Self-calibration of turntable sequences from silhouettes

This paper addresses the problem of recovering both the intrinsic and extrinsic parameters of a camera from the silhouettes of an object in a turntable sequence. Previous silhouette-based approaches have exploited correspondences induced by epipolar tangents to estimate the image invariants under turntable motion and achieved a weak calibration of the cameras. It is known that the fundamental matrix relating any two views in a turntable sequence can be expressed explicitly in terms of the image invariants, the rotation angle, and a fixed scalar. It will be shown that the imaged circular points for the turntable plane can also be formulated in terms of the same image invariants and fixed scalar. This allows the imaged circular points to be recovered directly from the estimated image invariants, and provide constraints for the estimation of the imaged absolute conic. The camera calibration matrix can thus be recovered. A robust method for estimating the fixed scalar from image triplets is introduced, and a method for recovering the rotation angles using the estimated imaged circular points and epipoles is presented. Using the estimated camera intrinsics and extrinsics, a Euclidean reconstruction can be obtained. Experimental results on real data sequences are presented, which demonstrate the high precision achieved by the proposed method. © 2009 IEEE.published_or_final_versio

3D Scanning System for Automatic High-Resolution Plant Phenotyping

Thin leaves, fine stems, self-occlusion, non-rigid and slowly changing structures make plants difficult for three-dimensional (3D) scanning and reconstruction -- two critical steps in automated visual phenotyping. Many current solutions such as laser scanning, structured light, and multiview stereo can struggle to acquire usable 3D models because of limitations in scanning resolution and calibration accuracy. In response, we have developed a fast, low-cost, 3D scanning platform to image plants on a rotating stage with two tilting DSLR cameras centred on the plant. This uses new methods of camera calibration and background removal to achieve high-accuracy 3D reconstruction. We assessed the system's accuracy using a 3D visual hull reconstruction algorithm applied on 2 plastic models of dicotyledonous plants, 2 sorghum plants and 2 wheat plants across different sets of tilt angles. Scan times ranged from 3 minutes (to capture 72 images using 2 tilt angles), to 30 minutes (to capture 360 images using 10 tilt angles). The leaf lengths, widths, areas and perimeters of the plastic models were measured manually and compared to measurements from the scanning system: results were within 3-4% of each other. The 3D reconstructions obtained with the scanning system show excellent geometric agreement with all six plant specimens, even plants with thin leaves and fine stems.Comment: 8 papes, DICTA 201

1D camera geometry and its application to the self-calibration of circular motion sequences

This paper proposes a novel method for robustly recovering the camera geometry of an uncalibrated image sequence taken under circular motion. Under circular motion, all the camera centers lie on a circle and the mapping from the plane containing this circle to the horizon line observed in the image can be modelled as a 1D projection. A 2×2 homography is introduced in this paper to relate the projections of the camera centers in two 1D views. It is shown that the two imaged circular points of the motion plane and the rotation angle between the two views can be derived directly from such a homography. This way of recovering the imaged circular points and rotation angles is intrinsically a multiple view approach, as all the sequence geometry embedded in the epipoles is exploited in the estimation of the homography for each view pair. This results in a more robust method compared to those computing the rotation angles using adjacent views only. The proposed method has been applied to self-calibrate turntable sequences using either point features or silhouettes, and highly accurate results have been achieved. © 2008 IEEE.published_or_final_versio

Camera Calibration from Dynamic Silhouettes Using Motion Barcodes

Computing the epipolar geometry between cameras with very different viewpoints is often problematic as matching points are hard to find. In these cases, it has been proposed to use information from dynamic objects in the scene for suggesting point and line correspondences. We propose a speed up of about two orders of magnitude, as well as an increase in robustness and accuracy, to methods computing epipolar geometry from dynamic silhouettes. This improvement is based on a new temporal signature: motion barcode for lines. Motion barcode is a binary temporal sequence for lines, indicating for each frame the existence of at least one foreground pixel on that line. The motion barcodes of two corresponding epipolar lines are very similar, so the search for corresponding epipolar lines can be limited only to lines having similar barcodes. The use of motion barcodes leads to increased speed, accuracy, and robustness in computing the epipolar geometry.Comment: Update metadat

Self-calibration and motion recovery from silhouettes with two mirrors

LNCS v. 7724-7727 (pts. 1-4) entitled: Computer vision - ACCV 2012: 11th Asian Conference on Computer Vision ... 2012: revised selected papersThis paper addresses the problem of self-calibration and motion recovery from a single snapshot obtained under a setting of two mirrors. The mirrors are able to show five views of an object in one image. In this paper, the epipoles of the real and virtual cameras are firstly estimated from the intersection of the bitangent lines between corresponding images, from which we can easily derive the horizon of the camera plane. The imaged circular points and the angle between the mirrors can then be obtained from equal angles between the bitangent lines, by planar rectification. The silhouettes produced by reflections can be treated as a special circular motion sequence. With this observation, technique developed for calibrating a circular motion sequence can be exploited to simplify the calibration of a single-view two-mirror system. Different from the state-of-the-art approaches, only one snapshot is required in this work for self-calibrating a natural camera and recovering the poses of the two mirrors. This is more flexible than previous approaches which require at least two images. When more than a single image is available, each image can be calibrated independently and the problem of varying focal length does not complicate the calibration problem. After the calibration, the visual hull of the objects can be obtained from the silhouettes. Experimental results show the feasibility and the preciseness of the proposed approach. © 2013 Springer-Verlag.postprin

3D object reconstruction using computer vision : reconstruction and characterization applications for external human anatomical structures

Tese de doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201

Robust recovery of shapes with unknown topology from the dual space

In this paper, we address the problem of reconstructing an object surface from silhouettes. Previous works by other authors have shown that, based on the principle of duality, surface points can be recovered, theoretically, as the dual to the tangent plane space of the object. In practice, however, the identification of tangent basis in the tangent plane space is not trivial given a set of discretely sampled data. This problem is further complicated by the existence of bi-tangents to the object surface. The key contribution of this paper is the introduction of epipolar parameterization in identifying a well-defined local tangent basis. This extends the applicability of existing dual space reconstruction methods to fairly complicated shapes, without making any explicit assumption on the object topology. We verify our approach with both synthetic and real-world data, and compare it both qualitatively and quantitatively with other popular reconstruction algorithms. Experimental results demonstrate that our proposed approach produces more accurate estimation, whilst maintaining reasonable robustness towards shapes with complex topologies. © 2007 IEEE.published_or_final_versio

Structure and motion estimation from apparent contours under circular motion

In this paper, we address the problem of recovering structure and motion from the apparent contours of a smooth surface. Fixed image features under circular motion and their relationships with the intrinsic parameters of the camera are exploited to provide a simple parameterization of the fundamental matrix relating any pair of views in the sequence. Such a parameterization allows a trivial initialization of the motion parameters, which all bear physical meaning. It also greatly reduces the dimension of the search space for the optimization problem, which can now be solved using only two epipolar tangents. In contrast to previous methods, the motion estimation algorithm introduced here can cope with incomplete circular motion and more widely spaced images. Existing techniques for model reconstruction from apparent contours are then reviewed and compared. Experiment on real data has been carried out and the 3D model reconstructed from the estimated motion is presented. © 2002 Elsevier Science B.V. All rights reserved.postprin