DistancePPG: Robust non-contact vital signs monitoring using a camera

Vital signs such as pulse rate and breathing rate are currently measured using contact probes. But, non-contact methods for measuring vital signs are desirable both in hospital settings (e.g. in NICU) and for ubiquitous in-situ health tracking (e.g. on mobile phone and computers with webcams). Recently, camera-based non-contact vital sign monitoring have been shown to be feasible. However, camera-based vital sign monitoring is challenging for people with darker skin tone, under low lighting conditions, and/or during movement of an individual in front of the camera. In this paper, we propose distancePPG, a new camera-based vital sign estimation algorithm which addresses these challenges. DistancePPG proposes a new method of combining skin-color change signals from different tracked regions of the face using a weighted average, where the weights depend on the blood perfusion and incident light intensity in the region, to improve the signal-to-noise ratio (SNR) of camera-based estimate. One of our key contributions is a new automatic method for determining the weights based only on the video recording of the subject. The gains in SNR of camera-based PPG estimated using distancePPG translate into reduction of the error in vital sign estimation, and thus expand the scope of camera-based vital sign monitoring to potentially challenging scenarios. Further, a dataset will be released, comprising of synchronized video recordings of face and pulse oximeter based ground truth recordings from the earlobe for people with different skin tones, under different lighting conditions and for various motion scenarios.Comment: 24 pages, 11 figure

Design of a Multi-Color Plenoptic Camera for Snapshot Hyperspectral Imaging

The design of a custom camera lens including: a two-lens optical system, filter array, and iris has been developed enabling a greyscale plenoptic camera to acquire full field-of-view, 2D, instantaneous hyperspectral measurements. This work focuses on the use of 7 discrete color filters and their effect on the image quality. It was determined that the placement of the filters inside the aperture plane of the camera was paramount to mitigating image artifacts. In addition, design rules were developed such that the optimal optical parameters (image distance, working distance, and focal length) can be easily determined from a few charts

Adaptive Smoothing of Digital Images: The R Package adimpro

Digital imaging has become omnipresent in the past years with a bulk of applications ranging from medical imaging to photography. When pushing the limits of resolution and sensitivity noise has ever been a major issue. However, commonly used non-adaptive filters can do noise reduction at the cost of a reduced effective spatial resolution only. Here we present a new package adimpro for R, which implements the propagationseparation approach by (Polzehl and Spokoiny 2006) for smoothing digital images. This method naturally adapts to different structures of different size in the image and thus avoids oversmoothing edges and fine structures. We extend the method for imaging data with spatial correlation. Furthermore we show how the estimation of the dependence between variance and mean value can be included. We illustrate the use of the package through some examples.

Spatio-Spectral Sampling and Color Filter Array Design

Owing to the growing ubiquity of digital image acquisition and display, several factors must be considered when developing systems to meet future color image processing needs, including improved quality, increased throughput, and greater cost-effectiveness. In consumer still-camera and video applications, color images are typically obtained via a spatial subsampling procedure implemented as a color filter array (CFA), a physical construction whereby only a single component of the color space is measured at each pixel location. Substantial work in both industry and academia has been dedicated to post-processing this acquired raw image data as part of the so-called image processing pipeline, including in particular the canonical demosaicking task of reconstructing a full-color image from the spatially subsampled and incomplete data acquired using a CFA. However, as we detail in this chapter, the inherent shortcomings of contemporary CFA designs mean that subsequent processing steps often yield diminishing returns in terms of image quality. For example, though distortion may be masked to some extent by motion blur and compression, the loss of image quality resulting from all but the most computationally expensive state-of-the-art methods is unambiguously apparent to the practiced eye. … As the CFA represents one of the first steps in the image acquisition pipeline, it largely determines the maximal resolution and computational efficiencies achievable by subsequent processing schemes. Here, we show that the attainable spatial resolution yielded by a particular choice of CFA is quantifiable and propose new CFA designs to maximize it. In contrast to the majority of the demosaicking literature, we explicitly consider the interplay between CFA design and properties of typical image data and its implications for spatial reconstruction quality. Formally, we pose the CFA design problem as simultaneously maximizing the allowable spatio-spectral support of luminance and chrominance channels, subject to a partitioning requirement in the Fourier representation of the sensor data. This classical aliasing-free condition preserves the integrity of the color image data and thereby guarantees exact reconstruction when demosaicking is implemented as demodulation (demultiplexing in frequency)

Efficient Encoding of Wireless Capsule Endoscopy Images Using Direct Compression of Colour Filter Array Images

Since its invention in 2001, wireless capsule endoscopy (WCE) has played an important role in the endoscopic examination of the gastrointestinal tract. During this period, WCE has undergone tremendous advances in technology, making it the first-line modality for diseases from bleeding to cancer in the small-bowel. Current research efforts are focused on evolving WCE to include functionality such as drug delivery, biopsy, and active locomotion. For the integration of these functionalities into WCE, two critical prerequisites are the image quality enhancement and the power consumption reduction. An efficient image compression solution is required to retain the highest image quality while reducing the transmission power. The issue is more challenging due to the fact that image sensors in WCE capture images in Bayer Colour filter array (CFA) format. Therefore, standard compression engines provide inferior compression performance. The focus of this thesis is to design an optimized image compression pipeline to encode the capsule endoscopic (CE) image efficiently in CFA format. To this end, this thesis proposes two image compression schemes. First, a lossless image compression algorithm is proposed consisting of an optimum reversible colour transformation, a low complexity prediction model, a corner clipping mechanism and a single context adaptive Golomb-Rice entropy encoder. The derivation of colour transformation that provides the best performance for a given prediction model is considered as an optimization problem. The low complexity prediction model works in raster order fashion and requires no buffer memory. The application of colour transformation yields lower inter-colour correlation and allows the efficient independent encoding of the colour components. The second compression scheme in this thesis is a lossy compression algorithm with a integer discrete cosine transformation at its core. Using the statistics obtained from a large dataset of CE image, an optimum colour transformation is derived using the principal component analysis (PCA). The transformed coefficients are quantized using optimized quantization table, which was designed with a focus to discard medically irrelevant information. A fast demosaicking algorithm is developed to reconstruct the colour image from the lossy CFA image in the decoder. Extensive experiments and comparisons with state-of-the-art lossless image compression methods establish the superiority of the proposed compression methods as simple and efficient image compression algorithm. The lossless algorithm can transmit the image in a lossless manner within the available bandwidth. On the other hand, performance evaluation of lossy compression algorithm indicates that it can deliver high quality images at low transmission power and low computation costs

2D Iterative MAP Detection: Principles and Applications in Image Restoration

The paper provides a theoretical framework for the two-dimensional iterative maximum a posteriori detection. This generalization is based on the concept of detection algorithms BCJR and SOVA, i.e., the classical (one-dimensional) iterative detectors used in telecommunication applications. We generalize the one-dimensional detection problem considering the spatial ISI kernel as a two-dimensional finite state machine (2D FSM) representing a network of the spatially concatenated elements. The cellular structure topology defines the design of the 2D Iterative decoding network, where each cell is a general combination-marginalization statistical element (SISO module) exchanging discrete probability density functions (information metrics) with neighboring cells. In this paper, we statistically analyse the performance of various topologies with respect to their application in the field of image restoration. The iterative detection algorithm was applied on the task of binarization of images taken from a CCD camera. The reconstruction includes suppression of the defocus caused by the lens, CCD sensor noise suppression and interpolation (demosaicing). The simulations prove that the algorithm provides satisfactory results even in the case of an input image that is under-sampled due to the Bayer mask

Natural images from the birthplace of the human eye

Here we introduce a database of calibrated natural images publicly available through an easy-to-use web interface. Using a Nikon D70 digital SLR camera, we acquired about 5000 six-megapixel images of Okavango Delta of Botswana, a tropical savanna habitat similar to where the human eye is thought to have evolved. Some sequences of images were captured unsystematically while following a baboon troop, while others were designed to vary a single parameter such as aperture, object distance, time of day or position on the horizon. Images are available in the raw RGB format and in grayscale. Images are also available in units relevant to the physiology of human cone photoreceptors, where pixel values represent the expected number of photoisomerizations per second for cones sensitive to long (L), medium (M) and short (S) wavelengths. This database is distributed under a Creative Commons Attribution-Noncommercial Unported license to facilitate research in computer vision, psychophysics of perception, and visual neuroscience.Comment: Submitted to PLoS ON

Color reproduction from noisy CFA data of single sensor digital cameras

2007-2008 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe