Extracting Straight Lines

Burns, J. Brian; Hanson, Allen R.; Riseman, Edward M.

doi:10.1109/tpami.1986.4767808

Cited by 621 publications

(175 citation statements)

References 12 publications

Supporting

Mentioning

167

Contrasting

Unclassified

Order By: Relevance

“…We use the phase-based line extraction frame as the basic frame for the extraction of LSR [14]. The frame groups the connected edge points that have similar edge orientation.…”

Section: Lsr Extractionmentioning

confidence: 99%

“…In this case the "equivalent" means same in quantified edge orientation. What is need to consider is that in this case the qualification of the orientation may cause serious break of the LSRs, and this can be solved with the overlapping method [14]. In order to filter out the scrappy structures, an individual LSR must cover enough pixels; otherwise it will be sign as background.…”

Section: Lsr Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Optical-to-Sar Image Registration Based on Gaussian Mixture Model

Wang

Chen

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Image registration is a fundamental in remote sensing applications such as inter-calibration and image fusion. Compared to other multi sensor image registration problems such as optical-to-IR, the registration for SAR and optical images has its specials. Firstly, the radiometric and geometric characteristics are different between SAR and optical images. Secondly, the feature extraction methods are heavily suffered with the speckle in SAR images. Thirdly, the structural information is more useful than the point features such as corners. In this work, we proposed a novel Gaussian Mixture Model (GMM) based Optical-to-SAR image registration algorithm. The feature of line support region (LSR) is used to describe the structural information and the orientation attributes are added into the GMM to avoid Expectation Maximization (EM) algorithm falling into local extremum in feature sets matching phase. Through the experiments it proves that our algorithm is very robust for optical-to-SAR image registration problem. * Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

show abstract

“…We use the phase-based line extraction frame as the basic frame for the extraction of LSR [14]. The frame groups the connected edge points that have similar edge orientation.…”

Section: Lsr Extractionmentioning

confidence: 99%

Section: Lsr Extractionmentioning

confidence: 99%

Optical-to-Sar Image Registration Based on Gaussian Mixture Model

Wang

Chen

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…this paper uses pattern recognition algorithms that has been addressed in its literature. Blob detection [27], line-segment extraction [28] and corner detection [29] are used to classify the features (See Table 2.2). A dead-end (shown in Fig 7) has only one blob that is a unique identification of this feature among all the other ones.…”

Section: Environmental Featuresmentioning

confidence: 99%

Multi-robot olfactory search in structured environments

Marjovi

Marques

2011

Robotics and Autonomous Systems

View full text Add to dashboard Cite

This paper presents a cooperative distributed approach for searching odor sources in unknown structured environments with multiple mobile robots . While searching and exploring the environment, the robots independently generate on-line local topological maps and by sharing them with each other they construct a global map. The proposed method is a decentralized frontier based algorithm enhanced by a cost/utility evaluation function that considers the odor concentration and airflow at each frontier. Therefore, frontiers with higher probability of containing an odor source will be searched and explored first. The method also improves path planning of the robots for exploration process by presenting a priority policy. Since there is no global positioning system and each robot has its own coordinate reference system for its localization, this paper uses topological graph matching techniques for map merging. The proposed method was tested in both simulation and real world environments with different number of robots and different scenarios. The search time, exploration time, complexity of the environment and number of double-visited map nodes were investigated in the tests. The experimental results validate the functionality of the method in different configurations.

show abstract

“…Schindler et al [27] extract salient linear features while assuming dominant orientations in the scene, similar to using a Manhattan World Assumption. Our algorithm, however, has to be able to identify linear features of all orientations, and thus is based on the more general framework of Burns et al [4], which we modify and implement in complete parallel.…”

Section: Extraction Of Salient Linear Featuresmentioning

confidence: 99%

Aerial 3D reconstruction with line-constrained dynamic programming

Liao

Lin

Medioni

2011

2011 International Conference on Computer Vision

View full text Add to dashboard Cite

Aerial imagery of an urban environment is often characterized by significant occlusions, sharp edges, and textureless regions, leading to poor 3D reconstruction using conventional multi-view stereo methods. In this paper, we propose a novel approach to 3D reconstruction of urban areas from a set of uncalibrated aerial images. A very general structural prior is assumed that urban scenes consist mostly of planar surfaces oriented either in a horizontal or an arbitrary vertical orientation. In addition, most structural edges associated with such surfaces are also horizontal or vertical. These two assumptions provide powerful constraints on the underlying 3D geometry. The main contribution of this paper is to translate the two constraints on 3D structure into intra-image-column and inter-imagecolumn constraints, respectively, and to formulate the dense reconstruction as a 2-pass Dynamic Programming problem, which is solved in complete parallel on a GPU. The result is an accurate cloud of 3D dense points of the underlying urban scene. Our algorithm completes the reconstruction of 1M points with 160 available discrete height levels in under a hundred seconds. Results on multiple datasets show that we are capable of preserving a high level of structural detail and visual quality.

show abstract

Extracting Straight Lines

Cited by 621 publications

References 12 publications

Optical-to-Sar Image Registration Based on Gaussian Mixture Model

Optical-to-Sar Image Registration Based on Gaussian Mixture Model

Multi-robot olfactory search in structured environments

Aerial 3D reconstruction with line-constrained dynamic programming

Contact Info

Product

Resources

About