Example apparatus disclosed herein include a memory and a processor to execute instructions to identify a first set of face rectangles and a second set of face rectangles in a frame pair of image data corresponding to a media environment, the first set of face rectangles corresponding to a first image sensor and the second set of face rectangles corresponding to a second image sensor, remove first face rectangles from the first set of face rectangles and the second set of face rectangles when the first face rectangles are determined to correspond to false positive face detections, group second face rectangles that remain in the first set of face rectangles and the second set of face rectangles after removal of the first face rectangles to form groups of face rectangles, and generate a count of people identified in the media environment based on a number of the groups.

An apparatus of this invention is directed to an information processing apparatus that effectively counts, on a type basis, articles of a plurality of types displayed in a depth direction on a display shelf. The information processing apparatus includes a display count acquirer that acquires display count information of articles using article presence/absence sensors provided on the display shelf on which the articles are placed, an article identifier that acquires article identification information capable of identifying the types of articles based on an image acquired by capturing the display shelf, and a display recognizer that recognizes, based on the display count information and the article identification information, display count of each type of the articles.

Described herein are methods, systems, and non-transitory computer-readable media to non-destructively acquire three-dimensional profiles of cellular microbiological samples growing on the surface of a solid growth medium. Acquisitions can be performed by an optical microscope that includes a vertical scanning interferometer. The three-dimensional profiles can enable measurement of sample parameters of microcolonies, which can be made of microbial colony forming units. The methods and systems enable early and rapid detection and quantification of microbes.

A fish counting system has: an image acquisition unit for acquiring a plurality of images wherein a fluid containing fish is imaged over time; a counting unit for counting the fish on the basis of the plurality of images; a fish count change display provision unit for providing a fish count change display, wherein a display corresponding to the number of fish counted per unit time is arranged in a time-series manner; a result provision unit for providing a counting result display wherein an image to which count completion marks indicating a counted fish have been added; and a correction unit for receiving a correction operation and correcting the number of fish.

An information processing apparatus includes, when a first determiner determines the amount of food or beverage in a platter being decreased, a second determiner to determine whether food or beverage on the platter was consumed by one person of multiple people, or the food or beverage was moved onto one small plate of small plates, a consumption information generator to generate, when the food or beverage on the platter was consumed by the one person, consumption information in which an amount of food or beverage consumed by the one person, the one person, and the type of the food or beverage are associated with each other, and an estimator to increase, when the food or beverage was moved to one small plate, the amount of food or beverage on the one small plate by an amount of decrease in the food or beverage in the platter.

Computer apparatuses, methods, and non-transitory computer-readable recording media having recorded thereon a program executed in a computer apparatus to perform functions of image processing are provided. An example non-transitory recording medium comprising a program executed in a computer apparatus causes the computer apparatus to perform functions comprising: identifying two or more subjects in a first image from an imaging device; and if the identified two or more subjects satisfy a predetermined condition, superimposing a predetermined effect either on the first image or on a second image from the imaging device that is different from the first image to generate a third image, and providing the third image.

A parking management system identifies vehicles in entrance lanes, exit lanes, or both, of a parking facility and provides a real-time vehicle inventory count. One or more beacons locate point of sale (POS) devices assigned to parking attendants to determine which of them are performing vehicle transactions. Vehicles can be associated with specific transactions, as well as the transaction-authorizing attendant and the barrier gate and vehicle travel lane to which the attendant is assigned. To reduce fraud, this transaction information can be monitored in real time by a web or mobile dashboard and analyzed in real time or upon attendant end-of-shift checkout. The parking management system also implements permit parking invitation techniques that provide registration, payment in advance or on demand, or both, for parking of an invitee's vehicle. These tasks are accomplished without an inviter's knowledge about the invitee, the invitee's vehicle, or both.

A method for detecting object paths in a workplace, at a sensor block, includes responsive to detecting absence of motion, capturing an initial sequence of frames at a baseline frame rate at a camera and for each frame in the initial sequence of frames: detecting an initial constellation of objects; and transmitting a container representing the initial constellation of objects to a computer system. The method further includes, responsive to detecting motion: capturing a frame at the baseline frame rate at the camera; detecting a first constellation of objects, including a set of humans, in the frame; calculating a quantity of humans; transitioning the camera to a first frame rate; capturing a second sequence of frames at the first frame rate; detecting a second constellation of objects in the second sequence of frames; deriving a set of object paths; and transmitting the set of object paths to the computer system.

A computer system that computes second tractography results is described. This computer may include: a computation device (such as a processor, a graphics processing unit or GPU, etc.) that executes program instructions; and memory that stores the program instructions. During operation, the computer system receives information specifying tractography results that specify a set of neurological fibers. Then, the computer system computes, using a predetermined (e.g., pretrained) autoencoder neural network, the second tractography results that specify a second set of neurological fibers based at least in part on the tractography results and information associated with a neurological anatomical region. For example, a subset of the set of neurological fibers may be anatomically implausible and the second set of fibers may exclude the subset. Note that the predetermined autoencoder neural network may be trained using an unsupervised-learning technique.

Disclosed is a fluorescence image analyzer for measuring and analyzing a sample that includes a plurality of cells in which target portions are labeled with fluorescent dyes, and the fluorescence image analyzer includes a light source configured to apply light to the sample; an imaging unit configured to take a fluorescence image of each of the cells by which fluorescence is generated by applying the light; a processing unit configured to process the fluorescence image having been taken; and a display unit. The processing unit obtains a bright point pattern of fluorescence in the fluorescence image, causes the display unit to display a plurality of positive patterns that are previously associated with at least one of a measurement item or a labeling reagent, and causes the display unit to display information of at least one of the number of abnormal cells included in the sample, a proportion of the number of the abnormal cells, the number of normal cells included in the sample, and a proportion of the normal cells, based on the bright point pattern having been obtained and the plurality of positive patterns.