Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The present report is submitted in response to General Assembly resolution 61/106, by which the Assembly adopted the Convention on the Rights of Persons with Disabilities and the Optional Protocol thereto. As requested by the Assembly, a brief overview of the status of the Convention as at 3 August 2007 is presented. The report also contains a brief description of technical arrangements on staff and facilities made necessary for the effective performance of the functions of the Conference of States Parties and the Committee under the Convention and the Optional Protocol, and a description on the progressive implementation of standards and guidelines for the accessibility of facilities and services of the United Nations system. Recent efforts of the United Nations and agencies to disseminate information on the Convention and the Optional Protocol are also described.

わかりやすい障害者の権利条約 : 知的障害のある人の権利のために = Convention on the rights of persons with disabilities

Roles of artificial intelligence in construction engineering and management: A critical review and future trends

Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2004 to 2018

This article presents a comprehensive survey of deep learning applications for object detection and scene perception in autonomous vehicles. Unlike existing review papers, we examine the theory underlying self-driving vehicles from deep learning perspective and current implementations, followed by their critical evaluations. Deep learning is one potential solution for object detection and scene perception problems, which can enable algorithm-driven and data-driven cars. In this article, we aim to bridge the gap between deep learning and self-driving cars through a comprehensive survey. We begin with an introduction to self-driving cars, deep learning, and computer vision followed by an overview of artificial general intelligence. Then, we classify existing powerful deep learning libraries and their role and significance in the growth of deep learning. Finally, we discuss several techniques that address the image perception issues in real-time driving, and critically evaluate recent implementations and tests conducted on self-driving cars. The findings and practices at various stages are summarized to correlate prevalent and futuristic techniques, and the applicability, scalability and feasibility of deep learning to self-driving cars for achieving safe driving without human intervention. Based on the current survey, several recommendations for further research are discussed at the end of this article.

Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues

Indoor furniture is of great relevance to building occupants in everyday life. Furniture occupies space in the building, gives comfort, establishes order in rooms and locates services and activities. Furniture is not always static; the rooms can be reorganized according to the needs. Keeping the building models up to date with the current furniture is key to work with indoor environments. Laser scanning technology can acquire indoor environments in a fast and precise way, and recent artificial intelligence techniques can classify correctly the objects that contain. The objective of this work is to study how to minimize the use of point cloud samples in Neural Network training, tedious to label, and replace them with images obtained from online sources. For this, point clouds are converted to images by means of rotations and projections. The conversion of a 3D vector data to a 2D raster allows the use of Convolutional Neural Networks, the achievement of several images for each acquired point cloud object and the combination with images obtained from online sources, such as Google Images. The images have been distributed among the validation and testing training sets following different percentages. The results show that, although point cloud images cannot be completely dispensed within the training set, only 10% of these achieve high accuracy in the classification.

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Transfer learning for indoor object classification: From images to point clouds

Multi feature-rich synthetic colour to improve human visual perception of point clouds

Virtual Seeds for Indoor Point Cloud Segmentation with Hololens 2

Abstract. Recently, the rapid development of new laser technologies has led to the continuous evolution of mobile laser systems, resulting in even greater capabilities for transport infrastructure. However, the market offers numerous MLS systems with varying specifications for global navigation satellite systems (GNSS), inertial measurement units (IMU), and laser scanners, which can result in different accuracies, resolutions, and densities. In this regard, this paper aims to compare two different MLS system, integrated with different GNSS and IMU for mapping in road and urban environments. The study evaluates the performance of these sensors using different classifiers and neighborhood sizes to determine which sensor produces better results. Random forest was found to be the most suitable classifier with an overall accuracy of (91.81% for Optech and 94.38% for Riegl) in road environment and (86.39% for Optech and 84.21% for Riegl) in urban environment. In terms of MLS, Optech achieved the highest accuracy in the road environment, while Riegl obtained the highest accuracy in the urban environment. This study provides valuable insights into the most effective MLS systems and approaches for accurate mapping in road and urban infrastructure.


https://isprs-archives.copernicus.org/articles/XLVIII-1-W1-2023/423/2023/isprs-archives-XLVIII-1-W1-2023-423-2023.pdf

J. Balado

Papers

Transfer learning for indoor object classification: From images to point clouds

Multi feature-rich synthetic colour to improve human visual perception of point clouds

Virtual Seeds for Indoor Point Cloud Segmentation with Hololens 2

Comparative study of road and urban object classification based on mobile laser scanners

A discordance analysis in manual labelling of urban mobile laser scanning data used for deep learning based semantic segmentation