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Interest-Point Detection on CUDA
Ryba, Jan ; Řezníček, Ivo (referee) ; Herout, Adam (advisor)
Corner point detection is one of many functions used in computer vision for tasks such as tracking, detecting objects, comparing images and much more. Many of the algorithms are complex and require a lot of CPU time. This is where the CUDA platform comes in. CUDA kernels run parallely on graphic accelerators can rapidly decrease time needed for execution, allowing even these complex calculations to work in real time or even better. Text focuces on Moravec and Harris corner detection algorithms and their effective implementation on CUDA. Examination of potetntial and performance of CUDA platform is also importatnt.
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AeroWorks: Visual Identification of Aircraft Flight Regimes
Kardoš, Juraj ; Dittrich, Petr (referee) ; Chudý, Peter (advisor)
This Bachelor thesis deals with the visual identification of an aircraft flight's regimes. It describes the spatial motion of an airplane along with the visualization of flight parameters and also proposes a system for a flight regime visual identification. The system processes the input video on a frame by frame basis in two steps. Initially, the video is being stabilized and the system subsequently proceeds in identification of flight related quantities describing the current flight state. Video stabilization is based on feature points detection and an optical flow calculation. Video frames are transformed in order to achieve sufficient consecutive frames overlap and thus to minimize the parasitic oscillations of the video acquisition system. Identification of values indicated by flight instruments is based on the Hough line transform approach. The thesis also includes a description of an application that analyzes a video from the cockpit of an aircraft and is able to recognize the instrument values displayed on specified flight instruments.
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AeroWorks: Visual Identification of Aircraft Flight Regimes
Kardoš, Juraj ; Dittrich, Petr (referee) ; Chudý, Peter (advisor)
This Bachelor thesis deals with the visual identification of an aircraft flight's regimes. It describes the spatial motion of an airplane along with the visualization of flight parameters and also proposes a system for a flight regime visual identification. The system processes the input video on a frame by frame basis in two steps. Initially, the video is being stabilized and the system subsequently proceeds in identification of flight related quantities describing the current flight state. Video stabilization is based on feature points detection and an optical flow calculation. Video frames are transformed in order to achieve sufficient consecutive frames overlap and thus to minimize the parasitic oscillations of the video acquisition system. Identification of values indicated by flight instruments is based on the Hough line transform approach. The thesis also includes a description of an application that analyzes a video from the cockpit of an aircraft and is able to recognize the instrument values displayed on specified flight instruments.
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Interest-Point Detection on CUDA
Ryba, Jan ; Řezníček, Ivo (referee) ; Herout, Adam (advisor)
Corner point detection is one of many functions used in computer vision for tasks such as tracking, detecting objects, comparing images and much more. Many of the algorithms are complex and require a lot of CPU time. This is where the CUDA platform comes in. CUDA kernels run parallely on graphic accelerators can rapidly decrease time needed for execution, allowing even these complex calculations to work in real time or even better. Text focuces on Moravec and Harris corner detection algorithms and their effective implementation on CUDA. Examination of potetntial and performance of CUDA platform is also importatnt.
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