Bradley W. Dickinson

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An adaptive watermarking technique is introduced in this work. A regional perceptual classi er is employed to assign a noise sensitivity index to each region. The watermark is inserted in the original image according to this index by using block DCT. The detection of the watermark is designed to achieve a desired false alarm probability.
The authors present a method for constructing a feedforward neural net implementing an arbitrarily good approximation to any L/sub 2/ function over (-1, 1)/sup n/. The net uses n input nodes, a single hidden layer whose width is determined by the function to be implemented and the allowable mean square error, and a linear output neuron. Error bounds and an(More)
In this paper we present a novel technique to dynamically adapt motion interpolation structures by temporal segmentation. The number of reference frames and the intervals between them are adjusted according to the temporal variation of the input video. Bit-rate control for this dynamic group of pictures (GOP) structure is achieved by taking advantage of(More)
In this paper, we present a multiresolution approach for video indexing and feature matching of subband coded video databases. Subband coding refers to a coding technique where the input images are quantized after being decomposed into several narrow spatial frequency bands by filtering and decimation. Five different approaches were tested for scene change(More)
In this paper, we present an algorithm for joint optimization of anchor frame separation and bit allocation for motion-compensated video coders. The anchor frame separation is optimized in the sense that the distortion is minimized under a bit budget constraint. At the same time, the quantization for each frame in a group of pictures is also optimized in an(More)
Relationships between locally interconnected neural networks that use receptive field representations and trellis or convolutional codes are explored. A fault tolerant neural network is described. It is patterned after the trellis graph description of convolutional codes and is able to tolerate errors in its inputs and failures of constituent neurons. This(More)