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Browsing by Subject "Feature extraction"

Now showing 1 - 2 of 2
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    Image registration by non-degenerate pixel detection
    (2012-11) Xing, Chen
    Image registration is used widely in applications for mapping one image to another. It is a fundamental task in many imaging applications. Existing image registration methods are either feature-based or intensity-based. Feature-based methods first extract relevant image features, and then find a geometrical transformation that best matches the two corresponding sets of features extracted from the two images. The geometrical transformation is estimated directly from the observed image intensities of the two images by an intensity-based image registration method. Most existing methods of both types assume that the mapping transformation has a parametric form or satisfies certain regularity conditions (e.g., it is a smooth function with continuous first or higher order derivatives). They often estimate the mapping transformation globally by solving a global minimization/maximization problem. Such global smoothing methods usually cannot uncover the ill-posed nature of the image registration problem, namely, the mapping transformation is not well defined at certain places, including places where the true image intensity surface is straight. In this thesis, we suggest solving the image registration problem locally, by first studying the local properties of a mapping transformation. Some concepts for describing such local properties are suggested, and an intensity-based local smoothing method for estimating the geometrical transformation is proposed. We also develop a feature-based method based on those concepts. Both theoretical and numerical studies show that our methods are effective in various applications.
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    Information Processing in Complex Environments: Insights from Treefrog Communication
    (2021-12) Gupta, Saumya
    Many animals use sounds to perform critical biological functions, such as choosing a mate or evading a predator, in environments where multiple sound sources are simultaneously active. Discerning a sound of interest in such complex acoustic environments, however, is not a trivial task. It requires animals to perceptually organize mixtures of auditory input into meaningful information about their external environment. In this dissertation research, my broad aim was to understand how animals parse their complex acoustic environments to perform acoustically guided behaviors. Using Cope’s gray treefrogs (Hyla chrysoscelis) as a model system, I investigated how animals accomplish the different perceptual tasks that are required for recognizing and responding to a signal of interest in noisy social environments. I discovered some of the processes that act together to extract information and facilitate signal recognition. Specifically, I found a perceptual mechanism that allows animals to perceive the different vocal signals in their environment as distinct sounds. I also found specific neural adaptations that allow them to extract and recognize biologically meaningful information from their vocal signals. Additionally, my research reveals that despite the evolution of these perceptual and sensorineural mechanisms, background sounds present in the social environment can interfere with the information processing capacity of animals, and thus, can critically constrain their ability to perform important biological functions. This research opens up an exciting and unknown question of how animals are evolutionarily adapted to overcome the limitations in information processing to perform acoustically guided behaviors.