A method for automated routing of pictures taken on mobile electronic devices to a digital picture frame including a camera integrated with the frame, and a network connection module allowing the frame for direct contact and upload of photos from electronic devices or from photo collections of community members. The integrated camera is used to automatically determine an identity of a frame viewer and can capture gesture-based feedback. The displayed photos are automatically shown and/or changed according to the detected viewers. The photos can be filtered and cropped at the receiver side. Clustering photos by content is used to improve display and to respond to photo viewer desires.

A computing entity is described that obtains at least one inspection image of an at least partially fabricated product and causes the at least one inspection image to be processed by a product inspection engine. The product inspection engine includes a machine learning-trained model. The computing entity obtains an inspection result determined based on the processing of the at least one inspection image by the product inspection engine; identifies one or more training images stored in an image database based at least in part on the at least one inspection image; associates automatically generated labeling data with the one or more training images based at least in part on the inspection result determined by the processing of the at least one inspection image; and causes training of the product inspection engine using the one or more training images and the associated labeling data.

A computing entity is described that obtains at least one inspection image of an at least partially fabricated product and causes the at least one inspection image to be processed by a product inspection engine. The product inspection engine includes a machine learning-trained model. The computing entity obtains an inspection result determined based on the processing of the at least one inspection image by the product inspection engine; identifies one or more training images stored in an image database based at least in part on the at least one inspection image; associates automatically generated labeling data with the one or more training images based at least in part on the inspection result determined by the processing of the at least one inspection image; and causes training of the product inspection engine using the one or more training images and the associated labeling data.

An acquisition unit (15a) detects an operation event of a user to acquire an occurrence position of the operation event in an operation screen and a captured image of the operation screen. An extraction unit (15b) extracts images that are able to become candidates for a GUI part from the acquired captured image, identifies which image the operation event has occurred on from the occurrence position of the operation event, and records an occurrence clock time of the operation event and the identified image in an associated manner. A classification unit (15c) classifies a group of recorded images into clusters in accordance with similarities of the images. A determination unit (15d) adds up the number of times the operation event has occurred in the images for each classified cluster, and in a case in which the aggregated value is equal to or greater than a predetermined threshold value, determines an image included in the cluster as an image of the GUI part that is an operation target at the occurrence clock time of the operation event.

An acquisition unit (15a) detects an operation event of a user to acquire an occurrence position of the operation event in an operation screen and a captured image of the operation screen. An extraction unit (15b) extracts images that are able to become candidates for a GUI part from the acquired captured image, identifies which image the operation event has occurred on from the occurrence position of the operation event, and records an occurrence clock time of the operation event and the identified image in an associated manner. A classification unit (15c) classifies a group of recorded images into clusters in accordance with similarities of the images. A determination unit (15d) adds up the number of times the operation event has occurred in the images for each classified cluster, and in a case in which the aggregated value is equal to or greater than a predetermined threshold value, determines an image included in the cluster as an image of the GUI part that is an operation target at the occurrence clock time of the operation event.

The present disclosure relates to the field of image processing and discloses an agricultural advisory system (100) comprising a user device (102) and a cloud server (104). The user device (102) captures a digital image of a scene, receives a sensed data corresponding to scene-related and environmental parameters, and transmits the image and the sensed data to the cloud server. The server (104) stores one or more pre-trained prediction models and a three-dimensional HyperIntelliStack which maps red green blue (RGB) pixel values with hyperspectral reflectance values. The server (104) receives the digital images and the sensed data, transforms the received image made of RGB pixel values into a hyperspectral image using the HyperIntelliStack data structure, computes vegetation indices for each pixel of the hyperspectral image to generate a segmented image, and generates at least one advisory for agriculture and allied areas using the segmented image and one or more prediction models.

Example implementations described herein involve a system for training and managing machine learning models in an industrial setting. Specifically, by leveraging the similarity across certain production areas, it is possible to group such areas together to train models efficiently that use human pose data to predict human activities or specific task(s) that the workers are engaged in. Example implementations remove previous methods of independent model construction for each production area and takes advantage of the commonality amongst different environments.

In some embodiments, apparatuses and methods are provided herein useful to train a machine learning algorithm to detect text of interest. In some embodiments, there is provided a system to detect vertically oriented text of interest including a first data set comprising a plurality of captured digital images each depicting an object of interest and a second data set comprising a plurality of augmented digital images each depicting a captured digital image augmented with a synthetic text image; a first control circuit configured to cause the machine learning algorithm to output a machine learning model trained to automatically detect occurrences of vertically oriented text of interest based on the first data set and the second data set; at least one camera; and a second control circuit configured to execute the machine learning model to automatically detect vertically oriented text of interest on the object of interest.

Methods, systems, and non-transitory computer readable media for analyzing type curve regions in a subsurface volume of interest are disclosed. Exemplary implementations may include: obtaining initial clusters of type curve regions in the subsurface volume of interest; obtaining production values as a function of position; generating an autocorrelation correction factor; attributing the autocorrelation correction factor to the production values as a function of position; generating type curve mean values; generating range distribution values; generating a type curve cluster probability value for each of the type curve regions; generating a first representation of the type curve regions as a function of position; clustering the type curve regions in updated clusters; generating a second representation of the type curve regions as a function of position; and displaying one or more of the first representation and the second representation.

A LiDAR-based object detection method includes clustering a point cloud acquired from LiDAR, selecting a to-be-divided cluster among clusters generated in the clustering, and selecting division points according to a geometrical feature formed with adjacent points from among points belonging to the to-be-divided cluster, and dividing the to-be-divided cluster based on a representative point determined by at least some of the division points.