A polarization image acquisition unit (11) acquires polarization images of three or more polarization directions. A feature quantity computation unit (15) computes image feature quantities on the basis of the acquired polarization images. For example, the luminance of each polarization image is normalized for each pixel, and the normalized luminance of the polarization image is used as the image feature quantity. The luminance of the polarization image changes according to the surface shape of an object. Thus, the image feature quantities computed on the basis of the polarization images are feature quantities corresponding to the surface shape of the object. Image processing, for example, image recognition, feature point detection, feature point matching, or the like, can be performed on the basis of the surface shape of the object using such image feature quantities.

The technology disclosed relates to identifying an object in a field of view of a camera. In particular, it relates to identifying a display in the field of view of the camera. This is achieved by monitoring a space including acquiring a series of image frames of the space using the camera and detecting one or more light sources in the series of image frames. Further, one or more frequencies of periodic intensity or brightness variations, also referred to as refresh rate, of light emitted from the light sources is measured. Based on the one or more frequencies of periodic intensity variations of light emitted from the light sources, at least one display that includes the light sources is identified.

Provided are a captured image evaluation apparatus, a captured image evaluation method, and a captured image evaluation program capable of evaluating a thickness and a density of stacked cultured cells in a short imaging time. The captured image evaluation apparatus includes: an image acquisition section 52 that acquires captured images obtained by imaging a subject under a condition in which a numerical aperture of an objective lens is changed; a thickness estimation section 53 that estimates a thickness of the subject on the basis of a low NA captured image obtained under a condition in which the numerical aperture of the objective lens is relatively small; and a density estimation section 54 that estimates a density of the subject on the basis of a high NA captured image obtained under a condition in which the numerical aperture of the objective lens is relatively large.

A 3D imaging system uses a depth sensor to produce a coarse depth map, and then uses the coarse depth map as a constraint in order to correct ambiguous surface normals computed from polarization cues. The imaging system outputs an enhanced depth map that has a greater depth resolution than the coarse depth map. The enhanced depth map is also much more accurate than could be obtained from the depth sensor alone. In many cases, the imaging system extracts the polarization cues from three polarized images. Thus, in many implementations, the system takes only three extra imagesin addition to data used to generate the coarse depth mapin order to dramatically enhance the coarse depth map.

Systems and methods are directed to object detection at various ranges for autonomous vehicles. In one example, a system includes a camera providing a first field of view; a machine-learned model that has been trained to generate object detection range estimates based at least in part on labeled training data representing image data having a second field of view different from the first field of view; and a computing system including one or more processors; and memory including instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include obtaining image data from the camera. inputting the image data from the camera to the machine-learned model; obtaining a first range estimate as an output of the machine-learned model, wherein the first range estimate represents estimates for the second field of view; generating transformed range estimate by applying a range estimate transform to the first range estimate output from the machine-learned model; and providing the transformed range estimate for use in controlling operation of an autonomous vehicle.

A system includes a sensor to capture multiple images of a portion of a first object illuminated by coherent illumination and a time of capture of each of the images; and a processor to compare two images of the multiple images to identify one or more touch points. Each touch point has a difference in value between the two images that is greater than a threshold. Upon determining a spatial shape formed by the identified touch points that corresponds to a pointing end of a pointing object, the system provides at least one of: i) a touch location of the pointing end relative to the first object, where the touch location is based on the spatial shape formed by the identified touch points, or ii) the time of capture of a second image of the two images that produced the spatial shape.

The disclosure relates to a method, apparatus and system for detecting and reducing the effects of color fringing in digital video acquired by a camera comprising an iris. The method comprises: acquiring, by the camera, a first digital image frame using a first camera setting, including a first iris aperture size; acquiring, by the camera, a second digital image frame using a second camera setting, including a second iris aperture size, wherein the second aperture size is smaller than the first aperture size; comparing the first and the second digital image frame, at least a specific color component thereof; localizing regions having a disproportional intensity ratio in the specific color component between the first digital image frame and the second digital image frame; and reducing the specific color component in the localized regions for subsequently acquired digital image frames.

Systems and method for expanding a dynamic range associated with an image are disclosed. The method includes capturing an image of a scene via an imaging device using a single exposure. The image of the scene includes a plurality of polarization images corresponding to different angles of polarization, and each of the polarization images comprise a plurality of color channels. The method further includes determining a criterion for each of the plurality of color channels; selecting one color channel of the plurality of color channels based on determining of the criterion; generating a reconstructed image irradiance for the one color channel based on pixels in two or more of the plurality of polarization images obtained for the one color channel; and outputting a reconstructed image with the reconstructed image irradiance.

A system and method for identifying developmental anomalies. The method includes obtaining a first set of at least one multimedia content element showing at least one crop and captured using a first set of at least one capturing parameter; obtaining normal development data for the at least one crop, wherein the normal development data represents at least one normal development characteristic of the at least one crop; analyzing, via machine vision, the first set of at least one multimedia content element to identify a first set of at least one characteristic of the at least one crop; determining, based on the first set of at least one characteristic and the normal development data, whether a suspected anomaly is identified; and verifying if the suspected anomaly is an anomaly using a second set of at least one multimedia content element captured using a second set of at least one capturing parameter.

An information processing method and an electronic device are provided. The method comprises: detecting a first movement parameter value of an image capture unit of the electronic device in its current movement state, the first movement parameter value being associated with the current movement state of the image capture unit; determining a first capturing frame rate corresponding to the first movement parameter value based on a correspondence between movement parameter values and capturing frame rates; and controlling the image capture unit to capture an image at the first capturing frame rate.