An flow meter includes a coupler, a support member, an image sensor, and one or more processors. The coupler is adapted to couple to a drip chamber. The support member is operatively coupled to the coupler. The image sensor has a field of view and is operatively coupled to the support member. The image sensor is positioned to view the drip chamber within its field of view. The one or more processors are operatively coupled to the image sensor to receive data thereform. The one or more processors (1) receive image data from the image sensor, and (2) determine an existence of a free flow condition by identifying an optical distortion of an area behind the free flow condition within the drip chamber using the received image data.

An apparatus and method to detect a line segment or arc using Hough transform. A Hough transform unit performs contour extraction on brightness image data to generate contour image data, with pixels having a pixel value of 0 to 255, performs the Hough transform on points in the contour image data, and counts additional values represented by pixel values of points in the contour image data in a Hough table. The Hough transform unit performs contour extraction on first to third component data to generate first to third contour data with pixels having a pixel value of 0 to 255, performs the Hough transform on points in the first to third contour data, and counts additional values represented by pixel values of points in the first to third contour data in the Hough table. The detection unit comprehensively evaluates the counts to detect a line segment or arc.

A device and method of dimensioning using digital images and depth data is provided. The device includes a camera and a depth sensing device whose fields of view generally overlap. Segments of shapes belonging to an object identified in a digital image from the camera are identified. Based on respective depth data, from the depth sensing device, associated with each of the segments of the shapes belonging to the object, it is determined whether each of the segments is associated with a same shape belonging to the object. Once all the segments are processed to determine their respective associations with the shapes of the object in the digital image, dimensions of the object are computed based on the respective depth data and the respective associations of the shapes.

The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes receiving receipt data associated with an entity. Policy questions associated with the entity are associated with at least one policy question answer that corresponds to a conformance or a violation of a policy selected by the entity. For each policy question, a machine learning policy model is identified for the policy question that includes, for each policy question answer, receipt data features that correspond to the policy question answer. The machine learning policy model is used to automatically determine a selected policy question answer to the policy question by comparing features of extracted tokens to respective receipt data features of the policy question answers that are included in the machine learning policy model. In response to determining that the selected policy question answer corresponds to a policy violation, an audit alert is generated.

A computer-based method for quantitative evaluation of computed tomography (CT) images of the head, particularly in circumstances of neurological emergency such as acute intracranial hemorrhage, evidence of intracranial mass effect, and acute stroke. The method comprises: calculation of volumes of abnormal areas such as locations of hemorrhage; quantification of severity of midline shift and basilar cistern effacement; and rapid identification of anatomical locations of abnormal findings. The methods comprise use of heuristics, convolutional neural networks, deep learning, edge detection, and Hough transform.

A method of detecting an edge of a support surface in an imaging controller includes: obtaining image data captured by an image sensor and a plurality of depth measurements captured by a depth sensor, the image data and the plurality of depth measurements corresponding to an area containing the support surface; detecting preliminary edges in the image data; applying a Hough transform to the preliminary edges to determine Hough lines representing candidate edges of the support surface; segmenting the plurality of depth measurements to assign classes to each pixel, each class defined by one of a plurality of seed pixels, wherein the plurality of seed pixels are identified from the depth measurements based on the Hough lines; and detecting the edge of the support surface by selecting a class of pixels and applying a line-fitting model to the selected class to obtain an estimated edge of the support surface.

A plurality of multi-camera systems and methods for imaging faint objects are disclosed, which includes an array of cameras that, when taken alone, are incapable of imaging such objects. The systems and methods may include common field arrays, hybrid field arrays, and/or adaptive field arrays.

A method for process control of a semiconductor structure fabricated by a series of fabrication steps, the method comprising obtaining an image of the semiconductor structure indicative of at least two individual fabrication steps; wherein the image is generated by scanning the semiconductor structure with a charged particle beam and collecting signals emanating from the semiconductor structure; and processing, by a hardware processor, the image to determining a parameter of the semiconductor structure, wherein processing includes measuring step/s from among the fabrication steps as an individual feature.

An Artificial Intelligence (AI) computational system for generating an embryo viability score from a single image of an embryo to aid selection of an embryo for implantation in an In-Vitro Fertilisation (IVF) procedure is described. The AI model uses a deep learning method applied to images in which the Zona Pellucida region in the image is identified using segmentation, and ground truth labels such as detection of a heartbeat at a six week ultrasound scan.

The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes receiving text submitted with a request. Token values are automatically extracted for selected token types from the text using a machine learning model. Tokens are identified in the text, and for each token, features and a token type of the token are determined, along with a score that indicates a likelihood that the token has the token type. A token value is extracted for the token from the text. Extracted token values are compared to request data values. A request value for the selected token type is identified in the data values for each selected token type. The extracted token value for each selected token type is compared to the request value. An audit alert is generated in response to determining that an extracted token value does not match a corresponding request value.