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

Stimulant

書目名稱Computer Vision Systems影響因子(影響力)




書目名稱Computer Vision Systems影響因子(影響力)學(xué)科排名




書目名稱Computer Vision Systems網(wǎng)絡(luò)公開度




書目名稱Computer Vision Systems網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computer Vision Systems被引頻次




書目名稱Computer Vision Systems被引頻次學(xué)科排名




書目名稱Computer Vision Systems年度引用




書目名稱Computer Vision Systems年度引用學(xué)科排名




書目名稱Computer Vision Systems讀者反饋




書目名稱Computer Vision Systems讀者反饋學(xué)科排名

違反

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

樹木中

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

反饋

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.