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Two video-processing problems by means of nontraditional methods
Kánský, Antonín ; Mangová, Marie (referee) ; Rajmic, Pavel (advisor)
The aim of this work is to solve two problems from the field of video editing by means of sparse representation of signals. The problematics of the traditional realisation of two effects, which are background separation of and separation background from moving foreground, is clarified here, as well as the problematics of sparse signals. The solutions was achieved through the method of Principal component analysis (PCP). The resulting algorithm is implemented and tested by simulated and real data.
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Decomposition of video-sequences into components with different dynamics
Gebrtová, Karolína ; Druckmüller, Miloslav (referee) ; Rajmic, Pavel (advisor)
The thesis is focused on the decomposition of video sequences, primarily on background and dynamic component separation, where the background remains the same and only small parts are in motion. Such a video is represented by a low-rank and sparse component. Thanks to the low-rank structure, the background can be separated using a median filter and dynamic mode decomposition. Furthermore, the robust principal component analysis, formulated as an optimization problem and its non-convex variant, will be employed. Also mentioned will be the multiresolution DMD, which is capable of decomposing the dynamic component based on its velocity.
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Two video-processing problems by means of nontraditional methods
Kánský, Antonín ; Mangová, Marie (referee) ; Rajmic, Pavel (advisor)
The aim of this work is to solve two problems from the field of video editing by means of sparse representation of signals. The problematics of the traditional realisation of two effects, which are background separation of and separation background from moving foreground, is clarified here, as well as the problematics of sparse signals. The solutions was achieved through the method of Principal component analysis (PCP). The resulting algorithm is implemented and tested by simulated and real data.
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