The present disclosure relates to a decoding device, a decoding method, an encoding device, and an encoding method, which are capable of enabling a decoding side to accurately recognize a color gamut of an encoding target image. The decoding device includes circuitry configured to receive an encoded stream including encoded data of an image and color primary information indicating a coordinate of at least one color primary of the image. The circuitry extracts the encoded data and the color primary information from the received encoded stream. The circuitry decodes the encoded data to generate the image. Further, the circuitry adjusts a color space of the generated image based on the extracted color primary information. The present disclosure can be applied to, for example, a decoding device of a high efficiency video coding (HEVC) scheme.

Systems, methods, and computer-readable media are provided for an autonomous and fully automated method of validating multi-signature decoys that are configured to release infrared flares at multiple points after launch. In one aspect, a method includes capturing, using at least one image capturing device, raw image data of a launched decoy, the decoy having one or more segments configured to be released after launch and automatically processing the raw image data to (1) identify a release point for each of the one or more segments and (2) identify an infrared signature associated with each release point. The method further includes generating a visual display of the release point(s) of the one or more segments and the infrared signature(s) originating from the release point(s).

An image processing method comprising steps of obtaining a first map representing a region outside a dynamic range of an input image based on a signal value in the input image and a threshold of the signal value, and inputting input data including the input image and the first map and executing a recognition task or a regression task.

Systems and methods for analyzing a fabric article to determine fiber damage are provided herein. In an embodiment of a method for analyzing a fabric article, the fabric article is provided and at least one image of the fabric article is captured using a three-dimensional imaging device. The at least one image is processed using a contrast function to produce a processed data set that is representative of fiber damage in a viewing area of the three-dimensional imaging device. Optionally, a processed image is produced based upon the processed data set.

Provided is an imaging control apparatus, method, and a vehicle, capable of efficiently analyzing an image. Among a short accumulation signal and a long accumulation signal of a photographed image, the short accumulation signal of a preceding frame is set to correspond to the short accumulation signal of a succeeding frame. The signal of the succeeding frame is processed by using the short accumulation signal of the preceding frame. The present technology is applicable to control imaging in an apparatus that supports driving of a vehicle by performing various processing, in each of frames of an N-frame sequence, for example, of recognizing various target objects necessary in supporting the driving of the vehicle, such as an obstacle, a guidance object, and other target objects, for example.

Embodiments of the present disclosure disclose an image details processing method, comprises: obtaining each luminance data in the image; performing non-linear transformation on the each luminance data to obtain corresponding transformed data; performing the low frequency processing on the transformed data to obtain low frequency data; based on the low-frequency data and the transformed data, determining the corrected luminance data. The embodiments of the present disclosure also disclose an image details processing apparatus, a terminal, and a storage medium.

The exemplary embodiments relate to converting Standard Dynamic Range (SDR) content to High Dynamic Range (HDR) content using a machine learning system. In some embodiments, the neural network is trained to convert an input SDR image into an HDR image using the encoded representation of a training SDR image and a training HDR image. In other embodiments, the neural network is trained to convert an input SDR image into an HDR image using a predefined set of color grading actions and the training images.

A sound-based activity recognition system has the potential to better detect and identify activity in an environment compared to video-only monitoring systems. However, conventional sound recognition systems are typically unable to provide sound recognition using a single device and have limited user control of data and video integration. These shortcomings may be overcome by a sound-based activity recognition system that incorporates computationally inexpensive methods to detect and identify sounds that can be performed on a single electronic device. The activity recognition system may further provide object recognition to enable both sound and object detection. In one example, the activity recognition system may include a microphone and a camera to record audio and video from the environment and a processor to filter background noise, which reduces the amount of data processed; to identify sounds and objects using a model; and to notify a user of the sounds and objects detected.

In an image generating apparatus, a setter is configured to variably set, for an image capturing task at a next capturing cycle, first and second shutter times and a total gain. The total gain is based on combination of an analog gain and a digital gain. An allocating unit is configured to obtain a threshold gain based on the first and second shutter times and a compression characteristic. The allocating unit is configured to variably allocate the total gain to at least one of the analog gain and the digital gain in accordance with a comparison among the total gain, the threshold gain, and an upper limit for the analog gain.

In a method for enhancing quality of a fingerprint image, a plurality of images is received, where the plurality of images comprises fingerprint images. An image of the plurality of images is selected as a reference image. Subsections of each image of the plurality of images are compared to corresponding subsections of the reference image. Based on the comparing, subsections of each image of the plurality of images are selected to utilize in generating an image average of the plurality of images. An image average of the plurality of images is generated utilizing selected subsections of the plurality of images.