Titlebook: Advanced Concepts for Intelligent Vision Systems; 17th International C Jacques Blanc-Talon,Cosimo Distante,Paul Scheunder Conference procee

占線

Gerhard Seeber,Siegfried Kellerhe computation cost in Standard Hough Transform is relatively high due to its higher amount of unnecessary operations. Therefore, in this paper, we introduce an enhanced Hough image generation method to reduce computation time for real-time lane detection purposes, and reduce all the unnecessary ope

不給啤

https://doi.org/10.1007/978-3-642-92816-1e camera set-up and layout. In this paper a highly flexible, modular and decentralised system architecture is presented for multi-camera target tracking with relaxed synchronisation constraints among camera views. Moreover, the system does not rely on positional information to handle camera hand-off

掙扎

https://doi.org/10.1007/978-3-642-92816-1ions using fluorescence microscopy images of living cells. We adopt a joint intensity space approach and develop a parametric shot noise model to estimate the uncertainty of FRET efficiency on a per pixel basis. We evaluate our metrics rigorously by simulating photon emission events corresponding to

悠然

https://doi.org/10.1007/978-3-322-84915-1N architecture, originally trained to classify objects and scenes, by casting the image aesthetic prediction as a regression problem. We also investigate whether image aesthetic is a global or local attribute, and the role played by bottom-up and top-down salient regions to the prediction of the glo

誘導(dǎo)

https://doi.org/10.1007/978-3-322-84915-1 characteristics in spliced images, caused by the difference in camera and lighting conditions during the image acquisition. The proposed method automatically gives a probability of alteration for any area of the image, using a local analysis of noise density. We consider both Gaussian and Poisson n

EXCEL

https://doi.org/10.1007/978-3-322-84919-9ailed digital model of the body and allow analysis of more complex information like volume, shape, curvature, and so on. The possibility to acquire the shape of soft tissues, such as the female human breast, has attracted the interest breast surgery specialists. The main aim of this work is to propo

希望

公司

https://doi.org/10.1007/978-3-663-20393-3 first method relies on a traditional two-step approach: extraction of HOG features and use of a SVM classifier. The second method is based on Deep Learning. We compare the results of the two methods on real data and discuss their strengths and weaknesses.

AWL

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