o allow for adoption of optical spectroscopy in mobile and consumer devices, truly miniaturized, low-cost, mass producible spectrometers are needed. We present such a miniature (4mm x 3.2mm x 3.3mm) LGA packaged CMOS spectrometer in the range of 650-900nm and 720-1000nm. It leverages a wafer-level patterned spectral filter technology. The devices include diffuse optics and integrated spectral calibration and embedded corrections processor for part-to-part stable and repeatable performance across input angles and temperature with 68dB dynamic range, 5nm spectral resolution and up to 70 spectra/s. The run-time spectral corrections allow for plug-and-play operation of the sensor, without any need for recalibration in the field. The sensor enables portable spectroscopic applications in smart agriculture, anti-counterfeit, food analysis and skin sensing. For example, the combination of high sensitivity and speed of the spectrometer enables high sampling rate measurement of PPG signals and accurate measurement of heart rate and blood oxygenation (SpO2).
During the last decade research in face recognition has shifted from 2D to 3D face representations. The need
for 3D face data has resulted in the advent of 3D databases. In this paper, we first give an overview of publicly
available 3D face databases containing expression variations, since these variations are an important challenge
in today's research. The existence of many databases demands a quantitative comparison of these databases in
order to compare more objectively the performances of the various methods available in literature. The ICP
algorithm is used as baseline algorithm for this quantitative comparison for the identification and verification
scenario, allowing to order the databases according to their inherent difficulty. Performance analysis using the
rank 1 recognition rate for identification and the equal error rate for verification reveals that the FRGC v2
database can be considered as the most challenging. Therefore, we recommend to use this database further as
reference database to evaluate (expression-invariant) 3D face recognition algorithms. As second contribution,
the main factors that influence the performance of the baseline technique are determined and attempted to be
quantified. It appears that (1) pose variations away from frontality degrade performance, (2) expression types
affect results, (3) more intense expressions degrade recognition, (4) an increasing number of expressions decreases
performance and (5) the number of gallery subjects degrades performace. A new 3D face recognition algorithm
should be evaluated for all these factors.
Aging and many neurological diseases cause progressive changes in brain morphology. Both subject-specific
detection and measurement of these changes, as well as their population-based analysis are of great interest in
many clinical studies. Generally, both problems are handled separately. However, as population-based knowledge
facilitates subject-specific analysis and vice versa, we propose a unified statistical framework for subject-specific
and population-based analysis of longitudinal brain MR image sequences of subjects suffering from the same
neurological disease. The proposed method uses a maximum a posteriori formulation and the expectation
maximization algorithm to simultaneously and iteratively segment all images in separate tissue classes, construct
a global probabilistic 3D brain atlas and non-rigidly deform the atlas to each of the images to guide their
segmentation. In order to enable a population-based analysis of the disease progression, an intermediate 4D
probabilistic brain atlas is introduced, representing a discrete set of disease progression stages. The 4D atlas is
simultaneously constructed with the 3D brain atlas by incorporating assignments of each input image (voxelwise)
to a particular disease progression stage in the statistical framework. Moreover, these assignments enable both
temporal and spatial subject-specific disease progression analysis. This includes detecting delayed or advanced
disease progression and indicating the affected regions. The method is validated on a publicly available data set
on which it shows promising results.
In forensic authentication, one aims to identify the perpetrator among a series of suspects or distractors. A
fundamental problem in any recognition system that aims for identification of subjects in a natural scene is
the lack of constrains on viewing and imaging conditions. In forensic applications, identification proves even
more challenging, since most surveillance footage is of abysmal quality. In this context, robust methods for pose
estimation are paramount. In this paper we will therefore present a new pose estimation strategy for very low
quality footage. Our approach uses 3D-2D registration of a textured 3D face model with the surveillance image
to obtain accurate far field pose alignment. Starting from an inaccurate initial estimate, the technique uses
novel similarity measures based on the monogenic signal to guide a pose optimization process. We will illustrate
the descriptive strength of the introduced similarity measures by using them directly as a recognition metric.
Through validation, using both real and synthetic surveillance footage, our pose estimation method is shown to
be accurate, and robust to lighting changes and image degradation.
During intensity-based 2D/3D registration of 3D CAD models of knee implant components to a calibrated
single-plane fluoroscopy image, a similarity measure between the fluoroscopy and a rendering of the model onto
the image plane is maximized w.r.t. the 3D pose parameters of the model. This work focuses on a robust
strategy for finding this maximum by extending the standard Powell optimization algorithm with problem-specific
knowledge. A combination of feature-based and intensity-based methods is proposed. At each iteration
of the optimization process, feature information is used to compute an additional search direction along which the
image-based similarity measure is maximized. Hence, the advantages of intensity-based registration (accuracy)
and feature-based registration (robustness) are combined. The proposed method is compared with the standard
Powell optimization strategy, using an image-based similarity measure only, on simulated fluoroscopy images.
It is shown that the proposed method generally has higher accuracy, more robustness and a better convergence
behavior. Although introduced for the registration of 3D CAD models of knee implant components to single-plane
fluoroscopy images, the optimization strategy is easily extendible and applicable to other 2D/3D registration
applications.
A fully automated initialization method is proposed for the 2D/3D registration of 3D CAD models of knee
implant components to a single-plane calibrated fluoroscopy. The algorithm matches edge segments, detected
in the fluoroscopy image, with pre-computed libraries of expected 2D silhouettes of the implant components.
Each library entry represents a different combination of out-of-plane registration transformation parameters.
Library matching is performed by computing point-based 2D/2D registrations in between each library entry
and each detected edge segment in the fluoroscopy image, resulting in an estimate of the in-plane registration
transformation parameters. Point correspondences for registration are established by template matching of the
bending patterns on the contours. A matching score for each individual 2D/2D registration is computed by
evaluating the transformed library entry in an edge-encoded (characteristic) image, which is derived from the
original fluoroscopy image. A matching scores accumulator is introduced to select and suggest one or more initial
pose estimates. The proposed method is robust against occlusions and partial segmentations. Validation results
are shown on simulated fluoroscopy images. In all cases a library match is found for each implant component
which is very similar to the shape information in the fluoroscopy. The feasibility of the proposed method is
demonstrated by initializing an intensity-based 2D/3D registration method with the automatically obtained
estimation of the registration transformation parameters.
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