32,535 research outputs found
Motion Segmentation Aided Super Resolution Image Reconstruction
This dissertation addresses Super Resolution (SR) Image Reconstruction focusing on motion segmentation. The main thrust is Information Complexity guided Gaussian Mixture Models (GMMs) for Statistical Background Modeling. In the process of developing our framework we also focus on two other topics; motion trajectories estimation toward global and local scene change detections and image reconstruction to have high resolution (HR) representations of the moving regions. Such a framework is used for dynamic scene understanding and recognition of individuals and threats with the help of the image sequences recorded with either stationary or non-stationary camera systems.
We introduce a new technique called Information Complexity guided Statistical Background Modeling. Thus, we successfully employ GMMs, which are optimal with respect to information complexity criteria. Moving objects are segmented out through background subtraction which utilizes the computed background model. This technique produces superior results to competing background modeling strategies.
The state-of-the-art SR Image Reconstruction studies combine the information from a set of unremarkably different low resolution (LR) images of static scene to construct an HR representation. The crucial challenge not handled in these studies is accumulating the corresponding information from highly displaced moving objects. In this aspect, a framework of SR Image Reconstruction of the moving objects with such high level of displacements is developed. Our assumption is that LR images are different from each other due to local motion of the objects and the global motion of the scene imposed by non-stationary imaging system. Contrary to traditional SR approaches, we employed several steps. These steps are; the suppression of the global motion, motion segmentation accompanied by background subtraction to extract moving objects, suppression of the local motion of the segmented out regions, and super-resolving accumulated information coming from moving objects rather than the whole scene. This results in a reliable offline SR Image Reconstruction tool which handles several types of dynamic scene changes, compensates the impacts of camera systems, and provides data redundancy through removing the background. The framework proved to be superior to the state-of-the-art algorithms which put no significant effort toward dynamic scene representation of non-stationary camera systems
Video foreground detection based on symmetric alpha-stable mixture models.
Background subtraction (BS) is an efficient technique for detecting moving objects in video sequences. A simple BS process involves building a model of the background and extracting regions of the foreground (moving objects) with the assumptions that the camera remains stationary and there exist no movements in the background. These assumptions restrict the applicability of BS methods to real-time object detection in video. In this paper, we propose an extended cluster BS technique with a mixture of symmetric alpha stable (SS) distributions. An on-line self-adaptive mechanism is presented that allows automated estimation of the model parameters using the log moment method. Results over real video sequences from indoor and outdoor environments, with data from static and moving video cameras are presented. The SS mixture model is shown to improve the detection performance compared with a cluster BS method using a Gaussian mixture model and the method of Li et al. [11]
Challenges in video based object detection in maritime scenario using computer vision
This paper discusses the technical challenges in maritime image processing
and machine vision problems for video streams generated by cameras. Even well
documented problems of horizon detection and registration of frames in a video
are very challenging in maritime scenarios. More advanced problems of
background subtraction and object detection in video streams are very
challenging. Challenges arising from the dynamic nature of the background,
unavailability of static cues, presence of small objects at distant
backgrounds, illumination effects, all contribute to the challenges as
discussed here
Large scale Micro-Photometry for high resolution pH-characterization during electro-osmotic pumping and modular micro-swimming
Micro-fluidic pumps as well as artificial micro-swimmers are conveniently
realized exploiting phoretic solvent flows based on local gradients of
temperature, electrolyte concentration or pH. We here present a facile
micro-photometric method for monitoring pH gradients and demonstrate its
performance and scope on different experimental situations including an
electro-osmotic pump and modular micro-swimmers assembled from ion exchange
resin beads and polystyrene colloids. In combination with the present
microscope and DSLR camera our method offers a 2 \mu m spatial resolution at
video frame rate over a field of view of 3920x2602 \mu m^2. Under optimal
conditions we achieve a pH-resolution of 0.05 with about equal contributions
from statistical and systematical uncertainties. Our quantitative
micro-photometric characterization of pH gradients which develop in time and
reach out several mm is anticipated to provide valuable input for reliable
modeling and simulations of a large variety of complex flow situations
involving pH-gradients including artificial micro-swimmers, microfluidic
pumping or even electro-convection.Comment: 5 figures, 15 page
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