Annals of physics

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

a ) p. 131 The discussion between eqs. (5.14) and (5.15) is incorrect (dA should be made as large as possible!). b ) p. 256 In the figure, the numbers 6) and 7) occur twice. c ) p. 292 At the end of section 12.5, it should be the space of isospectral 0Hermitian matrices. d ) p. 306 A ”Tr” is missing in eq. (13.43). e ) p. 327, Eq. (14.64b) is 〈Trρ〉B = N(14N+10) (5N+1)(N+3) should be 〈Trρ〉B = 8N+7 (N+2)(N+4)

Geometry of Quantum States: Frontmatter

https://chaos.if.uj.edu.pl/~karol/erratum10.pdf

Geometry of Quantum States

Geometry of quantum states: an introduction to quantum entanglement by Ingemar Bengtsson and Karol Zyczkowski

This paper introduces a new multi-lateral filter to fuse low-resolution depth maps with high-resolution images. The goal is to enhance the resolution of Time-of-Flight sensors and, at the same time, reduce the noise level in depth measurements. Our approach is based on the joint bilateral upsampling, extended by a new factor that considers the low reliability of depth measurements along the low-resolution depth map edges. Our experimental results show better performances than alternative depth enhancing data fusion techniques.

/pdf/pixel-weighted-average-strategy-for-depth-sensor-data-fusion-b0nujyaw09.pdf

Pixel weighted average strategy for depth sensor data fusion

One or more imaging device(s) inside a car that look(s) at occupants (driver, front passenger, rear passengers) and cover(s) multiple security, comfort, driver assistance and occupant state related functions, wherein the imaging device includes an illumination in the near infrared. An imaging device inside the car that can measure occupants' vital signs (heart rate, respiration rate, blood oxygen saturation) using contactless imaging photoplethysmography.

Imaging device based occupant monitoring system supporting multiple functions

A camera system comprises a 3D TOF camera for acquiring a camera-perspective range image of a scene and an image processor for processing the range image. The image processor contains a position and orientation calibration routine implemented therein in hardware and/or software, which position and orientation calibration routine, when executed by the image processor, detects one or more planes within a range image acquired by the 3D TOF camera, selects a reference plane among the at least one or more planes detected and computes position and orientation parameters of the 3D TOF camera with respect to the reference plane, such as, e.g., elevation above the reference plane and/or camera roll angle and/or camera pitch angle.

/pdf/3d-time-of-flight-camera-system-and-position-orientation-4r5h3ec3b9.pdf

3d time-of-flight camera system and position/orientation calibration method therefor

New experimental results on attachment reactions involving free electrons at sub-meV resolution allow for the first time a conclusive comparison of measured rate coefficients for Rydberg electron attachment with those calculated from the measured free electron cross sections on the basis of the quasi-free electron model for Rydberg electron collisions. Using classical velocity distributions for the highn Rydberg electrons and our measured free electron attachment cross sections, we calculate Rydberg electron attachment rate coefficientsknl for the two cases SF6 and HI for Rydberg binding energies |En| of 0.1–40 meV. We find a significant increase inknl towards lower binding energies, especially for HI, which is due to the deviation of the free electron cross section from the limitings-wave behaviour σ0∼E−1/2. The increase at |En|≲2 meV is in qualitative agreement with our highn Rydberg data (n≳80) if l-mixing due to residual electric fields is taken into account. For low l, Rydberg rate coefficientsknl(|En|) are significantly larger than free electron rate coefficientske(E=|En|), while for circular orbits (l=n−1) they agree. On average, attachment reactions of Rydberg electrons in low l orbits proceed with an effective collision energy substantially smaller than the binding energy |En|.

Comparison of rate coefficients for Rydberg electron and free electron attachment

A method for enhancing a depth image of a scene, comprises calculating an enhanced depth image by blending a first filtered depth image with a second filtered depth image or with the original depth image. The blending is achieved by application of a blending map, which defines, for each pixel, a contribution to the enhanced depth image of the corresponding pixel of the first filtered depth image and of the corresponding pixel of either the second filtered depth image or the original depth image. For pixels in the depth image containing no depth value or an invalid depth value, the blending map defines a zero contribution of the corresponding pixel of the second filtered depth image and a 100% contribution of the corresponding pixel of the first filtered image.

Bruno Mirbach

Papers

Pixel weighted average strategy for depth sensor data fusion

Imaging device based occupant monitoring system supporting multiple functions

3d time-of-flight camera system and position/orientation calibration method therefor

Comparison of rate coefficients for Rydberg electron and free electron attachment

Depth image enhancement method