Digital image ordering based on object position and aesthetics is leveraged in a digital medium environment. According to various implementations, an image analysis system is implemented to identify visual objects in digital images and determine aesthetics attributes of the digital images. The digital images can then be arranged in way that prioritizes digital images that include relevant visual objects and that exhibit optimum visual aesthetics.

A system and a method for automatic video redaction are provided herein. The method may include: receiving, an input video comprising a sequence of frames captured by a camera, wherein the input video includes live video obtained directly from the camera, wherein recordation of the video directly from the camera is disabled; performing visual analysis of the input video, to detect portions of the frames of the input video in which one of a plurality of predefined objects or a descriptor thereof is detected; generating a redacted input video by replacing the portions of the frames with new portions of another visual content; and recording the redacted input video on a data storage device, wherein the generating of thethe redacted input video, is carried out by a computer processor, after the input video is captured by the camera and before the recording of the redacted input video on the data storage device.

An information processing apparatus includes an input interface, a processor, and an output interface. The input interface obtains observation data obtained from an observation space. The processor detects a subject image of a detection target from the observation data, calculates a plurality of individual indices indicating degrees of reliability, each of which relates to at least identification information or measurement information regarding the detection target, and also calculates an integrated index, which is obtained by integrating a plurality of calculated individual indices. The output interface outputs the integrated index.

The present disclosure provides video processing methods, apparatuses and systems. The method includes: obtaining a to-be-processed video, where the to-be-processed video is obtained by performing feature removal processing for one or more objects in an original video; obtaining a feature restoration processing request for one or more to-be-processed objects; according to the feature restoration processing request for the one or more to-be-processed objects, obtaining feature image information corresponding to the one or more to-be-processed objects, where the feature image information for one of the one or more to-be-processed objects includes pixel position information of all or part of features for the one of the one or more to-be-processed objects in the original video; according to the feature image information for the one or more to-be-processed objects, performing feature restoration processing for the one or more to-be-processed objects in the to-be-processed video.

Example data processing methods and apparatus are provided. One example method includes obtaining an image captured by an in-vehicle camera. A to-be-detected target in the image is determined. A feature region corresponding to the to-be-detected target in the image is further determined based on a location of the to-be-detected target in the image. A first parking state is determined based on the image and wheel speedometer information. A first homography matrix corresponding to the first parking state is determined from a prestored homography matrix set, where different parking states correspond to different homography matrices. Image information of the feature region is processed based on the first homography matrix to obtain a detection result.

Systems and methods for self-supervised representation learning as a means to generate context-specific pretrained models include selecting data from a set of available data sets; selecting a pretext task from domain specific pretext tasks; selecting a target problem specific network architecture based on a user selection from available choices or any customized model as per user preference; and generating a pretrained model for the selected network architecture using the selected data obtained from the set of available data sets and a pretext task as obtained from domain specific pretext tasks.

There is provided with an information processing apparatus. An approximate discrimination unit discriminates an approximate type of an object from a first captured image obtained by capturing the object to which identification information is added. A setting unit sets, based on the approximate type of the object, an image capturing condition for capturing an image to obtain the identification information. A detail discrimination unit identifies the identification information from a second captured image obtained by capturing the object under the image capturing condition and discriminates a detailed type of the object based on a result of the identification.

In one embodiment, a computing system may update a first 3D model of a region of an environment based on comparisons between the first 3D model and first depth measurements of the region generated during a first time period. The computing system may determine that the region is static by comparing the first 3D model to second depth measurements of the region generated during a second time period. The computing system may in response to determining that the region is static, detect whether the region changed after the second time period based on comparisons between a second 3D model of the region and third depth measurements of the region generated after the second time period, the second 3D model having a lower resolution than the first 3D model. The computing system may in response to detecting a change in the region, update the first 3D model of the region.

A computer-implemented visual interface for identifying and revealing objects from video-based media provides visual cues to enable users to interact with video-based media. Objects in videos are inferred and identified based upon automatic interpretations of the video and/or audio that is associated with the video. The automatic interpretations may be performed by a computer-implemented neural network. The computer-implemented visual interface is integrated with the video to enable users to interact with the identified objects. User interactions with the visual interface may be through either touch or non-touch means. Information is delivered to users that is based upon the identified objects, including in augmented or virtual reality-based form, responsive to user interactions with the computer-implemented visual interface.

A radar system is provided that includes transmission signal generation circuitry, a transmit channel coupled to the transmission generation circuitry to receive a continuous wave test signal, the transmit channel configurable to output a test signal based on the continuous wave signal in which a phase angle of the test signal is changed in discrete steps within a phase angle range, a receive channel coupled to the transmit channel via a feedback loop to receive the test signal, the receive channel including an in-phase (I) channel and a quadrature (Q) channel, a statistics collection module configured to collect energy measurements of the test signal output by the I channel and the test signal output by the Q channel at each phase angle, and a processor configured to estimate phase and gain imbalance of the I channel and the Q channel based on the collected energy measurements.