Unsupervised learning-based semantic scene recognition and boundary visualization on category maps

2017 ◽  
Author(s):  
Hirokazu Madokoro ◽  
Kazuhito Sato ◽  
Kazuhisa Nakasho ◽  
Nobuhiro Shimoi
Keyword(s):  
Unsupervised Learning ◽  
Scene Recognition ◽  
Semantic Scene

Improving Registration of Augmented Reality by Incorporating DCNNS into Visual SLAM

2018 ◽  
Vol 32 (12) ◽  
pp. 1855022 ◽  
Author(s):  
Yongbin Chen ◽  
Hanwu He ◽  
Heen Chen ◽  
Teng Zhu
Keyword(s):  
Augmented Reality ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Recognition Algorithm ◽  
Scene Recognition ◽  
3D Registration ◽  
Deep Convolutional Neural Networks ◽  
Severe Problem ◽  
Localization And Mapping ◽  
Semantic Scene

Augmented reality (AR) by analyzing the characteristics of the scene, the computer-generated geometric information which can be added to the real environment in the way of visual fusion, reinforces the perception of the world. Three-dimensional (3D) registration is one of the core issues of in AR. The key issue is to estimate the visual sensor’s posture in the 3D environment and figure out the objects in the scene. Recently, computer vision has made significant progress, but the registration based on natural feature points in 3D space for AR system is still a severe problem. There is the difficulty of working out the mobile camera’s posture in the 3D scene precisely due to the unstable factors, such as the image noise, changing light and the complex background pattern. Therefore, to design a stable, reliable and efficient scene recognition algorithm is still very challenging work. In this paper, we propose an algorithm which combines Visual Simultaneous Localization and Mapping (SLAM) and Deep Convolutional Neural Networks (DCNNS) to boost the performance of AR registration. Semantic segmentation is a dense prediction task which aims to predict categories for each pixel in an image when applying to AR registration, and it will be able to narrow the searching range of the feature point between the two frames thus enhancing the stability of the system. Comparative experiments in this paper show that the semantic scene information will bring a revolutionary breakthrough to the AR interaction.

Primacy of emotional vs. semantic scene recognition in peripheral vision

2011 ◽  
Vol 25 (8) ◽  
pp. 1358-1375 ◽  
Author(s):  
Manuel G. Calvo ◽  
Pedro Avero ◽  
Lauri Nummenmaa
Keyword(s):  
Peripheral Vision ◽  
Scene Recognition ◽  
Semantic Scene

Scene Recognition in Pigeons

2000 ◽  
Author(s):  
Jennifer E. Sutton ◽  
William A. Roberts
Keyword(s):  
Scene Recognition

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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