Vanishing point detection using random forest and patch‐wise weighted soft voting

Xue, Fan; Riaz, Irfan; Rehman, Yawar; Shin, Hyunchul

doi:10.1049/iet-ipr.2016.0068

Cited by 7 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further reading Fan, X. and Shin, H. (2016), "Road vanishing point detection using weber adaptive local filter and salient-block-wise weighted soft voting", IET Computer Vision, Vol. 10 No.…”

Section: Corresponding Authormentioning

confidence: 99%

“…Moreover, the complex background that contains most of the interferences also severely affect the accuracy of vanishing point detection. Intrinsically, current methods for vanishing point detection can be generalized into two main categories (Fan et al, 2016): texture-based methods and edge-based methods. In the texture-based methods, texture orientation of each pixel is first calculated by the Gabor filters (Kong et al, 2009), the Laplacian of Gaussian filters (Kong et al, 2013) or the Weber local descriptors (Yang et al, 2016;Fan et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Intrinsically, current methods for vanishing point detection can be generalized into two main categories (Fan et al, 2016): texture-based methods and edge-based methods. In the texture-based methods, texture orientation of each pixel is first calculated by the Gabor filters (Kong et al, 2009), the Laplacian of Gaussian filters (Kong et al, 2013) or the Weber local descriptors (Yang et al, 2016;Fan et al, 2016). After that, the pixels that are considered as informative voters are selected to vote for the vanishing point location by the voting strategy.…”

Section: Introductionmentioning

confidence: 99%

“…Although the gLoG-based method is considered as the stateof-the-art method, it usually leads to high computational complexity, as both of the road trails and background noise can be regarded as voters or candidates. To overcome this problem, Yang et al (2016) and Fan et al (2016) apply the Weber local descriptors to distinguish the road clues from the background noise. However, these methods may fail when the background region contains strong texture noise.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust and efficient vanishing point detection in unstructured road scenes for assistive navigation

Han

Yang

et al. 2019

View full text Add to dashboard Cite

Purpose This paper aims to propose a robust and efficient method for vanishing point detection in unstructured road scenes. Design/methodology/approach The proposed method includes two main stages: drivable region estimation and vanishing point detection. In drivable region estimation stage, the road image is segmented into a set of patches; then the drivable region is estimated by the patch-wise manifold ranking. In vanishing point detection stage, the LSD method is used to extract the straight lines; then a series of principles are proposed to remove the noise lines. Finally, the vanishing point is detected by a novel voting strategy. Findings The proposed method is validated on various unstructured road images collected from the real world. It is more robust and more efficient than the state-of-the-art method and the other three recent methods. Experimental results demonstrate that the detected vanishing point is practical for vision-sensor-based navigation in complex unstructured road scenes. Originality/value This paper proposes a patch-wise manifold ranking method to estimate the drivable region that contains most of the informative clues for vanishing point detection. Based on the removal of the noise lines through a series of principles, a novel voting strategy is proposed to detect the vanishing point.

show abstract

Section: Corresponding Authormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust and efficient vanishing point detection in unstructured road scenes for assistive navigation

Han

Yang

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In view of the characteristics of hyperspectral remote sensing images, the classification of random forest algorithms may be a good choice. The random forest algorithm is a supervised self-training classifier [27][28][29][30] that consists of many classification trees, each of which completes its own sorting operation. The final classification results are determined by the voting results of each classification tree [31].…”

Section: Introductionmentioning

confidence: 99%

A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest

Sun

Wang

et al. 2018

Advances in Multimedia

View full text Add to dashboard Cite

The purposes of the algorithm presented in this paper are to select features with the highest average separability by using the random forest method to distinguish categories that are easy to distinguish and to select the most divisible features from the most difficult categories using the weighted entropy algorithm. The framework is composed of five parts: (1) random samples selection with (2) probabilistic output initial random forest classification processing based on the number of votes; (3) semisupervised classification, which is an improvement of the supervision classification of random forest based on the weighted entropy algorithm; (4) precision evaluation; and (5) a comparison with the traditional minimum distance classification and the support vector machine (SVM) classification. In order to verify the universality of the proposed algorithm, two different data sources are tested, which are AVIRIS and Hyperion data. The results show that the overall classification accuracy of AVIRIS data is up to 87.36%, the kappa coefficient is up to 0.8591, and the classification time is 22.72s. Hyperion data is up to 99.17%, the kappa coefficient is up to 0.9904, and the classification time is 8.16s. Classification accuracy is obviously improved and efficiency is greatly improved, compared with the minimum distance and the SVM classifier and the CART classifier.

show abstract

Accurate and Robust Vanishing Point Detection Method in Unstructured Road Scenes

Han

Yang

et al. 2018

J Intell Robot Syst

View full text Add to dashboard Cite

Vanishing point detection using random forest and patch‐wise weighted soft voting

Cited by 7 publications

References 17 publications

Robust and efficient vanishing point detection in unstructured road scenes for assistive navigation

Robust and efficient vanishing point detection in unstructured road scenes for assistive navigation

A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest

Accurate and Robust Vanishing Point Detection Method in Unstructured Road Scenes

Contact Info

Product

Resources

About