Three dimensional minimum variance recursive filtering with implicit spatiotemporal compensation

1999 ◽  
Vol 32 (2) ◽  
pp. 3850-3855
Author(s):  
L. Jetto
Keyword(s):  
Three Dimensional ◽  
Minimum Variance ◽  
Recursive Filtering

Minimum-variance cone-excitation ratios and the limits of relational color constancy

2004 ◽  
Vol 21 (3) ◽  
pp. 337-340 ◽  
Author(s):  
SÉRGIO M.C. NASCIMENTO ◽  
VASCO M.N. de ALMEIDA ◽  
PAULO T. FIADEIRO ◽  
DAVID H. FOSTER
Keyword(s):  
Color Constancy ◽  
Three Dimensional ◽  
Spectral Composition ◽  
Minimum Variance ◽  
Physical Basis ◽  
Test Surface ◽  
Two Dimensional ◽  
Surface Color

Relational color constancy refers to the constancy of the perceived relations between the colors of surfaces of a scene under changes in the spectral composition of the illuminant. Spatial ratios of cone excitations provide a natural physical basis for this constancy, as, on average, they are almost invariant under illuminant changes for large collections of natural surfaces and illuminants. The aim of the present work was to determine, computationally, for specific surfaces and illuminants, the constancy limits obtained by the application of a minimum-variance principle to cone-excitation ratios and to investigate its validity in predicting observers' surface-color judgments. Cone excitations and their changes due to variations in the color of the illuminant were estimated for colored surfaces in simulated two-dimensional scenes of colored papers and real three-dimensional scenes of solid colored objects. For various test surfaces, scenes, and illuminants, the estimated levels of relational color constancy mediated by cone-excitation ratios varied significantly with the test surface and only with certain desaturated surfaces corresponded to ideal matches. Observers' experimental matches were compared with predictions expressed in CIE 1976 (u′,v′) space and were found to be generally consistent with minimum-variance predictions.

scholarly journals Nanometric depth resolution from multi-focal images in microscopy

2011 ◽  
Vol 8 (60) ◽  
pp. 942-951 ◽  
Author(s):  
Heather I. C. Dalgarno ◽  
Paul A. Dalgarno ◽  
Adetunmise C. Dada ◽  
Catherine E. Towers ◽  
Gavin J. Gibson ◽  
...  
Keyword(s):  
Cell Biology ◽  
Photon Counting ◽  
Three Dimensional ◽  
Ccd Camera ◽  
Depth Resolution ◽  
Minimum Variance ◽  
Three Dimensions ◽  
Wide Field ◽  
Analytical Treatment ◽  
Depth Measurement

We describe a method for tracking the position of small features in three dimensions from images recorded on a standard microscope with an inexpensive attachment between the microscope and the camera. The depth-measurement accuracy of this method is tested experimentally on a wide-field, inverted microscope and is shown to give approximately 8 nm depth resolution, over a specimen depth of approximately 6 µm, when using a 12-bit charge-coupled device (CCD) camera and very bright but unresolved particles. To assess low-flux limitations a theoretical model is used to derive an analytical expression for the minimum variance bound. The approximations used in the analytical treatment are tested using numerical simulations. It is concluded that approximately 14 nm depth resolution is achievable with flux levels available when tracking fluorescent sources in three dimensions in live-cell biology and that the method is suitable for three-dimensional photo-activated localization microscopy resolution. Sub-nanometre resolution could be achieved with photon-counting techniques at high flux levels.

scholarly journals Modeling Respiratory Signals by Deformable Image Registration on 4DCT Lung Images

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Pham The Bao ◽  
Hoang Thi Kieu Trang ◽  
Tran Anh Tuan ◽  
Tran Thien Thanh ◽  
Vo Hong Hai
Keyword(s):  
Image Registration ◽  
Hybrid Approach ◽  
Three Dimensional ◽  
Deformable Image Registration ◽  
Minimum Variance ◽  
Human Anatomy ◽  
Breath Hold ◽  
Breathing Motion ◽  
Lung Motion ◽  
Overall Evaluation

The lung organ of human anatomy captured by a medical device reveals inhalation and exhalation information for treatment and monitoring. Given a large number of slices covering an area of the lung, we have a set of three-dimensional lung data. And then, by combining additionally with breath-hold measurements, we have a dataset of multigroup CT images (called 4DCT image set) that could show the lung motion and deformation over time. Up to now, it has still been a challenging problem to model a respiratory signal representing patients’ breathing motion as well as simulating inhalation and exhalation process from 4DCT lung images because of its complexity. In this paper, we propose a promising hybrid approach incorporating the local binary pattern (LBP) histogram with entropy comparison to register the lung images. The segmentation process of the left and right lung is completely overcome by the minimum variance quantization and within class variance techniques which help the registration stage. The experiments are conducted on the 4DCT deformable image registration (DIR) public database giving us the overall evaluation on each stage: segmentation, registration, and modeling, to validate the effectiveness of the approach.

scholarly journals Multi-target vital signs detection using frequency-modulated continuous wave radar

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yong Wang ◽  
Yuzhu Shui ◽  
Xiaobo Yang ◽  
Zhaoyu Li ◽  
Wen Wang
Keyword(s):  
Continuous Wave ◽  
Matching Pursuit ◽  
Three Dimensional ◽  
Vital Signs ◽  
Minimum Variance ◽  
Adaptive Beamforming ◽  
Discrete Wavelet ◽  
Fmcw Radar ◽  
Wave Radar ◽  
Spatially Distributed

AbstractRespiration and heartbeats rates are important physiological assessment indicators that provide valid prior-knowledge for the diagnosis of numerous diseases. However, most of the current research focuses on the vital signs measurement of single target, and multi-target vital signs detection has not received much attention. In this paper, we use frequency-modulated continuous wave (FMCW) radar to measure the vital signs signals of multi-target. First, we apply the three-dimensional fast Fourier transform (3D-FFT) method to separate multiple targets and get their distance and azimuth information. Subsequently, the linear constrained minimum variance-based adaptive beamforming (LCMV-ADBF) technique is proposed to form a spatially distributed beams on the targets of interest directions. Finally, a compressive sensing based on orthogonal matching pursuit (CS-OMP) method and rigrsure adaptive soft threshold noise reduction based on discrete wavelet transform (RA-DWT) method are present to extract the respiratory and heartbeat signals. We perform tests in a real experimental environment and compare the proposed method with reference devices. The results show that the degrees of agreement for respiratory and heartbeat are 89% and 87%, respectively, for two human targets. The level of agreement for respiratory and heartbeat is 87% and 85%, respectively, for three human targets, proving the effectiveness of the proposed method.

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