Abstract: Disclosed are a method, an apparatus, a device and a storage medium for reconstructing a 3D model from 2D images, comprising: obtaining two-dimensional images respectively corresponding to at least two viewing angles of a three-dimensional object; and inputting the two-dimensional images respectively corresponding to the at least two viewing angles into a set neural network for information fusion and 3D model reconstruction so as to obtain a 3D model of the three-dimensional object.

Abstract: A chart for calibrating a system of multiple cameras, the chart comprising: a background; an array of dots contrasting the background, wherein the array of dots are arranged in rows and columns, wherein the array of dots comprise a first dot array, and a second dot array, wherein the first dot array fully occupies a first region of evenly spaced dots with a first dot density, the second dot array fully occupies a second region of evenly spaced dots with a second dot density, and wherein the second region is enclosed within the first region; a group of first markers in the first region, a group of second markers in the second region, and a third marker at the center of the chart, wherein each second marker is closer to the third marker than each first marker.

Abstract: A multiple camera imaging system comprises a first camera image sensor to obtain a first image of a scene and a second camera image sensor to obtain a second image. The system further comprises an image signal processor (ISP) to produce a first roughly-aligned image from the first image and a second roughly-aligned image from the second image. A feature point in the first roughly-aligned image is identified. A center of a search zone is created, which is a combination of the position of the feature point and an associated disparity value from the first and second roughly-aligned images. The search zone is created within the second roughly-aligned image. Candidate feature points are identified within the search zone, which is much smaller than the entire image. A best-matched feature point pair in the first roughly-aligned image and the second roughly-aligned image is then identified.

Abstract: A multiple camera imaging system, comprising a first camera image sensor configured to obtain a first image of a scene from a first vantage perspective point; a second camera image sensor configured to obtain a second image of the scene from a second vantage perspective point; and an image signal processor (ISP), configured to process the first image and the second image by performing the following steps: producing a first monotone image from the first image, and a second monotone image from the second image; projecting the first monotone image to produce a first one-dimensional profile; and projecting the second monotone image to produce a second one-dimensional profile; extracting a matching information from the first and second one-dimensional profiles by matching the second one-dimensional profile to the first one dimensional profile; using the matching information produce a first processed two-dimensional image, and a second processed two-dimensional image that is aligned with the first processed two-dim

Abstract: A multiple camera imaging system, comprising: a first camera image sensor configured to obtain a first image of a scene from a first vantage perspective point; a second camera image sensor configured to obtain a second image of the scene from a second vantage perspective point; and an image signal processor (ISP), configured to process the first image and the second image by performing the following steps: producing a first roughly-aligned image from the first image, and a second roughly-aligned image from the second image; using the first and second roughly-aligned images to produce a disparity image; identifying a feature point within the first roughly-aligned image; utilizing the disparity image to create a search zone within the second roughly-aligned image; and identifying a group of candidate feature points within the search zone; identifying within the search zone a best-matched candidate feature point that best matches the feature point within the first roughly-aligned image to form a best-matched fea

Abstract: A multiple camera imaging system, comprising a first camera image sensor configured to obtain a first image of a scene from a first vantage perspective point; a second camera image sensor configured to obtain a second image of the scene from a second vantage perspective point; and an image signal processor (ISP), configured to process the first image and the second image by performing the following steps: producing a first monotone image from the first image, and a second monotone image from the second image; projecting the first monotone image to produce a first one-dimensional profile; and projecting the second monotone image to produce a second one-dimensional profile; extracting a matching information from the first and second one-dimensional profiles by matching the second one-dimensional profile to the first one dimensional profile; using the matching information produce a first processed two-dimensional image, and a second processed two-dimensional image that is aligned with the first processed two-dim

Abstract: A chart for calibrating a system of multiple cameras, the chart comprising: a background; an array of dots contrasting the background, wherein the array of dots are arranged in rows and columns, wherein the array of dots comprise a first dot array, and a second dot array, wherein the first dot array fully occupies a first region of evenly spaced dots with a first dot density, the second dot array fully occupies a second region of evenly spaced dots with a second dot density, and wherein the second region is enclosed within the first region; a group of first markers in the first region, a group of second markers in the second region, and a third marker at the center of the chart, wherein each second marker is closer to the third marker than each first marker.

Abstract: A method is proposed for automatically locating the optic disc or the optic cup in an image of the rear of an eye. A portion of the image containing the optic disc or optic cup is divided into sub-regions using a clustering algorithm. Biologically inspired features, and optionally other features, are obtained for each of the sub-regions. An adaptive model uses the features to generate data indicative of whether each sub-region is within or outside the optic disc or optic cup. The result is then smoothed, to form an estimate of the position of the optic disc or optic cup.

Abstract: A method is proposed for automatically locating the optic disc or the optic cup in an image of the rear of an eye. A portion of the image containing the optic disc or optic cup is divided into sub-regions using a clustering algorithm. Biologically inspired features, and optionally other features, are obtained for each of the sub-regions. An adaptive model uses the features to generate data indicative of whether each sub-region is within or outside the optic disc or optic cup. The result is then smoothed, to form an estimate of the position of the optic disc or optic cup.