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Ulvklo, Morgan
Publications (7 of 7) Show all publications
Ahlberg, J., Folkesson, M., Grönwall, C., Horney, T., Jungert, E., Klasén, L. & Ulvklo, M. (2006). Ground Target Recognition in a Query-Based Multi-Sensor Information System. Linköping, Sweden: Department of Electrical Engineering
Open this publication in new window or tab >>Ground Target Recognition in a Query-Based Multi-Sensor Information System
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2006 (English)Report (Other academic)
Abstract [en]

We present a system covering the complete process for automatic ground target recognition, from sensor data to the user interface, i.e., from low level image processing to high level situation analysis. The system is based on a query language and a query processor, and includes target detection, target recognition, data fusion, presentation and situation analysis. This paper focuses on target recognition and its interaction with the query processor. The target recognitionis executed in sensor nodes, each containing a sensor and the corresponding signal/image processing algorithms. New sensors and algorithms are easily added to the system. The processing of sensor data is performed in two steps; attribute estimation and matching. First, several attributes, like orientation and dimensions, are estimated from the (unknown but detected) targets. These estimates are used to select the models of interest in a matching step, where the targetis matched with a number of target models. Several methods and sensor data types are used in both steps, and data is fused after each step. Experiments have been performed using sensor data from laser radar, thermal and visual cameras. Promising results are reported, demonstrating the capabilities of the target recognition algorithms, the advantages of the two-level data fusion and the query-based system.

Place, publisher, year, edition, pages
Linköping, Sweden: Department of Electrical Engineering, 2006. p. 29
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2748
Keywords
Multi-sensor fusion, Query languages, Infrared sensors, Laser radar, Range data, Target recognition, Target detection
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-14124 (URN)LiTH-ISY-R-2748 (ISRN)
Available from: 2006-11-06 Created: 2006-11-06 Last updated: 2016-08-31Bibliographically approved
Ulvklo, M., Nygårds, J., Karlholm, J., Skoglar, P., Ahlberg, J. & Nilsson, J. (2005). A sensor management framework for autonomous UAV surveillance. In: Proceedings of SPIE 5787, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications II: . Paper presented at Airborne Intelligence, Surveillance, Reconnaissance Systems and Applications II, Orlando, Florida, USA, March 28, 2005 (pp. 48-61). SPIE - International Society for Optical Engineering
Open this publication in new window or tab >>A sensor management framework for autonomous UAV surveillance
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2005 (English)In: Proceedings of SPIE 5787, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications II, SPIE - International Society for Optical Engineering, 2005, p. 48-61Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents components of a sensor management architecture for autonomous UAV systems equipped with IR and video sensors, focusing on two main areas. Firstly, a framework inspired by optimal control and information theory is presented for concurrent path and sensor planning. Secondly, a method for visual landmark selection and recognition is presented. The latter is intended to be used within a SLAM (Simultaneous Localization and Mapping) architecture for visual navigation. Results are presented on both simulated and real sensor data, the latter from the MASP system (Modular Airborne Sensor Platform), an in-house developed UAV surrogate system containing a gimballed IR camera, a video sensor, and an integrated high performance navigation system.

Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2005
Series
Proceedings of SPIE, ISSN 0277-786X ; 5787
Keywords
UAV, surveillance, image processing, sensor management, path planning, autonomous systems, IR, tracking, detection, SLAM
National Category
Signal Processing Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-120562 (URN)10.1117/12.605023 (DOI)
Conference
Airborne Intelligence, Surveillance, Reconnaissance Systems and Applications II, Orlando, Florida, USA, March 28, 2005
Available from: 2015-08-14 Created: 2015-08-14 Last updated: 2018-01-11Bibliographically approved
Horney, T., Ahlberg, J., Grönwall, C., Folkesson, M., Silvervarg, K., Fransson, J., . . . Ulvklo, M. (2004). An information system for target recognition. In: Belur V. Dasarathy (Ed.), Volume 5434 Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications: . Paper presented at Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications, Orlando, FL, USA, April 12, 2004 (pp. 163-175). SPIE - International Society for Optical Engineering
Open this publication in new window or tab >>An information system for target recognition
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2004 (English)In: Volume 5434 Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications / [ed] Belur V. Dasarathy, SPIE - International Society for Optical Engineering, 2004, p. 163-175Conference paper, Published paper (Refereed)
Abstract [en]

We present an approach to a general decision support system. The aim is to cover the complete process for automatic target recognition, from sensor data to the user interface. The approach is based on a query-based information system, and include tasks like feature extraction from sensor data, data association, data fusion and situation analysis. Currently, we are working with data from laser radar, infrared cameras, and visual cameras, studying target recognition from cooperating sensors on one or several platforms. The sensors are typically airborne and at low altitude. The processing of sensor data is performed in two steps. First, several attributes are estimated from the (unknown but detected) target. The attributes include orientation, size, speed, temperature etc. These estimates are used to select the models of interest in the matching step, where the target is matched with a number of target models, returning a likelihood value for each model. Several methods and sensor data types are used in both steps. The user communicates with the system via a visual user interface, where, for instance, the user can mark an area on a map and ask for hostile vehicles in the chosen area. The user input is converted to a query in ΣQL, a query language developed for this type of applications, and an ontological system decides which algorithms should be invoked and which sensor data should be used. The output from the sensors is fused by a fusion module and answers are given back to the user. The user does not need to have any detailed technical knowledge about the sensors (or which sensors that are available), and new sensors and algorithms can easily be plugged into the system.

Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2004
Series
Proceedings of SPIE, ISSN 0277-786X ; 5434
Keywords
Target recognition ; Sensors ; Interfaces ; Algorithms ; Automatic target recognition ; Cameras ; Data fusion ; Decision support systems ; Feature extraction ; Infrared cameras
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-120917 (URN)10.1117/12.540968 (DOI)
Conference
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications, Orlando, FL, USA, April 12, 2004
Available from: 2015-08-31 Created: 2015-08-31 Last updated: 2018-01-11Bibliographically approved
Nygårds, J., Ulvklo, M., Skoglar, P. & Högström, T. (2004). Navigation aided image processing i UAV surveillance. Preliminary results and design of an airborne experimental systems. In: First Workshop on Integration of Vision and Intertial Sensors,2003. Coimbra, Portugal: Workshop on Integration of Vision and Intertial Sensors
Open this publication in new window or tab >>Navigation aided image processing i UAV surveillance. Preliminary results and design of an airborne experimental systems
2004 (English)In: First Workshop on Integration of Vision and Intertial Sensors,2003, Coimbra, Portugal: Workshop on Integration of Vision and Intertial Sensors , 2004Conference paper, Published paper (Refereed)
Abstract [en]

   

Place, publisher, year, edition, pages
Coimbra, Portugal: Workshop on Integration of Vision and Intertial Sensors, 2004
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-36438 (URN)31373 (Local ID)31373 (Archive number)31373 (OAI)
Available from: 2009-10-10 Created: 2009-10-10
Ulvklo, M., Granlund, G. H. & Knutsson, H. (1998). Adaptive Reconstruction Using Multiple Views. In: Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation: Tucson, Arizona, USA (pp. 47-52).
Open this publication in new window or tab >>Adaptive Reconstruction Using Multiple Views
1998 (English)In: Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation: Tucson, Arizona, USA, 1998, p. 47-52Conference paper, Published paper (Refereed)
Abstract [en]

This paper introduces a novel algorithm for extracting the optical flow obtained from a translating camera in a static scene. Occlusion between objects is incorporated as a natural component in a scene reconstruction strategy by first evaluate and reconstruct the foreground and then exclude its influence on the partly occluded objects behind.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-21744 (URN)
Note
Extended version in LiTH-ISY-R-2036Available from: 2009-10-25 Created: 2009-10-05 Last updated: 2013-08-28
Ulvklo, M., Knutsson, H. & Granlund, G. H. (1998). Depth Segmentation and Occluded Scene Reconstruction using Ego-motion. In: Proceedings of the SPIE Conference on Visual Information Processing: Orlando, Florida, USA (pp. 112-123).
Open this publication in new window or tab >>Depth Segmentation and Occluded Scene Reconstruction using Ego-motion
1998 (English)In: Proceedings of the SPIE Conference on Visual Information Processing: Orlando, Florida, USA, 1998, p. 112-123Conference paper, Published paper (Refereed)
Abstract [en]

This paper introduces a signal processing strategy for depth segmentation and scene reconstruction that incorporates occlusion as a natural component. The work aims to maximize the use of connectivity in the temporal domain as much as possible under the condition that the scene is static and that the camera motion is known. An object behind the foreground is reconstructed using the fact that different parts of the object have been seen in different images in the sequence. One of the main ideas in this paper is the use of a spatio- temporal certainty volume c(x) with the same dimension as the input spatio- temporal volume s(x), and then use c(x) as a 'blackboard' for rejecting already segmented image structures. The segmentation starts with searching for image structures in the foreground, eliminate their occluding influence, and then proceed. Normalized convolution, which is a Weighted Least Mean Square technique for filtering data with varying spatial reliability, is used for all filtering. High spatial resolution near object borders is achieved and only neighboring structures with similar depth supports each other.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-21721 (URN)
Available from: 2009-10-25 Created: 2009-10-05 Last updated: 2013-08-28
Ulvklo, M. (1995). Texture Analysis. In: Gösta H. Granlund and Hans Knutsson (Ed.), Signal Processing for Computer Vision: (pp. 399-418). Dordrecht: Kluwer
Open this publication in new window or tab >>Texture Analysis
1995 (English)In: Signal Processing for Computer Vision / [ed] Gösta H. Granlund and Hans Knutsson, Dordrecht: Kluwer , 1995, p. 399-418Chapter in book (Refereed)
Abstract [en]

This chapter deals with texture analysis, an important application of the methods described in earlier chapters. It introduces ideas from preattentive vision, which gives clues for the extraction of texture primitives. There is also a discussion on how to handle features whose significance varies with spatial position.

Place, publisher, year, edition, pages
Dordrecht: Kluwer, 1995
Keywords
Computer vision, Biological vision, Vision structure, Adaptive filtering, Seende datorer, Databehandling Bildbehandling
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-66942 (URN)0-7923-9530-1 (ISBN)978-0-7923-9530-0 (ISBN)
Available from: 2011-03-22 Created: 2011-03-22 Last updated: 2014-05-08Bibliographically approved
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