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In the field of augmented reality (AR), many kinds of vision-based extrinsic camera parameter estimation methods have been proposed to achieve geometric registration between real and virtual worlds. Previously, a feature landmark-based camera parameter estimation method was proposed. This is an effective method for implementing outdoor AR applications because a feature landmark database can be automatically constructed using the structure-from-motion (SfM) technique. However, the previous method cannot work in real time because it entails a high computational cost or matching landmarks in a database with image features in an input image. In addition, the accuracy of estimated camera parameters is insufficient for applications that need to overlay CG objects at a position close to the user's viewpoint. This is because it is difficult to compensate for visual pattern change of close landmarks when only the sparse depth information obtained by the SfM is available. In this paper, we achieve fast and accurate feature landmark-based camera parameter estimation by adopting the following approaches. First, the number of matching candidates is reduced to achieve fast camera parameter estimation by tentative camera parameter estimation and by assigning priorities to landmarks. Second, image templates of landmarks are adequately compensated for by considering the local 3-D structure of a landmark using the dense depth information obtained by a laser range sensor. To demonstrate the effectiveness of the proposed method, we developed some AR applications using the proposed method.

In this article, we propose an efficient method for estimating a depth map from long-baseline image sequences captured by a calibrated moving multi-camera system. Our concept for estimating a depth map is very simple; we integrate the counting of the total number of interest points (TNIP) in images with the original framework of multiple baseline stereo. Even by using a simple algorithm, the depth can be determined without computing similarity measures such as SSD (sum of squared differences) and NCC (normalized cross correlation) that have been used for conventional stereo matching. The proposed stereo algorithm is computationally efficient and robust for distortions and occlusions and has high affinity with omni-directional and multi-camera imaging. Although expected trade-off between accuracy and efficiency is confirmed for a naive TNIP-based method, a hybrid approach that uses both TNIP and SSD improve this with realizing high accurate and efficient depth estimation. We have experimentally verified the validity and feasibility of the TNIP-based stereo algorithm for both synthetic and real outdoor scenes.

In the field of augmented reality (AR), many kinds of vision-based extrinsic camera parameter estimation methods have been proposed to achieve geometric registration between real and virtual worlds. Previously, a feature landmark-based camera parameter estimation method was proposed. This is an effective method for implementing outdoor AR applications because a feature landmark database can be automatically constructed using the structure-from-motion (SfM) technique. However, the previous method cannot work in real time because it entails a high computational cost or matching landmarks in a database with image features in an input image. In addition, the accuracy of estimated camera parameters is insufficient for applications that need to overlay CG objects at a position close to the user's viewpoint. This is because it is difficult to compensate for visual pattern change of close landmarks when only the sparse depth information obtained by the SfM is available. In this paper, we achieve fast and accurate feature landmark-based camera parameter estimation by adopting the following approaches. First, the number of matching candidates is reduced to achieve fast camera parameter estimation by tentative camera parameter estimation and by assigning priorities to landmarks. Second, image templates of landmarks are adequately compensated for by considering the local 3-D structure of a landmark using the dense depth information obtained by a laser range sensor. To demonstrate the effectiveness of the proposed method, we developed some AR applications using the proposed method.

application/pdf, In this article, we propose an efficient method for estimating a depth map from long-baseline image sequences captured by a calibrated moving multi-camera system. Our concept for estimating a depth map is very simple; we integrate the counting of the total number of interest points (TNIP) in images with the original framework of multiple baseline stereo. Even by using a simple algorithm, the depth can be determined without computing similarity measures such as SSD (sum of squared differences) and NCC (normalized cross correlation) that have been used for conventional stereo matching. The proposed stereo algorithm is computationally efficient and robust for distortions and occlusions and has high affinity with omni-directional and multi-camera imaging. Although expected trade-off between accuracy and efficiency is confirmed for a naive TNIP-based method, a hybrid approach that uses both TNIP and SSD improve this with realizing high accurate and efficient depth estimation. We have experimentally verified the validity and feasibility of the TNIP-based stereo algorithm for both synthetic and real outdoor scenes.

In this article, we propose an efficient method for estimating a depth map from long-baseline image sequences captured by a calibrated moving multi-camera system. Our concept for estimating a depth map is very simple; we integrate the counting of the total number of interest points (TNIP) in images with the original framework of multiple baseline stereo. Even by using a simple algorithm, the depth can be determined without computing similarity measures such as SSD (sum of squared differences) and NCC (normalized cross correlation) that have been used for conventional stereo matching. The proposed stereo algorithm is computationally efficient and robust for distortions and occlusions and has high affinity with omni-directional and multi-camera imaging. Although expected trade-off between accuracy and efficiency is confirmed for a naive TNIP-based method, a hybrid approach that uses both TNIP and SSD improve this with realizing high accurate and efficient depth estimation. We have experimentally verified the validity and feasibility of the TNIP-based stereo algorithm for both synthetic and real outdoor scenes., journal article