Titlebook: Computer Vision Systems; 10th International C Lazaros Nalpantidis,Volker Krüger,Antonios Gastera Conference proceedings 2015 Springer Inter

Stimulant

书目名称Computer Vision Systems影响因子(影响力)




书目名称Computer Vision Systems影响因子(影响力)学科排名




书目名称Computer Vision Systems网络公开度




书目名称Computer Vision Systems网络公开度学科排名




书目名称Computer Vision Systems被引频次




书目名称Computer Vision Systems被引频次学科排名




书目名称Computer Vision Systems年度引用




书目名称Computer Vision Systems年度引用学科排名




书目名称Computer Vision Systems读者反馈




书目名称Computer Vision Systems读者反馈学科排名

违反

Integers and Fixed-Point Numbers. The algorithm runs at 22 Hz processing 2 MP image pairs and computing disparity maps with up to 255 disparities. The conducted evaluations on the KITTI Dataset and on a challenging bad weather dataset show that full depth resolution is obtained for small disparities and robustness of the method is

capsule

foppish

deceive

Yuri Demchenko,Juan J. Cuadrado-Gallegocan be preserved in the model training which consequently leads to performance improvement in the testing. Experiments conducted on commonly used benchmarks for cross-domain image classification show that our method significantly outperforms the state-of-the-art.

吞吞吐吐

Comparison of Statistical Features for Medical Colour Image Classificationix and Grey-Level Run-Length Matrix. Furthermore, we calculate Grey-Level Run-Length Matrix starting from the Grey-Level Difference Matrix. The resulting feature sets performances have been compared using the Support Vector Machine model. To validate our method we have used three different databases

吞吞吐吐

BAIT

Bayesian Formulation of Gradient Orientation Matchinghing. Another application is background/foreground segmentation. This paper will use the latter application as an example, but is focused on the general formulation. It is shown how the theory can be used to implement a very fast background/foreground segmentation algorithm that is capable of handli

树木中

Surface Reconstruction from Intensity Image Using Illumination Model Based Morphable Modelinganding different materials, lighting conditions and, the underneath sign matrix is also obtained by resizing/deforming Region of Interest(ROI) with respect to its counterpart of a similar object. The target object is then reconstructed from its still image. In addition to the process, delicate detai

反馈

An Informative Logistic Regression for Cross-Domain Image Classificationcan be preserved in the model training which consequently leads to performance improvement in the testing. Experiments conducted on commonly used benchmarks for cross-domain image classification show that our method significantly outperforms the state-of-the-art.