Seed growing for interactive image segmentation with geodesic voting

Park, Sunjeong; Lee, Han S.; Kim, Junmo

doi:10.1109/icip.2016.7532822

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…The interactive segmentation method is not a new topic in computer vision processing. The contourbased method and the bounding box method are widely used in interactive image segmentation [6][7][8]. However, a large amount of user interaction is required in these methods, and stroke operations are applied to each frame of video.…”

Section: Related Workmentioning

confidence: 99%

Deep Video Object Contour Extraction Using Fully Convolutional Network

2019

Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering

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Complex scene object contour extraction problem has become an important topic in computer vision. To solve the problem, a deep fully convolutional neural network model is established in this paper. The model tackles the task of semi-supervised video contour extract. In our model, the interactive segmentation method and Mask-RCNN algorithm are respectively applied to the first frame of a video to obtain the target semantic information and segmentation mask. Then the binary object mask is processed by the edge detection algorithm to generate a object contour mask. Next the video and the first frame contour mask are input to network of One-Shot Video Object Segmentation algorithm, and contour features and semantic information of the object are learned by the network. Finally the contour semantic information is automatically passed to subsequent frames, and the contours that particular objects in each frame of video are extracted. Experiments show that this model can detect and locate one or more objects quickly and accurately in a video sequence for various complex scene. Compared with the general edge detection operator, our algorithm does not need to extract redundant background edges and texture details and can be better applied to pose estimation and target recognition.

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Section: Related Workmentioning

confidence: 99%

Deep Video Object Contour Extraction Using Fully Convolutional Network

2019

Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering

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“…Rouchdy and Cohen proposed a geodesic voting model [10] , which is based on a fine-tree structure. This method improves the algorithm of single-source single-purpose to single-source multi-purpose, and achieves impressive performance in some segmentation problems [11][12][13][14] [15] . Some scholars defined the VOI boundary [16] to extend the two-dimensional voting formula and apply it to three-dimensional data.…”

Section: Introductionmentioning

confidence: 99%

Interactive image segmentation method based on asymmetric quadratic metric

Han,

zhou

2023

Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023)

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In this paper, we propose an interactive image segmentation method based on the geodesic voting model. Basically, the segmentation is implemented by thresholding a feature map generated by a geodesic voting strategy. In contrast to the classical geodesic voting method which considers isotropic metric, we exploit state-of-the-art asymmetric quadratic metric for geodesic computation. The main contribution of this work lies at an obstacle-based geodesic computation way, in order to encourage the computed geodesic paths to pass through image features of interest. The use of asymmetric quadratic metric for computing geodesic paths can take advantages from the anisotropy and asymmetry geometric features, thus overcoming the shortcuts problem suffered in the isotropic case. Experiments on real images demonstrate that the proposed method indeed can obtain promising results.

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“…• Stroke(s) [5][6][7][8]: the user is required to place stroke(s) on the object of interest and background on the image • Bounding box [9][10][11]: the user is required to put the bounding box on the object of interest in the image. • Seed point [12,13]: the user is required to put the seed points on the background and object of interest in the image.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Interactive Segmentation with Different Number of Strokes on Complex Images

Goh¹,

Ng²,

Hamzah³

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Interactive image segmentation is the way to extract an object of interest with the guidance of the user. The guidance from the user is an iterative process until the required object of interest had been segmented. Therefore, the input from the user as well as the understanding of the algorithms based on the user input has an essential role in the success of interactive segmentation. The most common user input type in interactive segmentation is using strokes. The different number of strokes are utilized in each different interactive segmentation algorithms. There was no evaluation of the effects on the number of strokes on this interactive segmentation. Therefore, this paper intends to fill this shortcoming. In this study, the input strokes had been categorized into single, double, and multiple strokes. The use of the same number of strokes on the object of interest and background on three interactive segmentation algorithms: i) Nonparametric Higher-order Learning (NHL), ii) Maximal Similarity-based Region Merging (MSRM) and iii) Graph-Based Manifold Ranking (GBMR) are evaluated, focusing on the complex images from Berkeley image dataset. This dataset contains a total of 12,000 test color images and ground truth images. Two types of complex images had been selected for the experiment: image with a background color like the object of interest, and image with the object of interest overlapped with other similar objects. This can be concluded that, generally, more strokes used as input could improve image segmentation accuracy.

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Seed growing for interactive image segmentation with geodesic voting

Cited by 6 publications

References 10 publications

Deep Video Object Contour Extraction Using Fully Convolutional Network

Deep Video Object Contour Extraction Using Fully Convolutional Network

Interactive image segmentation method based on asymmetric quadratic metric

A Comparative Study of Interactive Segmentation with Different Number of Strokes on Complex Images

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