3 research outputs found
ID3 Tag Editor Employing Image Processing Methods
Tato práce se zabĂ˝vá spojenĂm Ăşpravy tagĹŻ v hudebnĂch souborech a vyuĹľitĂ metod rozpoznávanĂ obrazu pro snazšà práci s obaly alb. V Ăşvodu je popsána distribuce hudby, rĹŻznĂ© podoby uchovávánĂ a pĹ™ehrávanĂ hudby. Je zde zmĂnÄ›n i problĂ©m legality stahovánĂ a distribuce hudby a vĂ˝mÄ›nnĂ˝ch sĂtĂ. Dále se text vÄ›nuje metodám pouĹľitĂ˝m pĹ™i transformacĂch obrazu pouĹľitĂ˝ch v aplikaci. SoučástĂ práce je i pĹ™Ăprava, implementace a demonstrace aplikace. V závÄ›ru práce je diskutováno moĹľnĂ© pouĹľitĂ nebo budoucĂ rozšĂĹ™enĂ.This thesis discusses the co-implementation of music tags editing and image processing methods for comfortable maintenance of album arts. The introduction deals with music distribution, different ways of storing and playing the music. Legality and peer-to-peer issue is also mentioned. Furthermore, the thesis deals with image transforming methods used in the application. A part of the work is the preparation, implementation and development of the application. The conclusion discusses future usage or extensions.
Geometric uncertainty models for correspondence problems in digital image processing
Many recent advances in technology rely heavily on the correct interpretation of an enormous amount of visual information. All available sources of visual data (e.g. cameras in surveillance networks, smartphones, game consoles) must be adequately processed to retrieve the most interesting user information. Therefore, computer vision and image processing techniques gain significant interest at the moment, and will do so in the near future.
Most commonly applied image processing algorithms require a reliable solution for correspondence problems. The solution involves, first, the localization of corresponding points -visualizing the same 3D point in the observed scene- in the different images of distinct sources, and second, the computation of consistent geometric transformations relating correspondences on scene objects.
This PhD presents a theoretical framework for solving correspondence problems with geometric features (such as points and straight lines) representing rigid objects in image sequences of complex scenes with static and dynamic cameras. The research focuses on localization uncertainty due to errors in feature detection and measurement, and its effect on each step in the solution of a correspondence problem.
Whereas most other recent methods apply statistical-based models for spatial localization uncertainty, this work considers a novel geometric approach. Localization uncertainty is modeled as a convex polygonal region in the image space. This model can be efficiently propagated throughout the correspondence finding procedure. It allows for an easy extension toward transformation uncertainty models, and to infer confidence measures to verify the reliability of the outcome in the correspondence framework. Our procedure aims at finding reliable consistent transformations in sets of few and ill-localized features, possibly containing a large fraction of false candidate correspondences.
The evaluation of the proposed procedure in practical correspondence problems shows that correct consistent correspondence sets are returned in over 95% of the experiments for small sets of 10-40 features contaminated with up to 400% of false positives and 40% of false negatives. The presented techniques prove to be beneficial in typical image processing applications, such as image registration and rigid object tracking