Multiresolution analysis

About: Multiresolution analysis is a research topic. Over the lifetime, 4032 publications have been published within this topic receiving 140743 citations. The topic is also known as: Multiresolution analysis, MRA.

Detection of epileptic events in electroencephalograms using wavelet analysis

Abstract: This study deals with the problem of identification of epileptic events in electroencephalograms using multiresolution wavelet analysis The following problems are analyzed: time localization and characterization of epileptiform events, and computational efficiency of the method The algorithm presented is based on a polynomial spline wavelet transform The multiresolution representation obtained from this wavelet transform and the corresponding digital filters derived allows time localization of epileptiform activity The proposed detector is based on the multiresolution energy function Electroencephalogram records from epileptic patients were analyzed, and results obtained are shown Some comparisons with other methods are given

Wavelet Convolutional Neural Networks.

Abstract: Spatial and spectral approaches are two major approaches for image processing tasks such as image classification and object recognition. Among many such algorithms, convolutional neural networks (CNNs) have recently achieved significant performance improvement in many challenging tasks. Since CNNs process images directly in the spatial domain, they are essentially spatial approaches. Given that spatial and spectral approaches are known to have different characteristics, it will be interesting to incorporate a spectral approach into CNNs. We propose a novel CNN architecture, wavelet CNNs, which combines a multiresolution analysis and CNNs into one model. Our insight is that a CNN can be viewed as a limited form of a multiresolution analysis. Based on this insight, we supplement missing parts of the multiresolution analysis via wavelet transform and integrate them as additional components in the entire architecture. Wavelet CNNs allow us to utilize spectral information which is mostly lost in conventional CNNs but useful in most image processing tasks. We evaluate the practical performance of wavelet CNNs on texture classification and image annotation. The experiments show that wavelet CNNs can achieve better accuracy in both tasks than existing models while having significantly fewer parameters than conventional CNNs.

FLIP-Q: A QCIF Resolution Focal-Plane Array for Low-Power Image Processing

Abstract: This paper reports a 176×144-pixel smart image sensor designed and fabricated in a 0.35 CMOS-OPTO process. The chip implements a massively parallel focal-plane processing array which can output different simplified representations of the scene at very low power. The array is composed of pixel-level processing elements which carry out analog image processing concurrently with photosensing. These processing elements can be grouped into fully-programmable rectangular-shape areas by loading the appropriate interconnection patterns into the registers at the edge of the array. The targeted processing can be thus performed block-wise. Readout is done pixel-by-pixel in a random access fashion. On-chip 8b ADC is provided. The image processing primitives implemented by the chip, experimentally tested and fully functional, are scale space and Gaussian pyramid generation, fully-programmable multiresolution scene representation-including foveation-and block-wise energy-based scene representation. The power consumption associated to the capture, processing and A/D conversion of an image flow at 30 fps, with full-frame processing but reduced frame size output, ranges from 2.7 mW to 5.6 mW, depending on the operation to be performed.

Multi-scale methods

Abstract: In this paper four multiple scale methods are proposed. The meshless hierarchical partition of unity is used as a multiple scale basis. The multiple scale analysis with the introduction of a dilation parameter to perform multiresolution analysis is discussed. The multiple field based on a 1-D gradient plasticity theory with material length scale is also proposed to remove the mesh dependency difficulty in softening/localization problems. A non-local (smoothing) particle integration procedure with its multiple scale analysis are then developed. These techniques are described in the context of the reproducing kernel particle method. Results are presented for elastic-plastic one-dimensional problems and 2-D large deformation strain localization problems to illustrate the effectiveness of these methods. Copyright © 2000 John Wiley & Sons, Ltd.