1,613 research outputs found
HP2IFS: Head Pose estimation exploiting Partitioned Iterated Function Systems
Estimating the actual head orientation from 2D images, with regard to its
three degrees of freedom, is a well known problem that is highly significant
for a large number of applications involving head pose knowledge. Consequently,
this topic has been tackled by a plethora of methods and algorithms the most
part of which exploits neural networks. Machine learning methods, indeed,
achieve accurate head rotation values yet require an adequate training stage
and, to that aim, a relevant number of positive and negative examples. In this
paper we take a different approach to this topic by using fractal coding theory
and particularly Partitioned Iterated Function Systems to extract the fractal
code from the input head image and to compare this representation to the
fractal code of a reference model through Hamming distance. According to
experiments conducted on both the BIWI and the AFLW2000 databases, the proposed
PIFS based head pose estimation method provides accurate yaw/pitch/roll angular
values, with a performance approaching that of state of the art of
machine-learning based algorithms and exceeding most of non-training based
approaches
Data comparison schemes for Pattern Recognition in Digital Images using Fractals
Pattern recognition in digital images is a common problem with application in
remote sensing, electron microscopy, medical imaging, seismic imaging and
astrophysics for example. Although this subject has been researched for over
twenty years there is still no general solution which can be compared with the
human cognitive system in which a pattern can be recognised subject to
arbitrary orientation and scale.
The application of Artificial Neural Networks can in principle provide a very
general solution providing suitable training schemes are implemented.
However, this approach raises some major issues in practice. First, the CPU
time required to train an ANN for a grey level or colour image can be very
large especially if the object has a complex structure with no clear geometrical
features such as those that arise in remote sensing applications. Secondly,
both the core and file space memory required to represent large images and
their associated data tasks leads to a number of problems in which the use of
virtual memory is paramount.
The primary goal of this research has been to assess methods of image data
compression for pattern recognition using a range of different compression
methods. In particular, this research has resulted in the design and
implementation of a new algorithm for general pattern recognition based on
the use of fractal image compression.
This approach has for the first time allowed the pattern recognition problem to
be solved in a way that is invariant of rotation and scale. It allows both ANNs
and correlation to be used subject to appropriate pre-and post-processing
techniques for digital image processing on aspect for which a dedicated
programmer's work bench has been developed using X-Designer
Fractal image compression and the self-affinity assumption : a stochastic signal modelling perspective
Bibliography: p. 208-225.Fractal image compression is a comparatively new technique which has gained considerable attention in the popular technical press, and more recently in the research literature. The most significant advantages claimed are high reconstruction quality at low coding rates, rapid decoding, and "resolution independence" in the sense that an encoded image may be decoded at a higher resolution than the original. While many of the claims published in the popular technical press are clearly extravagant, it appears from the rapidly growing body of published research that fractal image compression is capable of performance comparable with that of other techniques enjoying the benefit of a considerably more robust theoretical foundation. . So called because of the similarities between the form of image representation and a mechanism widely used in generating deterministic fractal images, fractal compression represents an image by the parameters of a set of affine transforms on image blocks under which the image is approximately invariant. Although the conditions imposed on these transforms may be shown to be sufficient to guarantee that an approximation of the original image can be reconstructed, there is no obvious theoretical reason to expect this to represent an efficient representation for image coding purposes. The usual analogy with vector quantisation, in which each image is considered to be represented in terms of code vectors extracted from the image itself is instructive, but transforms the fundamental problem into one of understanding why this construction results in an efficient codebook. The signal property required for such a codebook to be effective, termed "self-affinity", is poorly understood. A stochastic signal model based examination of this property is the primary contribution of this dissertation. The most significant findings (subject to some important restrictions} are that "self-affinity" is not a natural consequence of common statistical assumptions but requires particular conditions which are inadequately characterised by second order statistics, and that "natural" images are only marginally "self-affine", to the extent that fractal image compression is effective, but not more so than comparable standard vector quantisation techniques
Self-similarity and wavelet forms for the compression of still image and video data
This thesis is concerned with the methods used to reduce the data volume required to represent
still images and video sequences. The number of disparate still image and video
coding methods increases almost daily. Recently, two new strategies have emerged and
have stimulated widespread research. These are the fractal method and the wavelet transform.
In this thesis, it will be argued that the two methods share a common principle: that
of self-similarity. The two will be related concretely via an image coding algorithm which
combines the two, normally disparate, strategies.
The wavelet transform is an orientation selective transform. It will be shown that the
selectivity of the conventional transform is not sufficient to allow exploitation of self-similarity
while keeping computational cost low. To address this, a new wavelet transform
is presented which allows for greater orientation selectivity, while maintaining the
orthogonality and data volume of the conventional wavelet transform. Many designs for
vector quantizers have been published recently and another is added to the gamut by this
work. The tree structured vector quantizer presented here is on-line and self structuring,
requiring no distinct training phase. Combining these into a still image data compression
system produces results which are among the best that have been published to date.
An extension of the two dimensional wavelet transform to encompass the time dimension
is straightforward and this work attempts to extrapolate some of its properties into three
dimensions. The vector quantizer is then applied to three dimensional image data to
produce a video coding system which, while not optimal, produces very encouraging
results
Digital Signal Processing Research Program
Contains table of contents for Section 2, an introduction and reports on seventeen research projects.U.S. Navy - Office of Naval Research Grant N00014-91-J-1628Vertical Arrays for the Heard Island Experiment Award No. SC 48548Charles S. Draper Laboratories, Inc. Contract DL-H-418472Defense Advanced Research Projects Agency/U.S. Navy - Office of Naval Research Grant N00014-89-J-1489Rockwell Corporation Doctoral FellowshipMIT - Woods Hole Oceanographic Institution Joint ProgramDefense Advanced Research Projects Agency/U.S. Navy - Office of Naval Research Grant N00014-90-J-1109Lockheed Sanders, Inc./U.S. Navy - Office of Naval Research Contract N00014-91-C-0125U.S. Air Force - Office of Scientific Research Grant AFOSR-91-0034AT&T Laboratories Doctoral ProgramU.S. Navy - Office of Naval Research Grant N00014-91-J-1628General Electric Foundation Graduate Fellowship in Electrical EngineeringNational Science Foundation Grant MIP 87-14969National Science Foundation Graduate FellowshipCanada Natural Sciences and Engineering Research CouncilLockheed Sanders, Inc
Techniques and potential capabilities of multi-resolutional information (knowledge) processing
A concept of nested hierarchical (multi-resolutional, pyramidal) information (knowledge) processing is introduced for a variety of systems including data and/or knowledge bases, vision, control, and manufacturing systems, industrial automated robots, and (self-programmed) autonomous intelligent machines. A set of practical recommendations is presented using a case study of a multiresolutional object representation. It is demonstrated here that any intelligent module transforms (sometimes, irreversibly) the knowledge it deals with, and this tranformation affects the subsequent computation processes, e.g., those of decision and control. Several types of knowledge transformation are reviewed. Definite conditions are analyzed, satisfaction of which is required for organization and processing of redundant information (knowledge) in the multi-resolutional systems. Providing a definite degree of redundancy is one of these conditions
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