Jinlong Chen

Image Filtering Enhancement

With the development of science and technology, mankind has entered the information age. Image has become the main source of human access to information. However, in the actual process of image signal transmission, the loss and damage of data packet are inevitable due to the physical defects of the channel, which lead to a serious decline in the quality of the video stream. So it is necessary and even urgent now to do some research work on image enhancement technology. In this paper, the image enhancement algorithms that are commonly used, such as bilateral filtering algorithm, homomorphic filtering algorithm, are analyzed in image processing. In the design of the image enhancement, the best modeling and design schemes are chosen according to the comparison. The experimental results demonstrate that the bilateral filtering algorithm can effectively maintain the details of the image edges and make the image more smooth; the homomorphic filtering algorithm can effectively adjust the image gray range, so that the image details on the image area can be increased, and the algorithm can handle the image with inhomogeneous intensity. This work will lay a good foundation of further research.

The Analysis of Image Enhancement for Target Detection.

In the process of automatic detection and recognition based on image, the quality of the detected images affects the target detection and recognition results. To solve the problem of low contrast and high signal-to-noise ratio of the target image in the target detection process, this paper introduces two types of image detail enhancement algorithms which are widely used in recent years, including brightness contrast image enhancement algorithm and HSV color space based enhancement algorithm, and its impact on the target detection. Experiments show that the image detail enhancement can improve the overall and local contrast of the image, highlight the details of the image, and the enhanced image can effectively improve the number and accuracy of the target detection.

Random Forest Based Gesture Segmentation from Depth Image

Gesture image segmentation is a challenge task due to the high degree of freedom of human gestures, large differences in shape and high flexibility, traditional pattern recognition and image processing methods are not effective in gesture detection. The traditional image segmentation based on the detection of skin color and the image of the depth image are limited by the effects of ambient light, skin color difference and image depth variation, resulting in unsatisfactory results. Therefore, we propose a hand gesture depth image segmentation method based on random forest. The method learns the gesture image feature representation of the depth image by supervising learning. Experiments show that the proposed method segments the gesture s’ pixels from the backgrounds area of the depth image. The proposed method potential has widely usages in gesture tracking, gesture recognition and human computer interaction.

A Building Energy Saving Software System Based on Configuration.

A design method of building energy saving software system based on configuration is proposed, it can meet the needs of large-scale building energy efficiency through this method. The software system utilizes the configuration design concept to realize the process monitoring, analysis and evaluation functions for large-scale building energy consumption. It can find abnormal energy consumption equipment within the building, and reduce the peak power consumption to achieve the purpose of building energy efficiency. This paper first introduces the process control software development based on the idea of configuration, the overall framework of the building energy-saving configuration software system and the design process of each module is described in detailed. The software design practice validates the availability and good scalability of process control software based on configuration ideas. This system meets the needs of large building energy consumption monitoring and building energy efficiency.

Architecture and Parameter Analysis to Convolutional Neural Network for Hand Tracking

Currently, the hand tracking based on deep learning has made good progress, but these literatures have less influence on the tracking accuracy of Convolutional Neural Network (CNN) architecture and parameters. In this paper, we proposed a new method to analyze the influence factors of gesture tracking. Firstly, we establish the gesture image and corresponding gesture parameter database based on virtual 3D human hand, on which the convolutional neural network models are constructed, after that we research some related factors, such as network structure, iteration times, data augmentation and Dropout, etc., that affect the performance of hand tracking. Finally we evaluate the objective parameters of the virtual hand, and make the subjective evaluation of the real hand extracted in the video. The results show that, on the premise of the fixed training amount of the hand, the effect of increasing the number of convolutional cores or convolution layers on the accuracy of the real gesture is not obvious, the data augmentation is obvious. For the real gesture, when the number of iterations and the Dropout ratio is about 20%–30%, good results can be obtained. This work provides the foundation for future application research on hand tracking.

Accurate Hand Detection Method for Noisy Environments

For the problem of low manual detection accuracy under the conditions of illumination and occlusion, the detection of human hands based on common optical images was explored, and an accurate manual detection method under general conditions was proposed. The method based on skin color model combined with Convolutional Neural Network (CNN) was mainly used. Realize the detection of human hands. Firstly, the skin color model is obtained according to the characteristics of skin color in the HSV (Hue, Saturation and Value) space, which is used to segment skin area. On this basis, a convolutional neural network for the detection of human hand contours is constructed, which is used to extract the human hand contour features to constrain skin region to obtain the hand region. The results show that even in light and shielding, it also has adaptability, which improves the accuracy of hand detection.

Research on Building Energy Consumption Acquisition System Based on Configuration

Based on the problem of low stability and high network latency in the traditional building energy consumption acquisition system, in this paper, a building energy consumption acquisition system based on configuration is proposed. The system adopts the embedded technology and WAN communication technology such as TCP/IP, GSM, ZigBee, NB-loT and so on. Sensor-based system, the configuration system to support, embedded MCU as the core, a variety of network communication technologies complement each other, constitute the entire building energy collection system. Through experiments, the system can stably and quickly acquire the data information of the running equipment inside the building, and at the same time it can ensure the integrity and correctness of the data information transmission process. The system has the advantages of high automation, high reliability and fast transmission speed.

A Method for Energy Consumption Audit and Intelligent Decision of Green Buildings

According to the problem that the traditional building energy audit analysis and research methods are too single, which is not applicable to the analysis of specific survey objects such as campus buildings, this article has made an audit of water, electricity and gas energy consumption for campus buildings and put forward a new research program for water and electricity consumption. Compared with the traditional method, the research on electric energy consumption has improved the traditional formula and come up with a new research model for electric energy consumption in campus buildings. And the research on water energy consumption has added the analysis of daily water consumption per person, so the overall data will be accurate to the individual and make the results more accurate. Proved by the experiments, the improved model proposed in this paper can predict the energy consumption of the investigated objects more accurately.

Damage Estimation from Cues of Image Change

This paper proposes a damage estimation algorithm from cues of image changes. We get the feature map of damage area through comparing the Haar feature matrix and the LBP feature matrix by two images before and after the change. We then take the offset comparison method for fusion comparison results of different migration. At last, we get accurate location of damage detection by Gaussian filter and image morphology processing. Experimental results show that the algorithm can accurately detect the image damage area effectively, and is not too sensitive for the changes of light and color temperature. Furthermore this method does not need to establish different damage detection and evaluation models for different targets, and it can adapt to a variety of conditions of damage detection.

A Binaural Signal Synthesis Approach for Fast Rendering of Moving Sound

Considering the difficulty of modeling the individual head related transfer function (HRTF), a binaural moving sound source-oriented stereo audio synthesis approach is proposed, which refers to the interpolation method of HRTF. By means of spherical recording, the spatial audio of points from different directions can be obtained. In order to achieve more realistic effect, the spatial bilinear interpolation method is employed to calculate the weight of the relevant points which used to synthesize the moving sound source, and finally the Doppler Effect is simulated with the interpolation and extraction method in frequency domain. The experiment results show that, our method is capable of replacing HRTF to synthesize moving sound sources approximatively, and the generated performance of moving sound source is closer to the real recording.