Computer Vision for Human-Computer Interaction Using Noninvasive Technology

Abstract

Computer vision is a significant component of human-computer interaction (HCI) processes in interactive control systems. In general, the interaction between humans and computers relies on the flexibility of the interactive visualization system. Electromyography (EMG) is a bioelectric signal used in HCI that can be captured noninvasively by placing electrodes on the human hand. Due to the impact of complex background, accurate recognition and analysis of human motion in real-time multitarget scenarios are considered challenging in HCI. Further, EMG signals of human hand motions are exceedingly nonlinear, and it is important to utilize a dynamic approach to address the noise problem in EMG signals. Hence, in this paper, the Optimized Noninvasive Human-Computer Interaction (ONIHCI) model has been proposed to predict human motion recognition. Average Intrinsic Mode Function (AIMF) has been used to reduce the noise factor in EMG signals. Furthermore, this paper introduces spatial thermographic imaging to overcome the conventional sensor problem, such as gesture recognition and human target identification in multitarget scenarios. The human motion behavior in spatial thermographic images is examined by target trajectory, and body movement kinematics is employed to classify human targets and objects. The experimental findings demonstrate that the proposed method reduces noise by 7.2% and improves accuracy by 97.2% in human motion recognition and human target identification.

Sentiment Analysis of Image with Text Caption using Deep Learning Techniques

“…(i) Data preprocessing and preparation (ii) Possibility of feature extraction (iii) Developing and extracting views and information (iv) Classification of points of view e data that are obtained from the web content are not appropriately formatted at the time of extraction. e first step prepares the data in preparation for the sentiment analysis and extraction to take place later on [44,45]. Users' opinions in product evaluations are mined for product attributes using opinion mining as one possible method of discovering and matching up to the power and scarcity of things.…”

“…The opinions expressed by users and their ratings for the product not only help consumers in gaining a better understanding of the item's quality but also aid in the differentiation of various things. This satisfies the criteria by completing two significant tasks [ 44 ]. Feature identification, on the other hand, is the work of deriving and locating characteristics from the views of the user, and it is the first step [ 45 ].…”

Research on Face Attribute Recognition Technology Based on Fine-Grained Features

“…With the development of science and technology, all kinds of social media platforms and mobile devices have been rapidly popularized and updated, and the utilization of biometric information such as face pictures, human video, and voice has begun to gain attention in daily life [1][2][3]. Commonly used biometrics include face recognition, fingerprint recognition, iris recognition, voiceprint recognition, etc., which are widely used in security, public transportation, and medical fields [4][5][6]. These intelligent applications can provide more reliable security and convenience for people's lives.…”

The Innovative Model of Higher Education Management and Student Training Mechanism in the Internet Era

“…With the development and progress of network technology, face recognition technology is used in higher education management and student training, which has a significant impact on the relevant mechanisms of higher education. With the further development of the massification of higher education, higher education has contributed greatly to the promotion of economic development, scientific and technological innovation, cultural prosperity and social progress [1][2]. The promotion of quality education has resulted in a new perception of the education management mode of higher education in the new era.…”