Abstract: A method of image processing in a structured light imaging device is provided that includes capturing a plurality of images of a scene into which a structured light pattern is projected by a projector in the structured light imaging device, extracting features in each of the captured images, finding feature matches between a reference image of the plurality of captured images and each of the other images in the plurality of captured images, rectifying each of the other images to align with the reference image, wherein each image of the other images is rectified based on feature matches between the image and the reference image, combining the rectified other images and the reference image using interpolation to generate a high resolution image, and generating a depth image using the high resolution image.

Abstract: A method of image processing in a structured light imaging device is provided that includes capturing a plurality of images of a scene into which a structured light pattern is projected by a projector in the structured light imaging device, extracting features in each of the captured images, finding feature matches between a reference image of the plurality of captured images and each of the other images in the plurality of captured images, rectifying each of the other images to align with the reference image, wherein each image of the other images is rectified based on feature matches between the image and the reference image, combining the rectified other images and the reference image using interpolation to generate a high resolution image, and generating a depth image using the high resolution image.

Abstract: A method of image processing in a structured light imaging device is provided that includes capturing a plurality of images of a scene into which a structured light pattern is projected by a projector in the structured light imaging device, extracting features in each of the captured images, finding feature matches between a reference image of the plurality of captured images and each of the other images in the plurality of captured images, rectifying each of the other images to align with the reference image, wherein each image of the other images is rectified based on feature matches between the image and the reference image, combining the rectified other images and the reference image using interpolation to generate a high resolution image, and generating a depth image using the high resolution image.

Abstract: A method of image processing in a structured light imaging device is provided that includes capturing a plurality of images of a scene into which a structured light pattern is projected by a projector in the structured light imaging device, extracting features in each of the captured images, finding feature matches between a reference image of the plurality of captured images and each of the other images in the plurality of captured images, rectifying each of the other images to align with the reference image, wherein each image of the other images is rectified based on feature matches between the image and the reference image, combining the rectified other images and the reference image using interpolation to generate a high resolution image, and generating a depth image using the high resolution image.

Abstract: A method, apparatus and a system multi-camera image processing method. The method includes performing geometric alignment to produce a geometric output by estimating fish eye distortion correction parameters, performing initial perspective correction on related frame, running corner detection in the overlapping areas, locating the stronger corner, calculating BRIEF descriptors for features and match feature point from two cameras using BRIEF scores, performing checks and rejecting wrong feature matches, finding perspective matrices to minimize distance between matched features; and creating a geometric lookup table.

Abstract: An image fusing method, apparatus and system for fusing images from an array of cameras, method includes selecting a camera from the array of cameras as a reference camera, estimating misalignment between the retrieved input images from the reference camera and the retrieved input images from the other cameras, estimating misalignment parameters between the reference camera and the other cameras, estimating local disparity between the reference camera image data and the other cameras based on the estimated misalignment parameters, using the estimated misalignment parameters and the estimated disparity values, mapping the image data into a reference camera grid, the retrieved input image data from the other cameras in the array of cameras is fused in the reference camera grid utilizing fractional offsets from integer coordinates, and producing an output image grid on the reference camera grid and interpolate output pixels using processed data for producing a high resolution image.

Abstract: A method of misalignment correction in a structured light device is provided that includes extracting features from a first captured image of a scene, wherein the first captured image is captured by an imaging sensor component of the structured light device, and wherein the first captured image includes a pattern projected into the scene by a projector component of the structured light device, matching the features of the first captured image to predetermined features of a pattern image corresponding to the projected pattern to generate a dataset of matching features, determining values of alignment correction parameters of an image alignment transformation model using the dataset of matching features, and applying the image alignment transformation model to a second captured image using the determined alignment correction parameter values.

Abstract: A method, apparatus and a system multi-camera image processing method. The method includes performing geometric alignment to produce a geometric output by estimating fish eye distortion correction parameters, performing initial perspective correction on related frame, running corner detection in the overlapping areas, locating the stronger corner, calculating BRIEF descriptors for features and match feature point from two cameras using BRIEF scores, performing checks and rejecting wrong feature matches, finding perspective matrices to minimize distance between matched features; and creating a geometric lookup table.

Abstract: An image fusing method, apparatus and system for fusing images from an array of cameras, method includes selecting a camera from the array of cameras as a reference camera, estimating misalignment between the retrieved input images from the reference camera and the retrieved input images from the other cameras, estimating misalignment parameters between the reference camera and the other cameras, estimating local disparity between the reference camera image data and the other cameras based on the estimated misalignment parameters, using the estimated misalignment parameters and the estimated disparity values, mapping the image data into a reference camera grid, the retrieved input image data from the other cameras in the array of cameras is fused in the reference camera grid utilizing fractional offsets from integer coordinates, and producing an output image grid on the reference camera grid and interpolate output pixels using processed data for producing a high resolution image.

Abstract: A method of image processing in a structured light imaging device is provided that includes capturing a plurality of images of a scene into which a structured light pattern is projected by a projector in the structured light imaging device, extracting features in each of the captured images, finding feature matches between a reference image of the plurality of captured images and each of the other images in the plurality of captured images, rectifying each of the other images to align with the reference image, wherein each image of the other images is rectified based on feature matches between the image and the reference image, combining the rectified other images and the reference image using interpolation to generate a high resolution image, and generating a depth image using the high resolution image.

Abstract: A method of misalignment correction in a structured light device is provided that includes extracting features from a first captured image of a scene, wherein the first captured image is captured by an imaging sensor component of the structured light device, and wherein the first captured image includes a pattern projected into the scene by a projector component of the structured light device, matching the features of the first captured image to predetermined features of a pattern image corresponding to the projected pattern to generate a dataset of matching features, determining values of alignment correction parameters of an image alignment transformation model using the dataset of matching features, and applying the image alignment transformation model to a second captured image using the determined alignment correction parameter values.

Abstract: A method and apparatus for estimating stereo misalignment using modified affine or perspective model. The method includes dividing a left frame and a right frame into blocks, comparing horizontal and vertical boundary signals in the left frame and the right frame, estimating the horizontal and the vertical motion vector for each block in a reference frame, selecting a reliable motion vectors from a set of motion vectors, dividing the selected block into smaller features, feeding the data to an affine or a perspective transformation model to solve for the model parameters, running the model parameters through a temporal filter, portioning the estimated misalignment parameters between the left frame and right frame, and modifying the left frame and the right frame to save some boundary space.

Abstract: A method and apparatus for estimating stereo misalignment using modified affine or perspective model. The method includes dividing a left frame and a right frame into blocks, comparing horizontal and vertical boundary signals in the left frame and the right frame, estimating the horizontal and the vertical motion vector for each block in a reference frame, selecting a reliable motion vectors from a set of motion vectors, dividing the selected block into smaller features, feeding the data to an affine or a perspective transformation model to solve for the model parameters, running the model parameters through a temporal filter, portioning the estimated misalignment parameters between the left frame and right frame, and modifying the left frame and the right frame to save some boundary space.