Systems, methods, and other embodiments for selecting, enriching and sequencing digital media content to produce a narrative-oriented, ordered sub-collection of media such as for movie creation. The method identifies, evaluates, assesses, stores, enriches, groups, and sequences content. The method identifies the content metadata. When metadata are missing or anomalous, the method attempts to populate or correct the metadata and store that new content in the database. The method evaluates content for focus quality and may exclude content based on rules. The method assesses the content storing the people and their emotional level, animals, objects, locations, landmarks and date/time in the database. The method can then enrich the remaining content by providing map, photo, video, text, and audio content. The method uses selecting criteria for grouping and sequencing content by date, time, person, etc. and compiling the sequenced groups into the final narrative ready for distribution, e.g., movie creation.

A system and a method are disclosed for determining a heading of a vehicle and a heading of an implement of a farming machine when the farming machine is stationary or moving at a speed below a threshold speed. The vehicle and the heading are attached together via a pivot hitch. A farming machine management system receives coordinates from a first location sensor coupled to the vehicle and a second location sensor coupled to the implement. The farming machine management system determines intersection points between a first circle centered at the first location sensor and a second circle centered at the second location sensor. The farming machine management system selects one of the intersection points based on an output of a machine learning model. The farming machine management system determines the headings of the vehicle and the implement and generates instructions for operating the farming machine based on the headings.

An analysis apparatus according to one or more embodiments may identify the classes of features included in object data using a plurality of discriminators that are respectively configured to discriminate the presence of features of classes different to each other; and determines that a first data portion, with respect to which discrimination is established by one of the plurality of discriminators, but discrimination is not established by the remaining discriminators, includes a feature of the particular class that is discriminated by the one discriminator, and determines that a second data portion, with respect to which discrimination is established by all of the discriminators including the one discriminator, does not include a feature of that particular class.

An analysis apparatus according to one or more embodiments may identify the classes of features included in object data using a plurality of discriminators that are respectively configured to discriminate the presence of features of classes different to each other; and determines that a first data portion, with respect to which discrimination is established by one of the plurality of discriminators, but discrimination is not established by the remaining discriminators, includes a feature of the particular class that is discriminated by the one discriminator, and determines that a second data portion, with respect to which discrimination is established by all of the discriminators including the one discriminator, does not include a feature of that particular class.

A commodity sales data processing apparatus includes an image processor and a controller. The image processor captures an image including a symbol. The symbol includes discount information relating to a discount on a price of a commodity. The controller acquires commodity information that uniquely specifies the commodity. The controller extracts a region from the image captured by the camera. The region includes the symbol. The controller transmits, to a server, an image of the region extracted by the controller. The controller acquires, from the server, the discount information based on the image transmitted to the server. The controller registers the commodity information acquired by the controller and a discounted price of the commodity based on the discount information acquired by the controller in association with each other.

A commodity sales data processing apparatus includes an image processor and a controller. The image processor captures an image including a symbol. The symbol includes discount information relating to a discount on a price of a commodity. The controller acquires commodity information that uniquely specifies the commodity. The controller extracts a region from the image captured by the camera. The region includes the symbol. The controller transmits, to a server, an image of the region extracted by the controller. The controller acquires, from the server, the discount information based on the image transmitted to the server. The controller registers the commodity information acquired by the controller and a discounted price of the commodity based on the discount information acquired by the controller in association with each other.

An apparatus for constructing kinematic information of a robot manipulator is provided. The apparatus includes: a robot image acquisition part for acquiring a robot image containing shape information and coordinate information of the robot manipulator; a feature detection part for detecting the type of each of a plurality of joints of the robot manipulator and the three-dimensional coordinates of the joint using a feature detection model generated through deep learning based on the robot image containing shape information and coordinate information; and a variable derivation part for deriving Denavit-Hartenberg (DH) parameters based on the type of each of the plurality of joints of the robot manipulator and the three-dimensional coordinates of the joint.

A computer-implemented method for forming a dataset configured for learning a neural network. The neural network is configured for inference, from a freehand drawing representing a 3D shape, of a solid CAD feature representing the 3D shape. The method includes generating one or more solid CAD feature includes each representing a respective 3D shape. The method also includes, for each solid CAD feature, determining one or more respective freehand drawings each representing the respective 3D shape, and inserting in the dataset, one or more training samples. Each training sample includes the solid CAD feature and a respective freehand drawing. The method forms an improved solution for inference, from a freehand drawing representing a 3D shape, of a 3D modeled object representing the 3D shape.

An aspect of the disclosure is to determine a watch face image, and an operation method of an electronic device according to various embodiments may include: acquiring an image using a camera of the electronic device; displaying a watch face preview matching the acquired image on a display of the electronic device; determining a watch face image from the watch face preview; controlling a transceiver of the electronic device to transmit the watch face image to a smart watch; and applying the watch face image to the smart watch.

A shape identification program causes a computer to execute a process including: acquiring a third shape dataset and a fourth shape dataset, respectively generated by changing sizes of a first shape dataset and a second shape dataset in each of directions of a plurality of coordinate axes according to a specific rule; and generating a first plurality of images and a second plurality of images by capturing the third shape dataset and the fourth shape dataset, respectively, from both directions of the respective plurality of coordinate axes. The process further includes identifying a second portion in the second shape dataset corresponding to a first portion in the first shape dataset by aligning orientations of the first shape dataset and the second shape dataset based on a result of comparison between the first plurality of images and the second plurality of images.