Results 1  10
of
34,337
Estimation of Minimum Quantization Levels by Using Reconstructed Histogram
"... The OKquantization theory determines the minimum gray level by using the reproducibility of an image histogram. In many cases, it is ascertained by the human sense of sight that the minimum gray level obtained from this theory is appropriate. However, in order to put the OKquantization theory into ..."
Abstract
 Add to MetaCart
The OKquantization theory determines the minimum gray level by using the reproducibility of an image histogram. In many cases, it is ascertained by the human sense of sight that the minimum gray level obtained from this theory is appropriate. However, in order to put the OKquantization theory
Determining Optical Flow
 ARTIFICIAL INTELLIGENCE
, 1981
"... Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. A method for finding the optical flow pattern is presented which assumes that the apparent veloc ..."
Abstract

Cited by 2404 (9 self)
 Add to MetaCart
in space and time. It is also insensitive to quantization of brightness levels and additive noise. Examples are included where the assumption of smoothness is violated at singular points or along lines in the image.
Quantum Gravity
, 2004
"... We describe the basic assumptions and key results of loop quantum gravity, which is a background independent approach to quantum gravity. The emphasis is on the basic physical principles and how one deduces predictions from them, at a level suitable for physicists in other areas such as string theor ..."
Abstract

Cited by 572 (11 self)
 Add to MetaCart
We describe the basic assumptions and key results of loop quantum gravity, which is a background independent approach to quantum gravity. The emphasis is on the basic physical principles and how one deduces predictions from them, at a level suitable for physicists in other areas such as string
Frequency Band Dependent Quantization Level for Adaptive ECGDedicated Signal Compression
"... The paper is devoted to the ECGdedicated compression algorithm based on the eventdriven variable quantization level in three upper octaves of the timefrequency signal representation. The algorithm uses an integertointeger reversible wavelet transform and the segmentation procedure developed for ..."
Abstract
 Add to MetaCart
The paper is devoted to the ECGdedicated compression algorithm based on the eventdriven variable quantization level in three upper octaves of the timefrequency signal representation. The algorithm uses an integertointeger reversible wavelet transform and the segmentation procedure developed
English version Introduction to Symplectic Geometry and Deformation Quantization Level of course
"... ..."
On Distributed Averaging Algorithms and Quantization Effects
, 2009
"... We consider distributed iterative algorithms for the averaging problem over timevarying topologies. Our focus is on the convergence time of such algorithms when complete (unquantized) information is available, and on the degradation of performance when only quantized information is available. We stu ..."
Abstract

Cited by 133 (24 self)
 Add to MetaCart
establish bounds on the error and tight bounds on the convergence time, as a function of the number of quantization levels.
Visibility of Wavelet Quantization Noise
, 1996
"... The Discrete Wavelet Transform (DWT) decomposes an image into bands that vary in spatial frequency and orientation. It is widely used for image compression. Measures of the visibility of DWT quantization errors are required to achieve optimal compression. Uniform quantization of a single band of coe ..."
Abstract

Cited by 145 (1 self)
 Add to MetaCart
construct a mathematical model for DWT noise detection thresholds that is a function of level, orientation, and display visual resolution. This allows calculation of a "perceptually lossless" quantization matrix for which all errors are in theory below the visual threshold. The model may also
Learning midlevel features for recognition
, 2010
"... Many successful models for scene or object recognition transform lowlevel descriptors (such as Gabor filter responses, or SIFT descriptors) into richer representations of intermediate complexity. This process can often be broken down into two steps: (1) a coding step, which performs a pointwise tra ..."
Abstract

Cited by 228 (13 self)
 Add to MetaCart
to establish the relative importance of each step of midlevel feature extraction through a comprehensive cross evaluation of several types of coding modules (hard and soft vector quantization, sparse coding) and pooling schemes (by taking the average, or the maximum), which obtains state
Results 1  10
of
34,337