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Shape detection in images exploiting sparsity
2009 24th International Symposium on Computer and Information Sciences, 2009Detection of different kinds of shapes, i.e. lines, circles, hyperbolas etc., in varying kinds of images arises in diverse areas such as signal and image processing, computer vision or remote sensing. The generalized Hough Transform is a traditional approach to detect a specific shape in an image by transforming the problem into a parameter space ...
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Shape detection using range data
Proceedings. 1985 IEEE International Conference on Robotics and Automation, 2005The original algorithm of Hough transform was for detecting and finding straight lines in two dimensional images. Since then, it has been extended to detect and locate special planar curves such as circles, parabolas, etc. Recently, generalized Hough transform has been applied for recognizing and locating three dimensional objects using range data. Our
Xueyin Lin, William G. Wee
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Shape detector for generic ball detection
2015 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, 2015This paper describes a novel approach for generating object proposals for ball detection. Our method, called shape detector, captures the possible contours of balls and then transfers them into proposal bounding boxes which may contain the target object. These proposal bounding boxes can be further used in class-specific object detection task.
Yuncheng Li +3 more
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Fall detection based on shape deformation
Multimedia Tools and Applications, 2020Older people living alone are facing serious risks. Falls are the main risk that menace their lives. In this paper, a new vision-based method for fall detection is proposed to allow older people to live independently and safely. The proposed method uses shape deformation and motion information to distinguish between normal activity and fall.
Fairouz Merrouche, Nadia Baha
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Learning Shape-Aware Embedding for Scene Text Detection
Computer Vision and Pattern Recognition, 2019We address the problem of detecting scene text in arbitrary shapes, which is a challenging task due to the high variety and complexity of the scene. Specifically, we treat text detection as instance segmentation and propose a segmentation-based framework,
Zhuotao Tian +6 more
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BubCNN: Bubble detection using Faster RCNN and shape regression network
, 2020Detailed knowledge about gas-liquid multiphase flows is important to optimize industrial systems. Imaging with image processing is the most commonly used measurement technique.
Tim Haas +3 more
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Detection and Recognition Measures of Shape Discrimination
Nature, 1967THE ease with which an animal differentiates shapes is commonly measured by presenting it with two shapes, either simultaneously or successively, over a number of trials and rewarding a response specific to one shape but not to the other1. Measures of performance obtained in this way have recently been used to develop hypotheses2–4 about the way in ...
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Detection of Soliton Shape Modes in Polyacetylene
Physical Review Letters, 1986We observed soliton shape modes by photoinduced absorption in partially isomerized ${(\mathrm{CH})}_{x}$ and ${(\mathrm{CD})}_{x}$, coexisting with the soliton translational modes. The shape modes' ir activity, their dependence on the degree of isomerization, and the photoinduced spectra are explained in detail by a symmetry-breaking interaction which ...
, Vardeny +6 more
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Object Classification from Shape Detection
2019We evaluate the problem of object detection and classification based on a single model for five diverse classes. The class detection problem is implemented by enabling a method which detects the presence or absence of every shape-based model in every instance of a class. Low-level feature extraction is also performed to facilitate object categorization
Pragya Nagpal, Ankush Mittal
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Envelope Detection of Multi-object Shapes
2005The purpose of this paper is to allow for high level shape representation and matching in multi-object images by detecting and extracting the envelope of object groupings in the image. The proposed algorithm uses hierarchical clustering to find object groupings based on spatial proximity as well as low-level shape features of objects in the image. Each
Naif Alajlan +3 more
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