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.

An image capturing method and apparatus, an electronic photography device and a computer-readable storage medium are provided. The image capturing method includes: in a snapshot mode, performing image acquisition at a first frame rate and buffering an acquired first captured image; in a live mode, acquiring a second captured image at a second frame rate, where the second frame rate is less than the first frame rate; and processing the buffered first captured image and the second captured image at the second frame rate.

An intelligent item receptacle, system, and method for providing supplemental media content in response to depositing an item having media indicia disposed thereon. An intelligent item receptacle includes an item receptacle securely enclosing an interior volume, an item sensor configured to detect an inserted item, a camera within the interior volume configured to capture an image of the item, and an output configured to play audio or visual content to bystanders outside the item receptacle. Processing circuitry within the item receptacle analyzes the image to determine if the media indicia are present on the item and, if present, causes the output to play the audio or visual content associated with the media indicia.

An intelligent item receptacle, system, and method for providing supplemental media content in response to depositing an item having media indicia disposed thereon. An intelligent item receptacle includes an item receptacle securely enclosing an interior volume, an item sensor configured to detect an inserted item, a camera within the interior volume configured to capture an image of the item, and an output configured to play audio or visual content to bystanders outside the item receptacle. Processing circuitry within the item receptacle analyzes the image to determine if the media indicia are present on the item and, if present, causes the output to play the audio or visual content associated with the media indicia.

At least some example embodiments provide a fingerprint sensor, a fingerprint sensor package, and a fingerprint sensing system using light sources of a display panel. The fingerprint sensor includes an image sensor including a plurality of sensor pixels, the sensor pixels configured to sense light reflected by a fingerprint and generate image information corresponding to the fingerprint and a pinhole mask defining a plurality of pinholes, wherein each of the pinholes forms a focus for transmitting the light reflected by the fingerprint to the image sensor, wherein light is emitted from a plurality of organic light-emitting diodes (OLEDs) and is reflected by the fingerprint.

A computer-implemented method of using a machine learning model to categorize a sample in digital pathology may include receiving one or more cases, each associated with digital images of a pathology specimen; identifying, using the machine learning model, a case as ready to view; receiving a selection of the case, the case comprising a plurality of parts; determining, using the machine learning model, whether the plurality of parts are suspicious or non-suspicious; receiving a selection of a part of the plurality of parts; determining whether a plurality of slides associated with the part are suspicious or non-suspicious; determining, using the machine learning model, a collection of suspicious slides, of the plurality of slides, the machine learning model having been trained by processing a plurality of training images; and annotating the collection of suspicious slides and/or generating a report based on the collection of suspicious slides.

A computer-implemented method of using a machine learning model to categorize a sample in digital pathology may include receiving one or more cases, each associated with digital images of a pathology specimen; identifying, using the machine learning model, a case as ready to view; receiving a selection of the case, the case comprising a plurality of parts; determining, using the machine learning model, whether the plurality of parts are suspicious or non-suspicious; receiving a selection of a part of the plurality of parts; determining whether a plurality of slides associated with the part are suspicious or non-suspicious; determining, using the machine learning model, a collection of suspicious slides, of the plurality of slides, the machine learning model having been trained by processing a plurality of training images; and annotating the collection of suspicious slides and/or generating a report based on the collection of suspicious slides.

Fabrication processing is executed in a chip of an image sensor. An image capturing device includes an image capturing unit (11) mounted on a vehicle and configured to generate image data by performing image capturing of a peripheral region of the vehicle, a scene recognition unit (214) configured to recognize a scene of the peripheral region based on the image data, and a drive control unit (12) configured to control drive of the image capturing unit based on the scene recognized by the scene recognition unit.

Fabrication processing is executed in a chip of an image sensor. An image capturing device includes an image capturing unit (11) mounted on a vehicle and configured to generate image data by performing image capturing of a peripheral region of the vehicle, a scene recognition unit (214) configured to recognize a scene of the peripheral region based on the image data, and a drive control unit (12) configured to control drive of the image capturing unit based on the scene recognized by the scene recognition unit.

A method for semantic annotation of sensor data may include obtaining sensor data representing an image of a geographic area. The boundary points defining a first polygon in the image of the geographic area may be determined based on the sensor data. An overlap between the first polygon and a second polygon in the image of the geographic area may be detected based at least on the boundary points defining the first polygon. At least one of the first polygon or the second polygon may be modified to remove the overlap between the first polygon and the second polygon. An annotation corresponding to the first polygon may be generated based on the modifying of at least one of the first polygon or the second polygon. The annotation may identify a physical feature within the geographic area. Related systems and computer program products are also provided.