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Embedded Computer Vision Systems for Image Based Motion and Speckle Image Analysis
May 13, 2013 @ 3:00 pm - 4:00 pm PDT
Date/Time: Friday, May 10, 2013, 2:00 p.m. – 3: 00p.m.
Location: 4011 Donald Bren Hall
Committee Members:
Pai H. Chou (Chair)
Bernard Choi
Glenn Healey
Abstract:
Motion is common in taking images using conventional cameras. If there is motion either on camera or the object or on the both sides during the exposure time, the image is blurred, which is called motion blurred image. Motion blurred image is an interesting research subject in several areas including computer vision, biomedical engineering. In general, motion is undesirable to acquire clear image. Modern cameras are equipped with the mechanical structure to compensate motion artifact. However, mechanical structure has limit in compensating huge motion. To compensate huge motion, computer vision algorithm called motion deblurring is developed. Basically, it estimates the best latent image from the blurry image using several assumptions (or priors) on the latent image and camera motion. The information included in the motion blur can be useful for biomedical applications. Laser speckle imaging is one good example. When laser light of certain frequency (visible laser is widely used) is illuminated on the skin, the scattered laser light from the surface has random distribution if there is no motion. With motion, the speckle pattern is interfered by the motion and no longer random for the region with motion. Since the laser light can penetrate into skin, it is possible to measure blood perfusion near skin without invasion. Both motion deblurring and laser speckle imaging require huge computation time. That is the reason why they are developed on personal computers or workstations. However, the physical size, power consumption, and cost make them harder to penetrate into wider-range of applications (e.g. A super-fast motion deblurring algorithm that can run on embedded platforms like portable digital cameras or smart phones or portable laser speckle imaging system for an emergency vehicle). Nowadays, embedded processors become more and more powerful. The CPU clock frequency of those processors have already passed 1Ghz. Some of them use multiple number of cores for processing like x86 processors. However, even with such high performance processors, special design considerations are required. In this dissertation, we suggest embedded hardware and software systems for motion deblurring and laser speckle imaging applications. In the first part of the dissertation, we will discuss about our motion deblurring systems. We developed an extremely light-weight motion deblurring algorithm to make it fast enough to be suitable for embedded computing platforms. The developed algorithm is up to 100 times faster than the most recent deblurring algorithms. We also developed a hardware/software system for extremely accurate motion deblurring. The hardware system is composed of IMU, a control board, and the Microsoft Kinect. Using the hardware system and software algorithm, our system can reconstruct the relative motion of each pixel during the exposure time with 6-DoF. The second part is about the embedded laser speckle imaging systems that we developed. The mobile LSI (mLSI) system is a battery-powered laser speckle imaging system to meet the portability requirements of special applications (e.g. emergency vehicle, battle field). We designed the automated power control (APC) circuit based laser diode driver to reduce the power consumption while maintaining the accuracy of the data. We evaluated the pulsed operation of the laser diode with always-on operation using the driver. The mLSI system showed equivalent performance from the evaluation with the clinical LSI system. The small field of view of conventional LSI systems is not adequate to be used for the applications that require fast treatment on large area (e.g. burn wounds). The multi-camera LSI (mcLSI) system was developed to address the requirement. mcLSI shows significant improvement in spatio-temporal resolution over conventional LSI systems. The unit based design enables users to expand the FoV without the sacrifice of the system performance (e.g. average speckle contrast, spatial/temporal resolution).
Motion is common in taking images using conventional cameras. If there is motion either on camera or the object or on the both sides during the exposure time, the image is blurred, which is called motion blurred image. Motion blurred image is an interesting research subject in several areas including computer vision, biomedical engineering. In general, motion is undesirable to acquire clear image. Modern cameras are equipped with the mechanical structure to compensate motion artifact. However, mechanical structure has limit in compensating huge motion. To compensate huge motion, computer vision algorithm called motion deblurring is developed. Basically, it estimates the best latent image from the blurry image using several assumptions (or priors) on the latent image and camera motion. The information included in the motion blur can be useful for biomedical applications. Laser speckle imaging is one good example. When laser light of certain frequency (visible laser is widely used) is illuminated on the skin, the scattered laser light from the surface has random distribution if there is no motion. With motion, the speckle pattern is interfered by the motion and no longer random for the region with motion. Since the laser light can penetrate into skin, it is possible to measure blood perfusion near skin without invasion. Both motion deblurring and laser speckle imaging require huge computation time. That is the reason why they are developed on personal computers or workstations. However, the physical size, power consumption, and cost make them harder to penetrate into wider-range of applications (e.g. A super-fast motion deblurring algorithm that can run on embedded platforms like portable digital cameras or smart phones or portable laser speckle imaging system for an emergency vehicle). Nowadays, embedded processors become more and more powerful. The CPU clock frequency of those processors have already passed 1Ghz. Some of them use multiple number of cores for processing like x86 processors. However, even with such high performance processors, special design considerations are required. In this dissertation, we suggest embedded hardware and software systems for motion deblurring and laser speckle imaging applications. In the first part of the dissertation, we will discuss about our motion deblurring systems. We developed an extremely light-weight motion deblurring algorithm to make it fast enough to be suitable for embedded computing platforms. The developed algorithm is up to 100 times faster than the most recent deblurring algorithms. We also developed a hardware/software system for extremely accurate motion deblurring. The hardware system is composed of IMU, a control board, and the Microsoft Kinect. Using the hardware system and software algorithm, our system can reconstruct the relative motion of each pixel during the exposure time with 6-DoF. The second part is about the embedded laser speckle imaging systems that we developed. The mobile LSI (mLSI) system is a battery-powered laser speckle imaging system to meet the portability requirements of special applications (e.g. emergency vehicle, battle field). We designed the automated power control (APC) circuit based laser diode driver to reduce the power consumption while maintaining the accuracy of the data. We evaluated the pulsed operation of the laser diode with always-on operation using the driver. The mLSI system showed equivalent performance from the evaluation with the clinical LSI system. The small field of view of conventional LSI systems is not adequate to be used for the applications that require fast treatment on large area (e.g. burn wounds). The multi-camera LSI (mcLSI) system was developed to address the requirement. mcLSI shows significant improvement in spatio-temporal resolution over conventional LSI systems. The unit based design enables users to expand the FoV without the sacrifice of the system performance (e.g. average speckle contrast, spatial/temporal resolution).