Titlebook: Image and Graphics; 9th International Co Yao Zhao,Xiangwei Kong,David Taubman Conference proceedings 2017 Springer Nature Switzerland AG 20

HAUNT

強(qiáng)有力

Vulnerable

Jingwen Yan,Zhenguo Yuan,Tingting Xie,Huimin Zhaot in BNC. They are . and . for the verb ., . for the verb ., and . for the verb .. (3) Some colligational patterns occur less frequently in CCE than those in BNC, such as the patterns . and . for the verb . and . for the verb ., and . for the verb .. (4) No new colligational patterns have been found

先行

Canyon

Actual License Plate Images Clarity Classification via Sparse Representation in advance will help the license plate recognition algorithm set appropriate parameters to improve the accuracy of the recognition. In this paper, we propose a classification algorithm based on sparse representation and reconstruction error to divide license plate images into two categories: high-c

CREEK

Non-rigid 3D Object Retrieval with a Learned Shape Descriptordiscriminative shape descriptor for non-rigid 3D object retrieval. Compact low-level shape descriptors are designed from spectral descriptor, and the non-linear mapping of low level shape descriptors is carried out by a Siamese network. The Siamese network is trained to maximize the inter-class marg

hemorrhage

Adaptive Patch Quantization for?Histogram-Based Visual Trackingincapable of distinguishing objects from backgrounds robustly. In this paper, we propose an adaptive patch quantization approach for histogram-based visual tracking. We first exploit neighboring pixels in the form of local patches to improve the discrimination between objects and backgrounds. Then w

并置

Neural Image Caption Generation with Global Feature Based Attention Schememost attention scheme use the set of region features. Compared with global feature, the region features are lower level features. But we prefer high-level features in image caption generation because words are high-level concepts. So we explore a new attention scheme based on the global feature and

ERUPT

Activation-Based Weight Significance Criterion for Pruning Deep Neural Networksal neural networks (CNNs), can be inconvenient to implement for many real world applications. Therefore, sparsifying deep and densely connected neural networks is becoming a more and more important topic in the computer vision field for addressing these limitations. This paper starts from a very dee

拖網(wǎng)