A computer vision-based system provides for load distribution estimation and load rating and vibration serviceability assessment of structures. The system integrates evaluates the structural load carrying capacity, the diagnosis and prognosis of performance and safety, and vibration serviceability. Cameras record images of a structure, and regions of interest are monitored in those images for their displacement and velocity as loading varies. Where the displacement determined exceeds a predetermined threshold, or where the acceleration determined exceeds predetermined limits, or where the distribution of displacements of parts of the structure deviates substantially from an estimated displacement distribution, an output indicating potential problems with the structure is output.

A detection system method of color balancing an image includes receiving an image of a test area of a pad which includes a color of a reaction between a test substance and at least one reagent on the test area of the pad, an alignment code having detection system identification information, at least one color calibration block indicia for aligning with a camera as the image is being captured, and test identification information for analyzing the test substance during a colorimetric analysis. The method further includes collecting an array of pixels of RGB values for each pixel in the image, evaluating a captured color of the at least one color calibration block in the image, and performing the colorimetric analysis on the reaction between the test substance and the at least one reagent.

A User Interface (UI) interface object detection system employs an initial dataset comprising a set of images, that may include synthesized images, to train a Machine Learning (ML) engine to generate an initial trained model. A data point generator is employed to generate an updated synthesized image set which is used to further train the ML engine. The data point generator may employ images generated by an application program as a reference by which to generate the updated synthesized image set. The images generated by the application program may be tagged in advance. Alternatively, or in addition, the images generated by the application program may be captured dynamically by a user using the application program.

A chip recognition system in which a chip is configured to at least partially have a specific color indicative of a value of the chip includes: a recording device that uses a camera and records a state of the chip as an image; an image analysis device that subjects the image so recorded to image analysis and recognizes at least the specific color and a reference color that is present in the image and differs from the specific color; and a recognition device at least including an artificial intelligence device that uses a result of the image analysis by the image analysis device and specifies the specific color of the chip, wherein the artificial intelligence device of the recognition device has been subjected to teaching using, as training data, a plurality of images of the chip and the reference color irradiated with different illumination intensities.

A vehicle imaging station for capturing images of scratches on a vehicle, the vehicle imaging station including a tunnel having an entrance and an exit, with one or more walls defining an enclosure between the entrance and exit to define a tunnel volume containing a vehicle pathway having a central axis. The station further includes a reflection source surface and a camera arranged with a field of view including an imaging volume of the tunnel volume in which an image defined by the reflection source surface will be reflected to be visible to the camera as a reflected image by a vehicle moving along the vehicle pathway. The station also includes a color modifier arranged to cause the reflection source surface to adopt a first color of a plurality of possible colors in response to a color control signal such that the reflected image has the first color.

A method of identifying an item includes: capturing by a camera an image of the item, searching in the image a predefined dot start and end codes, determining in the image directions of the predefined dot start and end codes, interpolating in the image a reading line to extend from the predefined dot start code to the predefined dot end code, where a direction of the reading line at the predefined dot start code corresponds to the direction of the predefined dot start code, where the direction of the reading line at the predefined dot end code corresponds to the direction of the predefined dot end code, reading along the reading line the dots from the image, and deriving the identification code from the dots as read along the reading line between the predefined dot start code and the predefined dot end code.

An image acquiring unit of acquires a captured image in which two or more medicines of a plurality of objects (medicines) are in point-contact or line-contact with one another. A first recognizer receives the captured image and generates an edge image indicating only a part where medicines are in point-contact or line-contact with one another, in the captured image. A second recognizer receives the captured image and the edge image, recognizes each of the plurality of medicines from the captured image, and outputs a recognition result. Since the second recognizer receives edge image indicating only the part where medicines are in point-contact or line-contact useful for separating the areas of the medicines, even if two or more medicines of the plurality of medicines are in point-contact or line-contact with one another, it is possible to accurately separate and recognize the areas of the plurality of medicines from the captured image.

A method of recognizing an image is provided, which relates to a field of artificial intelligence technology, in particular to a field of image recognition. The method includes: recognizing a plurality of target object groups of different categories from an image to be recognized; intercepting an area of each target object group from the image to be recognized, so as to obtain a target image of the each target object group; recognizing a number of at least one target object in the each target object group from the target image of the each target object group; and generating a scheduling information for the each target object group according to the category of the each target object group and the number of the at least one target object in the each target object group. An electronic device and a storage medium are further provided.

A method for detecting stressed plants in an indoor farm includes the steps of receiving two consecutively taken images of a plantation area in the indoor farm captured consecutively at a predetermined interval. The two images are combined to form a composite image. To the composite image is applied an object detection network to segment the composite image into images of single plants. Pre-trained convolution neuronal networks can be applied to the images of single plants classifying the single plants as healthy or stressed.

An operating method with goods information is applicable to an electronic device. The operation method includes: obtaining image information associated with one or more goods objects on a target electronic shelf among a plurality of electronic shelves in a network; and performing first communicating with a server for controlling the electronic shelves in the network according to either or both of the image information and feature information associated with the one or more goods objects, wherein feature information is extracted from the image information and the first communicating includes wirelessly transmitting either or both of the image information and the feature information associated with the one or more goods objects to the server.