Modified two-dimensional Otsu image segmentation algorithm and fast realisation

Chen, Q.; Zhao, Lingjun; Lu, Junchi; Kuang, Gangyao; Wang, N.; Jiang, Yuwei

doi:10.1049/iet-ipr.2010.0078

Cited by 118 publications

(61 citation statements)

References 10 publications

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“…Therefore, the precise localization of IR objects is particularly important. Current methods of IR objects extraction are based on gray theory, 20 OTSU, 23 Renyi entropy, 24 and so on. These methods can extract small objects, but always include information from the background and cannot extract larger objects completely.…”

Section: Location Of the Objects Regionmentioning

confidence: 99%

Multi-sensor image fusion based on regional characteristics

Meng

Shi

Shan

et al. 2017

International Journal of Distributed Sensor Networks

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Multi-sensor data fusion method has been widely investigated in recent years. This article presents a novel fusion algorithm based on regional characteristics for combining infrared and visible light images in order to achieve an image with clear objects and high-resolution scene. First, infrared objects are extracted by region growing and guided filter. Second, the whole scene is divided into the objects region, the smooth region, and the texture region according to different regional characteristics. Third, the non-subsampled contourlet transform is used on infrared and visible images. Then, different fusion rules are applied to different regions, respectively. Finally, the fused image is constructed by the inverse non-subsampled contourlet transform with all coefficients. Experimental results demonstrate that the proposed objects extraction algorithm and the fusion algorithm have good performance in objective and subjective assessments.

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Section: Location Of the Objects Regionmentioning

confidence: 99%

Multi-sensor image fusion based on regional characteristics

Meng

Shi

Shan

et al. 2017

International Journal of Distributed Sensor Networks

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show abstract

“…In computer vision and image processing, Otsu's method is used to automatically perform histogram shape-based image segmentation or the reduction of a gray image to a binary image. In addition, it is proved one of the best methods for threshold selection [19]. Therefore, in this paper, we segment the vehicle image with L component in Lab color space based on this method.…”

Section: Hypothesis Generationmentioning

confidence: 99%

Improved Genetic Algorithm Optimization for Forward Vehicle Detection Problems

et al. 2015

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Automated forward vehicle detection is an integral component of many advanced driver-assistance systems. The method based on multi-visual information fusion, with its exclusive advantages, has become one of the important topics in this research field. During the whole detection process, there are two key points that should to be resolved. One is to find the robust features for identification and the other is to apply an efficient algorithm for training the model designed with multi-information. This paper presents an adaptive SVM (Support Vector Machine) model to detect vehicle with range estimation using an on-board camera. Due to the extrinsic factors such as shadows and illumination, we pay more attention to enhancing the system with several robust features extracted from a real driving environment. Then, with the introduction of an improved genetic algorithm, the features are fused efficiently by the proposed SVM model. In order to apply the model in the forward collision warning system, longitudinal distance information is provided simultaneously. The proposed method is successfully implemented on a test car and evaluation experimental results show reliability in terms of both the detection rate and potential effectiveness in a real-driving environment.

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“…So we was defined the probability of target and background. Make the target section satisfied [6][7]. Set up（i,j）as image coordinate points, and referred to the reference point.…”

Section: Article Algorithmmentioning

confidence: 99%

Target Segmentation Algorithm Based on the Discrete Energy Functional

Wan¹

2015

Advances in Social Science, Education and Humanities Research

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Abstract. Existing segmentation algorithm has the segmentation problem that is vulnerable to background noise and low accuracy. In order to address this problem, the discrete Technology is used in this research. And the target segmentation algorithm is based on discrete energy functional. It is effectively reduce the complexity and accuracy, which using discrete processing of the regional items and contour curve. Experimental results indicate that article algorithm can be maintained a good target extraction in the case of the target and the background which gradually increasing similarity. And the preponderance of article algorithm can be found through correctness and time-consuming experiment.

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Modified two-dimensional Otsu image segmentation algorithm and fast realisation

Cited by 118 publications

References 10 publications

Multi-sensor image fusion based on regional characteristics

Multi-sensor image fusion based on regional characteristics

Improved Genetic Algorithm Optimization for Forward Vehicle Detection Problems

Target Segmentation Algorithm Based on the Discrete Energy Functional

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