An autonomous driving system for a vehicle reduces the amount of computations for object extraction carried out by a DNN, using information a traveling environment or the like. An information processing apparatus including a processor, a memory, and an arithmetic unit that executes a computation using an inference model is provided. The information processing apparatus includes a DNN processing unit that receives external information, the DNN processing unit extracting an external object from the external information, using the inference model, and a processing content control unit that controls processing content of the DNN processing unit. The DNN processing unit includes an object extracting unit that executes the inference model in a deep neural network having a plurality of layers of neurons, and the processing content control unit includes an execution layer determining unit that determines the layers used by the object extracting unit.

The present disclosure discloses a microwave identification method, which is implemented on at least one device, including at least one processor and at least one storage device, the method including: the at least one processor obtains microwave data; the at least one processor generates an image of one or more objects based on the microwave data; the at least one processor obtains a model of each of the one or more objects; and based on the model of each of the one or more objects, the at least one processor identifies the one or more objects in the image of the one or more objects.

An electronic endoscope processor includes a converting means for converting each piece of pixel data that is made up of n (n≥3) types of color components and constitutes a color image of a biological tissue in a body cavity into a piece of pixel data that is made up of m (m≥2) types of color components, m being smaller than n; an evaluation value calculating means for calculating, for each pixel of the color image, an evaluation value related to a target illness based on the converted pieces of pixel data that are made up of m types of color components; and a lesion index calculating means for calculating a lesion index for each of a plurality of types of lesions related to the target illness based on the evaluation values calculated for the pixels of the color image.

According to an embodiment, an image processing device includes one or more processors. The one or more processors are configured to: acquire an image; detect a first repeated pattern from the image; detect an object included in the first repeated pattern; and output the object as a second repeated pattern.

A system and method for detecting changes in an agricultural field uses a time series of target images of the agricultural field in which a vegetation index value is calculated for each target image. A target trend line is calculated from the time series of the vegetation index values. A time series of candidate images of one or more candidate fields having one or more attributes that correspond to one or more attributes of the agricultural field is also acquired in which an expected trend line can be determined from calculated vegetation index values representative of respective candidate images. An alert is generated in response to a deviation of the target trend line from the expected trend line that meets alert criteria.

Techniques are disclosed for determining whether to include a bodyprint in a cluster of bodyprints associated with a recognized person. For example, a device performs facial recognition to identify the identity of a first person. The device also identifies and stores physical characteristic information of the first person, the stored information associated with the identity of the first person based on the recognized face. Subsequently, the device receives a second video feed showing an image of a second person whose face is also determined to be recognized by the device. The device then generates a quality score for physical characteristics in the image of the user. The device can then add the image with the physical characteristics to a cluster of images associated with the person if the quality score is above a threshold, or discard the image if not.

An image providing apparatus (10) includes a registration unit (12) for registering a plurality of captured images of a predetermined user taken at a plurality of locations, respectively, in a storage unit (11), an acquisition unit (13) for acquiring a first image of the user captured at a specific location, a specification unit (14) for specifying one or more second images including a face area whose degree of match with a face area of the user included in the first image is greater than or equal to a predetermined value from among the plurality of captured images, a generation unit (15) for generating a composite image including the specified second image, and an output unit (16) for outputting the composite image.

An image processing method whereby data pertaining to an estimated captured image obtained from reference data of a sample is acquired using an input acceptance unit, an estimation unit, and an output unit. The data is used when comparing the estimated image and an actual image of the sample, wherein the method includes: an input acceptance unit accepting input of the reference data, process information pertaining to the sample, and trained model data; the estimation unit using the reference data, the process information, and the model data to calculate captured image statistics representing a probabilistic distribution of values attained by the data of the captured image; and the output unit outputting the captured image statistics, and generating the estimated captured image from the captured image statistics. This permits reducing the time required for estimation and to perform comparison in real time.

A system for identifying markers on flowable-matter substrates, the system comprising a processing circuitry configured to: provide one or more reference images, each associated with (a) a corresponding marker, and (b) a corresponding action; obtain an image including a given marker applied on a flowable-matter substrate; identify a matching reference image of the reference images, the matching reference image being associated with the marker corresponding to the given marker; and upon identifying the matching reference image, perform the action associated with the matching reference image.

An electronic apparatus is provided. The electronic apparatus includes: a camera; a processor configured to control the camera; and a memory configured to be electrically connected to the processor and to store a network model trained to determine a degree of matching between an input image frame and predetermined feature information, wherein the memory stores at least one instruction, and wherein the processor is configured, by executing the at least one instruction, to: identify a representative image frame based on a degree of matching obtained by applying image frames, selected from among a plurality of image frames, to the trained network model, while the plurality of image frames are captured through the camera, identify a best image frame based on a degree of matching obtained by applying image frames within a specific section including the identified representative image frame, to the trained network model, from among the plurality of image frames, and provide the identified best image frame.