Abstract: An electronic device and a controlling method thereof are provided. An electronic device includes a memory configured to store at least one instruction and a processor configured to execute the at least one instruction and operate as instructed by the at least one instruction. The processor is configured to: obtain a first image; based on receiving a first user command to correct the first image, obtain a second image by correcting the first image; based on the first image and the second image, train a neural network model; and based on receiving a second user command to correct a third image, obtain a fourth image by correcting the third image using the trained neural network model.

Abstract: 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.

Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that extract multiple attributes from an object portrayed in a digital image utilizing a multi-attribute contrastive classification neural network. For example, the disclosed systems utilize a multi-attribute contrastive classification neural network that includes an embedding neural network, a localizer neural network, a multi-attention neural network, and a classifier neural network. In some cases, the disclosed systems train the multi-attribute contrastive classification neural network utilizing a multi-attribute, supervised-contrastive loss. In some embodiments, the disclosed systems generate negative attribute training labels for labeled digital images utilizing positive attribute labels that correspond to the labeled digital images.

Abstract: An electronic device includes a device housing having a front side and a rear side, a first image capture device positioned on the front side, and a second image capture device positioned on the rear side. One or more processors of the electronic device cause, in response to user input received at a user interface requesting the second image capture device capture an image of an object, the first image capture device to capture another image of a user delivering the user input. The one or more processors then define an image orientation of the image of the object to be the same as another image orientation of the other image of the user.

Abstract: A method for stabilizing an image based on artificial intelligence includes acquiring tremor detection data with respect to the image, the tremor detection data acquired from two or more sensors; outputting stabilization data for compensating for an image shaking, the stabilization data outputted using an artificial neural network (ANN) model trained to output the stabilization data based on the tremor detection data; and compensating for the image shaking using the stabilization data. A camera module includes a lens; an image sensor to output an image captured through the lens; two or more sensors to output tremor detection data with respect to the image; a controller to output stabilization data based on the tremor detection data using an ANN model; and a stabilization unit to compensate for an image shaking using the stabilization data. The ANN model is trained to output the stabilization data based on the tremor detection data.

Abstract: Systems and methods are provided that capture a user's physically observable state, such as body position. A processor analyzes the image to determine if the user's state is unhealthy and, if so whether or not the user is in the unhealthy state for longer than a threshold period of time. If the user is in an unhealthy state, the processor signals an alerting component to energize a circuit. Accordingly, the user may be prompted to alter their physical state to a less unhealthy more healthful state.

Abstract: A method and apparatus for processing an image based on partial images. The method includes extracting a feature of a current partial processing region of an input image frame by inputting pixel data of the current partial processing region into a convolutional neural network (CNN), updating a hidden state of a recurrent neural network (RNN) for a context between the current partial processing region and at least one previous partial processing region by inputting the extracted feature into the RNN, and generating an image processing result for the input image frame based on the updated hidden state.

Abstract: Disclosed are a data loading method and apparatus for a convolution operation, a computer device, a storage medium and a computer program product. The method includes: splitting a feature image to be loaded into the cache unit into a plurality of sub-feature images; determining a target cache line corresponding to each of the sub-feature images in each of the cache lines according to a positional relationship of each of the sub-feature images in the feature image; wherein target cache lines corresponding to at least two sub-feature images with the same positional relationship are located in the same cache set, and target cache lines corresponding to at least two sub-feature images with an adjacent positional relationship are located in different cache sets; loading a data content of each of the sub-feature images into the target cache line corresponding to each of the sub-feature images.

Abstract: An electronic device and a controlling method thereof are provided. An electronic device includes a memory configured to store at least one instruction and a processor configured to execute the at least one instruction and operate as instructed by the at least one instruction. The processor is configured to: obtain a first image; based on receiving a first user command to correct the first image, obtain a second image by correcting the first image; based on the first image and the second image, train a neural network model; and based on receiving a second user command to correct a third image, obtain a fourth image by correcting the third image using the trained neural network model.

Abstract: Automatic substance preparation and evaluation systems and methods are provided for preparing and evaluating a fluidic substance, such as e.g. a sample with bodily fluid, in a container and/or in a dispense tip. The systems and methods can detect volumes, evaluate integrities, and check particle concentrations in the container and/or the dispense tip.

Abstract: A computing system retrieves ball-by-ball data for a plurality of sporting events. The computing system generates a trained neural network based on ball-by-ball data supplemented with ball-by-ball data with ball-by-ball match context features and personalized embeddings based on a batsman and a bowler for each delivery. The computing system receives a target batsman and a target bowler for a pitch to be delivered in a target event. The computing system identifies target ball-by-ball data for a window of pitches preceding the to be delivered pitch. The computing system retrieves historical ball-by-ball data for each of the target batsman and the target bowler. The computing system generates personalized embeddings for both the target batsman and the target bowler based on the historical ball-by-ball data. The computing system predicts a shot type for the pitch to be delivered based on the target ball-by-ball data and the personalized embeddings.

Abstract: Method, apparatus, and non-transitory storage medium for end-to-end neural image compression using non-linear quantization with substitution, including receiving one or more input images, generating a substitute image associated with the input image based on the input image using a neural network based substitute feature generator, compressing the substitute image, quantizing the compressed substitute image to obtain a quantized representation of the input image with higher compression performance by using a non-linear quantizer, and entropy encoding the quantized substitute image using a neural network based encoder to generate a compressed representation of the quantized representation.

Abstract: An image processing apparatus includes an acquisition unit configured to acquire an image, and a classification unit configured to perform inference on the image by using a neural network and to output a classification result, wherein the neural network includes a common network that is common to a plurality of classes and is configured to extract a first feature from an input image, and a plurality of single-class networks each of which corresponds to a corresponding one of the plurality of classes and is configured to extract each of a plurality of second features based on the first feature. Each of the plurality of single-class networks has a single-class output layer that classifies the corresponding class.

Abstract: A method for perceiving a model of an environment, including: capturing a plurality of data while the robot moves within the environment, wherein: the plurality of data comprises at least a first data and a second data captured by a first sensor of a first sensor type and a second sensor of a second sensor type, respectively; the first sensor type is an imaging sensor; the second senor type captures movement data; an active source of illumination is positioned adjacent to the imaging sensor such that reflections of illumination light illuminating a path of the robot fall within a field of view of the imaging sensor; perceiving the model of the environment based on at least a portion of the plurality of data; storing the model of the environment in a memory; and transmitting the model of the environment to an application of a smartphone.

Abstract: The present disclosure is related to systems and methods for image processing. The method includes obtaining an image including a plurality of pixels. The method includes determining at least one brightness parameter based on the brightness values of the plurality of pixels. The method includes obtaining an image processing algorithm including at least one image processing parameter. The method includes determining a value of the at least one image processing parameter based on the at least one brightness parameter. The method includes generating a target image by processing the image according to the image processing algorithm upon the value of the at least one image processing parameter.

Abstract: A solid-state image capturing system (1) includes a solid-state image capturing device (100) and an information processing device (200). The solid-state image capturing device (100) includes a first DNN processing unit (130) that executes, on image data, a part of a DNN algorithm by a first DNN to generate a first result. The information processing device (200) includes a second DNN processing unit (230) that executes, on the first result acquired from the solid-state image capturing device, remaining of the DNN algorithm by a second DNN to generate a second result.

Abstract: An image processing device includes an image acquisition unit that acquires a captured image to be processed from a storage unit in which a captured image having first date/time information indicating a time of imaging is stored, a date/time information acquisition unit that acquires the first date/time information and acquires second date/time information indicating a date/time when the captured image to be processed is acquired, an elapsed date/time calculation unit that calculates an elapsed date/time from the time of imaging by making a comparison between the first date/time information and the second date/time information, and an image processing unit that selects image processing based on a length of the elapsed date/time from among a plurality of kinds of image processing that change according to the length of the elapsed date/time and performs the selected image processing on the captured image to be processed.

Abstract: A focus detection device includes: an imaging unit having a first pixel and a second pixel each of which receives light transmitted through an optical system and outputs signal used for focus detection; an input unit to which first information regarding a position on an image plane and an exit pupil distance of the optical system is input; a selection unit that selects a first focus detection based on the signal having been output from the first pixel or a second focus detection based on the signal having been output from the second pixel, based on the first information having been input to the input unit; and a focus detection unit that performs the first focus detection or the second focus detection based on a selection by the selection unit.

Abstract: Embodiments of the present disclosure relate to a texture unit circuit in a neural processor circuit. The neural processor circuit includes a tensor access operation circuit with the texture unit circuit, a data processor circuit, and at least one neural engine circuit. The texture unit circuit fetches a source tensor from a system memory by referencing an index tensor in the system memory representing indexing information into the source tensor. The data processor circuit stores an output version of the source tensor obtained from the tensor access operation circuit and sends the output version of the source tensor as multiple of units of input data to the at least one neural engine circuit. The at least one neural engine circuit performs at least convolution operations on the units of input data and at least one kernel to generate output data.

Abstract: Techniques are disclosed for using and training a descriptor network. An image may be received and provided to the descriptor network. The descriptor network may generate an image descriptor based on the image. The image descriptor may include a set of elements distributed between a major vector comprising a first subset of the set of elements and a minor vector comprising a second subset of the set of elements. The second subset of the set of elements may include more elements than the first subset of the set of elements. A hierarchical normalization may be imposed onto the image descriptor by normalizing the major vector to a major normalization amount and normalizing the minor vector to a minor normalization amount. The minor normalization amount may be less than the major normalization amount.