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We present a practical 3D reconstruction method to obtain a high-quality dense depth map from narrow-baseline image sequences captured by commercial digital cameras, such as DSLRs or mobile phones. Depth estimation from small motion has gained interest as a means of various photographic editing, but important limitations present themselves in the form of(More)
Given a set of surface normals, we pose a Manhattan Frame (MF) estimation problem as a consensus set maximization that maximizes the number of inliers over the rotation search space. We solve this problem through a branchand-bound framework, which mathematically guarantees a globally optimal solution. However, the computational time of conventional(More)
One of the most hazardous driving scenario is the overtaking of a slower vehicle, indeed, in this case the front vehicle (being overtaken) can occlude an important part of the field of view of the rear vehicle's driver. This lack of visibility is the most probable cause of accidents in this context. Recent research works tend to prove that augmented reality(More)
This paper presents a vision system and a depth processing algorithm for DRC-HUBO+, the winner of the DRC finals 2015. Our system is designed to reliably capture 3D information of a scene and objects and to be robust to challenging environment conditions. We also propose a depth-map upsampling method that produces an outliers-free depth map by explicitly(More)
—Most man-made environments, such as urban and indoor scenes, consist of a set of parallel and orthogonal planar structures. These structures are approximated by Manhattan world assumption and be referred to Manhattan Frame (MF). Given a set of inputs such as surface normals or vanishing points, we pose an MF estimation problem as a consensus set(More)
Body part (BP) classification is important in human image analysis. In this paper, we propose an optical flow based pixel-wise BP classifier using random forest (RF) in a monocular video. Running the BP classifier on each optical frame generates noisy mis classifications. We integrate the classifier with a human detector and temporal voting to reduce most(More)
We propose a novel camera calibration method for defocused images using a smartphone under the assumption that the defocus blur is modeled as a convolution of a sharp image with a Gaussian point spread function (PSF). In contrast to existing calibration approaches which require well-focused images, the proposed method achieves accurate camera calibration(More)
Recently, line feature has been used as addition feature in various computer vision applications such as 3D reconstruction and pose estimation, due to the lack of point feature in structured environments. In this paper, we review line assisted vision applications in structured environments: image stitching and segmentation. By showing the qualitative(More)