Projective reconstruction from line-correspondences in multiple uncalibrated images

A new approach is proposed for reconstructing 3D lines and cameras from 2D corresponding lines across multiple uncalibrated views. There is no requirement that the 2D corresponding lines on different images represent the same segment of a 3D line, which may not appear on all images. A 3D line is reconstructed by minimizing a geometric cost function that measures the distance of the reprojected end points of the 3D segment from the measured 2D lines on different images. An algorithmic procedure is provided with guaranteed convergence to a solution where the geometric cost function achieves a (local) minimum.     

Estimation of the fundamental matrix from uncalibrated stereo hand images for 3D hand gesture recognition

All 3D hand models employed for hand gesture recognition so far use kinematic models of the hand. We propose to use computer vision models of the hand, and recover hand gestures using 3D reconstruction techniques. In this paper, we present a new method to estimate the epipolar geometry between two uncalibrated cameras from stereo hand images. We first segmented hand images using the RCE neural network based color segmentation algorithm and extracted edge points of fingers as points of interest, then match them based on the topological features of the hand. The fundamental matrix is estimated using a combination of techniques such as input data normalization, rank-2 constraint, linear criterion, nonlinear criterion as well as M-estimator. This method has been tested with real calibrated and uncalibrated images. The experimental comparison demonstrates the effectiveness and robustness of the method.     

Projective reconstruction and invariants from multiple images

This correspondence investigates projective reconstruction of geometric configurations seen in two or more perspective views, and the computation of projective invariants of these configurations from their images. A basic tool in this investigation is the fundamental matrix that describes the epipolar correspondence between image pairs. It is proven that once the epipolar geometry is known, the configurations of many geometric structures (for instance sets of points or lines) are determined up to a collineation of projective 3-space 𝒫³ by their projection in two independent images. This theorem is the key to a method for the computation of invariants of the geometry. Invariants of six points in 𝒫³ and of four lines in 𝒫³ are defined and discussed. An example with real images shows that they are effective in distinguishing different geometrical configurations. Since the fundamental matrix is a basic tool in the computation of these invariants, new methods of computing the fundamental matrix from seven-point correspondences in two images or six-point correspondences in three images are given     

A quasi-dense approach to surface reconstruction from uncalibrated images

This paper proposes a quasi-dense approach to 3D surface model acquisition from uncalibrated images. First, correspondence information and geometry are computed based on new quasi-dense point features that are resampled subpixel points from a disparity map. The quasi-dense approach gives more robust and accurate geometry estimations than the standard sparse approach. The robustness is measured as the success rate of full automatic geometry estimation with all involved parameters fixed. The accuracy is measured by a fast gauge-free uncertainty estimation algorithm. The quasi-dense approach also works for more largely separated images than the sparse approach, therefore, it requires fewer images for modeling. More importantly, the quasi-dense approach delivers a high density of reconstructed 3D points on which a surface representation can be reconstructed. This fills the gap of insufficiency of the sparse approach for surface reconstruction, essential for modeling and visualization applications. Second, surface reconstruction methods from the given quasi-dense geometry are also developed. The algorithm optimizes new unified functionals integrating both 3D quasi-dense points and 2D image information, including silhouettes. Combining both 3D data and 2D images is more robust than the existing methods using only 2D information or only 3D data. An efficient bounded regularization method is proposed to implement the surface evolution by level-set methods. Its properties are discussed and proven for some cases. As a whole, a complete automatic and practical system of 3D modeling from raw images captured by hand-held cameras to surface representation is proposed. Extensive experiments demonstrate the superior performance of the quasi-dense approach with respect to the standard sparse approach in robustness, accuracy, and applicability.     

Quasi-Euclidean epipolar rectification of uncalibrated images

This paper deals with the problem of epipolar rectification in the uncalibrated case. First the calibrated (Euclidean) case is recognized as the ideal one, then we observe that in that case images are transformed with a collineation induced by the plane at infinity, which has a special structure. Hence, that structure is imposed to the sought transformation while minimizing a rectification error. Experiments show that this method yields images that are close to the ones produced by Euclidean rectification.     

Projective reconstruction of ellipses from multiple images

This paper presents a new approach for reconstructing 3D ellipses (including circles) from a sequence of 2D images taken by uncalibrated cameras. Our strategy is to estimate an ellipse in 3D space by reconstructing N(≥5) 3D points (called representative points) on it, where the representative points are reconstructed by minimizing the distances from their projections to the measured 2D ellipses on different images (i.e., 2D reprojection error). This minimization problem is transformed into a sequence of minimization sub-problems that can be readily solved by an algorithm which is guaranteed to converge to a (local) minimum of the 2D reprojection error. Our method can reconstruct multiple 3D ellipses simultaneously from multiple images and it readily handles images with missing and/or partially occluded ellipses. The proposed method is evaluated using both synthetic and real data.     

一种非定标图像高精度三维重建算法

由非定标图像重建三维场景有着广泛的应用。给出了一种非定标多视图像三维重建算法。该算法主要基于因子分解和光束法平差技术。首先用因子分解方法得到射影空间下相机投影矩阵和物点坐标,以旋转矩阵的正交性以及对偶绝对二次曲面秩为3为约束,将射影空间升级到欧式空间,最后用光束法平差进行优化。该方法可同时获得相机的内外参数、畸变系数和场景的三维坐标。仿真实验表明,在1000 mm×1000 mm×400mm的范围内,当像点检测误差在0-1pixel和0-2pixel内,所重建三维点的误差分别为0.1530 mm和0.6712 mm。在500 mm×500 m×200 mm下,真实实验重构三维点的误差在0.3 mm以内。所提出的算法稳定可靠,可对实际工程进行指导。     

Affine structure from line correspondences with uncalibrated affinecameras

This paper presents a linear algorithm for recovering 3D affine shape and motion from line correspondences with uncalibrated affine cameras. The algorithm requires a minimum of seven line correspondences over three views. The key idea is the introduction of a one-dimensional projective camera. This converts 3D affine reconstruction of “line directions” into 2D projective reconstruction of “points”. In addition, a line-based factorization method is also proposed to handle redundant views. Experimental results both on simulated and real image sequences validate the robustness and the accuracy of the algorithm     

基于未标定多幅图的三维重建算法

为得到鲁棒的三维重建效果,提出了一种基于未标定多幅图像的三维重建算法。该算法首先采用Harris算法检测特征点,针对双向匹配算法匹配速度慢的缺点,使用改进的双向匹配算法进行特征点匹配,在已知摄像机参数的情况下进行两幅图的三维重建;接着采用四元数算法进行坐标转换,将由每两幅图得到的不同部分的重建结果转移到同一坐标系下,实现了多幅图像的三维重建;最后利用集束调整优化重建结果。实验结果证明,该算法能获得比较满意的重建效果。     

Quaternion wavelet phase based stereo matching for uncalibrated images

This paper proposes a quaternion wavelet phase based stereo matching (QWPSM) scheme for uncalibrated image pairs. In this scheme, we estimate the disparity by directly establishing correspondences between quaternionic phase structures of two quaternion wavelet filtered (QWF) images. Firstly, linear-phase quaternion wavelet filters (LPQWFs) are constructed from real biorthogonal wavelet bases. Then, quaternion phases are extracted under each scale through quaternion wavelet filtering of the multiscale transformed image pyramids. The disparity estimation is formed as a minimization process of a local energy weighted cost function, and propagated from coarse to fine scales. Costs can adaptively alleviate the negative effects of phase singularities, which are the main causes of mismatches in phase-based stereo matching. Multiscale matching strategy is used to avoid phase wrapping and improve convergence speed. Experimental results are promising in various image pairs.     

Comparison of edge detection algorithms using a structure frommotion task

This paper presents an evaluation of edge detector performance. We use the task of structure from motion (SFM) as a "black box" through which to evaluate the performance of edge detection algorithms. Edge detector goodness is measured by how accurately the SFM could recover the known structure and motion from the edge detection of the image sequences. We use a variety of real image sequences with ground truth to evaluate eight different edge detectors from the literature. Our results suggest that ratings of edge detector performance based on pixel-level metrics and on the SFM are well correlated and that detectors such as the Canny detector and Heitger detector offer the best performance.     

基于极线几何约束的非标定图像的立体匹配

立体匹配是计算机视觉领域的一个关键问题，是计算机三维重建中的一个核心问题。基于极线几何约束是特征匹配中最强有力的约束，独立于场景结构，因此将特征匹配与极线几何有机结合起来，实现了一个鲁棒的匹配算法在非标定图像中的应用。其中，对基础矩阵估计及其引导匹配均采用点到极线距离最小约束准则。对真实图像的实验表明，该算法具有较好的实用性。     

Robust iris segmentation on uncalibrated noisy images using mathematical morphology

This paper proposes a new approach for fast iris segmentation that relies on the closed nested structures of iris anatomy (the sclera is brighter than the iris, and the iris is brighter than the pupil) and on its polar symmetry. The described method applies mathematical morphology for polar/radial-invariant image filtering and for circular segmentation using shortest paths from generalized grey-level distances. The proposed algorithm obtained good results on the NICE-I contest and showed a very robust behavior, especially when dealing with half-closed eyes, different skin colours/illumination or subjects wearing glasses.     

Applications of a direct algorithm for the rectification of uncalibrated images

An algorithm for the rectification of uncalibrated images is presented and applied to a variety of cases. The algorithm generates the rectifying transformations directly from the geometrical relationship between the images, using any three correspondences in the images to define a reference plane. A small set of correspondences is used to calculate an initial rectification. Additional correspondences are introduced semi-automatically, by correlating regions of the rectified images. Since the rectified images of surfaces in the reference plane have no relative distortion, features can be matched very accurately by correlation, allowing small changes in disparity to be detected. In the 3-d reconstruction of an architectural scene, differences in depth are resolved to about 0.001 of the distance from camera to subject.     

一种用于未标定图像三维重建的立体匹配算法*

提出了一种适用于未标定图像三维重建的立体匹配算法。该算法首先引入限制因子来消除Harris角点聚簇的现象,使用高斯曲面拟合内插使Harris角点达到亚像素级;接着采用特征点的Sift特征描述符进行初始匹配,利用随机抽样算法估计基础矩阵的同时剔除误匹配点对;最后在估计的基础矩阵的引导下进行双向匹配。实验证明,该算法能够很好地恢复物体的结构,是一种有效的用于未标定图像三维重建的立体匹配算法。     

Three-dimensional object recognition from single two-dimensional images

A computer vision system has been implemented that can recognize three-dimensional objects from unknown viewpoints in single gray-scale images. Unlike most other approaches, the recognition is accomplished without any attempt to reconstruct depth information bottom-up from the visual input. Instead, three other mechanisms are used that can bridge the gap between the two-dimensional image and knowledge of three-dimensional objects. First, a process of perceptual organization is used to form groupings and structures in the image that are likely to be invariant over a wide range of viewpoints. Second, a probabilistic ranking method is used to reduce the size of the search space during model-based matching. Finally, a process of spatial correspondence brings the projections of three-dimensional models into direct correspondence with the image by solving for unknown viewpoint and model parameters. A high level of robustness in the presence of occlusion and missing data can be achieved through full application of a viewpoint consistency constraint. It is argued that similar mechanisms and constraints form the basis for recognition in human vision.     

Gender recognition from visible and thermal infrared facial images

Most present research of gender recognition focuses on visible facial images, which are sensitive to illumination changes. In this paper, we proposed hybrid methods for gender recognition by fusing visible and thermal infrared images. First, the active appearance model is used to extract features from visible images, as well as local binary pattern features and several statistical temperature features are extracted from thermal infrared images. Then, feature selection is performed by using the F-test statistic. Third, we propose using Bayesian Networks to perform explicit and implicit fusion of visible and thermal infrared image features. For explicit fusion, we propose two Bayesian Networks to perform decision-level and feature-level fusion. For implicit fusion, we propose using features from one modality as privileged information to improve gender recognition by another modality. Finally, we evaluate the proposed methods on the Natural Visible and Infrared facial Expression spontaneous database and the Equinox face database. Experimental results show that both feature-level and decision-level fusion improve the gender recognition performance, compared to that achieved from one modality. The proposed implicit fusion methods successfully capture the role of privileged information of one modality, thus enhance the gender recognition from another modality.     

Projective invariants of co-moments of 2D images

Functions of moments of 2D images that are invariant under some changes are important in image analysis and pattern recognition. One of the most basic changes to a 2D image is geometric change. Two images of the same plane taken from different viewpoints are related by a projective transformation. Unfortunately, it is well known that geometric moment invariants for projective transformations do not exist in general. Yet if we generalize the standard definition of the geometric moments and utilize some additional information from the images, certain type of projective invariants of 2D images can be derived. This paper first defines co-moment as a moment-like function of image that contains two reference points. Then a set of functions of co-moments that is invariant under general projective transformations is derived. The invariants are simple and in explicit form. Experimental results validated the mathematical derivations.     

Fruit recognition from images using deep learning applications

Multimedia Tools and Applications - Smart imaging devices have been used at a rapid rate in the agriculture sector for the last few years. Fruit recognition and classification is noticed as one of...     

一种有效的可见光图像中水坝目标的识别方法

提出了一种在中高分辨率可见光图像中识别水坝的有效方法。根据源图像的分辨率采样图像,利用梯度直方图及区域增长获得水坝上游的大面积水域,用链码表示大面积水域,找寻下游水域,分割可能包含水坝的区域;在初步分割的基础上,提取目标的一组特征参数。算法具有检测率高、扩展性好等特点。实验证明该算法能够有效地在中高分辨率可见光图像上识别水坝。