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Automatic description of complex buildings from multiple images

by Zuwhan Kim, Ramakant Nevatia - Comput. Vis. Image Underst , 2004
"... www.elsevier.com/locate/cviu We present an approach for detecting and describing complex buildings with flat or complex rooftops by using multiple, overlapping images of the scene. We find 3-D rooftop boundary hypotheses from the line and junction features of the images by applying consecutive group ..."
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www.elsevier.com/locate/cviu We present an approach for detecting and describing complex buildings with flat or complex rooftops by using multiple, overlapping images of the scene. We find 3-D rooftop boundary hypotheses from the line and junction features of the images by applying consecutive grouping procedures. First, 3-D features are generated by grouping image features over multiple images, and rooftop hypotheses are generated by neighborhood searches on those features. Probabilistic reasoning, level-of-details, and cues from image-derived unedited elevation data are used at various stages to manage the huge search space for rooftop boundary hypotheses. Three-dimensional rooftop hypotheses generated by above procedures are verified with evidence collected from the images and the elevation data. Expandable Bayesian networks are used to combine evidence from multiple images. Finally, overlap and rooftop analyses are performed to find the final building models. Experimental results are shown on complex buildings.
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Flight demonstrations of self-directed collaborative navigation of small unmanned aircraft

by Eric W. Frew - In AIAA 3 rd Unmanned Unlimited Technical Conference, September 2004. 48 Journal of Computer Applications (0975 – 8887) Volume 47– No.23 , 2012
"... The development of small, autonomous UAVs that can operate in complex environments as part of large coordinated groups will enable many new applications at fractions of the cost of current systems. A fleet of fixed-wing aircraft has been developed to create an intelligent aerial platform that has de ..."
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The development of small, autonomous UAVs that can operate in complex environments as part of large coordinated groups will enable many new applications at fractions of the cost of current systems. A fleet of fixed-wing aircraft has been developed to create an intelligent aerial platform that has demonstrated various autonomous capabilities. By means of a downward-looking camera, a single aircraft autonomously follows a roadway using the natural features of the scene in conjunction with onboard sensors, without the use of GPS or prior knowledge of the road’s coordinates. A forward looking camera is used to perceive obstacles in the aircraft’s flight path by segmenting images into sky/no-sky regions and classifying no-sky regions above the horizon as obstacles. The tracking of friendly ground vehicles – for which GPS information is known but path information is not – is performed using circular and sinusoidal orbits to maintain desired proximity regardless of ground vehicle motion. Teams of two or three aircraft demonstrate convoy protection by providing persistent surveillance around a moving ground vehicle. The team either flies in rigid formation, providing a large area of coverage around the ground vehicle, or the aircraft coordinate several separate actions that provide both lateral (side to side) and longitudinal

A Supervised Classification Approach Towards Quality Self-Diagnosis Of 3D Building Models Using

by Laurence Boudet, Nicolas Paparoditis, Franck Jung, Gilles Martinoty, Marc Pierrot-deseilligny - Digital Aerial Imagery, IAPRS
"... In the context of 3D building model production or updating, the models have to be manually checked one by one by a human operator in order to ensure their quality. In this paper, we investigate a new approach to perform a quality self-diagnosis of building models in dense urban areas from high resol ..."
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In the context of 3D building model production or updating, the models have to be manually checked one by one by a human operator in order to ensure their quality. In this paper, we investigate a new approach to perform a quality self-diagnosis of building models in dense urban areas from high resolution aerial images. Hence, we aim at reliably identifying roof facets that do not comply with quality specifications. The self-diagnosis process will highlight potential incorrect facets for their inspection by a human operator. A set of calibrated aerial images enable us to collect positive or negative evidences of roof facet existence and consistency. A particular attention has been paid to the definition of a set of low-level, complementary, robust and consistent image processing measures. Four quality classes have been defined and are used to classify roof facet quality. A supervised classifier and robust decision rules are then applied to perform an effective self-diagnosis according to the traffic light paradigm. Finally, the work in progress leads to a promising quantitative and qualitative evaluation in the context of dense urban areas. 1.1 Motivation 1.
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Object Extraction for Digital Photogrammetric Workstations

by Helmut Mayer - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences , 2004
"... This paper deals with the state and with promising directions of automated object extraction for digital photogrammetric workstations (DPW). A review of the state of the art shows that there are only few success stories. Therefore, important areas for a practical success are identified. A solid and ..."
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This paper deals with the state and with promising directions of automated object extraction for digital photogrammetric workstations (DPW). A review of the state of the art shows that there are only few success stories. Therefore, important areas for a practical success are identified. A solid and most important powerful theoretical background is the basis. Here, we advocate particularly statistical modeling. Testing makes clear which of the approaches are best suited and how useful they are for praxis. A key for commercial success is user interaction, an area where much work still has to be done. As the means for data acquisition are changing, new promising application areas such as extremely detailed three-dimensional (3D) urban models for virtual television or mission rehearsal evolve. 1
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Incremental Unsupervised Three-Dimensional Vehicle Model Learning From Video

by Nirmalya Ghosh, Bir Bhanu
"... Abstract—In this paper, we present a new generic model-based approach for building 3-D models of vehicles from color video from a single uncalibrated traffic-surveillance camera. We pro-pose a novel directional template method that uses trigonometric relations of the 2-D features and geometric relat ..."
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Abstract—In this paper, we present a new generic model-based approach for building 3-D models of vehicles from color video from a single uncalibrated traffic-surveillance camera. We pro-pose a novel directional template method that uses trigonometric relations of the 2-D features and geometric relations of a single 3-D generic vehicle model to map 2-D features to 3-D in the face of pro-jection and foreshortening effects. We use novel hierarchical struc-tural similarity measures to evaluate these single-frame-based 3-D estimates with respect to the generic vehicle model. Using these similarities, we adopt a weighted clustering technique to build a 3-D model of the vehicle for the current frame. The 3-D features are then adaptively clustered again over the frame sequence to generate an incremental 3-D model of the vehicle. Results are shown for several simulated and real traffic videos in an uncontrolled setup. Finally, the results are evaluated by the same structural performance measure, underscoring the useful-ness of incremental learning. The performance of the proposed method for several types of vehicles in two considerably different traffic spots is very promising to encourage its applicability in 3-D reconstruction of other rigid objects in video. Index Terms—Clustering, generic vehicle models, traffic sur-veillance, video-based 3-D modeling, 3-D vehicle modeling. I.
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Bayesian based 3D shape reconstruction from video

by Nirmalya Ghosh, Bir Bhanu - in Proc. IEEE ICIP
"... In a video sequence with a 3D rigid object moving, changing shapes of the 2D projections provide interrelated spatio-temporal cues for incremental 3D shape reconstruction. This paper describes a probabilistic approach for intelligent view-integration to build 3D model of vehicles from traffic videos ..."
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In a video sequence with a 3D rigid object moving, changing shapes of the 2D projections provide interrelated spatio-temporal cues for incremental 3D shape reconstruction. This paper describes a probabilistic approach for intelligent view-integration to build 3D model of vehicles from traffic videos collected from an uncalibrated static camera. The proposed Bayesian net framework allows the handling of uncertainties in a systematic manner. The performance is verified with several types of vehicles in different videos. Index Terms – Learning, 3D shape from video 1.
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OBJECT EXTRACTION FOR DIGITAL PHOTOGRAMMETRIC WORKSTATIONS Helmut Mayer

by unknown authors
"... This paper deals with the state and with promising directions of automated object extraction for digital photogrammetric workstations (DPW). A review of the state of the art shows that there are only few success stories. Therefore, important areas for a practical success are identified. A solid and ..."
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This paper deals with the state and with promising directions of automated object extraction for digital photogrammetric workstations (DPW). A review of the state of the art shows that there are only few success stories. Therefore, important areas for a practical success are identified. A solid and most important powerful theoretical background is the basis. Here, we advocate particularly statistical modeling. Testing makes clear which of the approaches are best suited and how useful they are for praxis. A key for commercial success is user interaction, an area where much work still has to be done. As the means for data acquisition are changing, new promising application areas such as extremely detailed three-dimensional (3D) urban models for virtual television or mission rehearsal evolve. 1
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Recursive Tower of Knowledge for Learning to Interpret Scenes

by Mai Xu, Maria Petrou
"... The Tower of Knowledge architecture integrates probability theory and logic for making decisions. The scheme models the causal dependencies between the functionalities of objects and their descriptions, and then employs the maximum expected utility principle, which combines probability theory and lo ..."
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The Tower of Knowledge architecture integrates probability theory and logic for making decisions. The scheme models the causal dependencies between the functionalities of objects and their descriptions, and then employs the maximum expected utility principle, which combines probability theory and logic, to select the most appropriate label for the object. Since most existing scene interpretation methods rely heavily on training data, we develop in this paper a recursive version of ToK to avoid such dependency. Recursive ToK learns the prior distributions iteratively from the decisions of labelling components made in the last iteration, partly by functionalities of components, and partly by the already learnt prior distributions in previous iterations. To validate our method in the domain of 3D outdoor scene interpretation, we compare ToK against a state-of-the-art method, Expandable Bayesian Networks (EBN), for labelling components of buildings. Experimental results then show that the labelling accuracy of ToK is superior to that of EBN. Also, these results reveal that recursive ToK improves the accuracy of ToK for labelling 3D components in the worst case when lacking any training data. 1
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by unknown authors
"... ram ..."
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978-1-4244-4620-9/09/$25.00 c©2009 IEEE Continuously Evolvable Bayesian Nets for Human Action Analysis in Videos

by Nirmalaya Ghosh, Bir Bhanu, Giovanni Denina
"... Abstract—This paper proposes a novel data driven continuously evolvable Bayesian Net (BN) framework to analyze human actions in video. In unpredictable video streams, only a few generic causal relations and their interrelations together with the dynamic changes of these interrelations are used to pr ..."
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Abstract—This paper proposes a novel data driven continuously evolvable Bayesian Net (BN) framework to analyze human actions in video. In unpredictable video streams, only a few generic causal relations and their interrelations together with the dynamic changes of these interrelations are used to probabilistically estimate relatively complex human activities. Based on the available evidences in streaming videos, the proposed BN can dynamically change the number of nodes in every frame and different relations for the same nodes in different frames. The performance of the proposed BN framework is shown for complex movie clips where actions like hand on head or waist, standing close, and holding hands take place among multiple individuals under changing pose conditions. The proposed BN can represent and recognize the human activities in a scalable manner Keywords-human action recognition; Bayesian Nets; interactions of multiple people; behavior analysis; I.
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