7 Result(s)
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Chapter and Conference Paper
Potential-based modeling of 2D regions using non-uniform source distributions
One of existing approaches to path planning problems uses a potential function to represent the topological structure of the free space. 1 Newtonian potential was used in [1] to represent object and obstacles ...
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Chapter and Conference Paper
Drawing Graphs with Nonuniform Nodes Using Potential Fields
A potential field approach, coupled with force-directed methods, is proposed in this paper for drawing graphs with nonuniform nodes in 2-D and 3-D. In our framework, nonuniform nodes are uniformly or nonuniformly...
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Chapter and Conference Paper
Automatic Chromosome Classification Using Medial Axis Approximation and Band Profile Similarity
Automated chromosome classification is an essential task in cytogenetics and has been an important pattern recognition problem. Numerous attempts were made in the past to characterize chromosome to perform cli...
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Chapter and Conference Paper
Direct Energy Minimization for Super-Resolution on Nonlinear Manifolds
We address the problem of single image super-resolution by exploring the manifold properties. Given a set of low resolution image patches and their corresponding high resolution patches, we assume they respect...
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Chapter and Conference Paper
Practical Error Analysis of Cross-Ratio-Based Planar Localization
Recently, more and more computer vision researchers are paying attention to error analysis so as to fulfill various accuracy requirements arising from different applications. As a geometric invariant under pro...
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Chapter and Conference Paper
Investigating Size Personalization for More Accurate Eye Tracking Glasses
Personalized eyewear frame could improve the accuracy of eye tracking. To obtain the personalized frame size (temple length), we propose a new measuring instrument that consists of (i) the hardware, a 3D print...
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Chapter and Conference Paper
Point MixSwap: Attentional Point Cloud Mixing via Swap** Matched Structural Divisions
Data augmentation is developed for increasing the amount and diversity of training data to enhance model learning. Compared to 2D images, point clouds, with the 3D geometric nature as well as the high collecti...
