Techniques are provided detecting diluted drugs using machine learning. Measurements and images corresponding to a product are obtained, wherein the product is formulated as a liquid, and wherein the measurements and images capture physical, spectral, optical, and/or chemical properties of the product. The measurements and images are provided to a machine learning model, wherein the machine learning model is trained using data generated from interactive learning modules (e.g., a generative adversarial network). The machine learning model detects whether the product or chemical is a real or counterfeit product. In addition, these techniques may be used by practitioners (e.g., medical personnel dispensing a prescribed dosage of a drug with a specific dilution level) to detect prescription errors at the point of administration.

A vehicle information detection method, a method for training a detection model, an electronic device and a storage medium are provided, and relates to the technical field of artificial intelligence, in particular to the technical field of computer vision and deep learning. The method includes: performing a first target detection operation based on an image of a target vehicle, to obtain a first detection result for target information of the target vehicle; performing an error detection operation based on the first detection result, to obtain error information; and performing a second target detection operation based on the first detection result and the error information, to obtain a second detection result for the target information.

An information processing apparatus includes a processor configured to acquire a result of character recognition of a handwritten character string and in response to a determination that the handwritten character string includes a character or a symbol that refers to a reference character string that has appeared previously, replace the character or the symbol with the reference character string that is referred to by the character or the symbol.

Methods, systems, and computer program products for vision-based cell structure recognition using hierarchical neural networks and cell boundaries to structure clustering are provided herein. A computer-implemented method includes detecting a style of the given table using at least one style classification model; selecting, based at least in part on the detected style, a cell detection model appropriate for the detected style; detecting cells within the given table using the selected cell detection model; and outputting, to at least one user, information pertaining to the detected cells comprising image coordinates of one or more bounding boxes associated with the detected cells.

A system and method, after performing the comparison, identifies at least one a match of the target feature from the imaging source with a library of target features. The system and method receive at least one numerical representation from a diagnostic source of a target feature. The system and method compares the at least one numerical representation of the target feature with a library of numerical representations. The system and method, after performing the comparison, identifies at least one match of the target feature from the imaging source with the library.

Embodiments may provide techniques that provide identification of images that can provide reduced resource utilization due to reduced sampling of video frames for visual recognition. For example, in an embodiment, a method of visual recognition processing may be implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method comprising: coarsely segmenting video frames of video stream into a plurality of clusters based on scenes of the video stream, sampling a plurality of video frames from each cluster; determining a quality of each cluster, re-clustering the video frames of video stream to improve the quality of at least some of the clusters.

An image processing apparatus comprises a detection circuit that, by referencing dictionary data acquired by machine learning corresponding to a target photographic subject to be detected in an obtained image, detects the target photographic subject; a selection unit that selects one of a plurality of dictionary data items corresponding to the target photographic subject; and a control circuit that, in a case where a detection evaluation value in a case where the photographic subject is detected by using the dictionary data selected by the selection unit is lower than a predetermined value, controls the detection circuit to detect the target photographic subject by using the selected dictionary data and dictionary data different to the selected dictionary data.

In one example of the disclosure, a set of electronic document templates is accessed and instances of duplicated document content are identified. Display of a user notice for first duplicated document content is caused. Responsive to receipt of data indicative of a user instruction to create a component template for the first duplicated content, the component template is created and stored.

Images of optical codes are mapped to an overview image to localize optical codes within a space. By localizing optical codes, information about locations of various products can be ascertained. One or more techniques can be used to map the images of optical codes to the overview image. The overview image can be a composite image formed by stitching together several images.

In an illustrative embodiment, a system for identifying products on a production line includes image capturing devices that acquire images of containers moving along a production line at an inspection location. The system also includes a rejection device and a controller that configures the image capturing devices for image acquisition based on properties of the containers, identifies a product associated with each of the containers based on a portion of a product identification code and a portion of additional features detected in the images, and determines whether the identified product matches predetermined properties or characteristics, resulting in a pass result, otherwise a non-pass result occurs. When a non-pass result occurs, the controller outputs a signal to actuate the rejection device that removes the container from the production line.