In a computer implemented sketch-based education or training system, guidance elements are generated and output to users both on an affirmative request of the user and in an automated manner without a request for guidance from the user. Automated guidance may take the form of a mini-hint that does not provide explicit information about a solution. The automatically provided guidance elements may contain numerical measures of correspondence between a user submitted sketch and a model sketch.

In examples, a non-transitory computer-readable storage medium stores executable code, which, when executed by a processor, causes the processor to receive a semiconductor package image, the image including semiconductor package surface codes, the codes including a semiconductor package identifier. The executable code causes the processor to transmit at least one of the semiconductor package identifier, the codes, or the image. The executable code causes the processor to receive information associated with the semiconductor package identifier. The executable code causes the processor to output the information via at least one of a display coupled to the processor, a speaker coupled to the processor, or the wireless transceiver.

An image processing method performed by a computing device deployed with a deep-learning neural network is provided. An image, including an object to be segmented from the image, is acquired. The object is segmented from the image by using the deep-learning neural network to acquire a first segmentation result. Correction information input by a user with respect to the first segmentation result is acquired. Based on correction information, the first segmentation result is modified by using the deep-learning neural network, to acquire a second segmentation result.

An automated inspection method and system are provided, for identifying and assessing the condition of shipping containers. The method includes analysing images, each including at least a portion of one of the shipping container's underside, rear, front, sides and/or roof; detecting a container code appearing in at least one of said images; identifying, based at least on said plurality of images, one or more characteristics of the shipping container and determining a condition of the shipping container based on said physical characteristics identified, the container code and characteristics being determined by machine learning algorithms previously trained on shipping container images captured in various lighting and environmental conditions; associating said container code with said condition of the shipping container and transmitting the container inspection results to a terminal operating system.

An apparatus includes a display control unit, a receiving unit, an adjusting unit, and a determination unit. The display control unit is configured to display an image showing a result of detection of a defect from a captured image of a structure on a display device. The receiving unit is configured to receive an operation to specify part of the displayed image as a first region and an operation to give an instruction to correct at least part of the detection data corresponding to the first region. The adjusting unit is configured to adjust a parameter to be applied to the first region according to the instruction. The determination unit is configured to determine one or more second regions to which the adjusted parameter is to be applied from a plurality of segmented regions of the image.

Methods, apparatus, and systems for generating a price of a target device are disclosed herein. An evaluator device obtains technical properties associated with the target device. The technical properties include a make and a model of the target device. Physical properties associated with the target device are obtained. The physical properties include information related to wear and tear of the target device. Obtaining the physical properties includes indicating to a user that the user should position the target device in multiple predetermined positions and that the evaluator device records an image of the target device in each of the multiple predetermined positions. A video of the target device is recorded while the target device is positioned in the multiple predetermined positions. The obtained physical properties are evaluated to generate a condition metric value of the target device. Based on the generated condition metric value, the price of the target device is determined.

In examples, a non-transitory computer-readable storage medium stores executable code, which, when executed by a processor, causes the processor to receive a semiconductor package image, the image including semiconductor package surface codes, the codes including a semiconductor package identifier. The executable code causes the processor to transmit at least one of the semiconductor package identifier, the codes, or the image. The executable code causes the processor to receive information associated with the semiconductor package identifier. The executable code causes the processor to output the information via at least one of a display coupled to the processor, a speaker coupled to the processor, or the wireless transceiver.

A video annotation system for deep learning based video analytics and corresponding methods of use and operation are described that significantly improve the efficiency of video data frame labeling and the user experience. The video annotation system described herein may be deployed at a network edge and may support various intelligent annotation functionality including annotation tracking, adaptive video segmentation, and execution of predictive annotation algorithms. In addition, the video annotation system described herein supports team collaboration functionality in connection with large-scale labeling tasks.

Automatic document classification using machine learning may involve receiving inputs that assign documents to classifiers, which define document classification rules for a classification model. The computing device may train the classification model using a machine learning technique that assigns each document of a second set of documents to destinations based on the document classification rules. The computing device may also receive a template design for each destination that specifies metadata to extract for a document type corresponding to documents assigned to the destination. The computing device may subsequently classifying a particular document using the classification model, which may involve assigning the particular document to a given destination of the plurality of destinations based on the document classification rules, and exporting metadata from the particular document using the template design associated with the given destination.

Disclosed is a device for inputting symbols in an entry field. An example of the device includes an interface unit having a touch screen and a module for processing graphics objects. The module may include a detection unit for detecting a start and an end of a current graphics object being input; a storage unit for storing graphics data corresponding to the input graphics object; a recognition unit for generating a list of candidate strings of symbols from the graphics data, each candidate string being associated with a pertinence value; and an insertion unit for inserting into the entry field a string selected by the user from the list, a data group comprising the graphics data, the candidate strings, the pertinence values, and an identifier of the selected string being stored during a predetermined duration.