Dr. Matthias F. Carlsohn Profile

Dr. Matthias F. Carlsohn

Engineer Consultant at Ingenieurberatung Dr. Carlsohn

SPIE Involvement:

Conference Chair | Editor | Author

Area of Expertise:

Image Processing , Computer Vision , Real-time , Pattern Recognition , Machine Learning , Neural Neworks

Proceedings Article | 7 June 2023 Presentation + Paper

Evolution of real-time processing of visual information over four decades: a retrospective as outlook to the future of real-time imaging

Matthias Carlsohn

Proceedings Volume 12571, 1257103 (2023) https://doi.org/10.1117/12.2666707

KEYWORDS: Image processing, Visualization, Video, Data processing, Cameras, Real time image processing, Video processing, Information visualization, Computer hardware, Computing systems

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Proceedings Article | 29 April 2016 Paper

3D real-time visualization of blood flow in cerebral aneurysms by light field particle image velocimetry

Matthias Carlsohn, André Kemmling, Arne Petersen, Lennart Wietzke

Proceedings Volume 9897, 989703 (2016) https://doi.org/10.1117/12.2220417

KEYWORDS: 3D modeling, Particles, 3D image processing, Cameras, Brain, 3D metrology, Visualization, Image processing, Video, Blood circulation

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Cerebral aneurysms require endovascular treatment to eliminate potentially lethal hemorrhagic rupture by hemostasis of blood flow within the aneurysm. Devices (e.g. coils and flow diverters) promote homeostasis, however, measurement of blood flow within an aneurysm or cerebral vessel before and after device placement on a microscopic level has not been possible so far. This would allow better individualized treatment planning and improve manufacture design of devices. For experimental analysis, direct measurement of real-time microscopic cerebrovascular flow in micro-structures may be an alternative to computed flow simulations. An application of microscopic aneurysm flow measurement on a regular basis to empirically assess a high number of different anatomic shapes and the corresponding effect of different devices would require a fast and reliable method at low cost with high throughout assessment. Transparent three dimensional 3D models of brain vessels and aneurysms may be used for microscopic flow measurements by particle image velocimetry (PIV), however, up to now the size of structures has set the limits for conventional 3D-imaging camera set-ups. On line flow assessment requires additional computational power to cope with the processing large amounts of data generated by sequences of multi-view stereo images, e.g. generated by a light field camera capturing the 3D information by plenoptic imaging of complex flow processes. Recently, a fast and low cost workflow for producing patient specific three dimensional models of cerebral arteries has been established by stereo-lithographic (SLA) 3D printing. These 3D arterial models are transparent an exhibit a replication precision within a submillimeter range required for accurate flow measurements under physiological conditions. We therefore test the feasibility of microscopic flow measurements by PIV analysis using a plenoptic camera system capturing light field image sequences. Averaging across a sequence of single double or triple shots of flashed images enables reconstruction of the real-time corpuscular flow through the vessel system before and after device placement. This approach could enable 3D-insight of microscopic flow within blood vessels and aneurysms at submillimeter resolution. We present an approach that allows real-time assessment of 3D particle flow by high-speed light field image analysis including a solution that addresses high computational load by image processing. The imaging set-up accomplishes fast and reliable PIV analysis in transparent 3D models of brain aneurysms at low cost. High throughput microscopic flow assessment of different shapes of brain aneurysms may therefore be possibly required for patient specific device designs.

Proceedings Article | 15 May 2014 Paper

Comparison of two real-time hand gesture recognition systems involving stereo cameras, depth camera, and inertial sensor

Kui Liu, Nasser Kehtarnavaz, Matthias Carlsohn

Proceedings Volume 9139, 91390C (2014) https://doi.org/10.1117/12.2051342

KEYWORDS: Sensors, Gesture recognition, Imaging systems, Cameras, Stereoscopic cameras, Image fusion, Sensor fusion, Computing systems, Calibration, Image segmentation

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Proceedings Article | 2 May 2012 Paper

Real-time FPGA implementation of recursive wavelet packet transform

Vanishree Gopalakrishna, Nasser Kehtarnavaz, Chandrasekhar Patlolla, Matthias Carlsohn

Proceedings Volume 8437, 84370P (2012) https://doi.org/10.1117/12.924156

KEYWORDS: Wavelets, Field programmable gate arrays, Digital filtering, LabVIEW, Computer programming, Image filtering, Discrete wavelet transforms, Wavelet transforms, Data conversion, Image processing

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Proceedings Article | 5 May 2010 Paper

Real-time logo detection and tracking in video

M. George, N. Kehtarnavaz, M. Rahman, M. Carlsohn

Proceedings Volume 7724, 77240B (2010) https://doi.org/10.1117/12.853598

KEYWORDS: Detection and tracking algorithms, Calibration, Image processing, Video, Binary data, Digital filtering, Electronic filtering, Light sources and illumination, Associative arrays, Cell phones

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Showing 5 of 6 publications

SPIE Conference Volume | 21 June 2023

Real-time Processing of Image, Depth and Video Information 2023

SPIE Conference Volume | 21 June 2022

Real-Time Image Processing and Deep Learning 2022

SPIE Conference Volume | 10 May 2021

Real-Time Image Processing and Deep Learning 2021

SPIE Conference Volume | 26 May 2020

Real-Time Image Processing and Deep Learning 2020

SPIE Conference Volume | 26 July 2019

Real-Time Image Processing and Deep Learning 2019

Showing 5 of 17 publications

Conference Committee Involvement (22)

Real-time Processing of Image, Depth and Video Information 2024

8 April 2024 | Strasbourg, France

Real-time Processing of Image, Depth and Video Information 2023

24 April 2023 | Prague, Czech Republic

Real-Time Image Processing and Deep Learning 2022

4 April 2022 | Orlando, Florida, United States

Real-Time Image Processing and Deep Learning 2021

12 April 2021 | Online Only, Florida, United States

Real-Time Image Processing and Deep Learning 2020

27 April 2020 | Online Only, California, United States

Real-Time Image Processing and Deep Learning 2019

15 April 2019 | Baltimore, MD, United States

Real-Time Image and Video Processing 2018

16 April 2018 | Orlando, FL, United States

Real-Time Image and Video Processing 2017

10 April 2017 | Anaheim, CA, United States