Titlebook: Industrial Competitiveness; Cost Reduction GIDEON HALEVI Book 2006 Springer Science+Business Media B.V. 2006 Controlling.Management.Manufac

Juvenile

ataset shows that our best model detects laser-scar images with sensitivity of 0.962, specificity of 0.999, precision of 0.974 and AP of 0.988 and AUC of 0.999. The same model is tested on the public LMD-BAPT test set, obtaining sensitivity of 0.765, specificity of 1, precision of 1, AP of 0.975 and

GET

ataset shows that our best model detects laser-scar images with sensitivity of 0.962, specificity of 0.999, precision of 0.974 and AP of 0.988 and AUC of 0.999. The same model is tested on the public LMD-BAPT test set, obtaining sensitivity of 0.765, specificity of 1, precision of 1, AP of 0.975 and

不可比擬

一致性

el are unpaired sets of noisy and clean images. This paper explores the use of Generative Adversarial Networks (GAN) to generate denoised versions of the noisy documents. In particular, where paired information is available, we formulate the problem as an image-to-image translation task i.e, transla

強所

plate diagrams, unstructured shape of graphical objects to be identified and variability in the strokes of handwritten text. The proposed pipeline incorporates a capsule and spatial transformer network based classifier for accurate text reading, and a customized CTPN [.] network for text detection i

血統(tǒng)

plate diagrams, unstructured shape of graphical objects to be identified and variability in the strokes of handwritten text. The proposed pipeline incorporates a capsule and spatial transformer network based classifier for accurate text reading, and a customized CTPN [.] network for text detection i

DEMUR

性冷淡

radation kernel as the weighted combination of the basis kernels. With the learned degradation model, a large number of realistic HR-LR pairs can be easily generated to train a more robust SISR model. Extensive experiments are performed to quantitatively and qualitatively validate the proposed degra

FANG

節(jié)省

of a particular location in the 2D map. At test time, this embedding is extracted from a panoramic building instance label and depth images. It is then used to retrieve the closest match in the database..We evaluate our localization framework on two large-scale datasets consisting of Cambridge and