Traditional approaches to line segment detection typically involve perceptual grouping in the image domain and/or global accumulation in the Hough domain. Here we propose a probabilistic algorithm that merges the advantages of both approaches. In a first stage lines are detected using a global probabilistic Hough approach. In the second stage each detected line is analyzed in the image domain to localize the line segments that generated the peak in the Hough map. By limiting search to a line, the distribution of segments over the sequence of points on the line can be modeled as a Markov chain, and a probabilistically optimal labelling can be computed exactly using a standard dynamic programming algorithm, in linear time. The Markov assumption also leads to an intuitive ranking method that uses the local marginal posterior probabilities to estimate the expected number of correctly labelled points on a segment. To assess the resulting Markov Chain Marginal Line Segment Detector (MCMLSD) we develop and apply a novel quantitative evaluation methodology that controls for under- and over-segmentation. Evaluation on the YorkUrbanDB and Wireframe datasets shows that the proposed MCMLSD method outperforms prior traditional approaches, as well as more recent deep learning methods.

Prior approaches to line segment detection typically involve perceptual grouping in the image domain or global accumulation in the Hough domain. Here we propose a probabilistic algorithm that merges the advantages of both approaches. In a first stage lines are detected using a global probabilistic Hough approach. In the second stage each detected line is analyzed in the image domain to localize the line segments that generated the peak in the Hough map. By limiting search to a line, the distribution of segments over the sequence of points on the line can be modeled as a Markov chain, and a probabilistically optimal labelling can be computed exactly using a standard dynamic programming algorithm, in linear time. The Markov assumption also leads to an intuitive ranking method that uses the local marginal posterior probabilities to estimate the expected number of correctly labelled points on a segment. To assess the resulting Markov Chain Marginal Line Segment Detector (MCMLSD) we develop and apply a novel quantitative evaluation methodology that controls for under-and over-segmentation. Evaluation on the YorkUrbanDB dataset shows that the proposed MCMLSD method outperforms the state-of-the-art by a substantial margin.

We address the problem of estimating the rotation of a camera relative to the canonical frame of an urban scene, from a single image. Solutions generally rely on the so-called ‘Manhattan World’ assumption [1] that the major structures in the scene conform to three orthogonal principal directions. This can be expressed as a generative model in which the dense gradient map of the image is explained by a mixture of the three principal directions and a background process [2]. It has recently been shown that using sparse oriented edges rather than the dense gradient map leads to substantial gains in both accuracy and speed [3]. Here we explore whether further gains can be made by basing inference on even sparser extended lines. Standard Houghing techniques suffer from quantization errors and noise that make line extraction unreliable. Here we introduce a probabilistic line extraction technique that eliminates these problems through two innovations. First, we accurately propagate edge uncertainty from the image to the Hough map through a bivariate normal kernel that uses natural image statistics, resulting in a non-stationary ‘soft-voting’ technique. Second, we eliminate multiple responses to the same line by updating the Hough map dynamically as each line is extracted. We evaluate the method on a standard benchmark dataset [3], showing that the resulting line representation supports reliable estimation of the Manhattan frame, bettering the accuracy of previous edge-based methods by a factor of 2 and the gradient-based Manhattan World method by a factor of 5.

In this work the goal is to develop a distributed system for sensing, interpreting and visualizing the real-time dynamics of urban life within the 3D context of a city focusing on typical, useful dynamic information such as walking pedestrians and moving vehicles captured by pan-tilt-zoom (PTZ) video cameras. Three-dimensionalization of the data extracted from video cameras is achieved by an algorithm that uses the Manhattan structure of the urban scene to automatically estimate the camera pose. Thus, if the pose of the video camera changes, our system will automatically update the corresponding projection matrix to maintain accurate geo-location of the scene dynamics.

In this report we introduce a novel approach for determining correspondence in a sequence of images. We formulate a probabilistic framework that relates a feature's appearance and its position under relaxed statistical assumptions. We employ a Monte-Carlo approximation for the joint probability density of the feature position and its appearance that uses a flexible noise and motion model to generate random samples. The joint probability density is modeled by a Gaussian Mixture. The feature's position given its appearance is then determined by maximizing its posterior. We evaluate our method using real and synthetic sequences and compare its performance with leading or popular algorithms from the literature. The noise robustness of our algorithm is superior under a wide variety of conditions. The method can be applied in the context of optical flow, tracking and any application that needs feature point matching.