Method for compressing a sequence of images comprising a first image and a second image, the method comprising the steps of: generating a first descriptor comprising parameters for displaying a computer-generated graphical element in the first image, the graphical element being of non-photographic origin, and the display parameters not comprising pixel values; processing the second image so as to determine an event which gave rise to a potential variation in the parameters for displaying the graphical element between the first image and the second image; generating a second descriptor comprising an event code indicating the determined event.

In a crack detection device (10), an image acquisition unit (21) acquires image data acquired by taking an image of a road surface from an oblique direction with respect to the road surface, An image classification unit (22) classifies image data acquired into an acceptable range with a resolution higher than a standard value, and an unacceptable range with a resolution equal to or less than the standard value. A data output unit (23) outputs acceptable data being image data of a part classified into the acceptable range as data to detect a crack on the road surface. An image display unit (24) displays data output.

Texture filtering is applied to a texture represented with a mipmap comprising a plurality of levels, wherein each level of the mipmap comprises an image representing the texture at a respective level of detail. A texture filtering unit has minimum and maximum limits on an amount by which it can alter the level of detail when it filters texels from an image of a single level of the mipmap. The range of level of detail between the minimum and maximum limits defines an intrinsic region of the texture filtering unit. If it is determined that a received input level of detail is in an intrinsic region of the texture filtering unit, texels are read from a single mipmap level of the mipmap, and the read texels from the single mipmap level are filtered to determine a filtered texture value representing part of the texture at the input level of detail. If it is determined that the received input level of detail is in an extrinsic region of the texture filtering unit: texels are read from two mipmap levels of the mipmap, and the read texels from the two mipmap levels are processed to determine a filtered texture value representing part of the texture at the input level of detail.

One embodiment provides a computer-implemented method that includes providing a dynamic list structure that stores one or more detected object bounding boxes. Temporal analysis is applied that updates the dynamic list structure with object validation to reduce temporal artifacts. A two-dimensional (2D) buffer is utilized to store a luminance reduction ratio of a whole video frame. The luminance reduction ratio is applied to each pixel in the whole video frame based on the 2D buffer. One or more spatial smoothing filters are applied to the 2D buffer to reduce a likelihood of one or more spatial artifacts occurring in a luminance reduced region.

A depth measuring apparatus includes a camera assembly configured to capture a plurality of images of a target at a plurality of distances from the target. The depth measuring apparatus further includes a controller configured to, for each of a plurality of regions within the plurality of images: determine corresponding gradient values within the plurality of images; determine a corresponding maximum gradient value from the corresponding gradient values; and determine, based on the corresponding maximum gradient value, a depth measurement for a region of the plurality of regions.

In one embodiment, a computer-based method includes obtaining a first image where the first image includes one or more patterns, generating a second image that substantially removes or reduces the one or more patterns from the first image at least partially by automatically detecting the one or more patterns and a zone where the one or more patterns occur in the first image, converting the first image to frequency domain data, applying a multi-parameter filter to the frequency domain data to substantially remove or reduce the one or more patterns. The parameters may include bandwidths in a depth and azimuthal direction. The parameters may be adapted in the multi-parameter filter based on the one or more patterns. The method also includes transforming the frequency domain data to spatial domain data and outputting the second image based at least in part on the spatial domain data.

Implementations of the present disclosure include receiving a training image, providing a hash pattern that is representative of the training image, applying a plurality of filters to the training image to provide a respective plurality of filtered training images, identifying a filter to be associated with the hash pattern based on the plurality of filtered training images, and storing a mapping of the filter to the hash pattern within a set of mapping in a data store.

The present invention provides an auxiliary berthing method and system for a vessel. Position information of a vessel relative to a berth is determined by a solar blind ultraviolet imaging method; meanwhile, by a GPS method, an attitude angle of the vessel relative to the berth is determined by at least two GPS receivers. Thus, the vessel can be berthed safely when getting close to the shore at low visibility. Further, in the method and device of the present invention, it can be preferable to integrate coordinate data and angle data received by a solar blind ultraviolet imaging module and GPS signal receiving modules by a normalized correlation algorithm and a data fusion algorithm, so as to improve the positioning accuracy.

The present invention provides an auxiliary berthing method and system for a vessel. In the berthing method, by a solar blind ultraviolet imaging method, a position and an attitude of a vessel relative to a shoreline of a port berth during berthing are calculated by at least two solar blind ultraviolet imaging modules according to light signals received by a solar blind ultraviolet light source array arranged in advance on the shore. Further, when more than three solar blind ultraviolet imaging modules are used, in the method and device of the present invention, a normalized correlation algorithm and a data fusion algorithm are used to improve the accuracy of the position and attitude data of the vessel.

An approach is provided for using a machine learning model for identifying planar region(s) in an image. The approach involves, for example, determining the model for performing image segmentation. The model comprises at least: a trainable filter that convolves the image to generate an input volume comprising a projection of the image at different resolution scales; and feature(s) to identify image region(s) having a texture within a similarity threshold. The approach also involves processing the image using the model by generating the input volume from the image using the trainable filter and extracting the feature(s) from the input volume to determine the region(s) having the texture. The approach further involves determining the planar region(s) by clustering the image regions. The approach further involves generating a planar mask based on the planar region(s). The approach further involves providing the planar mask as an output of the image segmentation.