A system for identifying desired information from collected sensor data includes a collection device and a processing module. The collection device collects sensor data, coarsely filters the sensor data according to predefined rules to generate filter matched data, and securely transmits the filter matched data to the processing module. The processing module finely filters the filter matched data to generate desired information, provides the desired information to an authorized actor, and deletes the filter matched data.

A modular control system for controlling an AGV includes an interface, a processor, a memory, and a plurality of programs. The plurality of programs include a task scheduling module, a sensor fusion module, a mapping module, and a localization module. The interface receives a command signal from an AGV management system and sensor signals from a plurality of sensors. The memory stores a surrounding map and the plurality of programs to be executed by the processor. The task scheduling module converts the command signal to generate an enabling signal. The sensor fusion module processes the received sensor signals according to the enabling signal and generates an organized sensor data. The mapping module processes the organized sensor data and the surrounding map to generate an updated surrounding map. The localization module processes the organized sensor data and the updated surrounding map to generate a location and pose signal.

Computing devices and methods for reading gauges are disclosed. A gauge reading method includes capturing image data corresponding to a captured image of one or more gauges, detecting one or more gauges in the captured image, cropping a detected gauge in the captured image to provide a use image including the detected gauge, and classifying the detected gauge to correlate the detected gauge with a template image. The gauge reading method also includes attempting to perform feature detection rectification on the use image to produce a rectified image of the detected gauge, performing template matching rectification on the use image to produce the rectified image responsive to a failure to perform the feature detection rectification, and estimating a gauge reading responsive to the rectified image. A computing device may implement at least a portion of a gauge reading method.

Computer-implemented methods and systems for identifying corresponding slices in medical image data sets are provided. For example, the systems and methods are based on identifying corresponding slices by systematically quantifying image similarities between the slices comprised in one medical image data set and the slices comprised in another medical image data set.

This application relates to automated processes for determining item placement compliance within retail locations. For example, a computing device may obtain an image of a fixture within a store. The image may be captured by a camera with a field of view directed at the fixture. The computing device may apply a segmentation process to the image to determine a portion of the image. Further, the computing device may determine a correlation between the portion of the image and each of a plurality of item image templates. Each item image template may include an image of an item the retail location sells in the retail location. The computing device may determine, based on the correlations, one of the plurality of item image templates and its corresponding item. The computing device may then determine whether the item should be located at the fixture based on a planogram.

Systems and methods for classifying at least a portion of an image as being textured or textureless are presented. The system receives an image generated by an image capture device, wherein the image represents one or more objects in a field of view of the image capture device. The system generates one or more bitmaps based on at least one image portion of the image. The one or more bitmaps describe whether one or more features for feature detection are present in the at least one image portion, or describe whether one or more visual features for feature detection are present in the at least one image portion, or describe whether there is variation in intensity across the at least one image portion. The system determines whether to classify the at least one image portion as textured or textureless based on the one or more bitmaps.

Multiple field of view (FOV) systems are disclosed herein. An example system includes a bioptic barcode reader having a target imaging region. The bioptic barcode reader includes at least one imager having a first FOV and a second FOV and is configured to capture an image of a target object from each FOV. The example system includes one or more processors configured to receive the images and a trained object recognition model stored in memory communicatively coupled to the one or more processors. The memory includes instructions that, when executed, cause the one or more processors to analyze the images to identify at least a portion of a barcode and one or more features associated with the target object. The instructions further cause the one or more processors to determine a target object identification probability and to determine whether a predicted product identifies the target object.

The present invention is in the field of experimental data acquisition. In particular, the present invention relates to a live-cell imaging method and a corresponding system for acquiring experimental data of one or more biological probes. More specifically, the present invention relates to methods and systems for evaluating an optimized concentration trajectory for administration of a drug, in particular a chemotherapeutic drug.

An information processing device calculates a rate related to growth of a plant from sensing data, and performs control to display a sample position image arranged according to a sensing position of each piece of the sensing data in a map region of a user interface screen in an image mode determined according to a rate calculated from each piece of the sensing data.

A detection system includes an acquisition portion scanning light to acquire point-cloud information corresponding to a plurality of positions of a detection target object; an estimation portion using consistency with an outer shape model of the detection target object to estimate a location and attitude of the detection target object based on the point-cloud information; and an output portion outputting information relating to a movement target location based on an estimation result, wherein the estimation portion fits an outer shape model indicating an outer shape of the detection target object to a point cloud according to the point-cloud information, and uses point-cloud information existing outside the outer shape model to estimate the location and the attitude of the detection target object.