3D Image Processing using Machine Learning based Input Processing for Man-Machine Interaction

Zengyi Huang Chang Che Bo Liu + 2 lainnya

1 April 2024

Computer vision is a kind of simulation of biological vision using computers and related equipment. It is an important part of the field of artificial intelligence. Its research goal is to make computers have the ability to recognize three-dimensional environmental information through two-dimensional images. Computer vision is based on image processing technology, signal processing technology, probability statistical analysis, computational geometry, neural network, machine learning theory and computer information processing technology, through computer analysis and processing of visual information.The article explores the intersection of computer vision technology and robotic control, highlighting its importance in various fields such as industrial automation, healthcare, and environmental protection. Computer vision technology, which simulates human visual observation, plays a crucial role in enabling robots to perceive and understand their surroundings, leading to advancements in tasks like autonomous navigation, object recognition, and waste management. By integrating computer vision with robot control, robots gain the ability to interact intelligently with their environment, improving efficiency, quality, and environmental sustainability. The article also discusses methodologies for developing intelligent garbage sorting robots, emphasizing the application of computer vision image recognition, feature extraction, and reinforcement learning techniques. Overall, the integration of computer vision technology with robot control holds promise for enhancing human-computer interaction, intelligent manufacturing, and environmental protection efforts.

Nicole L. Robinson Brendan Tidd D. Kulić + 2 lainnya

5 Desember 2022

Robotic vision, otherwise known as computer vision for robots, is a critical process for robots to collect and interpret detailed information related to human actions, goals, and preferences, enabling robots to provide more useful services to people. This survey and systematic review presents a comprehensive analysis on robotic vision in human-robot interaction and collaboration (HRI/C) over the past 10 years. From a detailed search of 3,850 articles, systematic extraction and evaluation was used to identify and explore 310 papers in depth. These papers described robots with some level of autonomy using robotic vision for locomotion, manipulation, and/or visual communication to collaborate or interact with people. This article provides an in-depth analysis of current trends, common domains, methods and procedures, technical processes, datasets and models, experimental testing, sample populations, performance metrics, and future challenges. Robotic vision was often used in action and gesture recognition, robot movement in human spaces, object handover and collaborative actions, social communication, and learning from demonstration. Few high-impact and novel techniques from the computer vision field had been translated into HRI/C. Overall, notable advancements have been made on how to develop and deploy robots to assist people.

Longfei Zhou Lin Zhang N. Konz

11 Desember 2021

Computer vision (CV) techniques have played an important role in promoting the informatization, digitization, and intelligence of industrial manufacturing systems. Considering the rapid development of CV techniques, we present a comprehensive review of the state of the art of these techniques and their applications in manufacturing industries. We survey the most common methods, including feature detection, recognition, segmentation, and three-dimensional modeling. A system framework of CV in the manufacturing environment is proposed, consisting of a lighting module, a manufacturing system, a sensing module, CV algorithms, a decision-making module, and an actuator. Applications of CV to different stages of the entire product life cycle are then explored, including product design, modeling and simulation, planning and scheduling, the production process, inspection and quality control, assembly, transportation, and disassembly. Challenges include algorithm implementation, data preprocessing, data labeling, and benchmarks. Future directions include building benchmarks, developing methods for nonannotated data processing, developing effective data preprocessing mechanisms, customizing CV models, and opportunities aroused by 5G.

Yanwei Bao F. Ren

1 Januari 2020

In the field of artificial intelligence, human–computer interaction (HCI) technology and its related intelligent robot technologies are essential and interesting contents of research. From the perspective of software algorithm and hardware system, these above-mentioned technologies study and try to build a natural HCI environment. The purpose of this research is to provide an overview of HCI and intelligent robots. This research highlights the existing technologies of listening, speaking, reading, writing, and other senses, which are widely used in human interaction. Based on these same technologies, this research introduces some intelligent robot systems and platforms. This paper also forecasts some vital challenges of researching HCI and intelligent robots. The authors hope that this work will help researchers in the field to acquire the necessary information and technologies to further conduct more advanced research.

Linqin Cai Jianrong Zhang Jun Xu + 1 lainnya

2022

To naturally interact with virtual environment by hand gesture, this paper presents a robust RGB-D data based recognition method of static and dynamic hand gesture. Firstly, for static hand gesture recognition, starting from the hand gesture contour extraction, the palm center is identified by Distance Transform (DT) algorithm. The fingertips are localized by employing the K-Curvature-Convex Defects Detection algorithm (K-CCD). On the basis, the distances of the pixels on hand gesture contour to palm center and the angle between fingertips are considered as the auxiliary features to construct a multimodal feature vector, and then recognition algorithm is presented to robustly recognize the static hand gestures. Secondly, combining Euclidean distance between hand joints and shoulder center joint with the modulus ratios of skeleton features, this paper generates a unifying feature descriptor for each dynamic hand gesture and proposes an improved dynamic time warping (IDTW) algorithm to obtain recognition results of dynamic hand gestures. Finally, we conduct extensive experiments to test and verify the static and dynamic hand gesture recognition algorithm and realize a low-cost real-time application of natural interaction with virtual environment by hand gestures.