We present two simple approaches to calibrate a stereo camera setup with heterogeneous lenses: a wide-angle fish-eye lens and a narrow-angle lens in left and right sides, respectively. Instead of using a conventional black-white checkerboard pattern, we design an embedded checkerboard pattern by combining two differently colored patterns. In both approaches, we split the captured stereo images into RGB channels and extract R and inverted G channels from left and right camera images, respectively. In our first approach, we consider the checkerboard pattern as the world coordinate system and calculate left and right transformation matrices corresponding to it. We use these two transformation matrices to estimate the relative pose of the right camera by multiplying the inversed left transformation with the right. In the second approach, we calculate a planar homography transformation to identify common object points in left-right image pairs and treat them with the well-known Zhangs camera calibration method. We analyze the robustness of these two approaches by comparing reprojection errors and image rectification results. Experimental results show that the second method is more accurate than the first one.
This paper proposes a calibration technique of a stereo gamma detection camera. Calibration of the internal and external parameters of a stereo vision camera is a well-known research problem in the computer vision society. However, few or no stereo calibration has been investigated in the radiation measurement research. Since no visual information can be obtained from a stereo radiation camera, it is impossible to use a general stereo calibration algorithm directly. In this paper, we develop a hybrid-type stereo system which is equipped with both radiation and vision cameras. To calibrate the stereo radiation cameras, stereo images of a calibration pattern captured from the vision cameras are transformed in the view of the radiation cameras. The homography transformation is calibrated based on the geometric relationship between visual and radiation camera coordinates. The accuracy of the stereo parameters of the radiation camera is analyzed by distance measurements to both visual light and gamma sources. The experimental results show that the measurement error is about 3%.
This paper presents an improved dense disparity estimating technique for a collection of multi-baseline stereo (referred to as MBS in the text) images. The flow of the proposed system consists of two main frameworks: a preliminary cost calculation and initial disparity estimating framework, and an iterative cost refinement framework. The first framework implements an accurate multi-baseline stereo cost (referred to as MBSC in the text) calculation method, and a scan line optimization inspired by the Semi Global Matching (SGM) algorithm. Cost volumes of each two-view camera pair are calculated by fusing two pixel dissimilarity measures: i) weighted Census transformation and ii) sum of absolute difference color consistency term (SAD-Census). The initial disparity map between reference and the matching view with the largest baseline displacement is calculated by summing-and-interpolating SAD-Census costs of the current and all neighboring camera pairs in-between, and taking the minimum after aggregating for sixteen directions. The second framework refines the aggregated MBSC volume recursively. In each iteration, individual pair-wise disparity maps are used to warp matching views towards the reference to create binary masks that resemble overlapping differences. White locations in the mask represent incorrect correspondence matches, thus a penalty is added for costs associated with, adapting a Gaussian modulating function. This significantly reduces the selection probability of incorrect disparity minima in proceeding iterations. A Guided filter-based Rolling Guidance filter is applied to further up-vote the probability of pixels with the lowest costs, which are similar or close enough to ground truth readings. Through experimental results evaluated on the Middlebury dataset, we show that our method leads to effective and efficient multi-baseline disparity estimations.
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