Aptive Image Segmentation based on Saliency Detection

Shui, Linlin

doi:10.21307/ijssis-2017-765

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Selecting the appropriate color space and method according to different occasions can get the segmentation results which are in line with people's subjective visual perception. present color spaces can be converted to RGB color space [11]. In RGB color space, the three components R, G, B should be processed respectively in the image segmentation.…”

Section: B Color Image Segmentationmentioning

confidence: 99%

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Image Segmentation Algorithm Based On Color Features: Case Study With Giant Panda

Wang

Xiao

Zhang

2016

International Journal on Smart Sensing and Intelligent Systems

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Color which is one of the basic features of the image is widely used in image processing. The choice of color space is a primary issue for the color image segmentation based on color features. In this paper, giant pandas are chosen as the research objects. In order to achieve good segmentation results, different color spaces and the corresponding algorithms are chosen for image segmentation according to the color characteristics of different background of panda images. There are three kinds of color spaces introduced in detail and its advantages and disadvantages for the giant panda image segmentation are also summarized in this paper.

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Section: B Color Image Segmentationmentioning

confidence: 99%

“…RGB color model is also called computer graph color model, it converted the colors of images into information which can be expressed by computers. It is a widely used color space and all present color spaces can be converted to RGB color space[11]. In RGB color space, the three components R, G, B should be processed respectively in the image segmentation.…”

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confidence: 99%

Image Segmentation Algorithm Based On Color Features: Case Study With Giant Panda

Wang

Xiao

Zhang

2016

International Journal on Smart Sensing and Intelligent Systems

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Surface Following With An Rgb-D Vision-Guided Robotic System For Automated And Rapid Vehicle Inspection

Nakhaeinia

Payeur

Chávez-Aragón

et al. 2016

International Journal on Smart Sensing and Intelligent Systems

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This paper presents the design and integration of a vision-guided robotic system for automated and rapid vehicle inspection. The main goal of this work is to scan and explore regions of interest over an automotive vehicle while a manipulator's end effector operates in close proximity of the vehicle and safely accommodates its curves and inherent surface obstacles, such as outside mirrors or door handles, in order to perform a series of close inspection tasks. The project is motivated by applications in automated vehicle inspection, cleaning, and security screening. In order to efficiently navigate the robotic manipulator along the vehicle's surface within regions of interest that are selectively identified, an efficient and accurate integration of information from multiple RGB-D sensors and robotic components is proposed. The main components of the proposed approach include: automated vehicle category recognition from visual information; RGB-D sensors calibration; extraction of specific areas to inspect over the vehicle body, and path planning from an efficiently

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Road Damage Identification and Degree Assessment Based on UGV

Song

Gao

Liu

et al. 2016

International Journal on Smart Sensing and Intelligent Systems

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Abstract-Aiming at the problem of automatic identification and evaluation of road damage degree, the road damage identification and degree assessment algorithms based on unmanned vehicles experimental platform are studied. The road crack segmentation extraction method based on adaptive sliding window is studied. On this basis, the road damage crack classifies and identifies according to the crack geometry information and the principle of template matching. The road damage degree assessment algorithm based on fuzzy decision is proposed based on the quantitative analysis of the road crack and the corresponding parameters information. The experimental results demonstrate that the road damage identification and degree assessment algorithms proposed in this paper are effective and stable.

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Aptive Image Segmentation based on Saliency Detection

Cited by 3 publications

References 19 publications

Image Segmentation Algorithm Based On Color Features: Case Study With Giant Panda

Image Segmentation Algorithm Based On Color Features: Case Study With Giant Panda

Surface Following With An Rgb-D Vision-Guided Robotic System For Automated And Rapid Vehicle Inspection

Road Damage Identification and Degree Assessment Based on UGV

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