Grab Cut Image Segmentation Based on Image Region

Li, Yubing; Zhang, Jinbo; Gao, Peng; Jiang, Liangcheng; Chen, Ming

doi:10.1109/icivc.2018.8492818

Cited by 45 publications

(21 citation statements)

References 4 publications

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“…GrabCut [28] is an image segmentation method, based on iterative graph cuts as in [29], [30], [31], [32]. The algorithm estimates the colour distribution of the target object and that of the background using a Gaussian mixture model.…”

Section: Hazmat Sign Segmentationmentioning

confidence: 99%

DeepHAZMAT: Hazardous Materials Sign Detection and Segmentation with Restricted Computational Resources

Sharifi¹,

Zibaei²,

Rezaei³

2020

Preprint

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One of the most challenging and non-trivial tasks in robotics-based rescue operations is Hazardous Materials or HAZMATs sign detection within the operation field, in order to prevent other unexpected disasters. Each Hazmat sign has a specific meaning that the rescue robot should detect and interpret it to take a safe action, accordingly. Accurate Hazmat detection and real-time processing are the two most important factors in such robotics applications. Furthermore, we also have to cope with some secondary challengers such as image distortion problems and restricted CPU and computational resources which are embedded in a rescue robot. In this paper, we propose a CNN-Based pipeline called DeepHAZMAT for detecting and segmenting Hazmats in four steps; 1) optimising the number of input images that are fed into the CNN network, 2) using the YOLOv3-tiny structure to collect the required visual information from the hazardous areas, 3) Hazmat sign segmentation and separation from the background using GrabCut technique, and 4) post-processing the result with morphological operators and convex hall algorithm. In spite of the utilisation of a very limited memory and CPU resources, the experimental results show the proposed method has successfully maintained a better performance in terms of detection-speed and detection-accuracy, compared with the state-of-the-art methods.

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Section: Hazmat Sign Segmentationmentioning

confidence: 99%

DeepHAZMAT: Hazardous Materials Sign Detection and Segmentation with Restricted Computational Resources

Sharifi¹,

Zibaei²,

Rezaei³

2020

Preprint

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show abstract

“…A meaningful part of the image is obtained using image segmentation process. The most problem that frequently occur is the background is similar to the object [17]. Currently some images also have depth data, with this data we can make segmentation more easily as in some existing research [18] [19].…”

Section: Related Workmentioning

confidence: 99%

DBSCAN for Hand Tracking and Gesture Recognition

Aditya

Tolle

Shih

2020

JITeCS

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Hand segmentation and tracking are important issues for hand-gesture recognition. Using depth data, it can speed up the segmentation process because we can delete unnecessary data like the background of the image easily. In this research, we modify DBSCAN clustering algorithm to make it faster and suitable for our system. This method is used in both hand tracking and hand gesture recognition. The results show that our method performs well in this system. The proposed method can outperform the original DBSCAN and the other clustering method in terms of computational time.

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“…In this paper they are using SURF algorithm for feature detection of the foreground object and eventually segmenting them effectively. Paper [17] talks about Grab Cut Algorithm which is one of the familiar approaches for foreground extraction in the domain of Image processing. In paper [1] FCFN mechanism is used for background subtraction.…”

Section: Introductionmentioning

confidence: 99%

Foreground algorithms for detection and extraction of an object in multimedia

Rekha¹,

Natarajan²,

Rose³

2020

IJECE

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Background Subtraction of a foreground object in multimedia is one of the major preprocessing steps involved in many vision-based applications. The main logic for detecting moving objects from the video is difference of the current frame and a reference frame which is called “background image” and this method is known as frame differencing method. Background Subtraction is widely used for real-time motion gesture recognition to be used in gesture enabled items like vehicles or automated gadgets. It is also used in content-based video coding, traffic monitoring, object tracking, digital forensics and human-computer interaction. Now-a-days due to advent in technology it is noticed that most of the conferences, meetings and interviews are done on video calls. It’s quite obvious that a conference room like atmosphere is not always readily available at any point of time. To eradicate this issue, an efficient algorithm for foreground extraction in a multimedia on video calls is very much needed. This paper is not to just build Background Subtraction application for Mobile Platform but to optimize the existing OpenCV algorithm to work on limited resources on mobile platform without reducing the performance. In this paper, comparison of various foreground detection, extraction and feature detection algorithms are done on mobile platform using OpenCV. The set of experiments were conducted to appraise the efficiency of each algorithm over the other. The overall performances of these algorithms were compared on the basis of execution time, resolution and resources required.

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Grab Cut Image Segmentation Based on Image Region

Cited by 45 publications

References 4 publications

DeepHAZMAT: Hazardous Materials Sign Detection and Segmentation with Restricted Computational Resources

DeepHAZMAT: Hazardous Materials Sign Detection and Segmentation with Restricted Computational Resources

DBSCAN for Hand Tracking and Gesture Recognition

Foreground algorithms for detection and extraction of an object in multimedia

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