Object Detection Using Straight Line Matching in θ-ρ Space

The contours of many industrial parts contain straight lines and positions of the lines are therefore useful information for object detection. This paper presents a matching technique of straight lines in θ-ρ space. Any lines in 2D space are represented with parameters θ and ρ by Hough transform. To find an object is to find combination of line parameters in θ-ρ space. Our matching method contains edge detection, line detection and matching processes. At first, we perform edge detection for model and scene image to detect contour of objects. Next, we extract the straight lines using Hough transform for provided edge image. Then in the matching process, we perform matching with parameters of straight lines. Matching process contains θ matching, ρ matching and pose estimation. In the θ matching, we use the relative θ values of corresponding lines. In the ρ matching, we compute parameters of transfer and deviations of ρ values. Finally, in the pose estimation we compute transfer parameters using corresponding intersection points of the straight lines. The experimental results show our method is robust for rotation, occlusion and scaling of objects

DISPARITY REFINEMENT OF BUILDING EDGES USING ROBUSTLY MATCHED STRAIGHT LINES FOR STEREO MATCHING

Stereo dense matching has already been one of the dominant tools in 3D reconstruction of urban regions, due to its low cost and high flexibility in generating 3D points. However, the image-derived 3D points are often inaccurate around building edges, which limit its use in several vision tasks (e.g. building modelling). To generate 3D point clouds or digital surface models (DSM) with sharp boundaries, this paper integrates robustly matched lines for improving dense matching, and proposes a non-local disparity refinement of building edges through an iterative least squares plane adjustment approach. In our method, we first extract and match straight lines in images using epipolar constraints, then detect building edges from these straight lines by comparing matching results on both sides of straight lines, and finally we develop a non-local disparity refinement method through an iterative least squares plane adjustment constrained by matched straight lines to yield sharper and more accurate edges. Experiments conducted on both satellite and aerial data demonstrate that our proposed method is able to generate more accurate DSM with sharper object boundaries

A practical technique for gait recognition on curved and straight trajectories

Many studies show the effectiveness of gait in surveillance and access control scenarios. However, appearance changes due to walking direction changes impose a challenge for gait recognition techniques that assume people only walk in a straight line. In this paper, the effect of walking along straight and curved path is studied by proposing a practical technique which is based on the three key frames in the start, middle and end of the gait cycle. The position of these frames is estimated in 3D space which is then used to estimate the local walking direction in the first and second part of the cycle. The technique used 3D volume sequences of the people to adapt to changes in the walking direction. The performance is evaluated using a newly collected dataset and the Kyushu University 4D Gait Dataset, containing people walking in straight lines and curves. With the proposed technique, we obtain a correct classification rate of 98% for matching straight with straight walking and 81% for matching straight with curved walking averaged over both datasets. The variation in walking patterns when a person walks along a straight or curved path is most likely to be responsible for the difference. In support of this, the recognition rate when matching curved with curved walking is 99% on our dataset

Linear Features in Photogrammetry

This research addresses the task of including points as well as linear features in photogrammetric applications. Straight lines in object space can be utilized to perform aerial triangulation. Irregular linear features (natural lines) in object space can be utilized to perform single photo resection and automatic relative orientation. When working with primitives, it is important to develop appropriate representations in image and object space. These representations must accommodate for the perspective projection relating the two spaces. There are various options for representing linear features in the above applications. These options have been explored, and an optimal representation has been chosen. An aerial triangulation technique that utilizes points and straight lines for frame and linear array scanners has been implemented. For this task, the MSAT (Multi Sensor Aerial Triangulation) software, developed at the Ohio State University, has been extended to handle straight lines. The MSAT software accommodates for frame and linear array scanners. In this research, natural lines were utilized to perform single photo resection and automatic relative orientation. In single photo resection, the problem is approached with no knowledge of the correspondence of natural lines between image space and object space. In automatic relative orientation, the problem is approached without knowledge of conjugate linear features in the overlap of the stereopair. The matching problem and the appropriate parameters are determined by use of the modified generalized Hough transform. These techniques were tested using simulated and real data sets for frame imagery

Parallel algorithm for determining motion vectors in ice floe images by matching edge features

A parallel algorithm is described to determine motion vectors of ice floes using time sequences of images of the Arctic ocean obtained from the Synthetic Aperture Radar (SAR) instrument flown on-board the SEASAT spacecraft. Researchers describe a parallel algorithm which is implemented on the MPP for locating corresponding objects based on their translationally and rotationally invariant features. The algorithm first approximates the edges in the images by polygons or sets of connected straight-line segments. Each such edge structure is then reduced to a seed point. Associated with each seed point are the descriptions (lengths, orientations and sequence numbers) of the lines constituting the corresponding edge structure. A parallel matching algorithm is used to match packed arrays of such descriptions to identify corresponding seed points in the two images. The matching algorithm is designed such that fragmentation and merging of ice floes are taken into account by accepting partial matches. The technique has been demonstrated to work on synthetic test patterns and real image pairs from SEASAT in times ranging from .5 to 0.7 seconds for 128 x 128 images

From on-line sketching to 2D and 3D geometry: A fuzzy knowledge based system

The paper describes the development of a fuzzy knowledge based prototype system for conceptual design. This real time system is designed to infer user’s sketching intentions, to segment sketched input and generate corresponding geometric primitives: straight lines, circles, arcs, ellipses, elliptical arcs, and B-spline curves. Topology information (connectivity, unitary constraints and pairwise constraints) is received dynamically from 2D sketched input and primitives. From the 2D topology information, a more accurate 2D geometry can be built up by applying a 2D geometric constraint solver. Subsequently, 3D geometry can be received feature by feature incrementally. Each feature can be recognised by inference knowledge in terms of matching its 2D primitive configurations and connection relationships. The system accepts not only sketched input, working as an automatic design tools, but also accepts user’s interactive input of both 2D primitives and special positional 3D primitives. This makes it easy and friendly to use. The system has been tested with a number of sketched inputs of 2D and 3D geometry

Robust extended Kalman filtering for camera pose tracking using 2D to 3D lines correspondences

International audienceIn this paper we present a new robust camera pose estimation approach based on 3D lines tracking. We used an Extended Kalman Filter (EKF) to incrementally update the camera pose in real-time. The principal contributions of our method includes first, the expansion of the RANSAC scheme in order to achieve a robust matching algorithm that associates 2D edges from the image with the 3D line segments from the input model. And second, a new framework for camera pose estimation using 2D-3D straight-lines within an EKF. Experimental results on real image sequences are presented to evaluate the performances and the feasibility of the proposed approach

The largest crossing number of tanglegrams

A tanglegram $\cal T$ consists of two rooted binary trees with the same number of leaves, and a perfect matching between the two leaf sets. In a layout, the tanglegrams is drawn with the leaves on two parallel lines, the trees on either side of the strip created by these lines are drawn as plane trees, and the perfect matching is drawn in straight line segments inside the strip. The tanglegram crossing number ${\rm cr}({\cal T})$ of $\cal T$ is the smallest number of crossings of pairs of matching edges, over all possible layouts of $\cal T$. The size of the tanglegram is the number of matching edges, say $n$. An earlier paper showed that the maximum of the tanglegram crossing number of size $n$ tanglegrams is $<\frac{1}{2}\binom{n}{2}$; but is at least $\frac{1}{2}\binom{n}{2}-\frac{n^{3/2}-n}{2}$ for infinitely many $n$. Now we make better bounds: the maximum crossing number of a size $n$ tanglegram is at most $\frac{1}{2}\binom{n}{2}-\frac{n}{4}$, but for infinitely many $n$, at least $\frac{1}{2}\binom{n}{2}-\frac{n\log_2 n}{4}$. The problem shows analogy with the Unbalancing Lights Problem of Gale and Berlekamp

Matching Points with Things

Given an ordered set of points and an ordered set of geometric objects in the plane, we are interested in finding a non-crossing matching between point-object pairs. We show that when the objects we match the points to are finite point sets, the problem is NP-complete in general, and polynomial when the objects are on a line or when their number is at most 2. When the objects are line segments, we show that the problem is NP-complete in general, and polynomial when the segments form a convex polygon or are all on a line. Finally, for objects that are straight lines, we show that the problem of finding a min-max non-crossing matching is NP-complete

Insulin/IGF and Sex Hormone Axes in Human Endometrium and Associations with Endometrial Cancer Risk Factors

Given an ordered set of points and an ordered set of geometric objects in the plane, we are interested in finding a non-crossing matching between point-object pairs. In this paper, we address the algorithmic problem of determining whether a non-crossing matching exists between a given point-object pair. We show that when the objects we match the points to are finite point sets, the problem is NP-complete in general, and polynomial when the objects are on a line or when their size is at most 2. When the objects are line segments, we show that the problem is NP-complete in general, and polynomial when the segments form a convex polygon or are all on a line. Finally, for objects that are straight lines, we show that the problem of finding a min-max non-crossing matching is NP-complete. © 2012 Elsevier B.V.SCOPUS: ar.jinfo:eu-repo/semantics/publishe