Results 221 to 230 of about 61,033 (257)
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Monocular Visual Scene Understanding: Understanding Multi-Object Traffic Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Following recent advances in detection, context modeling, and tracking, scene understanding has been the focus of renewed interest in computer vision research. This paper presents a novel probabilistic 3D scene model that integrates state-of-the-art multiclass object detection, object tracking and scene labeling together with geometric 3D reasoning ...
Stefan Roth, Konrad Schindler
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Computer Vision and Image Understanding, 1995
Abstract Most computer vision systems are concerned with computing the whats and wheres of a scene. We describe a set of programs concerned instead with computing the whys and hows—why the scene is the way it is, and how an agent can interact with it.
Paul R. Cooper +2 more
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Abstract Most computer vision systems are concerned with computing the whats and wheres of a scene. We describe a set of programs concerned instead with computing the whys and hows—why the scene is the way it is, and how an agent can interact with it.
Paul R. Cooper +2 more
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A Parallel Framework for Scene Understanding
2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI), 2021Different related computer vision tasks make it possible to understand and analyze the scene totally. However, there is no useful framework combining different tasks for scene understanding. In this paper, we investigate the current state of scene understanding from different aspects relying on different computer vision tasks and propose an effective ...
Wenwen Zhang +2 more
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Understanding scenes on many levels
2011 International Conference on Computer Vision, 2011This paper presents a framework for image parsing with multiple label sets. For example, we may want to simultaneously label every image region according to its basic-level object category (car, building, road, tree, etc.), superordinate category (animal, vehicle, manmade object, natural object, etc.), geometric orientation (horizontal, vertical, etc.),
Joseph Tighe, Svetlana Lazebnik
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An Architecture for Automated Scene Understanding
Seventh International Workshop on Computer Architecture for Machine Perception (CAMP'05), 2006This paper presents distributed, automated, scene surveillance architecture. Object detection and tracking is performed by a set of region and object agents. The area under surveillance is divided in several sub-areas. One camera is assigned to each sub-area. A region agent is responsible for monitoring a given sub-area. Background subtraction is first
Ruth Aguilar-Ponce +3 more
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Context-Based Scene Understanding
International Journal of Multimedia Data Engineering and Management, 2016Context plays an important role in performance of object detection. There are two popular considerations in building context models for computer vision applications; type of context (semantic, spatial, scale) and scope of the relations (pairwise, high-order).
Esfandiar Zolghadr, Borko Furht
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Convex Optimization for Scene Understanding
2013 IEEE International Conference on Computer Vision Workshops, 2013In this paper we give a convex optimization approach for scene understanding. Since segmentation, object recognition and scene labeling strongly benefit from each other we propose to solve these tasks within a single convex optimization problem. In contrast to previous approaches we do not rely on pre-processing techniques such as object detectors or ...
Mohamed Souiai +3 more
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Scene Understanding With Automotive Radar
IEEE Transactions on Intelligent Vehicles, 2020Extracting semantic information solely from automotive radar data is a relatively new topic in the radar community. We present a complete pipeline to obtain semantic information for each target measured by a network of radar sensors. Static and dynamic objects are treated in two separate branches: In the first branch, a convolutional neural network ...
Ole Schumann +4 more
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IEEE Signal Processing Letters, 2019
Road scene understanding is a difficult task in autonomous driving. In this letter, we propose a novel deep encoder–decoder architecture for road scene understanding in an end-to-end manner. This core trainable understanding engine includes an encoder network, a decoder network with two streams, and a pixel-level fusion network with classification ...
Wujie Zhou +3 more
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Road scene understanding is a difficult task in autonomous driving. In this letter, we propose a novel deep encoder–decoder architecture for road scene understanding in an end-to-end manner. This core trainable understanding engine includes an encoder network, a decoder network with two streams, and a pixel-level fusion network with classification ...
Wujie Zhou +3 more
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Scene understanding by rule evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997We consider how machine learning can be used to help solve the problem of identifying objects or structures composed of parts in complex scenes. We first discuss a conditional rule generation technique that is designed to describe structures using part attributes and their relations.
Walter F. Bischof, Terry Caelli
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