Blob detection

Résumé

In computer vision, blob detection methods are aimed at detecting regions in a that differ in properties, such as brightness or color, compared to surrounding regions. Informally, a blob is a region of an image in which some properties are constant or approximately constant; all the points in a blob can be considered in some sense to be similar to each other. The most common method for blob detection is convolution. Given some property of interest expressed as a function of position on the image, there are two main classes of blob detectors: (i) differential methods, which are based on derivatives of the function with respect to position, and (ii) methods based on local extrema, which are based on finding the local maxima and minima of the function. With the more recent terminology used in the field, these detectors can also be referred to as interest point operators, or alternatively interest region operators (see also interest point detection and corner detection). The

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https://en.wikipedia.org/wiki/Blob_detection

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Contactless Respiration Monitoring in Real-Time via a Video Camera

Olivier Grossenbacher, Alia Lemkaddem

Until today, vital signs monitoring in neonatal intensive care units (NICUs) is based on wired sensors, known to cause discomfort and false alarms. In view of overcoming such issues we investigate a contactless method for respiration monitoring by means of a simple video camera. Unlike many other solutions proposed in the literature, our approach makes use of a motion estimation with low computational complexity which facilitates a real-time implementation. To do so, the input image is split into blocks, for each of which motion is estimated. Thereafter, these block motions are classified according to their likelihood to contain true respiratory activity, enabling an automatic region of interest detection. Aside from the respiratory rate (RR) our algorithm also computes a quality index, representing the confidence of the given RR. The proposed approach was tested and evaluated on 16 healthy adults, both during illuminated and dark conditions, using a color or near-infrared camera, respectively. On more than 2 hours of recording, Bland-Altman analysis reveals an error of 0.2 +/- 2.3 bpm (breaths-per-minute) when compared to the reference measure, a thoracic strain gauge belt. Our analysis further indicates that independent of light or dark conditions the near-infrared camera alone is sufficient to achieve satisfying results. These findings pave the way towards a simple, low-cost and con tactless RR monitoring. While currently only tested on healthy adults, future work includes the evaluation of this approach in clinical scenarios, such as NICUs in particular.

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Electrochemical biosensors are promoting point-of-care and wearable instrumentation due to their high versatility in measuring human metabolites. There is a considerable number of biological compounds that can be detected and measured through voltammetry based techniques. Voltmmetry some times requires peak identification and quantification that are non-trivial to be efficiently implemented by automatic instrumentation. To overcome the complexity of automatic peak estimation, we propose here an instrumentation circuit for edge-computing in pharmacology relying on an entirely novel measurement method via TotalCharge Detection in Cyclic voltammetry (TCDC). Namely, our TCDC method innovatively applies the coulometry measurement to the well-established voltammetry procedure. The proposed instrumentation accumulates the total charge exchanged in the faradaic process, exploiting a Nagaraj integrator as charge suppressor to fit the application-specific constraints. The work shows accurate simulations of the TCDC circuit on a set of experimental measures, acquired on paracetamol as benchmark drug. The proposed measurement technique and the developed circuit are compared to the peak detection method usually adopted in literature. The results demonstrate that the proposed system is a perfect trade-off between the doubled limit-of-detection and a tenfold reduction in measurement errors. At the same time, we eliminate any need for data oversampling and processing, promoting the TCDC as an efficient new measurement method for point-of-care and wearable monitoring of biological compounds.

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