Omnivision Technologies, Inc.

Agency for Science, Technology and Research

Singapore Health Services Pte Ltd

Xinting Gao

Lu Chang

Guansong Liu

Shan Xu

Jiang Liu

Wing Kee Damon Wong

Tien Yin Wong

Ying Zhou

Beng Hai Lee

Ngan Meng Tan

Zhuo Zhang

Jun Cheng

Wenshou Chen

Fengshou Yin

Tin Aung

Baskaran Mani

Ye Tao

Bin Chen

Yanwu Xu

Shimiao Li

Yanfang Huang

Xiangang Cheng

Ching-Yu Cheng

Yim-lui Carol Cheung

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.

Multiple camera calibration chart

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.

Aligning two images by matching their feature points

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-dimensional image.

Alignment of multiple camera images by matching projected one dimensional image profiles

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.

Methods and systems for automatic location of optic structures in an image of an eye, and for automatic retina cup-to-disc ratio computation

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Singapore, Singapore

Xinting Gao