Provided is a work system including: an object imaging unit configured to acquire an object image by photographing an object from a work direction; a work position acquisition unit configured to acquire a work position based on an existence region of the object obtained from a machine learning model; and a work unit configured to execute work on the object based on a work position obtained by inputting the object image to the work position acquisition unit.

A system and method for generating and displaying a print product offering including a plurality of digital images is provided. The method comprises: providing a photo lab computing device comprising a memory, wherein a plurality of digital images are stored in the memory; generating a group of digital images from the plurality of digital images; classifying each of the digital images within the group based on at least one image quality parameter; selecting one or more of the digital images in the group which conform to the at least one image quality parameter; generating an image product template design including the digital images which conform to the at least one image quality parameter; and displaying the image product template design as a print product offering.

An image recognition apparatus is provided which comprises a first extracting means for extracting, from every registration image previously registered, a set of registration partial images of a predetermined size, and a second extracting means for extracting, from an input new image, a set of new partial images of a predetermined size. The apparatus further comprises a discriminating means for discriminating an attribute of the new partial image based on a rule formed by dividing the set of the registration partial images extracted by the first extracting means, and a collecting means for deriving a final recognition result of the new image by collecting discrimination results by the discriminating means at the time when the new partial images as elements of the set of the new partial images are input.

A system, method, and non-transitory computer readable medium for ichnological classification of geological images are described. The method of ichnological classification of geological images includes receiving a geological image by a computing device having circuitry including a memory storing program instructions and one or more processors configured to perform the program instructions, formatting the geological image to generate a formatted geological image, applying the formatted geological image to a deep convolutional neural network (DCNN) trained to classify bioturbation indices, and matching the formatted geological image to a bioturbation index class.

An apparatus for detecting a moving object is provided. In the apparels, a moving object, such as a pedestrian, is imaged repeatedly. As a dictionary, image patterns indicative of features of the moving object are stored in a storage in advance. Feature points indicating a position of an object in the images are extracted based on the images. An optical flow of the feature points is calculated based on the features points. The feature points are grouped and a reference rectangle encompassing the grouped feature points is set, based on the optical flow. An identification region encompassing a region showing the moving object is set, the identification region encompassing the reference rectangle. The moving object is identified in the identification region, using the dictionaries. The region of the moving object is specified, based on identified results and an image region having the highest degree of match with the dictionaries.

Methods and apparatus associated with producing a quantification of differences associated with biochemical recurrence (BcR) in a region of tissue demonstrating prostate cancer (PCa) are described. One example apparatus includes a set of logics, and a data store that stores a set of magnetic resonance (MR) images acquired from a population of subjects. The set of logics includes an image acquisition logic that acquires a diagnostic image of a region of tissue in a patient demonstrating PCa, a morphology logic that extracts a shape feature, a volume feature, or an intensity feature from the diagnostic image or from a member of the set of MR images, a differential atlas construction logic that constructs a statistical shape differential atlas from the set of MR images, and a quantification logic that produces a quantification of differences based on the shape feature, the volume feature, or the intensity feature, and the differential atlas.

The present disclosure provides devices, systems and computer-readable media for identifying object characteristics using a machine learning model trained to de-emphasize brightness values. The machine learning model can be trained using modified training data. Modifying the training data can include converting the training data from an original color space into a color space having a brightness channel. The values of the brightness channel for the, training data can then be modified. After the values of the brightness channel are modified, the training data can be converted back into the original color space and used to train the machine learning model. A detection device can be configured with the machine learning model and used to identify object characteristics.

The present disclosure provides devices, systems and computer-readable media for identifying object characteristics using a machine learning model trained to de-emphasize brightness values. The machine learning model can be trained using modified training data. Modifying the training data can include converting the training data from an original color space into a color space having a brightness channel. The values of the brightness channel for the, training data can then be modified. After the values of the brightness channel are modified, the training data can be converted back into the original color space and used to train the machine learning model. A detection device can be configured with the machine learning model and used to identify object characteristics.

A computer-implemented method of grouping faces in large user account for creating an image product includes adding the face images obtained from an image album in a user's account into a first chunk; if the chunk size of the first chuck is smaller than a maximum chuck value, keeping the face images from the image album into the first chunk; otherwise, automatically separating the face images from the image album into a first portion and one or more second portions; keeping the first portion in the first chunk; automatically moving the second portions to subsequent chunks; automatically grouping face images in the first chunk to form face groups; assigning the face groups to known face models associated with the user account; and creating a design for an image-based product based on the face images in the first chunk associated with the face models.

A method and system for training a neural network are provided. The method includes receiving an input image, selecting at least one data augmentation method from a pool of data augmentation methods, generating an augmented image by applying the selected at least one data augmentation method to the input image, and generating a mixed image from the input image and the augmented image.