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检索条件"主题词=Interactive Image segmentation"

共 189 条记录，以下是171-180 订阅

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interactive Object Class segmentation for Mobile Devices 27

Interactive Object Class Segmentation for Mobile Devices

引用

27th SIBGRAPI Conference on Graphics, Patterns and images (SIBGRAPI)

作者： Gallo, Ignazio Zamberletti, Alessandro Noce, Lucia Univ Insubria Dept Theoret & Appl Sci DiSTA Varese Italy

ISBN: (纸本)9781479942602

In this paper we propose an interactive approach for object class segmentation of natural images on touch-screen capable mobile devices. The key research question to which this paper tries to give an answer is: can we effectively correct the errors committed by an automatic or semi-automatic figure-ground segmentation algorithm while also providing real time feedback to the user on a low computational power mobile device? Many research works focused on improving automatic or semi-automatic figure-ground segmentation algorithms, but none tried to take advantage of the existing touch-screen technology integrated in most modern mobile devices to optimize the segmentation results of these algorithms. Our key idea is to use superpixels as interactive buttons that can be quickly tapped by the user to be added or removed from an initial low quality segmentation mask, with the aim of correcting the segmentation errors and produce a satisfying final result. We performed an extensive analysis of the proposed approach by implementing it both on a desktop computer and a mid-range Android device;even though our method is extremely simple, the results we obtained are comparable with those achieved by other state-of-the-art interactive segmentation algorithms. As such, we believe that the proposed approach can be exploited by most image editing mobile applications to provide a simple but highly effective method for interactive object class segmentation.

关键词： interactive image segmentation Object Class segmentation GrabCut segmentation Superpixel segmentation

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CONTENT-SENSITIVE COLLECTION SNAPPING

CONTENT-SENSITIVE COLLECTION SNAPPING

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Fu, Yanwei Guo, Yanwen Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Jiangyin Informat Technol Res Inst Nanjing Peoples R China

ISBN: (纸本)9781612843490

interactive segmentation methods have greatly simplified the task of object cutout from an image. However, segmenting a large number of images in a collection is still a tedious task. In this paper, we present a content-sensitive group segmentation method that iteratively segments the images in a collection and incrementally refines the results. With our method, a user only provides a small number of strokes to segment and refine a few sample images. For each of the rest of images, our method finds relevant sample images and applies the corresponding appearance models to guide the segmentation. To improve the segmentation results using the user strokes on a few images with unsatisfactory segmentation results, our method calculates the relevance map that measures the probability that a stroke can be appropriately applied at each pixel/region of an image, and applies it accordingly. Our experiments show that our method can effectively segment an image collection with a wide variety of image content and significantly reduce user input.

关键词： interactive image segmentation Collection segmentation Cosegmentation

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Auto-Labeling Algorithms on CA Based interactive segmentation for High Resolution Remote Sensing images 6

Auto-Labeling Algorithms on CA Based Interactive Segmentatio...

引用

IEEE Proceedings Sixth Int Conference Intelligent Control Inform Processing

作者： Zhang, Xiaopan Liu, Jing Chi, Xu Wuhan Univ Technol Wuhan 430070 Peoples R China

ISBN: (纸本)9781479917174

interactive image segmentation based on Cellular Automata (CA) has shown its effectivity of object extraction in photographs or video frames. But in high resolution remote sensing images it will be a heavy work to manually label out all of objects, especially the mixture-up objects, in large scale of map area. Three kinds of auto-labelling algorithms are studied to generate labels automatically just according to a few of artificial sample labels. These algorithms deal with the similarity between unlabeled area and the labeled samples in the aspects of spectrum, shapes, and mixture pattern respectively, and then make the use of maximum likelihood labelling, shape calculation by screening contours, and spatial clustering comprehensively to extract some feature pixels to construct new labels. The feasibility of the algorithms has been shown by experimental results of different types of high resolution remote sensing images retrieved from google earth.

关键词： interactive image segmentation high resolution remote sensing images Cellular Automata maximum likelihood labeling spatial clustering

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A Foreground Extraction Approach Using Convolutional Neural Network with Graph Cut 3

A Foreground Extraction Approach Using Convolutional Neural ...

引用

3rd IEEE International Conference on image, Vision and Computing (ICIVC)

作者： Utah, Matee Iltaf, Adnan Hou, Qiujun Ali, Farman Liu, Chuancai Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538649916

In the literature of computer vision, many techniques have demonstrated their potentials for interactive image segmentation. However, most of these state-of-the-art algorithms are unable to produce accurate boundaries without more user interaction, as they are highly sensitive to the seed's quantity and quality. These techniques frequently depend on more user interaction to refine the boundaries. In order to solve the problem and get accurate boundaries via less user interaction, in this work, a robust interactive image segmentation method is proposed based on generic multiscale oriented contours via single forward pass Convolutional Neural Networks (CNNs) and graph cut framework. We first utilize CNN to construct the boundary-level information and then combine this boundary-level information with the boundary energy term of graph cut framework. The proposed method exhibits a significant leap in robustness to user interaction, smooth boundaries, accurate segmentation and the ability to handle the changes. We show that the sensitivity to the seeds can be controlled and accuracy can be improved via boundary-level information. We further conduct both qualitative and quantitative experiments on benchmark datasets, showing that our proposed method outperforms the state-of-the-art interactive image segmentation techniques.

关键词： interactive image segmentation deep learning CNN graph cut

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"GrabCut" - interactive foreground extraction using iterated graph cuts

"GrabCut" - Interactive foreground extraction using iterated...

引用

Annual Symposium of the ACM SIGGRAPH

作者： Rother, C Kolmogorov, V Blake, A Microsoft Res Cambridge Cambridge England

The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical image segmentation tools use either texture (colour) information, e.g. Magic Wand, or edge (contrast) information, e.g. Intelligent Scissors. Recently, an approach based on optimization by graph-cut has been developed which successfully combines both types of information. In this paper we extend the graph-cut approach in three respects. First, we have developed a more powerful, iterative version of the optimisation. Secondly, the power of the iterative algorithm is used to simplify substantially the user interaction needed for a given quality of result. Thirdly, a robust algorithm for "border matting" has been developed to estimate simultaneously the alpha-matte around an object boundary and the colours of foreground pixels. We show that for moderately difficult examples the proposed method outperforms competitive tools.

关键词： interactive image segmentation graph cuts image editing foreground extraction alpha matting

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An Improved Intelligent Scissors Algorithm for the segmentation of Vessels Segments in Coronary Angiography Imaging 3

An Improved Intelligent Scissors Algorithm for the Segmentat...

引用

3rd International Conference on Intelligent Robotics and Control Engineering (IRCE)

作者： Li, Yue Pan, Hangyi Jiang, Lurong Tong, Jijun Shen, Yuqiang Zhejiang Sci Tech Univ Hangzhou 310018 Peoples R China Zhejiang Univ Affiliated Hosp 4 Sch Med Hangzhou 310018 Peoples R China

ISBN: (纸本)9781728189727

interactive segmentation is widely used to precisely segment the parts of interest in medical images. Among them, the intelligent scissors algorithm is an efficient interactive segmentation algorithm, but it has poor performance in processing images with a high similarity between foreground and background. And it is easily affected by the strong edge, which leads to more seed points and longer time to segment the interested vessel segments in the coronary angiography images. In order to improve the segmentation efficiency, we redesign the cost function, using Canny operator, Scharr operator, to replace the Laplace operator in the traditional algorithm. Moreover, gradient magnitude adjustment function and histogram adjustment function are introduced to improve the segmentation effect. Experiment results show that the improved algorithm can shorten the segmentation time and improve the segmentation efficiency without the loss of performance compared with the traditional algorithm.

关键词： intelligent scissors live-wire interactive image segmentation medical image processing

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Lazy snapping

Lazy snapping

引用

Annual Symposium of the ACM SIGGRAPH

作者： Li, Y Sun, J Tang, CK Shum, IY Hong Kong Univ Sci & Technol Hong Kong Hong Kong Peoples R China Microsoft Res Asia Beijing Peoples R China

In this paper, we present Lazy Snapping, an interactive image cutout tool. Lazy Snapping separates coarse and fine scale processing, making object specification and detailed adjustment easy. Moreover, Lazy Snapping provides instant visual feedback, snapping the cutout contour to the true object boundary efficiently despite the presence of ambiguous or low contrast edges. Instant feedback is made possible by a novel image segmentation algorithm which combines graph cut with pre-computed over-segmentation. A set of intuitive user interface (UI) tools is designed and implemented to provide flexible control and editing for the users. Usability studies indicate that Lazy Snapping provides a better user experience and produces better segmentation results than the state-of-the-art interactive image cutout tool, Magnetic Lasso in Adobe Photoshop.

关键词： user interface image cutout interactive image segmentation graph cut

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Slim Scissors: Segmenting Thin Object from Synthetic Background 17th

Slim Scissors: Segmenting Thin Object from Synthetic Backgro...

引用

17th European Conference on Computer Vision (ECCV)

作者： Han, Kunyang Liew, Jun Hao Feng, Jiashi Tian, Huawei Zhao, Yao Wei, Yunchao Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing Peoples R China ByteDance Beijing Peoples R China Peoples Publ Secur Univ China Beijing Peoples R China

ISBN: (纸本)9783031198175;9783031198182

Existing interactive segmentation algorithms typically fail when segmenting objects with elongated thin structures (e.g., bicycle spokes). Though some recent efforts attempt to address this challenge by introducing a new synthetic dataset and a three-stream network design, they suffer two limitations: 1) large performance gap when tested on real image domain;2) still requiring extensive amounts of user interactions (clicks) if the thin structures are not well segmented. To solve them, we develop Slim Scissors, which enables quick extraction of elongated thin parts by simply brushing some coarse scribbles. Our core idea is to segment thin parts by learning to compare the original image to a synthesized background without thin structures. Our method is model-agnostic and seamlessly applicable to existing state-of-the-art interactive segmentation models. To further reduce the annotation burden, we devise a similarity detection module, which enables the model to automatically synthesize background for other similar thin structures from only one or two scribbles. Extensive experiments on COIFT, HRSOD and ThinObject-5K clearly demonstrate the superiority of Slim Scissors for thin object segmentation: it outperforms TOS-Net by 5.9% IoUthin and 3.5% F score on the real dataset HRSOD.

关键词： interactive image segmentation Thin object segmentation

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interactive TRAINING AND ARCHITECTURE FOR DEEP OBJECT SELECTION

INTERACTIVE TRAINING AND ARCHITECTURE FOR DEEP OBJECT SELECT...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Forte, Marco Price, Brian Cohen, Scott Xu, Ning Pitie, Francois Trinity Coll Dublin Dublin Ireland Adobe Inc San Jose CA USA

ISBN: (纸本)9781728113319

interactive object cutout tools are the cornerstone of the image editing workflow. Algorithms that can reduce the number of interactions are clearly valuable. Recent deep-learning based interactive segmentation algorithms are capable of rough binary selections with a handful of clicks, yet, they tend to plateau once this rough selection has been reached. In this work, we interpret this plateau as an inability of the algorithm to precisely leverage each user interaction. We introduce a novel interactive architecture and a training scheme that are both tailored to better exploit the user input at higher numbers of clicks. Comprehensive experiments support our approach, and our network achieves state of the art performance.

关键词： interactive image segmentation Semantic segmentation image Matting Deep Learning

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Haptic image Exploration

Haptic Image Exploration

引用

作者： Lareau, David Uottawa

学位级别：master

The haptic exploration of 2-D images is a challenging problem in computer haptics. Research on the topic has primarily been focused on the exploration of maps and curves. This thesis describes the design and implementation of a system for the haptic exploration of photographs. The system builds on various research directions related to assistive technology, computer haptics, and image segmentation. An object-level segmentation hierarchy is generated from the source photograph to be rendered haptically as a contour image at multiple levels-of-detail. A tool for the authoring of object-level hierarchies was developed, as well as an innovative type of user interaction by region selection for accurate and efficient image segmentation. According to an objective benchmark measuring how the new method compares with other interactive image segmentation algorithms shows that our region selection interaction is a viable alternative to marker-based interaction. The hierarchy authoring tool combined with precise algorithms for image segmentation can build contour images of the quality necessary for the images to be understood by touch with our system. The system was evaluated with a user study of 24 sighted participants divided in different groups. The first part of the study had participants explore images using haptics and answer questions about them. The second part of the study asked the participants to identify images visually after haptic exploration. Results show that using a segmentation hierarchy supporting multiple levels-of-detail of the same image is beneficial to haptic exploration. As the system gains maturity, it is our goal to make it available to blind users

关键词： Haptics interactive image segmentation image Exploration Human Computer Interaction Hierarchical segmentation Assistive Technology

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