Research on image processing of intelligent building environment based on pattern recognition technology

Cai, Wei; Wen, Xiangming; Tu, Qiu; Guo, Xin

doi:10.1016/j.jvcir.2019.03.014

Cited by 13 publications

(8 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on video continuous frame image data, the human contour, bones, or points of interest that can be tracked are extracted, and then, machine learning or deep learning models are trained to recognize human actions. Although video-based approaches can provide rich visual information, articles [15], more attentions should be put into the combination with the wearable sensors to make sure that the classification is accurate. First, privacy is the first issue, and people are wary of cameras, fearing information leakage or being spied on, thus making it difficult to promote vision-based methods in practical applications.…”

Section: Related Workmentioning

confidence: 99%

Recognition Technology of Athlete’s Limb Movement Combined Based on the Integrated Learning Algorithm

Tan

Xie

2021

Journal of Sensors

View full text Add to dashboard Cite

Human motion recognition based on inertial sensor is a new research direction in the field of pattern recognition. It carries out preprocessing, feature selection, and feature selection by placing inertial sensors on the surface of the human body. Finally, it mainly classifies and recognizes the extracted features of human action. There are many kinds of swing movements in table tennis. Accurately identifying these movement modes is of great significance for swing movement analysis. With the development of artificial intelligence technology, human movement recognition has made many breakthroughs in recent years, from machine learning to deep learning, from wearable sensors to visual sensors. However, there is not much work on movement recognition for table tennis, and the methods are still mainly integrated into the traditional field of machine learning. Therefore, this paper uses an acceleration sensor as a motion recording device for a table tennis disc and explores the three-axis acceleration data of four common swing motions. Traditional machine learning algorithms (decision tree, random forest tree, and support vector) are used to classify the swing motion, and a classification algorithm based on the idea of integration is designed. Experimental results show that the ensemble learning algorithm developed in this paper is better than the traditional machine learning algorithm, and the average recognition accuracy is 91%.

show abstract

Section: Related Workmentioning

confidence: 99%

Recognition Technology of Athlete’s Limb Movement Combined Based on the Integrated Learning Algorithm

Tan

Xie

2021

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…( It is found that 5 * 5 template smoothing filter has a good effect on removing Gaussian noise and can retain more details. For salt and pepper noise, its amplitude is basically the same, but the position of noise points is random, and the average value of noise in statistical sense is not 0, so the filtering effect of average filter on salt and pepper noise is not ideal [13].…”

Section: Image Denoisingmentioning

confidence: 99%

Design of Process Products Based on Image Processing Multimode Interaction

Sun

2021

Scientific Programming

View full text Add to dashboard Cite

With the development of modern science and technology and more and more image processing systems, related technologies are becoming more and more complex. The application of image processing technology can be seen in various fields of society, such as medical field, aerospace field and life, and entertainment field. Due to the increasing amount of information on the picture, the requirements for the speed and clarity of image processing are also increasing. The existence of various external factors will lead to the production of image products and objects between the error and distortion problems. In order to make the process product design more authentic and reliable, this paper studies the process product design based on image processing multimode interaction. It uses radiometric correction and geometric correction to process distorted images and uses GPU parallel computing technology to accelerate the correction process. In this paper, this technology is applied to the visual recognition of welding robot, and the experiment shows that the product produced by the image processed by this module can obviously reduce the error.

show abstract

“…The method was found to effectively reduce the Poisson noise level without losing diagnostic function and exhibit high performance in both qualitative and quantitative evaluations. Cai et al 6 used the image processing of intelligent buildings as the basic theoretical platform, and based on the pattern recognition technology, studied image processing, image extraction, and image recognition in image processing of building intelligent environment, and proposed reasonable solutions. De Silva et al 7 proposed and evaluated two wavelet-based edge feature enhancement methods to pre-process the input image by CNN, developed a new pre-processing layer, and attached it to the network architecture.…”

Section: Related Workmentioning

confidence: 99%

A novel image edge smoothing method based on convolutional neural network

2020

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

In the field of visual perception, the edges of images tend to be rich in effective visual stimuli, which contribute to the neural network’s understanding of various scenes. Image smoothing is an image processing method used to highlight the wide area, low-frequency components, main part of the image or to suppress image noise and high-frequency interference components, which could make the image’s brightness smooth and gradual, reduce the abrupt gradient, and improve the image quality. At present, there are still problems such as easy blurring of the edges of the image, poor overall smoothing effect, obvious step effect, and lack of robustness to noise on image smoothing. Based on the convolutional neural network, this article proposes a method for edge detection and deep learning for image smoothing. The results show that the research method proposed in this article solves the problem of edge detection and information capture better, significantly improves the edge effect, and protects the effectiveness of edge information. At the same time, it reduces the signal-to-noise ratio of the smoothed image and greatly improves the effect of image smoothing.

show abstract

Research on image processing of intelligent building environment based on pattern recognition technology

Cited by 13 publications

References 24 publications

Recognition Technology of Athlete’s Limb Movement Combined Based on the Integrated Learning Algorithm

Recognition Technology of Athlete’s Limb Movement Combined Based on the Integrated Learning Algorithm

Design of Process Products Based on Image Processing Multimode Interaction

A novel image edge smoothing method based on convolutional neural network

Contact Info

Product

Resources

About