Systems, methods, apparatuses, and computer program products for contactless image-based blood oxygen estimation. A method may include receiving an image or video of a part of a subject captured by a camera of a computing device. The method may also include extracting a region of interest of the part of the subject from the image or video. The method may further include performing feature extraction of the region of interest. In addition, the method may include estimating a blood oxygen saturation level of the subject based on a spatial and temporal data analysis of more than two color channels. Feature extraction and estimation of the blood oxygen saturation level may include implementing a combination of spatial averaging, color channel mixing, and temporal trend analysis.

There is provided with an image processing apparatus. A detection unit detects an object from a captured image. A generation unit generates a map representing a correspondence between objects detected in a plurality of captured images. A determination unit matches the objects detected in the plurality of captured images based on the generated map.

A system, method, and computer program product for predicting, anticipating, and/or assessing tissue characteristics obtains measurement information associated with a parameter of a voxel of tissue of a patient measured at two or more time points, the two or more time points occurring before one or more characteristics of the voxel of the tissue are separable in an image generated based on the parameter of the voxel measured at a single time point of the two or more time points, and determines, based on the parameter of the voxel at the two or more time points, the one or more characteristics of the voxel of the tissue.

A system, method, and computer program product for predicting, anticipating, and/or assessing tissue characteristics obtains measurement information associated with a parameter of a voxel of tissue of a patient measured at two or more time points, the two or more time points occurring before one or more characteristics of the voxel of the tissue are separable in an image generated based on the parameter of the voxel measured at a single time point of the two or more time points, and determines, based on the parameter of the voxel at the two or more time points, the one or more characteristics of the voxel of the tissue.

In example implementations, an apparatus is provided. The apparatus includes a channel, a camera, and a processor. The channel contains a fluid and an object. The fluid is to move the object through the channel. The camera system is to capture video images of the object in the channel. The processor is to track movement of the object in the channel via the video images based on known flow dynamics of the channel.

An information processing system (10) includes: an acquisition unit (50) configured to sequentially acquire a plurality of elements included in sequential data; a first calculation unit (110) configured to calculate, for each of the plurality of elements, a first indicator indicating which one of a plurality of classes the element belongs to; a weight calculation unit (130) configured to calculate, for each of the plurality of elements, a weight according to a confidence related to calculation of the first indicator; a second calculation unit (120) configured to calculate, based on the first indicators each weighted with the weight, a second indicator indicating which one of the plurality of classes the sequential data belongs to; and a classification unit (60) configured to classify the sequential data as any one of the plurality of classes, based on the second indicator. According to such an information processing system, sequential data can be appropriately classified.

A three-dimensional (3D) printer includes a nozzle and a camera configured to capture a real image or a real video of a liquid metal while the liquid metal is positioned at least partially within the nozzle. The 3D printer also includes a computing system configured to perform operations. The operations include generating a model of the liquid metal positioned at least partially within the nozzle. The operations also include generating a simulated image or a simulated video of the liquid metal positioned at least partially within the nozzle based at least partially upon the model. The operations also include generating a labeled dataset that comprises the simulated image or the simulated video and a first set of parameters. The operations also include reconstructing the liquid metal in the real image or the real video based at least partially upon the labeled dataset.

The present subject matter relates to a system and method of operating one or more self-checkout (SCO) terminals of a SCO environment. The system comprises one or more video sensors configured to capture a plurality of video frames. The video frames are processed by a processing unit to detect a primary subject of interest and a second subject of interest post detection of the primary subject of interest. Further, change in location and time of appearance of the primary subject of interest and the secondary subject of interest is determined, which generates a motion trigger. Based on the motion trigger, a transaction data is received which is compared with the detected secondary subject of interest. A non-scan event alert is generated based on a mismatch in the comparison of the transaction data and the detected one or more secondary subject of interest.

The present subject matter relates to a system and method of operating one or more self-checkout (SCO) terminals of a SCO environment. The system comprises one or more video sensors configured to capture a plurality of video frames. The video frames are processed by a processing unit to detect a primary subject of interest and a second subject of interest post detection of the primary subject of interest. Further, change in location and time of appearance of the primary subject of interest and the secondary subject of interest is determined, which generates a motion trigger. Based on the motion trigger, a transaction data is received which is compared with the detected secondary subject of interest. A non-scan event alert is generated based on a mismatch in the comparison of the transaction data and the detected one or more secondary subject of interest.

The present invention relates to landmark and/or temporal event detection. It is proposed to utilize previously learned spatial statistical correlations between multiple landmarks in order to regularize convolutional neural networks (CNNs) either as a post-processing step or during training in order to utilize anatomical prior knowledge, reduce the false-positive prediction rate, and/or increase the accuracy and stability of the algorithm. The proposed apparatus and method may also be applied to improve the detection of correlated events in e.g., time-series by leveraging prior knowledge.