A method for chip collection and a method for chip positioning are provided. The method for chip collection includes that: an image to be detected is obtained; chip position information of a comparison image with a highest matching degree with the image to be detected is obtained from a database; a position of each of detection regions in the image to be detected is obtained based on the chip position information; an image of the detection region is obtained based on the position of each detection region; it is determined whether the image of the detection region includes the chip code image; and when the image of the detection region includes the chip code image, a chip code corresponding to the chip code image identified and the chip code is stored in the database.

Computer vision systems and methods for generating building models using three-dimensional sensing and augmented reality (AR) techniques are provided. Image frames including images of a structure to be modeled are captured by a camera of a mobile device such as a smart phone, as well as three-dimensional data corresponding to the image frames. An object of interest, such as a structural feature of the building, is detected using both the image frames and the three-dimensional data. An AR icon is determined based upon the type of object detected, and is displayed on the mobile device superimposed on the image frames. The user can manipulate the AR icon to better fit or match the object of interest in the image frames, and can capture the object of interest using a capture icon displayed on the display of the mobile device.

A device configured to capture a first overhead depth image of the platform using a three-dimensional (3D) sensor at a first time instance and a second overhead depth image of a first object using the 3D sensor at a second time instance. The device is further configured to determine that a first portion of the first object is within a region-of-interest and a second portion of the first object is outside the region-of-interest in the second overhead depth image. The device is further configured to capture a third overhead depth image of a second object placed on the platform using the 3D sensor at a third time instance. The device is further configured to capture a first image of the second object using a camera in response to determining that the first object is outside of the region-of-interest and the second object is within the region-of-interest for the platform.

A device configured to receive a first encoded vector and receive one or more feature descriptors for a first object. The device is further configured to remove one or more encoded vectors from an encoded vector library that are not associated with the one or more feature descriptors and to identify a second encoded vector in the encoded vector library that most closely matches the first encoded vector based on the numerical values within the first encoded vector. The device is further configured to identify a first item identifier in the encoded vector library that is associated with the second encoded vector and to output the first item identifier.

A method in a server includes receiving sensor data from an edge computing device, the sensor data generated by a sensor array disposed on a support surface carrying items; detecting, from the sensor data, a set of regions of the sensor array corresponding to respective items carried on the support surface; generating, for each region, a corresponding monitoring definition, each monitoring definition containing: a location of the region within the array, a noise compensation indicator, and an item presence indicator; sending the monitoring definitions to the edge computing device; subsequent to sending the monitoring definitions, receiving item count data from the edge computing device, the item count data derived at the edge computing device from further sensor data generated by the sensor array, based on the monitoring definitions.

A method comprising: receiving a set of images associated with each of a plurality of plants in a plantation; estimating, with respect to each of the plants, based, at least in part, on the set of images associated with the plant, the following data: (i) a count of fruits detected in the plant, and (ii) one or more features associated with the plant; at a training stage, training a machine learning model on a training set comprising, with respect to a subset of the plurality of plants: (iii) the data, and (iv) labels indicating an actual a number of fruits in each of the plants in the subset; and at an inference stage, applying the trained machine learning model to the data associated with the rest of the plurality of plants, to predict a number of fruits in each of the rest of the plurality of plants.

The present invention provides a processing apparatus (10) including an acquisition unit (11) that acquires a capture image capturing a movement means, an image analysis unit (12) that computes, based on the capture image, the number of persons each having an attribute satisfying a first criterion among persons inside the movement means, and the number of persons targeted for comparison, a computation unit (13) that computes a ratio of the number of persons satisfying the first criterion to the number of persons targeted for comparison, and an output unit (14) that outputs alert information when the ratio satisfies an alert condition.

Provided are a leukocyte detection method, a system, an electronic device and a computer readable medium. The method comprises: acquiring a microcirculation image (S1); determining a location of an intra-tubular space of a capillary vessel from the microcirculation image (S2); and determining a leukocyte index based on image information of the intra-tubular space of the capillary vessel (S3).

The automatic control system of the smart bus platform includes a plurality of electronic devices provided in the shelter and a control unit. The control unit controls at least one of the plurality of electronic devices based on at least one of the number of users and a prospective staying time of the users in the shelter.

The present disclosure provides a method and an apparatus for processing a lane line, and relates to the field of data processing and, in particular, to the fields of intelligent transportation, Internet of Vehicles and intelligent cockpit. A specific implementation scheme is: obtaining a lane edge line of a road and a lane dividing line of the road according to point cloud data and image information of the road; acquiring breakpoints of the lane edge line, and acquiring breakpoints of the lane dividing line; completing the lane edge line according to the breakpoints of the lane edge line, to obtain a continuous lane edge line; completing the lane dividing line according to the breakpoints of the lane dividing line and the continuous lane edge line, to obtain a continuous lane dividing line.