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Context-Aware Systems and Applications - Second International Conference, ICCASA 2013, Phu Quoc Island, Vietnam, November 25-26, 2013, Revised Selected Papers (Paperback, 2014 ed.)
Phan Cong-Vinh, Vangalur Alagar, Emil Vassev, Ashish Khare
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R2,763
Discovery Miles 27 630
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Ships in 10 - 15 working days
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This book constitutes the thoroughly refereed proceedings of the
Second International Conference on Context-Aware Systems and
Applications, ICCASA 2013, held in Phu Quoc Island, Vietnam in
November 2013. The 36 revised full papers presented were carefully
selected and reviewed from over 100 submissions and cover a wide
spectrum of issues in the area of context-aware systems (CAS) and
context-based recommendation systems.
This book presents select papers from the International Conference
on Emerging Trends in Communication, Computing and Electronics
(IC3E 2018). Covering the latest theories and methods in three
related fields - electronics, communication and computing, it
describes cutting-edge methods and applications in the areas of
signal and image processing, cyber security, human-computer
interaction, machine learning, electronic devices,
nano-electronics, wireless sensor networks, antenna and wave
propagation, and mobile communication. The contents of this book
will be beneficial to students, researchers, and professionals
working in the field of networks and communications.
Noise reduction is an important step for development of any
sophisticated algorithms in computer vision and image processing. A
tradeoff between the removed noise and the blur in the image always
exists. The capability of the wavelets to give detail
spatial-frequency information is the main reason for the use of
wavelets. This property promises a possibility for better
discrimination between the noise and the real data. Successful
exploitation of the wavelet transform might reduce the blurring
effect or even overcome it completely. Object tracking is a problem
of estimating the positions and other relevant information of
moving objects in the sequences of image video. The main
difficulties in reliable tracking of moving objects include: rapid
appearance changes caused by image noise and interaction between
multiple objects. In a long sequence of image video, if the
dynamics of the moving object is known, prediction can be made
about the positions of the object in a particular frame. This
information can be combined with the actual image observation to
achieve more robust results. We have explored a possibility to use
wavelet transform for object tracking.
Medical images are generally of poor contrast and they also get
complex type of noise and blur. The noise also has variability from
one condition to other. So it is very difficult to suggest a robust
method for noise removal which works equally well for different
modalities of medical images. During the denoising process of a
noisy image, it is usually helpful to look at an image at different
resolutions so that important information about both the image and
the noise can emerge easily. If the chosen resolution is too
coarse, fine details will not be visible. On the other hand,
looking too closely at an object can cause surroundings to
disappear, so the noise and the object cannot be distinguished
easily. This is where wavelets can be useful. But unfortunately the
present wavelet based techniques for medical image denoising are
too particular and are useful in particular situations only. Here,
it is important to mention that complex wavelet transform has not
found its deserving place in many applications, and one of the
major challenging tasks taken up in this work is to apply complex
wavelet transform for denoising.
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