An image acquisition unit 341-1 acquires a polarization image and a non-polarization image indicating a peripheral area of a moving body, such as the peripheral area of a vehicle. A discrimination information generation unit 342-1 uses the polarization image acquired by the image acquisition unit 341-1 and generates analysis object discrimination information indicating a road surface or the like. An image analysis unit 344-1 uses an image of an image analysis area set on the basis of the analysis object discrimination information generated by the discrimination information generation unit 342-1 with respect to the non-polarization image acquired by the image acquisition unit 341-1, and performs a discrimination of an object, such as an obstacle on the road surface. It is possible to efficiently perform a determination of the presence of the object from the non-polarization image of the peripheral area of the moving body.

The present disclosure provides a method and device for filtering sensor data. Signals from an array of sensor pixels are received and checked for changes in pixel values. Motion is detected based on the changes in pixel values, and motion output signals are transmitted to a processing station. If the sum of correlated changes in pixel values across a predetermined field of view exceeds a predetermined value, indicating sensor jitter, the motion output signals are suppressed. If a sum of motion values within a defined subsection of the field of view exceeds a predetermined threshold, indicating the presence of a large object of no interest, the motion output signals are suppressed for that subsection.

A camera system for inventory monitoring includes a movable base that supports multiple cameras. The multiple cameras are directed toward inventory to take a series of pictures along aisle of a retail store or warehouse. A processing module connected to the multiple cameras is used to stitch together the pictures, and along with depth information and product identification information, construct a real time or near real time inventory mapping of products positioned on aisle shelves. This information can be transferred to remote locations to simplify and speed product ordering, and assist in maintenance of appropriate product stock levels.

The present disclosure describes an imaging system, method, and apparatus for identifying a latent image of a hidden object. A light source generates a first beam of narrow-band light and a second beam of narrow-band light that has temporal fluctuations correlated with the first beam. A frequency modulator shifts a temporal frequency of at least one of the first beam or the second beam. The first beam is directed towards a first scattering surface and the second beam is directed towards a second scattering surface. The first scattering surface scatters the first beam to a scattered light that illuminates a hidden object. The hidden object reflects at least a portion of the scattered light towards the second scattering surface, the reflected light interferes with the second beam and produces an interference pattern on the second scattering surface. A lock-in camera detects an irradiance of the interference pattern, monitors temporal variations of the irradiance caused by the temporal frequency shift introduced by the frequency modulator, and identifies a complex-valued light field that represents information of the hidden object based on the temporal variations of the irradiance.

In some arrangements, product packaging is digitally watermarked over most of its extent to facilitate high-throughput item identification at retail checkouts. Imagery captured by conventional or plenoptic cameras can be processed (e.g., by GPUs) to derive several different perspective-transformed viewsfurther minimizing the need to manually reposition items for identification. Crinkles and other deformations in product packaging can be optically sensed, allowing such surfaces to be virtually flattened to aid identification. Piles of items can be 3D-modelled and virtually segmented into geometric primitives to aid identification, and to discover locations of obscured items. Other data (e.g., including data from sensors in aisles, shelves and carts, and gaze tracking for clues about visual saliency) can be used in assessing identification hypotheses about an item. A great variety of other features and arrangements are also detailed.

A method for assisting operation of a vehicle traveling on a roadway includes acquiring visual images around the vehicle with at least one visual camera having a field of view and acquiring thermal images around the vehicle with at least one thermal camera having the field of view. The thermal images are superimposed over the visual images to produce composite images. An object is detected in the composite images. A vehicle assist system adjusts at least one of a direction of travel and speed of the vehicle in response to detecting the object.

In one embodiment, a method includes accessing first image data generated by a first image sensor having a first filter array that has a first filter pattern. The first filter pattern includes a number of first filter types. The method also includes accessing second image data generated by a second image sensor having a second filter array that has a second filter pattern different from the first filter pattern. The second filter pattern includes a number of second filter types, the number of second filter types and the number of first filter types have at least one filter type in common. The method also includes determining a correspondence between one or more first pixels of the first image data and one or more second pixels of the second image data based on a portion of the first image data associated with the filter type in common.

The present disclosure includes apparatuses and methods related to a command bus in memory. A memory module may be equipped with multiple memory media types that are responsive to perform various operations in response to a common command. The operations may be carried out during the same clock cycle in response to the command. An example apparatus can include a first number of memory devices coupled to a host via a first number of ports and a second number of memory devices each coupled to the first number of memory devices via a second number of ports, wherein the second number of memory devices each include a controller, and wherein the first number of memory devices and the second number of memory devices can receive a command from the host to perform the various (e.g., the same or different) operations, sometime concurrently.

A method and system for object recognition via a computer vision application including an object to be recognized, the object having an object specific luminescence spectral pattern, a light source including at least two illuminants for illuminating a scene including the object to be recognized by switching between the two illuminants, a sensor configured to capture radiance data of the scene including the object when the scene is illuminated by the light source, and a data storage unit storing fluorescence spectral patterns together with appropriately assigned respective objects. The method and system further include a data processing unit configured to extract the object specific fluorescence spectral pattern from the radiance data of the scene and to match the extracted object specific fluorescence spectral pattern with the fluorescence spectral patterns stored in the data storage unit, and to identify a best matching fluorescence spectral pattern and its assigned object.

When a computer image is generated from a real-world scene having a semi-reflective surface (e.g. window), the computer image will create, at the semi-reflective surface from the viewpoint of the camera, both a reflection of a scene in front of the semi-reflective surface and a transmission of a scene located behind the semi-reflective surface. Similar to a person viewing the real-world scene from different locations, angles, etc., the reflection and transmission may change, and also move relative to each other, as the viewpoint of the camera changes. Unfortunately, the dynamic nature of the reflection and transmission negatively impacts the performance of many computer applications, but performance can generally be improved if the reflection and transmission are separated. The present disclosure uses deep learning to separate reflection and transmission at a semi-reflective surface of a computer image generated from a real-world scene.