30,411 research outputs found
General Dynamic Scene Reconstruction from Multiple View Video
This paper introduces a general approach to dynamic scene reconstruction from
multiple moving cameras without prior knowledge or limiting constraints on the
scene structure, appearance, or illumination. Existing techniques for dynamic
scene reconstruction from multiple wide-baseline camera views primarily focus
on accurate reconstruction in controlled environments, where the cameras are
fixed and calibrated and background is known. These approaches are not robust
for general dynamic scenes captured with sparse moving cameras. Previous
approaches for outdoor dynamic scene reconstruction assume prior knowledge of
the static background appearance and structure. The primary contributions of
this paper are twofold: an automatic method for initial coarse dynamic scene
segmentation and reconstruction without prior knowledge of background
appearance or structure; and a general robust approach for joint segmentation
refinement and dense reconstruction of dynamic scenes from multiple
wide-baseline static or moving cameras. Evaluation is performed on a variety of
indoor and outdoor scenes with cluttered backgrounds and multiple dynamic
non-rigid objects such as people. Comparison with state-of-the-art approaches
demonstrates improved accuracy in both multiple view segmentation and dense
reconstruction. The proposed approach also eliminates the requirement for prior
knowledge of scene structure and appearance
The aceToolbox: low-level audiovisual feature extraction for retrieval and classification
In this paper we present an overview of a software platform
that has been developed within the aceMedia project,
termed the aceToolbox, that provides global and local lowlevel feature extraction from audio-visual content. The toolbox is based on the MPEG-7 eXperimental Model (XM),
with extensions to provide descriptor extraction from arbitrarily shaped image segments, thereby supporting local descriptors reflecting real image content. We describe the architecture of the toolbox as well as providing an overview of the descriptors supported to date. We also briefly describe the segmentation algorithm provided. We then demonstrate the usefulness of the toolbox in the context of two different content processing scenarios: similarity-based retrieval in large collections and scene-level classification of still images
Real-Time fusion of visual images and laser data images for safe navigation in outdoor environments
[EN]In recent years, two dimensional laser range finders mounted on vehicles is becoming a
fruitful solution to achieve safety and environment recognition requirements (Keicher &
Seufert, 2000), (Stentz et al., 2002), (DARPA, 2007). They provide real-time accurate range
measurements in large angular fields at a fixed height above the ground plane, and enable
robots and vehicles to perform more confidently a variety of tasks by fusing images from
visual cameras with range data (Baltzakis et al., 2003). Lasers have normally been used in
industrial surveillance applications to detect unexpected objects and persons in indoor
environments. In the last decade, laser range finder are moving from indoor to outdoor rural
and urban applications for 3D imaging (Yokota et al., 2004), vehicle guidance (Barawid et
al., 2007), autonomous navigation (Garcia-Pérez et al., 2008), and objects recognition and
classification (Lee & Ehsani, 2008), (Edan & Kondo, 2009), (Katz et al., 2010). Unlike
industrial applications, which deal with simple, repetitive and well-defined objects, cameralaser
systems on board off-road vehicles require advanced real-time techniques and
algorithms to deal with dynamic unexpected objects. Natural environments are complex
and loosely structured with great differences among consecutive scenes and scenarios.
Vision systems still present severe drawbacks, caused by lighting variability that depends
on unpredictable weather conditions. Camera-laser objects feature fusion and classification
is still a challenge within the paradigm of artificial perception and mobile robotics in
outdoor environments with the presence of dust, dirty, rain, and extreme temperature and
humidity. Real time relevant objects perception, task driven, is a main issue for subsequent
actions decision in safe unmanned navigation. In comparison with industrial automation
systems, the precision required in objects location is usually low, as it is the speed of most
rural vehicles that operate in bounded and low structured outdoor environments.
To this aim, current work is focused on the development of algorithms and strategies for
fusing 2D laser data and visual images, to accomplish real-time detection and classification
of unexpected objects close to the vehicle, to guarantee safe navigation. Next, class
information can be integrated within the global navigation architecture, in control modules,
such as, stop, obstacle avoidance, tracking or mapping.Section 2 includes a description of the commercial vehicle, robot-tractor DEDALO and the
vision systems on board. Section 3 addresses some drawbacks in outdoor perception.
Section 4 analyses the proposed laser data and visual images fusion method, focused in the
reduction of the visual image area to the region of interest wherein objects are detected by
the laser. Two methods of segmentation are described in Section 5, to extract the shorter area
of the visual image (ROI) resulting from the fusion process. Section 6 displays the colour
based classification results of the largest segmented object in the region of interest. Some
conclusions are outlined in Section 7, and acknowledgements and references are displayed
in Section 8 and Section 9.projects: CICYT- DPI-2006-14497 by the Science
and Innovation Ministry, ROBOCITY2030 I y II: Service Robots-PRICIT-CAM-P-DPI-000176-
0505, and SEGVAUTO: Vehicle Safety-PRICIT-CAM-S2009-DPI-1509 by Madrid State
Government.Peer reviewe
Research in interactive scene analysis
An interactive scene interpretation system (ISIS) was developed as a tool for constructing and experimenting with man-machine and automatic scene analysis methods tailored for particular image domains. A recently developed region analysis subsystem based on the paradigm of Brice and Fennema is described. Using this subsystem a series of experiments was conducted to determine good criteria for initially partitioning a scene into atomic regions and for merging these regions into a final partition of the scene along object boundaries. Semantic (problem-dependent) knowledge is essential for complete, correct partitions of complex real-world scenes. An interactive approach to semantic scene segmentation was developed and demonstrated on both landscape and indoor scenes. This approach provides a reasonable methodology for segmenting scenes that cannot be processed completely automatically, and is a promising basis for a future automatic system. A program is described that can automatically generate strategies for finding specific objects in a scene based on manually designated pictorial examples
Cognitive visual tracking and camera control
Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision
Positional estimation techniques for an autonomous mobile robot
Techniques for positional estimation of a mobile robot navigation in an indoor environment are described. A comprehensive review of the various positional estimation techniques studied in the literature is first presented. The techniques are divided into four different types and each of them is discussed briefly. Two different kinds of environments are considered for positional estimation; mountainous natural terrain and an urban, man-made environment with polyhedral buildings. In both cases, the robot is assumed to be equipped with single visual camera that can be panned and tilted and also a 3-D description (world model) of the environment is given. Such a description could be obtained from a stereo pair of aerial images or from the architectural plans of the buildings. Techniques for positional estimation using the camera input and the world model are presented
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