Detecting multiple planes in images is a challenging problem, but one with many applications. Recent work such as J-Linkage and Ordered Residual Kernels have focussed on developing a domain independent approach to detect multiple structures. These multiple structure detection methods are then used for estimating multiple homographies given feature matches between two images. Features participating in the multiple homographies detected, provide us the multiple scene planes. We show that these methods provide locally optimal results and fail to merge detected planar patches to the true scene planes. These methods use only residues obtained on applying homography of one plane to another as cue for merging. In this paper, we develop additional cues such as local consistency of planes, local normals, texture etc. to perform better classification and merging . We formulate the classification as an MRF problem and use TRWS message passing algorithm to solve non metric energy terms and complex sparse graph structure. We show results on challenging dataset common in robotics navigation scenarios where our method shows accuracy of more than 85 percent on average while being close or same as the actual number of scene planes.
@cite_9 observe that J-linkage uses only nearby feature matches to generate initial homographies. This is done to ensure that the computed homographies correspond to real scene planes. Though, the downside of such an approach is that the homographies output by J-linkage are also locally optimal and do not fit a large scene plane. The scene plane is output as multiple small planar patches by J-linkage. @cite_9 solve this by continuing to cluster the matches (after J-linkage) using the distance measure:
@cite_9
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We present a new method for the robust detection and matching of multiple planes in pairs of images. Such planes can serve as stable landmarks for vision-based urban navigation. Our approach starts from SIFT matches and generates multiple local homography hypotheses using the recent J-linkage technique by Toldo and Fusiello, a robust randomized multi-model estimation algorithm. These hypotheses are then globally merged, spatially analyzed, robustly fitted, and checked for stability. When tested on more than 30,000 image pairs taken from panoramic views of a college campus, our method yields no false positives and recovers 72 of the matchable building walls identified by a human, despite significant occlusions and viewpoint changes. A novel method able to recover scene planes of arbitrary position and orientation from oriented images using homographies is presented. Planar regions are reconstructed using only sparse, affine-invariant sets of corresponding seed regions. These regions are iteratively expanded and refined using plane-induced homographies. Experiments on synthetic data show the high accuracy of the reconstruction and demonstrate that the reconstruction method can cope with large baseline changes. Experiments on real images show the performance of the method on practically relevant scenes. Because of their abundance and simplicity, planes are used in several computer vision tasks. Their simplicity results in that, under perspective projection, the transformation between a world plane and its corresponding image plane is projective linear, or a homography. These relations also hold between perspective views of a plane in different images. This paper proposes an algorithm that detects planar homographies in uncalibrated image pairs. It then demonstrates how this plane identification method can be used as a first step in an image analysis process, when point matching between images is unreliable. The detection is performed using a RANSAC scheme based on the linear computation of the homography matrix elements using four points. Results are shown on real image pairs. The importance of precise homography estimation is often underestimated even though it plays a crucial role in various vision applications such as plane or planarity detection, scene degeneracy tests, camera motion classification, image stitching, and many more. Ignoring the radial distortion component in homography estimation—even for classical perspective cameras—may lead to significant errors or totally wrong estimates. In this paper, we fill the gap among the homography estimation methods by presenting two algorithms for estimating homography between two cameras with different radial distortions. Both algorithms can handle planar scenes as well as scenes where the relative motion between the cameras is a pure rotation. The first algorithm uses the minimal number of five image point correspondences and solves a nonlinear system of polynomial equations using Grobner basis method. The second algorithm uses a non-minimal number of six image point correspondences and leads to a simple system of two quadratic equations in two unknowns and one system of six linear equations. The proposed algorithms are fast, stable, and can be efficiently used inside a RANSAC loop. Motion in the image plane is ultimately a function of 3D motion in space. We propose to compute optical flow using what is ostensibly an extreme overparameterization: depth, surface normal, and frame-to-frame 3D rigid body motion at every pixel, giving a total of 9 DoF. The advantages of such an overparameterization are twofold: first, geometrically meaningful reasoning can be called upon in the optimization, reflecting possible 3D motion in the underlying scene; second, the ‘fronto-parallel’ assumption implicit in the use of traditional matching pixel windows is ameliorated because the parameterization determines a plane-induced homography at every pixel. We show that optimization over this high-dimensional, continuous state space can be carried out using an adaptation of the recently introduced PatchMatch Belief Propagation (PMBP) energy minimization algorithm, and that the resulting flow fields compare favorably to the state of the art on a number of small- and large-displacement datasets. We propose a robust method for detecting local planar regions in a scene with an uncalibrated stereo. Our method is based on random sampling using distributions of feature point locations. For doing RANSAC, we use the distributions for each feature point defined by the distances between the point and the other points. We first choose a correspondence by using an uniform distribution and next choose candidate correspondences by using the distribution of the chosen point. Then, we compute a homography from the chosen correspondences and find the largest consensus set of the homography. We repeat this procedure until all regions are detected. We demonstrate that our method is robust to the outliers in a scene by simulations and real image examples. This paper proposes a novel multi-layer homography algorithm for background modeling from a free-moving camera. Background is composed of many planes. Different planes satisfy with different homographies which can be found by our algorithm. Each pixel except for the moving pixel definitely belongs to some plane. Rectified by the corresponding homography, each static pixel in the shared view can find its match in the previous frame. Thus, frames can be rectified to a specific viewpoint for background modeling. Experiment shows it is effective. Our approach can be used in motion detection from a free-moving camera. We introduce a new approach to structure and motion recovery directly from one or more large planes in the scene. When such a plane exists, we demonstrate how to automatically detect and track it robustly and consistently over a long video sequence, and how to efficiently self-calibrate the camera using the homographies induced by this plane. We build a complete structure from motion system which does not use any additional off-the-plane information about the scene, and show its advantage over conventional systems in handling two important issues which often occur in real world videos, namely, the plane degeneracy and the dynamic foreground problems. Experimental results on a variety of real video sequences verify the effectiveness and efficiency of our system. This paper describes a method to construct seamless image mosaics of a panoramic scene containing two predominate planes: a distant back plane and a ground plane that sweeps out from the camera's location. While this type of panorama can be stitched when the camera is carefully rotated about its optical center, such ideal scene capture is hard to perform correctly. Existing techniques use a single homography per image to perform alignment followed by seam cutting or image blending to hide inevitable alignments artifacts. In this paper, we demonstrate how to use two homographies per image to produce a more seamless image. Specifically, our approach blends the homographies in the alignment procedure to perform a nonlinear warping. Once the images are geometrically stitched, they are further processed to blend seams and reduce curvilinear visual artifacts due to the nonlinear warping. As demonstrated in our paper, our procedure is able to produce results for this type of scene where current state-of-the-art techniques fail. We present a real-time camera pose tracking and mapping system which uses the assumption of a planar scene to implement a highly efficient mapping algorithm. Our light-weight mapping approach is based on keyframes and plane-induced homographies between them. We solve the planar reconstruction problem of estimating the keyframe poses with an efficient image rectification algorithm. Camera pose tracking uses continuously extended and refined planar point maps and delivers robustly estimated 6DOF poses. We compare system and method with bundle adjustment and monocular SLAM on synthetic and indoor image sequences. We demonstrate large savings in computational effort compared to the monocular SLAM system while the reduction in accuracy remains acceptable. This paper presents a novel method for estimating the unknown 6DOF pose of a mobile device. The method is based on matching between the mobile image and the virtual city model which is merely composed of 3D points on planar building facade. The main contributions of this paper are as follows: firstly, we design a new plane generation strategy which fuses the 3D model points, photo homography and the orientation of the buildings together within RANSAC framework. Secondly, we propose a novel energy-based method which can parallel solve the mobile poses as well as the best 2D-3D matches. Thirdly, a client server mode is established to support a speedy localization experience on the mobile devices. To the best of our knowledge, this is the first implementation that uses such a multi-planar model to accurately locate the mobile device in a scene of city scale. Experiment shows that the localization performance becomes faster and more robust comparing with other methods when adding the multi-planar information of the buildings to localization algorithm. This paper presents an improvement of the J-linkage algorithm for fitting multiple instances of a model to noisy data corrupted by outliers. The binary preference analysis implemented by J-linkage is replaced by a continuous (soft, or fuzzy) generalization that proves to perform better than J-linkage on simulated data, and compares favorably with state of the art methods on public domain real datasets. The problem of record linkage focuses on determining whether two object descriptions refer to the same underlying entity. Addressing this problem effectively has many practical applications, e.g., elimination of duplicate records in databases and citation matching for scholarly articles. In this paper, we consider a new domain where the record linkage problem is manifested: Internet comparison shopping. We address the resulting linkage setting that requires learning a similarity function between record pairs from streaming data. The learned similarity function is subsequently used in clustering to determine which records are co-referent and should be linked. We present an online machine learning method for addressing this problem, where a composite similarity function based on a linear combination of basis functions is learned incrementally. We illustrate the efficacy of this approach on several real-world datasets from an Internet comparison shopping site, and show that our method is able to effectively learn various distance functions for product data with differing characteristics. We also provide experimental results that show the importance of considering multiple performance measures in record linkage evaluation. In this paper, we describe a real-time algorithm for computing the ego-motion of a vehicle relative to the road. The algorithm uses as input only those images provided by a single omnidirectional camera mounted on the roof of the vehicle. The front ends of the system are two different trackers. The first one is a homography-based tracker that detects and matches robust scale-invariant features that most likely belong to the ground plane. The second one uses an appearance-based approach and gives high-resolution estimates of the rotation of the vehicle. This planar pose estimation method has been successfully applied to videos from an automotive platform. We give an example of camera trajectory estimated purely from omnidirectional images over a distance of 400 m. For performance evaluation, the estimated path is superimposed onto a satellite image. In the end, we use image mosaicing to obtain a textured 2-D reconstruction of the estimated path. A novel stereo matching algorithm is proposed that utilizes color segmentation on the reference image and a self-adapting matching score that maximizes the number of reliable correspondences. The scene structure is modeled by a set of planar surface patches which are estimated using a new technique that is more robust to outliers. Instead of assigning a disparity value to each pixel, a disparity plane is assigned to each segment. The optimal disparity plane labeling is approximated by applying belief propagation. Experimental results using the Middlebury stereo test bed demonstrate the superior performance of the proposed method We present an efficient method for feature correspondence and object-based image matching, which exploits both photometric similarity and pairwise geometric consistency from local invariant features. We formulate object-based image matching as an unsupervised multi-class clustering problem on a set of candidate feature matches, and propose a novel pairwise dissimilarity measure and a robust linkage model in the framework of hierarchical agglomerative clustering. The algorithm handles significant amount of outliers and deformation as well as multiple clusters, thus enabling simultaneous feature matching and clustering from real-world image pairs with significant clutter and multiple deformable objects. The experimental evaluation on feature correspondence, object recognition, and object-based image matching demonstrates that our method is robust to both outliers and deformation, and applicable to a wide range of image matching problems. Robust image registration is a challenging problem, especially when dealing with severe changes in illumination and viewpoint. Previous methods assume a global geometric model (e.g., homography) and, hence, are only able to align images under predefined constraints (e.g., planar scenes and parallax-free camera motion). However, these constraints may not hold for natural scenes and uncontrolled imaging conditions. Therefore, this paper proposes a novel method which approximates image regions with planes by incorporating piecewise local geometric models. The approximated planar regions are obtained by exploiting a hierarchical figure-ground segmentation method. Each such planar region assumes an affine transformation. To achieve the alignment of the planar regions, an energy function is defined which employs intensity, a key-point descriptor, and geometric information under a global constraint. By re-segmenting and re-merging planar regions iteratively in an energy minimization framework, the method is able to align images even under significant changes in illumination and viewpoint. Experiments on two datasets show that the proposed method outperforms state-of-the-art, especially in the case of large appearance variations and it is, therefore, applicable to web-images (i.e., unconstrained setting) which are taken from the same scene with different viewpoints. This paper offers a real-time approach for simultaneously determining spacecraft motion and multiple 3D planar surfaces for spacecraft safe landing. The approach contains three algorithms: a multiple homography alignment algorithm, which constructs homographies under unified epipolar geometry; a closed-form motion estimation algorithm; and a simple routine for surface slope estimation. This approach has three significant advantages: first, it works well for both a simple planar scene and more complex 3D world containing many smaller planar surfaces; second, it decomposes a very large optimization problem into much smaller sub-problems that are computationally less expensive. This computational advantage means that this approach can be exploited in real time robotic operations such as during time critical spacecraft landing. Finally, this approach is very reliable and accurate. The effectiveness of this approach is determined quantitatively through extensive simulations and qualitatively with actual images This paper tackles the problem of self-calibration of multiple cameras which are very far apart. Given a set of feature correspondences one can determine the camera geometry. The key problem we address is finding such correspondences. Since the camera geometry (location and orientation) and photometric characteristics vary considerably between images one cannot use brightness and or proximity constraints. Instead we propose a three step approach: first we use moving objects in the scene to determine a rough planar alignment, next we use static features to improve the alignment, finally we compute the epipolar geometry from the the homography matrix of the planar alignment. We do not assume synchronized cameras and we show that enforcing geometric constraints enables us to align the tracking data in time. We present results on challenging outdoor scenes using real time tracking data. To realistically integrate 3D graphics into an unprepared environment, camera position must be estimated by tracking natural image features. We apply our technique to cases where feature positions in adjacent frames of an image sequence are related by a homography, or projective transformation. We describe this transformation's computation and demonstrate several applications. First, we use an augmented notice board to explain how a homography, between two images of a planar scene, completely determines the relative camera positions. Second, we show that the homography can also recover pure camera rotations, and we use this to develop an outdoor AR tracking system. Third, we use the system to measure head rotation and form a simple low-cost virtual reality (VR) tracking solution.