3 research outputs found
Hierarchical Bin Buffering: Online Local Moments for Dynamic External Memory Arrays
Local moments are used for local regression, to compute statistical measures
such as sums, averages, and standard deviations, and to approximate probability
distributions. We consider the case where the data source is a very large I/O
array of size n and we want to compute the first N local moments, for some
constant N. Without precomputation, this requires O(n) time. We develop a
sequence of algorithms of increasing sophistication that use precomputation and
additional buffer space to speed up queries. The simpler algorithms partition
the I/O array into consecutive ranges called bins, and they are applicable not
only to local-moment queries, but also to algebraic queries (MAX, AVERAGE, SUM,
etc.). With N buffers of size sqrt{n}, time complexity drops to O(sqrt n). A
more sophisticated approach uses hierarchical buffering and has a logarithmic
time complexity (O(b log_b n)), when using N hierarchical buffers of size n/b.
Using Overlapped Bin Buffering, we show that only a single buffer is needed, as
with wavelet-based algorithms, but using much less storage. Applications exist
in multidimensional and statistical databases over massive data sets,
interactive image processing, and visualization
Feature Extraction for image super-resolution using finite rate of innovation principles
To understand a real-world scene from several multiview pictures, it is necessary to find
the disparities existing between each pair of images so that they are correctly related to one
another. This process, called image registration, requires the extraction of some specific
information about the scene. This is achieved by taking features out of the acquired
images. Thus, the quality of the registration depends largely on the accuracy of the
extracted features.
Feature extraction can be formulated as a sampling problem for which perfect re-
construction of the desired features is wanted. The recent sampling theory for signals with
finite rate of innovation (FRI) and the B-spline theory offer an appropriate new frame-
work for the extraction of features in real images. This thesis first focuses on extending the
sampling theory for FRI signals to a multichannel case and then presents exact sampling
results for two different types of image features used for registration: moments and edges.
In the first part, it is shown that the geometric moments of an observed scene can
be retrieved exactly from sampled images and used as global features for registration. The
second part describes how edges can also be retrieved perfectly from sampled images for
registration purposes. The proposed feature extraction schemes therefore allow in theory
the exact registration of images. Indeed, various simulations show that the proposed
extraction/registration methods overcome traditional ones, especially at low-resolution.
These characteristics make such feature extraction techniques very appropriate for
applications like image super-resolution for which a very precise registration is needed. The
quality of the super-resolved images obtained using the proposed feature extraction meth-
ods is improved by comparison with other approaches. Finally, the notion of polyphase
components is used to adapt the image acquisition model to the characteristics of real
digital cameras in order to run super-resolution experiments on real images
Feature Extraction for Image Super-resolution using Finite Rate of Innovation Principles
To understand a real-world scene from several multiview pictures, it is necessary to find the disparities existing between each pair of images so that they are correctly related to one another., This process. called image registration, reguires the extraction of some specific information about the scene. This is achieved by taking features out of the acquired imaqes. Thus, the quality of the, registration depends largely on the accuracy of the extracted features. Feature extraction can be formulated as a sampling problem for which perfect reconstruction of the, desired features is wanted. The recent sampling theory for signals with finite rate of innovation (FR/), and the B-spline theory offer an appropriate new framework for the extraction of features in real, images. This thesis first focuses on extending the sampling theory for FRI signals to a multichannel, case and then presents exact sampling results for two different types of image features used for, registration: moments and edges. In the first part, it is shown that the geometric moments of an observed scene can be retrieved exactly from sampled images and used as global features for registration. The second part describes how edges can also be retrieved perfectly from sampled images for registration purposes. The proposed feature extraction schemes therefore allow in theory the exact registration of images. Indeed, various simulations show that the proposed extraction/registration methods overcome traditional ones, especially at low-resolution. These characteristics make such feature extraction techniques very appropriate for applications like image super-resolution for which a very precise registration is needed. The quality of the superresolved images obtained using the proposed feature extraction methods is improved by comparison with other approaches. Finally, the notion of polyphase components is used to adapt the imaqe acquisition model to the characteristics of real digital cameras in order to run super-resolution experiments on real images