An imaging apparatus according to an embodiment includes: an imaging unit having a pixel region in which a plurality of pixels is arranged; a readout controller that controls readout of pixel signals from pixels included in the pixel region; a first unit-of-readout setting unit that sets a unit of readout as a part of the pixel region, for which the readout controller performs readout of the pixel signal; an image output unit that outputs a first image based on the pixel signal read out from the unit of readout to a subsequent stage; a second unit-of-readout controller that controls the unit of readout in which the readout controller performs readout of the pixel signal; and a recognition unit that learns training data for each of the units of readout, performs a recognition process on the pixel signal for each of the units of readout, and outputs a recognition result.

There is provided an information processing apparatus including a processor and a memory connected to or built into the processor. The processor is configured to process a captured image by using an AI method that uses a neural network and perform composition processing of combining a first image obtained by processing the captured image by using the AI method and a second image obtained by processing the captured image without using the AI method.

Technologies for source degradation and auto-tuning of cameras are disclosed. In one embodiment, a system includes a compute node connected to one or more cameras. The cameras can monitor an environment, such as a manufacturing process in a factory. The compute node may use image processing, such as a machine-learning-based algorithm, to monitor processes. A system integrator may adjust parameters of the camera and/or the algorithm, such as focus, gain, contrast, white balance, etc. The compute node can monitor the system integrator and learn how to adjust parameters in order to improve key performance indicators (KPIs) of the monitored process. In a production environment, the compute node may then automatically adjust parameters of the camera and/or analysis algorithm based on the actions of the system integrator in order to improve performance of the analysis algorithm.

Systems and techniques described herein relate to techniques for improving image detection. In some examples, aspects relate to systems and techniques for improving image detection by performing tracking of objects within captured image frames. A process can include obtaining, from an image capture device, a first image frame including an object. The process can further include determining, using an object detector, an object validation score associated with detection of the object in the first image frame, and determining the object validation score is less than a validation threshold. Based on the object validation score being less than the validation threshold, the process can include tracking the object for one or more image frames received subsequent to the first image frame.

An information processing device includes: a determination unit that determines retrieval information used to retrieve at least one photographing recipe including information on photographing; an acquisition unit that acquires relative setting values representing relative values of photographing setting values, corresponding to one of the at least one photographing recipe retrieved on a basis of the retrieval information; and a setting unit that sets a photographing device to perform photographing on a basis of the relative setting values.

System, methods, devices, and instructions are described for fast boot of a processor as part of camera operation. In some embodiments, in response to a camera input, a digital signal processor (DSP) of a device is booted using a first set of instructions. Capture of image sensor data is initiated using the first set of instructions at the DSP. The DSP then receives a second set of instructions and the DSP is programmed using the second set of instructions after at least a first frame of the image sensor data is stored in a memory of the device. The first frame of the image sensor data is processed using the DSP as programmed by the second set of instructions. In some embodiments, the first set of instructions includes only instructions for setting camera sensor values, and the second set of instructions includes instructions for processing raw sensor data into formatted image files.

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.

An example electronic device includes a first housing; a second housing; a camera module; a flexible display including a first area exposed at the front surface of an electronic device in a state in which the electronic device is closed, and a second area extended from the first area and exposed at the back surface of the electronic device; a memory; and a processor operatively connected to the camera module, the flexible display and the memory. The processor is configured to control to drive the camera module based on user input, display, in the first area and the second area, an image acquired from the camera module, provide first photography guidance information through the first area, and provide, through the second area, second photography guidance information that differs from the first photography guidance information.

An example electronic device includes a first housing; a second housing; a camera module; a flexible display including a first area exposed at the front surface of an electronic device in a state in which the electronic device is closed, and a second area extended from the first area and exposed at the back surface of the electronic device; a memory; and a processor operatively connected to the camera module, the flexible display and the memory. The processor is configured to control to drive the camera module based on user input, display, in the first area and the second area, an image acquired from the camera module, provide first photography guidance information through the first area, and provide, through the second area, second photography guidance information that differs from the first photography guidance information.

Methods, systems, and storage media for dynamically identifying visual media capture formats based upon at least one of capture device, resultant visual medium, and/or application-supported conditions are disclosed. Exemplary implementations may: query a visual media capture device for at least a portion of the device-supported visual media capture formats; receive information regarding one or more capture device, resultant visual medium, and/or application-supported conditions relevant to capture of a resultant visual medium; execute a plurality of rules to dynamically identify one or more device-supported visual media capture formats of the device-supported visual media capture formats that is configured to determine the resultant visual medium; and cause the visual media capture device to initiate capture of the resultant visual medium using one of the identified device-supported visual media capture formats.