Line matching leveraged by point correspondences

This correspondence shows that line and point correspondences can obtain orientation with the same accuracy, and that when using line correspondence, using more matching lines does not guarantee that the orientation error can be reduced. Orientation detection using line correspondence is very sensitive to errors in image location. We provide a sensitivity analysis of line correspondence, and conditions which lead to orientation sensitivity due to errors in image location. The orientation error using line correspondence due to offset from the image center is obtained analytically. A method for selecting three matching lines, such that orientation is not sensitive to image location errors and using more matching edges to improve the accuracy of orientation is given. Finally three examples are given to illustrate the concepts and support the theoretical results. The first example shows that an orientation with reasonable accuracy may be obtained by using three matching lines. The second example demonstrates that both point and line correspondences can obtain orientation with the same accuracy. The last example supports experimentally the result regarding the orientation error using line correspondence due to offset of the image center and lens distortion.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Vanishing points and vanishing lines are classical geometrical concepts in perspective cameras that have a lineage dating back to 3 centuries. A vanishing point is a point on the image plane where parallel lines in 3D space appear to converge, whereas a vanishing line passes through 2 or more vanishing points. While such concepts are simple and intuitive in perspective cameras, their counterparts in catadioptric cameras (obtained using mirrors and lenses) are more involved. For example, lines in the 3D space map to higher degree curves in catadioptric cameras. The projection of a set of 3D parallel lines converges on a single point in perspective images, whereas they converge to more than one point in catadioptric cameras. To the best of our knowledge, we are not aware of any systematic development of analytical models for vanishing points and vanishing curves in different types of catadioptric cameras. In this paper, we derive parametric equations for vanishing points and vanishing curves using the calibration parameters, mirror shape coefficients, and direction vectors of parallel lines in 3D space. We show compelling experimental results on vanishing point estimation and absolute pose estimation for a wide range of catadioptric cameras in both simulations and real experiments.

Line matching plays an important role in vision localization and three-dimensional reconstruction of building structures. The conventional method of line matching is not effective for processing stereo images with wide baselines and large viewing angles. This paper proposes a line matching method in an affine projection space, aiming to solve the problem of change of viewing angles in aerial oblique images. Firstly, monocular image orientation can be performed through geometric structures of buildings. Secondly, according to the pose information of the camera, the affine projection matrix is obtained. The original image can be rectified as a conformal image based on this projection matrix, thereby reducing the difference in the viewing angle between images. Then, line matching is performed on the rectified images to get the matched line pairs. Finally, the inverse affine projection matrix is used to back-project the matched line pairs to the original images. The experimental results of five groups of aerial oblique images show that the matched line segments obtained by the proposed method are basically superior to those of the methods which are directly processed on the original image in terms of quantity, correctness, and efficiency.

Synovial sarcoma (SS) is initiated by a t(X;18) chromosomal translocation and resultant SS18-SSX fusion oncogene. Only a few SS cell lines exist. None has been compared to its source tumor. In order to compare matched tumor and cell line pairs, we performed RNAseq on 3 tumor/cell line pairs from a genetically engineered mouse model of SS, as well as 2 pairs from human SS tumors. Transcriptomes of mouse tumors and derivative cell lines deviated significantly. Differentially expressed genes highlighted inflammatory infiltrates and metabolism. The same was found for the human tumor and cell line pairs. More was shared between different tumors than between any tumor and its cell line. Direct xenografting generated transcriptomes that more closely resembled the primary tumor than did its derivative cell line. SS tumor transcriptomes are powerfully impacted by the environment wherein they reside, especially with regard to immune interaction and metabolism.

Deep feature descriptor based hierarchical dense matching for X-ray angiographic images

Abstract. This paper presents an enhanced method for extracting invariant features from images based on Scale Invariant Feature Transform (SIFT). Although SIFT features are invariant to image scale and rotation, additive noise, and changes in illumination but we think this algorithm suffers from excess keypoints. Besides, by adding the hue feature, which is extracted from combination of hue and illumination values in HSI colour space version of the target image, the proposed algorithm can speed up the matching phase. Therefore, we proposed the Scale Invariant Feature Transform plus Hue (SIFTH) that can remove the excess keypoints based on their Euclidean distances and adding hue to feature vector to speed up the matching process which is the aim of feature extraction. In this paper we use the difference of hue features and the Mean Square Error (MSE) of orientation histograms to find the most similar keypoint to the under processing keypoint. The keypoint matching method can identify correct keypoint among clutter and occlusion robustly while achieving real-time performance and it will result a similarity factor of two keypoints. Moreover removing excess keypoint by SIFTH algorithm helps the matching algorithm to achieve this goal.

Detection of articulated objects in images, including location and state, is an important and challenging task in many object tracking applications. Image edges have proven to be a key feature, although their quality is influenced by many factors. In this paper, we propose a novel edge gradient-based template matching method for object detection. In contrast to other methods, ours does not perform any binarization or discretization during the online matching. This is facilitated by a new continuous edge gradient similarity measure. Its main components are a novel edge gradient operator, which is applied to query and template images, and the formulation as a convolution, which can be computed very efficiently in Fourier space. Our method consists of a preprocessing stage for the template images, a simple preprocessing of the query image, and our similarity measure between template and query image, which yields a confidence map for the query image. The resulting confidence map can be used for the final object localization. We compared our method to a state-of-the-art chamfer-based matching method. The results demonstrate that our method is much more robust against weak edge response and yields much better confidence maps with fewer maxima that are also more significant. In addition, our method lends itself well to efficient implementation on GPUs: at a query image resolution of 320× 256 and a template resolution of 80× 80 we can generate about 330 confidence maps per second.

153 CARBONIC ANHYDRASE XII: POTENTIAL ROLE IN LYMPHATIC METASTASES OF RENAL CELL CARCINOMA

We consider the problem of nonrigid shape and motion recovery from point correspondences in multiple perspective views. It is well known that the constraints among multiple views of a rigid shape are multilinear on the image points and can be reduced to bilinear (epipolar) and trilinear constraints among two and three views, respectively. In this paper, we generalize this classic result by showing that the constraints among multiple views of a nonrigid shape consisting of K shape bases can be reduced to multilinear constraints among K + ⌈ (K + 1)/2⌉, ⋯, 2K + 1 views. We then present a closed form solution to the reconstruction of a nonrigid shape consisting of two shape bases. We show that point correspondences in five views are related by a nonrigid quintifocal tensor, from which one can linearly compute nonrigid shape and motion. We also demonstrate the existence of intrinsic ambiguities in the reconstruction of camera translation, shape coefficients and shape bases. Examples show the effectiveness of our method on nonrigid scenes with significant perspective effects.

Change detection, or anomaly detection, from street-view images acquired by an autonomous robot at multiple different times, is a major problem in robotic mapping and autonomous driving. Formulation as an image comparison task, which operates on a given pair of query and reference images is common to many existing approaches to this problem. Unfortunately, providing relevant reference images is not straightforward. In this paper, we propose a novel formulation for change detection, termed compressive change retrieval, which can operate on a query image and similar reference images retrieved from the web. Compared to previous formulations, there are two sources of difficulty. First, the retrieved reference images may frequently contain non-relevant reference images, because even state-of-the-art place-recognition techniques suffer from retrieval noise. Second, image comparison needs to be conducted in a compressed domain to minimize the storage cost of large collections of street-view images. To address the above issues, we also present a practical change detection algorithm that uses compressed bag-of-words (BoW) image representation as a scalable solution. The results of experiments conducted on a practical change detection task, "moving object detection (MOD)," using the publicly available Malaga dataset validate the effectiveness of the proposed approach.

Triangulation is an important task in the 3D reconstruction of computer vision. It seems simple to find the position of a point in 3D space when its 2D perspective projections in multi-view images are given and the corresponding camera projection matrices are known. However, in practice, multiple lines in 3D space do not intersect at one point because of noise. Then how to calculate the optimal 3D point of intersection becomes difficult. While there have been multiple methods trying to solve this problem, there is no systematic comparison between them. In this paper, we reviewed various currently existing variants of triangulation method and compared them through extensive experiments. The speed and accuracy of these methods have been compared using both synthetic and real datasets. We presented the results of experi-ments and summarized the advantages, limitations, and applicability of these methods so that to provide a guide for users when they need to choose an appropriate triangulation method for their given applications. Moreover, based on above analysis we proposed an improved method which shows better performance.

Pipelines are a safe energy-efficient mode of transporting fluids and gases. However, risks of pipe leaks still exist. Various pipe leak detection methods are in use; some traditional methods rely on acoustic receivers that must be located on the pipe, thereby restricting their implementation. Microseismic monitoring resolves this restriction because the ground rather than the pipe is monitored. However, microseismic monitoring has, thus far, only been applied using 2D methods for pipeline leak detection, and its accuracy is limited. To address this problem, a new 3D leak detection and location method for buried pipelines is proposed. First, using a 3D staggered-grid finite-difference method for acoustic equations, we simulated a microseismic wave field generated by a leaking pipeline, and recorded it with a ground surface geophone array. Then, a generalized cross-correlation technique was used to extract the time difference. Finally, the simulated annealing method was used to obtain the leak location in 3D space. Simulation and field data results show that the proposed method can locate the leakage point and has good anti-noise capability. Accurate 3D leak location requires at least two survey lines. Non-parallel survey lines can locate the approximate plane position of the leakage. Parallel line and orthogonal line pairs can be used to obtain the leak position accurately; the parallel line pair is the most accurate. The proposed method was concluded to be effective and accurate at detecting and locating leakages in buried pipeline networks.

Recently, efficient spatial data acquisition and visualization have been receiving more attention from the view point of digital city, VR museum including digital archives. Generally, in order to perform object modeling through digital images, line or feature extraction and stereo matching are performed, and many matching methods such as area based matching, feature based matching have been proposed. In particular, line gives important information for object extraction, and satisfied 3D results depend on rigorous line extraction and matching.With this motive, the authors have developed robust line matching which is performed by line extraction and line tracking. The line extraction was performed by Canny operator, and line matching was performed using optical flow and trifocal tensor.In order to apply the line matching method for modeling of historical object, 3D modeling for the “Koma house” through image sequences was investigated in this paper. The Koma house was built in 17th century, and designated as national important cultural assets in 1971.

Finding an image's exact GPS location is a challenging computer vision problem that has many real-world applications. In this paper, we address the problem of finding the GPS location of images with an accuracy which is comparable to hand-held GPS devices. We leverage a structured data set of about 100,000 images build from Google Maps Street View as the reference images. We propose a localization method in which the SIFT descriptors of the detected SIFT interest points in the reference images are indexed using a tree. In order to localize a query image, the tree is queried using the detected SIFT descriptors in the query image. A novel GPS-tag-based pruning method removes the less reliable descriptors. Then, a smoothing step with an associated voting scheme is utilized; this allows each query descriptor to vote for the location its nearest neighbor belongs to, in order to accurately localize the query image. A parameter called Confidence of Localization which is based on the Kurtosis of the distribution of votes is defined to determine how reliable the localization of a particular image is. In addition, we propose a novel approach to localize groups of images accurately in a hierarchical manner. First, each image is localized individually; then, the rest of the images in the group are matched against images in the neighboring area of the found first match. The final location is determined based on the Confidence of Localization parameter. The proposed image group localization method can deal with very unclear queries which are not capable of being geolocated individually.

Purpose: Arbitrary Lagrangian‐Eulerian (ALE) moving mesh is a finite‐element based technique that preserves mass and topology during deformation. We have developed a two‐dimensional (2D) image registration method using ALE moving mesh. This study is aimed to extend the method for three‐dimensional (3D) deformable image registration.Method and Materials: The image registration is contour‐based. 3D structures are reconstructed using slice images. A dynamic contour method is used to establish the correspondence of the surface points between a reference image and a target image. ALE mesh is generated in the reference image. With the correspondence of the surface points, the surface of the reference image is deformed to match the surface of the target image. The displacement of the moving surface propagates to the interior nodes throughout the domain. Displacement vectors of the volume can be obtained and the deformation of the interior can be tracked. Results: Two sets of 3D CTimages of a patient's head and neck tumor were used in the study, which were taken on different dates. The external contour (volume) of the first set of images was taken as the reference and that of the second set of images was taken as the target. By moving the surface of the reference to match the surface of the target, displacement vectors of the domain enclosed by the surface were derived from the moving mesh, which were then examined with the deformation of gross target volume (GTV). The image‐warped GTV was found similar to the GTV of the second set of images.Conclusions: It is feasible to perform 3D deformable image registration using ALE moving mesh. Further studies are needed to verify the accuracy of this method by either improving the technique for surface matching or comparing with other more established method of deformable image registration.