1,394 research outputs found
Reversible watermarking scheme with image-independent embedding capacity
Permanent distortion is one of the main drawbacks of all the irreversible watermarking schemes. Attempts to recover the original signal after the signal passing the authentication process are being made starting just a few years ago. Some common problems, such as salt-and-pepper artefacts owing to intensity wraparound and low embedding capacity, can now be resolved. However, some significant problems remain unsolved. First, the embedding capacity is signal-dependent, i.e., capacity varies significantly depending on the nature of the host signal. The direct impact of this is compromised security for signals with low capacity. Some signals may be even non-embeddable. Secondly, while seriously tackled in irreversible watermarking schemes, the well-known problem of block-wise dependence, which opens a security gap for the vector quantisation attack and transplantation attack, are not addressed by researchers of the reversible schemes. This work proposes a reversible watermarking scheme with near-constant signal-independent embedding capacity and immunity to the vector quantisation attack and transplantation attack
Dimension reduction for linear separation with curvilinear distances
Any high dimensional data in its original raw form may contain obviously classifiable clusters which are difficult to identify given the high-dimension representation. In reducing the dimensions it may be possible to perform a simple classification technique to extract this cluster information whilst retaining the overall topology of the data set. The supervised method presented here takes a high dimension data set consisting of multiple clusters and employs curvilinear distance as a relation between points, projecting in a lower dimension according to this relationship. This representation allows for linear separation of the non-separable high dimensional cluster data and the classification to a cluster of any successive unseen data point extracted from the same higher dimension
Neural Discrete Representation Learning
Learning useful representations without supervision remains a key challenge
in machine learning. In this paper, we propose a simple yet powerful generative
model that learns such discrete representations. Our model, the Vector
Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways:
the encoder network outputs discrete, rather than continuous, codes; and the
prior is learnt rather than static. In order to learn a discrete latent
representation, we incorporate ideas from vector quantisation (VQ). Using the
VQ method allows the model to circumvent issues of "posterior collapse" --
where the latents are ignored when they are paired with a powerful
autoregressive decoder -- typically observed in the VAE framework. Pairing
these representations with an autoregressive prior, the model can generate high
quality images, videos, and speech as well as doing high quality speaker
conversion and unsupervised learning of phonemes, providing further evidence of
the utility of the learnt representations
Virtual environment trajectory analysis:a basis for navigational assistance and scene adaptivity
This paper describes the analysis and clustering of motion trajectories obtained while users navigate within a virtual environment (VE). It presents a neural network simulation that produces a set of five clusters which help to differentiate users on the basis of efficient and inefficient navigational strategies. The accuracy of classification carried out with a self-organising map algorithm was tested and improved to in excess of 85% by using learning vector quantisation. This paper considers how such user classifications could be utilised in the delivery of intelligent navigational support and the dynamic reconfiguration of scenes within such VEs. We explore how such intelligent assistance and system adaptivity could be delivered within a Multi-Agent Systems (MAS) context
An ECG compression scheme based on vector quantisation
An ECG signal compression scheme based on the vector quantisation (VQ) method is proposed. The compression is performed by quantising the ECG samples into a reduced set of reference vectors, the codebook. The vector representing elements with a high frequency of occurrence is selected for further coding, while the rest of the signal containing all the values occurring with lower probabilities are kept unchanged. The proposed scheme is superior to other available direct compression methods in retaining the most important part of the ECG signal
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