Atmospheric turbulence has a degrading effect on the image quality of long-range observation systems. As a result of various elements such as temperature, wind velocity, humidity, etc., turbulence is characterized by ...
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ISBN:
(数字)9781665487399
ISBN:
(纸本)9781665487405
Atmospheric turbulence has a degrading effect on the image quality of long-range observation systems. As a result of various elements such as temperature, wind velocity, humidity, etc., turbulence is characterized by random fluctuations in the refractive index of the atmosphere. It is a phenomenon that may occur in various imaging spectra such as the visible or the infrared bands. In this paper, we analyze the effects of atmospheric turbulence on object detection performance in thermal imagery. We use a geometric turbulence model to simulate turbulence effects on a medium-scale thermal image set, namely "FLIR ADAS v2". We apply thermal domain adaptation to state-of-the-art object detectors and propose a data augmentation strategy to increase the performance of object detectors which utilizes turbulent images in different severity levels as training data. Our results show that the proposed data augmentation strategy yields an increase in performance for both turbulent and non-turbulent thermal test images.
We introduce a nonparametric approach to multiscale segmentation of images using a hierarchical matrix analysis framework called diffusion wavelets. This approach benefits from the advantages of both graph theory and ...
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ISBN:
(纸本)9781479928941
We introduce a nonparametric approach to multiscale segmentation of images using a hierarchical matrix analysis framework called diffusion wavelets. This approach benefits from the advantages of both graph theory and wavelet transform. Till now a broad range of multiscale transforms like wavelets (and other x-lets) have been introduced for image segmentation task. The graph theoretic formulation of grouping is also well-known to deal with this problem. The combination of multiscale transforms and graph based partitioning results in a scale-spectral method exploring through different scales of the image, over a great deal of spectral methods in graph partitioning. The method constructs multiscale basis functions and a series of dilation and orthogonalizations build a hierarchy, automatically. At each level, a set of basis functions is built by applying dyadic powers of a diffusion operator on the bases at the lower level. Two approaches are proposed for multiscale segmentation of images using diffusion wavelets. The first method is based on extended bases functions at each level and designing a competition between the bases value for partitioning. The second approach is defining a new distance for each level and clustering based on such distances.
In contrast with the growth of plants and trees, human organs can undergo significant changes in shape through a variety of global transformations during the growth period, such as bending or twisting. In our approach...
In contrast with the growth of plants and trees, human organs can undergo significant changes in shape through a variety of global transformations during the growth period, such as bending or twisting. In our approach, the topology of a human organ is represented by a skeleton in the form of a tree or cycled graph. The length of skeleton growth can be simulated by an algebraic L-system that also produces discrete events. The paper shows how to include global transformations into the formalism of L-systems to obtain a continuous proc.ss. The shape of the organ is approximated by a number of ellipsoidal clusters centred at points on the skeleton. The proposed growth model of the organ continually responds to the positional changes of surrounding organs, thereby changing the organ shape locally. In our study, the stomach of a human embryo is used for the demonstration of organ development, and the methodology employed is also applicable to the animation of animal organs and their development.
Data Modeling is an essential first step for data preparation in any data mining proc.dure. Conventional entity-relational (E-R) data modeling is lossy, irreproducible, and time-consuming especially when dealing with ...
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We study the problem of representing images within a multimedia Database Management System (DBMS), in order to support fast retrieval operations without compromising storage efficiency. To achieve this goal, we propos...
We study the problem of representing images within a multimedia Database Management System (DBMS), in order to support fast retrieval operations without compromising storage efficiency. To achieve this goal, we propose new image coding techniques which combine a wavelet representation, embedded coding of the wavelet coefficients, and segmentation of image-domain regions in the wavelet domain. A bitstream is generated in which each image region is encoded independently of other regions, without having to explicitly store information describing the regions. Simulation results show that our proposed algorithms achieve coding performance which compares favorably, both perceptually and objectively, to that achieved using state-of-the-art image/video coding techniques while additionally providing region-based support.
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