An apparatus includes a parser configured to decimate a source image to produce a decimated image according to a pre-distortion geometry for the source image, and partition the decimated image into image portions according to a first clock rate. The apparatus includes warping engines coupled to the parser and configured to pre-distort, according to a second clock rate, respective image portions to produce respective pre-distorted image portions according to the pre-distortion geometry. The apparatus also includes a combiner coupled to the warping engines and configured to combine the pre-distorted image portions, according to the first clock rate, to form a pre-distorted image of the source image. The apparatus further includes a processor configured to process the pre-distorted image to produce a processed image for projection by a light modulator and one or more light sources.

A method includes creating a point cloud model of an environment, applying at least one filter to the point cloud model to produce a filtered model of the environment and defining a plane in the filtered model corresponding to a horizontal expanse associated with a floor of the environment.

Disclosed herein is a full-screen display device capable of sufficiently securing light transmittance of a sensor area overlapping a sensor unit in a pixel array and minimizing deterioration in perceived image quality of the sensor area. The pixels are arranged in the sensor area overlapping the sensor unit in the pixel array of the full-screen display device such that the number of pixels gradually decreases from the outer periphery toward the center of the sensor area in units of masks, and the area of a transmission portion gradually increases from the outer periphery toward the center of the sensor area in units of masks.

The present technology relates to an imaging device, an imaging method, and a program capable of setting a resolution based on a distance to a subject. A control unit which changes a resolution of a captured image on the basis of distance information, corresponding to the captured image, regarding a detected distance to a subject included in the image is included. The control unit changes a resolution of a portion of a region of the captured image on the basis of the distance information. The portion of the region is a region distant from another region. The control unit changes the resolution of the portion of the region such that the portion of the region becomes higher than a resolution of another region.

Disclosed herein are a method and apparatus for image compression based on machine learning using a global context. The disclosed image compression network employs an existing image quality enhancement network for an end-to-end joint learning scheme. The image compression network may jointly optimize image compression enhancement and quality enhancement. The image compression networks and image quality enhancement networks may be easily combined within a unified architecture which minimizes total loss, and may be easily jointly optimized.

Embodiments relate to reducing logging entries based on contents are disclosed. According to the embodiments, the logging entries are converted into an image with pixels whose values are related with the log content and the image size is reduced by applying an image processing technology. The reduced image is converted back to the log entries. In this way, the number of logging entries is reduced without losing important information.

A method for computing, a computing device, and a computer-readable storage medium are provided. The method includes determining a first pixel block in a cache. The first pixel block is composed of a 2m row×2n column pixel matrix and includes original pixel data and pixel data related to the original pixel data. The first pixel block is read from the cache. At least part of the pixel data related to the original pixel data is used for padding related to the original pixel data. The original pixel data includes pixel data from the (n+1).sup.th column to the 2n.sup.th column in the (m+1).sup.th row to the 2m.sup.th row in the 2m row×2 n column pixel matrix. When reading data from the cache, pixel data that needs to be obtained after insert-zero and padding operations on the original pixel data in back propagation can be read at one time.

This application is directed to vectoring a raster image in which an electronic device detects a contour of a component in the raster image, builds tangent vectors for each point of the contour and identifies a plurality of segmentation points on the contour. One or more points of sharp angle are identified on the contour in accordance with a determination that each point of sharp angle corresponds to two distinct tangent vectors and that an angle between the two distinct tangent vectors falls below a predefined threshold. A respective one of the segmentation points is positioned at each identified point of shape angle. The electronic device approximates a piecewise smooth fitting curve (e.g., a piecewise Bezier curve) having two or more fitting segments to connect the plurality of segmentation points on the contour. The piecewise smooth fitting curve is thereby provided to vectorize the raster image.

A method of inspecting a composite structure formed of plies of tows is provided. The method involves receiving an image of an upper ply overlapping lower plies, the upper ply tow ends defining a boundary between plies, and applying extracted sub-images to a trained machine learning model to detect the upper or lower ply. Probability maps are produced in which pixels of the sub-images are associated with probabilities the pixels belong to an object class for the upper or lower ply. The method may also involve transforming the probability maps into reconstructed sub-images, stitching together a composite image, and applying the composite image to a feature detector to detect locations of tow ends of the upper ply. The method may also involve comparing the locations to as-designed locations of the tow ends, inspecting the composite structure, and indicating a result of the comparison.

Embodiments relate to generating an image pyramid for feature extraction. A pyramid image generator circuit includes a first image buffer that stores image data at a first octave, a first blur filter circuit, a first spatial filter circuit, and a first decimator circuit. The first blur filter circuit generates a first pyramid image for a first scale of the first octave by applying a first amount of smoothing to the first image data stored in the first image buffer. The first spatial filter circuit and the first decimator generate second image data of a second octave that is higher than the first octave by applying a smoothing and a decimation to the first image data stored in the first image buffer. The first spatial filter circuit begins processing the first image data before the first blur filter circuit begins to process the first image data.