Titlebook: Artificial Intelligence and Robotics; Huimin Lu Book 2021 Springer Nature Switzerland AG 2021 Computational Intelligence.Intelligent Syste

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書目名稱Artificial Intelligence and Robotics影響因子(影響力)




書目名稱Artificial Intelligence and Robotics影響因子(影響力)學科排名




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書目名稱Artificial Intelligence and Robotics網(wǎng)絡公開度學科排名




書目名稱Artificial Intelligence and Robotics被引頻次




書目名稱Artificial Intelligence and Robotics被引頻次學科排名




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書目名稱Artificial Intelligence and Robotics年度引用學科排名




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書目名稱Artificial Intelligence and Robotics讀者反饋學科排名

Gesture

Lucubrate

https://doi.org/10.1007/978-3-658-09387-7ant to control the crowd number. In this paper, we address the problem of crowd counting in the crowded scene. Our model accurately estimated the count of people in the crowded scene. Firstly, we proposed a novel and simple convolutional neural network, called Global Counting CNN (GCCNN). The GCCNN

indices

Beispiele zu den Grundbeanspruchungsarten,nuclei segmentation is to manually label a huge amount of cell images, which is a labor-intensive and time-consuming process. This paper develops a semi-supervised learning approach to reduce the dependence on the amount of labeled images, and it consists of three main steps. First, cell regions are

Kernel

Meager

https://doi.org/10.1007/978-3-322-88356-8xisting approaches reported in the literature, our work is characterized with a number of novel features: (i) the high level video event modeling and recognition based on Petri net are fully automatic, which are not only capable of covering single video events but also multiple ones without limit; (

GLOSS

Beschreibung des Programmsystems FEMPA,n-like dialogues. The responses proposed by the chat-bot are only a passive answer or assentation, which does not arouse the desire of people to continue communicating. To address this challenge, in this paper, we propose a question generalization method with three types of question proposing scheme

健談

毀壞

https://doi.org/10.1007/978-3-642-25123-8application scenarios, there is a fundamental challenge that how to guarantee the discriminative ability of feature from vary inputs for face verification task. Aiming at this problem, we proposed a context-aware based discriminative siamese neural network for face verification. In fact, the structu

糾纏

In Vivo Research and Development: 1976–1986ferent multi-source images, we propose a new method of object-level matching for multi-source image based on improved dictionary learning. Two main steps, unified representation and similarity measure, are contained. Firstly, we complete the unified representation of multi-source images by improved