scholarly journals Recognition of Teaching Features and Behaviors in Online Open Courses Based on Image Processing

2021 ◽  
Vol 38 (1) ◽  
pp. 155-164
Author(s):  
Sheliang Li ◽  
Huaqi Chai
Keyword(s):  
Image Processing ◽  
Support Vector ◽  
Teaching Behaviors ◽  
High Quality ◽  
Behavior Recognition ◽  
Wide Audience ◽  
Teaching Behavior ◽  
And Behaviors ◽  
Open Courses

High-quality online open courses have a wide audience. To further improve the quality of these courses, it is critical to analyze the teaching behaviors in class, which are the manifestation of the overall quality of the teacher. Considering the popularity of image processing-based behavior recognition in many disciplines, this paper explores deep into the teaching features and behaviors in online open courses based on image processing. Firstly, a coding scale was designed for teaching behaviors in online open courses. Next, the principle of optical flow solving was explained for teaching video images. Then, a teaching behavior feature extraction model was established based on dual-flow deep CNN, and used to extract the key points of teacher body and the behavior features of the teacher. After that, a teaching behavior recognition method was developed combining histogram of oriented gradients (HOG) and support vector machine (SVM) to accurately allocate the teaching features and behaviors to the corresponding teaching links. Finally, the proposed model was proved effective through experiments. Based on the recognized teaching behaviors, the frequency and duration of such behaviors were subject to comparative analysis, revealing the teaching features in high-quality online open courses.

scholarly journals Enhancement of Satellite Imagesusing Image Sharpening Technique

2019 ◽  
Vol 8 (6S2) ◽  
pp. 351-354
Keyword(s):  
Image Processing ◽  
Color Image ◽  
Histogram Equalization ◽  
Grayscale Image ◽  
Original Image ◽  
High Quality ◽  
Wide Range ◽  
Equalization Technique ◽  
The Difference

In many image processing applications, a wide range of image enhancement techniques are being proposed. Many of these techniques demanda lot of critical and advance steps, but the resultingimage perception is not satisfactory. This paper proposes a novel sharpening method which is being experimented with additional steps. In the first step, the color image is transformed into grayscale image, then edge detection process is applied using Laplacian technique. Then deduct this image from the original image. The resulting image is as expected; After performing the enhancement process,the high quality of the image can be indicated using the Tenengrad criterion. The resulting image manifested the difference in certain areas, the dimension and the depth as well. Histogram equalization technique can also be applied to change the images color.

scholarly journals Vision Based Algorithm for People Counting Using Deep Learning

2018 ◽  
Vol 7 (3.6) ◽  
pp. 74
Author(s):  
M Padmashini ◽  
R Manjusha ◽  
Latha Parameswaran
Keyword(s):  
Image Processing ◽  
Deep Neural Networks ◽  
Local Binary Pattern ◽  
Support Vector ◽  
Shopping Malls ◽  
Energy Usage ◽  
People Detection ◽  
False Negatives ◽  
People Counting

Estimating the number of people in a particular scene has always been an important topic of research in computer vision and digital image processing. People counting has wide applications in scenario ranging from analyzing the customer's choice and improving the quality of service in retail stores, supermarkets and shopping malls to managing human resources and optimizing the energy usage in office buildings. While there exists algorithms for counting people in a scene, some algorithm have set their benchmark in performance with respect to efficiency, flexibility and accuracy. In this paper, an attempt has been made to perform people counting using Deep Neural Networks (DNN) on comparison with existing image processing based algorithms like Histogram of Oriented Gradients with Support Vector Machine (HoG with SVM), Local Binary Pattern (LBP) based Adaboost classifier and contour based people detection. The proposed DNN based approach has higher accuracy at 90% and less false negatives.  

Steganographic data hiding in image processing using predictive differencing

2012 ◽  
Vol 20 (2) ◽  
Author(s):  
C. Weng ◽  
H. Tso ◽  
S. Wang
Keyword(s):  
Image Processing ◽  
Data Hiding ◽  
Predictive Value ◽  
Experimental Result ◽  
Embedding Capacity ◽  
High Quality ◽  
Pixel Value ◽  
Pixel Value Differencing

AbstractIn this paper, we propose a stenography scheme based on predictive differencing to embed data in a grey-image. In order to promote the embedding capacity of pixel-value differencing (PVD), we use differencing between a predictive value and an input pixel as the predictive differencing to embed the message where a predictive value is calculated by using various predictors. If the predictive differencing is large, then it means that the input pixel is located in the edge area and, thus, has a larger embedding capacity than the pixel in a smooth area. The experimental result shows that our proposed scheme is capable of providing greater embedding capacity and high quality of stego-images then previous works. Furthermore, we have also applied various predictors to evaluate our proposed scheme.

Automated Headstone Photo Engraving

2015 ◽  
Vol 816 ◽  
pp. 313-320
Author(s):  
Daniela Perdukova ◽  
Mišel Batmend ◽  
Pavol Fedor
Keyword(s):  
Image Processing ◽  
Poor Quality ◽  
Cnc Machine ◽  
Solid Materials ◽  
High Quality ◽  
Simple Method ◽  
Cnc Machines ◽  
Data Coding ◽  
Better Than

Nowadays, machine engraving of photos into solid materials such as marble or granite is becoming very popular. Relatively cheap CNC machines are available. The problem is that high quality photos are essential to obtain good results. The first part of the paper describes a model of a CNC machine used for engraving and puts down the principles of image processing applied to poor quality photos in order to get the best results, as well as the fundamental image processing methods necessary for achieving satisfactory results when using an electromagnetic diamond percussion tool for engraving. The second part of the paper describes a very simple method of data coding and the algorithm of engraving tool movement for image engraving process by means of a control system based on ATmega16 microcontroller. The quality of the engraved images is comparable, or even better, than that of manually engraved images or images engraved by other competitive CNC machines.

scholarly journals Aplication of Image Processing to Determine The Freshness of Tilapia Fish (Oreochromis niloticus)

2019 ◽  
Vol 22 (2) ◽  
pp. 229-235
Author(s):  
Ayu Kalista ◽  
Amin Redjo ◽  
Umi Rosidah
Keyword(s):  
Image Processing ◽  
Storage Time ◽  
High Quality ◽  
Fresh Fish ◽  
A Value ◽  
Red Color ◽  
Quality Category ◽  
Independent Variable ◽  
High Quality Category

The quality of fresh fish will decrease immediately after death. One of the indicators of fish quality is the changes of the gills color. The aim of this research was to determine the changes of red color in the gills of tilapia using image processing as an indicator of fish freshness. The research method used is the Explanatory Research where the independent variable (x) is tilapia which stored at room temperature for 12 hours. The dependent variable (y) was the intensity value of red. The quality of fish could be grouped into several categories, such as high quality, good quality, limit of acceptability and spoilt. The Observation was carried out with a time of 0 hours to 12 hours (4 hour interval). The results showed that storage time affected the deterioration of fresh quality. The high quality category has a red percentage value of 82.18%. Fresh category has a red percentage value of 67.10%. The limit of acceptability category has a value of 38.52% and the spoiltcategory has a red percentage value of 9.92%.

scholarly journals A Multi-Sensor System for Silkworm Cocoon Gender Classification via Image Processing and Support Vector Machine

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2656 ◽  
Author(s):  
Alex Noel Joseph Raj ◽  
Rahul Sundaram ◽  
Vijayalakshmi G.V. Mahesh ◽  
Zhemin Zhuang ◽  
Alessandro Simeone
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Digital Camera ◽  
Computation Time ◽  
Sensor System ◽  
Support Vector ◽  
Gender Classification ◽  
Load Sensor ◽  
Silkworm Cocoon

Sericulture is traditionally a labor-intensive rural-based industry. In modern contexts, the development of process automation faces new challenges related to quality and efficiency. During the silkworm farming life cycle, a common issue is represented by the gender classification of the cocoons. Improper cocoon separation negatively affects quantity and quality of the yield resulting in disruptive bottlenecks for the productivity. To tackle this issue, this paper proposes a multi sensor system for silkworm cocoons gender classification and separation. Utilizing a load sensor and a digital camera, the system acquires weight and digital images from individual silkworm cocoons. An image processing procedure is then applied to extract significant shape-related features from each image instance, which, combined with the weight data, are provided as inputs to train a Support Vector Machine-based pattern classifier for gender classification. Subsequently, an air blower mechanism and a conveyor system sort the cocoons into their respective bins. The developed system was trained and tested on two different types of silkworm cocoons breeds, respectively CSR2 and Pure Mysore. The system performances are finally discussed in terms of accuracy, robustness and computation time.

Mathematical models for some problems in textile engineering and its corresponding discussions

2014 ◽  
Vol 05 (02) ◽  
pp. 1350030
Author(s):  
Li Zhang ◽  
Xinhua You ◽  
Jun Chen ◽  
Liang Zhang
Keyword(s):  
Image Processing ◽  
Cluster Analysis ◽  
Textile Industry ◽  
Programming Algorithm ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Image Processing Techniques ◽  
Chinese Industry ◽  
Processing Techniques

Textile industry is very important to the development of Chinese industry and economy. Image processing techniques are beneficial to improve the quality of cotton goods. Suitable blending ratio of yarn is good for it. It is significant to measure the blending ratio of yarn in the practice of textile engineering. Combined with results done by other scholars, this paper uses the concepts of acreage index, abnormity index and fluctuation index. Based on these morphologic indices, it is convenient to construct corresponding eigenvector and to discuss useful mathematical method for the cluster analysis during the measurement of the blending ratio. This paper also sets up some kinds of nonlinear optimization model for the problem. Using classical integer programming algorithm, support vector machine algorithm and genetic algorithm to the problem, we get fine results for cluster analysis. Finally, we give out another problem of image processing and have some discussions about it.

scholarly journals Reduction of Artefacts in JPEG-XR Compressed Images

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1214 ◽  
Author(s):  
Kai-Lung Hua ◽  
Ho Trang ◽  
Kathiravan Srinivasan ◽  
Yung-Yao Chen ◽  
Chun-Hao Chen ◽  
...  
Keyword(s):  
Image Processing ◽  
Document Image ◽  
File Size ◽  
Quantization Parameter ◽  
High Quality ◽  
Compressed Images ◽  
Extra Step ◽  
Image Displays ◽  
Jpeg Xr

The JPEG-XR encoding process utilizes two types of transform operations: Photo Overlap Transform (POT) and Photo Core Transform (PCT). Using the Device Porting Kit (DPK) provided by Microsoft, we performed encoding and decoding processes on JPEG XR images. It was discovered that when the quantization parameter is >1-lossy compression conditions, the resulting image displays chequerboard block artefacts, border artefacts and corner artefacts. These artefacts are due to the nonlinearity of transforms used by JPEG-XR. Typically, it is not so visible; however, it can cause problems while copying and scanning applications, as it shows nonlinear transforms when the source and the target of the image have different configurations. Hence, it is important for document image processing pipelines to take such artefacts into account. Additionally, these artefacts are most problematic for high-quality settings and appear more visible at high compression ratios. In this paper, we analyse the cause of the above artefacts. It was found that the main problem lies in the step of POT and quantization. To solve this problem, the use of a “uniform matrix” is proposed. After POT (encoding) and before inverse POT (decoding), an extra step is added to multiply this uniform matrix. Results suggest that it is an easy and effective way to decrease chequerboard, border and corner artefacts, thereby improving the image quality of lossy encoding JPEG XR than the original DPK program with no increased calculation complexity or file size.

scholarly journals Face Transferring on Webcam images using StyleGAN

2021 ◽  
Vol 2062 (1) ◽  
pp. 012011
Author(s):  
J. Prasanthi ◽  
G. Anuradha
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Facial Expressions ◽  
Privacy Protection ◽  
Affine Transformation ◽  
Image Transfer ◽  
Bilinear Interpolation ◽  
High Quality ◽  
The Face

Abstract In image processing technology, face transfer is broadly used for privacy protection, picture enhancement, and entertainment applications. Face transfer is the domain that maps one image into another image and extracts several features of the face from one person to morph that face to another person. This face transfer will carry the facial expressions also. This is also called face morph, face swap, etc. Here we propose StyleGAN technology using face transfer with the image to get high quality. In this StyleGAN contribute the bilinear interpolation and affine transformation. Bilinear interpolation is to remove the noise and increase the quality of images. Affine transformation is to supply the images with 2d warping to improve the image quantity. To upgrade the quality of the images with face transfer is adopted to increase the accuracy of the image quality after image transfer.

scholarly journals Automatic Extraction of Color Features from Landscape Images Based on Image Processing

2021 ◽  
Vol 38 (3) ◽  
pp. 747-755
Author(s):  
Cong Tan ◽  
Shaoyu Yang
Keyword(s):  
Image Processing ◽  
Color Space ◽  
Color Constancy ◽  
Support Vector ◽  
Automatic Extraction ◽  
Color Features ◽  
Image Library ◽  
Presentation Effect ◽  
High Level

The dominant color features determine the presentation effect and visual experience of landscapes. The existing studies rarely quantify the application effect of landscape colors through image colorization. Besides, it is unreasonable to analyze landscape images with multiple standard colors with a single color space. To solve the problem, this paper proposes an automatic extraction method for color features from landscape images based on image processing. Firstly, a landscape lighting model was constructed based on color constancy theories, and the quality of landscape images was improved with color constant image enhancement technology. In this way, the low-level color features were extracted from the landscape image library. Next, support vector machine (SVM) and fuzzy c-means (FCM) were innovatively integrated to extract high-level color features from landscape images. The proposed method was proved effective through experiments.

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