street scenes due to the observation that pedestrians indeed
exhibit discriminative contrast texture. Our main contributions are first to
design a local, statistical multi-channel descriptorin order to incorporate
both color and gradient information. Second, we introduce a multi-direction and
multi-scale contrast scheme based on grid-cells in order to integrate
expressive local variations. Contributing to the issue of selecting most
discriminative features for assessing and classification, we perform extensive
comparisons w.r.t. statistical descriptors, contrast measurements, and scale
structures. This way, we obtain reasonable results under various
configurations. Empirical findings from applying our optimized detector on the
INRIA and Caltech pedestrian datasets show that our features yield
state-of-the-art performance in pedestrian detection.Comment: Accepted for publication in IEEE Transactions on Circuits and Systems
for Video Technology (TCSVT
Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd
Object detection and 6D pose estimation in the crowd (scenes with multiple
object instances, severe foreground occlusions and background distractors), has
become an important problem in many rapidly evolving technological areas such
as robotics and augmented reality. Single shot-based 6D pose estimators with
manually designed features are still unable to tackle the above challenges,
motivating the research towards unsupervised feature learning and
next-best-view estimation. In this work, we present a complete framework for
both single shot-based 6D object pose estimation and next-best-view prediction
based on Hough Forests, the state of the art object pose estimator that
performs classification and regression jointly. Rather than using manually
designed features we a) propose an unsupervised feature learnt from
depth-invariant patches using a Sparse Autoencoder and b) offer an extensive
evaluation of various state of the art features. Furthermore, taking advantage
of the clustering performed in the leaf nodes of Hough Forests, we learn to
estimate the reduction of uncertainty in other views, formulating the problem
of selecting the next-best-view. To further improve pose estimation, we propose
an improved joint registration and hypotheses verification module as a final
refinement step to reject false detections. We provide two additional
challenging datasets inspired from realistic scenarios to extensively evaluate
the state of the art and our framework. One is related to domestic environments
and the other depicts a bin-picking scenario mostly found in industrial
settings. We show that our framework significantly outperforms state of the art
both on public and on our datasets.Comment: CVPR 2016 accepted paper, project page:
http://www.iis.ee.ic.ac.uk/rkouskou/6D_NBV.htm
Occlusion-Aware Object Localization, Segmentation and Pose Estimation
We present a learning approach for localization and segmentation of objects
in an image in a manner that is robust to partial occlusion. Our algorithm
produces a bounding box around the full extent of the object and labels pixels
in the interior that belong to the object. Like existing segmentation aware
detection approaches, we learn an appearance model of the object and consider
regions that do not fit this model as potential occlusions. However, in
addition to the established use of pairwise potentials for encouraging local
consistency, we use higher order potentials which capture information at the
level of im- age segments. We also propose an efficient loss function that
targets both localization and segmentation performance. Our algorithm achieves
13.52% segmentation error and 0.81 area under the false-positive per image vs.
recall curve on average over the challenging CMU Kitchen Occlusion Dataset.
This is a 42.44% decrease in segmentation error and a 16.13% increase in
localization performance compared to the state-of-the-art. Finally, we show
that the visibility labelling produced by our algorithm can make full 3D pose
estimation from a single image robust to occlusion.Comment: British Machine Vision Conference 2015 (poster
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