Towards Robotic Semantic Segmentation of Supporting Surfaces

Wang, Sen; Zuo, Xinxin; Yu, Weiwei; Wang, Runxiao; Madani, Kurosh

doi:10.1109/cict.2015.89

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…For example, the number of fixed cameras depended on the area covered; obstacles or objects with the same colour as the floor were not identified. Wang et al [12] proposed the floor segmentation framework based on gravity vector estimation and developed semantic segmentation for indoor environment analysis. In this proposed system, RGB-D image sensor was used for surface segmentation.…”

Section: Literature Reviewmentioning

confidence: 99%

Floor Segmentation Approach Using FCM and CNN

Ravishankar¹,

Devaraj²,

Hanumathaiah³

2023

ATAIML

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Floor plans play an essential role in the architecture design and construction, which serves as an important communication tool between engineers, architects and clients. Automatic identification of various design elements in a floor plan image can improve work efficiency and accuracy. This paper proposed a method consists of two stages, Fuzzy C-Means (FCM) segmentation and Convolutional Neural Network (CNN) segmentation. In FCM stage, the given input image was partitioned into homogeneous regions based on similarity for merging. In CNN stage, the interactive information was introduced as markers of the object area and background area, which were input by the users to roughly indicate the position and main features of the object and background. The segmentation evaluation was measured using probabilistic rand index, variation of information, global consistency error, and boundary displacement error. Experiments were conducted on real dataset to evaluate performance of the proposed model. The experimental results revealed the proposed model was successful.

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Section: Literature Reviewmentioning

confidence: 99%

Floor Segmentation Approach Using FCM and CNN

Ravishankar¹,

Devaraj²,

Hanumathaiah³

2023

ATAIML

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“…Robotics machine learning language is used for scene understanding with robotics applications the calculated design scene are with better results in semantic segmentation in robotics Daniel Wolf associated Johann Prankl proposed an improvement in novel, fast, and compact technique to enhance semantic segmentation of three-dimensional (3-D) purpose clouds, that is in a position Sen Wang and Xinxin Zuo that major work of robotics for indoor location is geometry environment enhancement. And for that surface segmentation is done for RGB-D pictures [6] …”

Section: Related Workmentioning

confidence: 99%

Neuro-Fuzzy Network Technique for Semantic Segmentation Robotics

Kaur¹,

Singh²

2017

IJCA

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The various techniques are proposed for better semantic segmentation. The neuro-fuzzy technique is proposed for learning common nature between object and structure. The proposed technique work better for robotic environment for fast and efficient results.The proposed technique provides better accuracy as compared to previous technique and work better in semantic segmentation as compared to previous technique.

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Semantic segmentation for robotic using neuro-fuzzy network technique

Kaur

Singh

2017

2017 IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Mate

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Towards Robotic Semantic Segmentation of Supporting Surfaces

Cited by 4 publications

References 13 publications

Floor Segmentation Approach Using FCM and CNN

Floor Segmentation Approach Using FCM and CNN

Neuro-Fuzzy Network Technique for Semantic Segmentation Robotics

Semantic segmentation for robotic using neuro-fuzzy network technique

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