Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Hyper-Spectral Image Analysis with Partially-Latent Regression and Spatial Markov Dependencies

Hyper-spectral data can be analyzed to recover physical properties at large planetary scales. This involves resolving inverse problems which can be addressed within machine learning, with the advantage that, once a relationship between physical parameters and spectra has been established in a data-driven fashion, the learned relationship can be used to estimate physical parameters for new hyper-spectral observations. Within this framework, we propose a spatially-constrained and partially-latent regression method which maps high-dimensional inputs (hyper-spectral images) onto low-dimensional responses (physical parameters such as the local chemical composition of the soil). The proposed regression model comprises two key features. Firstly, it combines a Gaussian mixture of locally-linear mappings (GLLiM) with a partially-latent response model. While the former makes high-dimensional regression tractable, the latter enables to deal with physical parameters that cannot be observed or, more generally, with data contaminated by experimental artifacts that cannot be explained with noise models. Secondly, spatial constraints are introduced in the model through a Markov random field (MRF) prior which provides a spatial structure to the Gaussian-mixture hidden variables. Experiments conducted on a database composed of remotely sensed observations collected from the Mars planet by the Mars Express orbiter demonstrate the effectiveness of the proposed model.Comment: 12 pages, 4 figures, 3 table

Video enhancement : content classification and model selection

The purpose of video enhancement is to improve the subjective picture quality. The field of video enhancement includes a broad category of research topics, such as removing noise in the video, highlighting some specified features and improving the appearance or visibility of the video content. The common difficulty in this field is how to make images or videos more beautiful, or subjectively better. Traditional approaches involve lots of iterations between subjective assessment experiments and redesigns of algorithm improvements, which are very time consuming. Researchers have attempted to design a video quality metric to replace the subjective assessment, but so far it is not successful. As a way to avoid heuristics in the enhancement algorithm design, least mean square methods have received considerable attention. They can optimize filter coefficients automatically by minimizing the difference between processed videos and desired versions through a training. However, these methods are only optimal on average but not locally. To solve the problem, one can apply the least mean square optimization for individual categories that are classified by local image content. The most interesting example is Kondo’s concept of local content adaptivity for image interpolation, which we found could be generalized into an ideal framework for content adaptive video processing. We identify two parts in the concept, content classification and adaptive processing. By exploring new classifiers for the content classification and new models for the adaptive processing, we have generalized a framework for more enhancement applications. For the part of content classification, new classifiers have been proposed to classify different image degradations such as coding artifacts and focal blur. For the coding artifact, a novel classifier has been proposed based on the combination of local structure and contrast, which does not require coding block grid detection. For the focal blur, we have proposed a novel local blur estimation method based on edges, which does not require edge orientation detection and shows more robust blur estimation. With these classifiers, the proposed framework has been extended to coding artifact robust enhancement and blur dependant enhancement. With the content adaptivity to more image features, the number of content classes can increase significantly. We show that it is possible to reduce the number of classes without sacrificing much performance. For the part of model selection, we have introduced several nonlinear filters to the proposed framework. We have also proposed a new type of nonlinear filter, trained bilateral filter, which combines both advantages of the original bilateral filter and the least mean square optimization. With these nonlinear filters, the proposed framework show better performance than with linear filters. Furthermore, we have shown a proof-of-concept for a trained approach to obtain contrast enhancement by a supervised learning. The transfer curves are optimized based on the classification of global or local image content. It showed that it is possible to obtain the desired effect by learning from other computationally expensive enhancement algorithms or expert-tuned examples through the trained approach. Looking back, the thesis reveals a single versatile framework for video enhancement applications. It widens the application scope by including new content classifiers and new processing models and offers scalabilities with solutions to reduce the number of classes, which can greatly accelerate the algorithm design

Robust detail-preserving signal extraction

We discuss robust filtering procedures for signal extraction from noisy time series. Particular attention is paid to the preservation of relevant signal details like abrupt shifts. moving averages and running medians are widely used but have shortcomings when large spikes (outliers) or trends occur. Modifications like modified trimmed means and linear median hybrid filters combine advantages of both approaches, but they do not completely overcome the difficulties. Better solutions can be based on robust regression techniques, which even work in real time because of increased computational power and faster algorithms. Reviewing previous work we present filters for robust signal extraction and discuss their merits for preserving trends, abrupt shifts and local extremes as well as for the removal of outliers. --