Topological structural analysis of digitized binary images by border following

Suzuki, Satoshi; Abe, Keiichi

doi:10.1016/0734-189x(85)90016-7

Cited by 2,004 publications

(661 citation statements)

References 6 publications

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“…Candidate contours are extracted using the border following method of Suzuki [17]. Contours that do not encapsulate any of the four corners are rejected.…”

Section: Pre-processing and Horizon Detectionmentioning

confidence: 99%

“…The three feature maps are combined into a single saliency map for lo-100 cal conspicuity over the entire visual scene. Walther et al [15] (STB (Saliency Toolbox)) extends this concept towards modelling visual attention in terms of proto-objects (the generated saliency maps are used for the deduction of a proto-object (via a winner-take-all neural network framework) at the attended location), (see [16,17] for further literature on proto-objects). Harel et al [18] (GBVS) uses the computational power, topographical structure, and parallel nature of probabilistic graphical models in order to describe visually salient regions in an image.…”

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confidence: 99%

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Structure augmented monocular saliency for planetary rovers

Spiteri

Shaukat

Gao

2017

Robotics and Autonomous Systems

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“…Candidate contours are extracted using the border following method of Suzuki [17]. Contours that do not encapsulate any of the four corners are rejected.…”

Section: Pre-processing and Horizon Detectionmentioning

confidence: 99%

mentioning

confidence: 99%

Structure augmented monocular saliency for planetary rovers

Spiteri

Shaukat

Gao

2017

Robotics and Autonomous Systems

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“…This is followed by Canny edge detection, which creates a binary image, which in turn places a pixel at regions with a large gradient change. This is then followed by the Suzuki algorithm [14] which is used to find closed contours in the image. It can be seen in Figure 5 (b) that the feature detection algorithm converted the raw image into a binary image which consists of only the edges of the objects in the images.…”

Section: Application To Real Time Feature Detectionmentioning

confidence: 99%

Development of a Control and Vision Interface for an AR.Drone

Cheema

Luo

Gibbens

2016

MATEC Web of Conferences

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Abstract. The AR.Drone is a remote controlled quadcopter which is low cost, and readily available for consumers. Therefore it represents a simple test-bed on which control and vision research may be conducted. However, interfacing with the AR.Drone can be a challenge for new researchers as the AR.Drone's application programming interface (API) is built on low-level, bit-wise, C instructions. Therefore, this paper will demonstrate the use of an additional layer of abstraction on the AR.Drone's API via the Robot Operating System (ROS). Using ROS, the construction of a high-level graphical user interface (GUI) will be demonstrated, with the explicit aim of assisting new researchers in developing simple control and vision algorithms to interface with the AR.Drone. The GUI, formally known as the Control and Vision Interface (CVI) is currently used to research and develop computer vision, simultaneous localisation and mapping (SLAM), and path planning algorithms by a number of postgraduate and undergraduate students at the school of Aeronautical, Mechanical, and Mechatronics Engineering (AMME) in The University of Sydney.

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“…points. In the case in question, we used the contour-point extraction algorithm by Satoshi Suzuki and Keiichi Abe [23]. For more information on image segmentation see [24].…”

Section: Image Recognition Modelmentioning

confidence: 99%

Evaluating Image Recognition Efficiency using a Self-Organizing Map

Ligeiro¹,

Utkin²

2016

IJIGSP

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Abstract-Recognition and classification of images is an extremely topical interdisciplinary area that covers image processing, computer graphics, artificial intelligence, computer vision, and pattern recognition, resulting in many applications based on contemporary mobile devices. Developing reliable recognition schemes is a difficult task to accomplish. It depends on many factors, such as illumination, acquisition quality and the database images, in particular, their diversity. In this paper we study how the data diversity affects decision making in image recognition, presenting a database driven classificationerror predictor. The predictor is based on a hybrid approach that combines a self-organizing map together with a probabilistic logical assertion method. By means of a clustering approach, the model provides fast and efficient assessment of the image database heterogeneity and, as expected, indicates that such heterogeneity is of paramount importance for robust recognition. The practicality of the model is demonstrated using a set of image samples collected from a standard traffic sign database publicly available by the UK Department for Transport.

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Topological structural analysis of digitized binary images by border following

Cited by 2,004 publications

References 6 publications

Structure augmented monocular saliency for planetary rovers

Structure augmented monocular saliency for planetary rovers

Development of a Control and Vision Interface for an AR.Drone

Evaluating Image Recognition Efficiency using a Self-Organizing Map

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