scholarly journals Colour Recognition Algorithm Based on Colour Mapping Knowledge for Wooden Building Image

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Siyi Jia ◽  
Heng Chen
Keyword(s):  
Mutual Influence ◽  
Block Diagram ◽  
Recognition Algorithm ◽  
Linear Mapping ◽  
Display Device ◽  
Mapping Algorithm ◽  
Image Reproduction ◽  
Reproduction Process ◽  
Mapping Technology ◽  
Image Pixels

In the cross-media image reproduction technology, the accurate transfer and reproduction of colour between different media are an important issue in the reproduction process, and the colour mapping technology is the key technology to effectively maintain the image details and improve the level of colour reproduction. Wooden structure in the image colour and colour piece is different, the image of each colour of visual perception is not independent, and every colour in the image pixels is affected by the surrounding pixels, but in the process of image map, without thinking of the pixel space, adjacent pixels of mutual influence in particular, do not let a person particularly be satisfied with the resulting map figure. In the process of image processing by traditional colour mapping algorithm, the colour distortion caused by colour component is ignored and the block diagram of colour mapping system is constructed. With the continuous development of mapping recognition algorithms, the maximum and minimum brightness values in the image are mapped to the maximum and minimum brightness values of the display device by linear mapping algorithm according to the flow of the established recognition algorithm. By establishing the colour adjustment method of the colour mapping image, the processing effect of the mapping algorithm is analysed. The results show that the brightness deviation of the image is reduced and the colour resolution is improved by the colour brightness compensation.

The Ontology Mapping Technology Based on the Information Coding Ontology Model

2010 ◽  
Vol 139-141 ◽  
pp. 1022-1027
Author(s):  
Jian Jun Jiang ◽  
Jun Biao Wang ◽  
Nan Liu
Keyword(s):  
Semantic Similarity ◽  
Information Integration ◽  
Implementation Process ◽  
Information Coding ◽  
Mapping Algorithm ◽  
Ontology Model ◽  
Similarity Algorithm ◽  
Mapping Technology ◽  
Model Section ◽  
Semantic Similarity Analysis

To our knowledge, there is no paper in the open literature dealing with the topic in the title. This paper provides an Information Coding Ontology Model (ICOM) to shield semantic heterogeneous of information resources, and to achieve information integration. Sections 1 and 2 of the full paper present the information coding ontology model and its formal definition, then design the mapping framework of the information coding ontology model. Section 3 discusses the mapping algorithm of the information coding ontology model from three aspects: semantic similarity analysis, obtaining semantic similarity algorithm, and semantic mapping and its implementation. Section 4 gives Fig. 7 as the mapping flow chart of the information coding ontology model and uses a numerical example to illustrate the four-step procedure of the mapping implementation process.

scholarly journals A Framework of an Intelligent Education System for Higher Education Based on Deep Learning

2021 ◽  
Vol 16 (07) ◽  
pp. 233
Author(s):  
Xiuli Zhang ◽  
Zhongqiu Cao
Keyword(s):  
Neural Network ◽  
Higher Education ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Education System ◽  
Block Diagram ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Functional Block Diagram ◽  
Specific Teaching

Intelligent learning platforms and education information application platforms are gaining ground, owing to the wide application of modern technologies such as the Internet of Things, big data analysis, artificial intelligence, and cloud computing. However, the current platforms cannot solve specific teaching problems, and the relevant research mostly focuses on primary and secondary education. Therefore, this paper constructs and analyzes a framework of intelligent education system for higher education based on the deep learning. Firstly, the functional block diagram of the system was built up. Next, a face detection algorithm was proposed based on the multi-task convolutional neural network, a face recognition algorithm was developed based on the improved deep convolutional neural network, and the knowledge learning status of students was tracked based on the memory augmented neural network. Finally, the proposed framework was proved effective and swift through experiments. The research results expand the application scope of the deep learning in education.

scholarly journals FPGA Implementation of Archery Target Detection using Color Sequence Recognition Algorithm

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1391-1397
Keyword(s):  
Central Region ◽  
Color Space ◽  
Recognition Algorithm ◽  
Fpga Implementation ◽  
Target Distance ◽  
Color Classification ◽  
Sequence Recognition ◽  
Field Programmable ◽  
Image Pixels ◽  
Color Sequence

In this paper, an implementation of image processing methods to extract and recognize a standard tri-colored archery target to a field-programmable gate array is demonstrated. Detection and recognition of the archery target was never been done on an FPGA platform. The platform used to realize the design was the ZedBoard™ Development Kit equipped with Xilinx Zynq®-7000 All Programmable system on chip. The algorithms used to extract the central region is based on color classification in HSV color space. Once each image pixels are classified, the color sequence recognition algorithm attempts to look for the target and extract the central region of the archery target if present. Image filtering techniques and analysis such as morphological filtering and contour feature analysis are used to properly identify the shape and location of the extracted pixels. Discussed next is the implementation of the algorithm both in the software and hardware aspects and a comparison between their response time and accuracy is demonstrated. There was about two-fold decrease in processing time when FPGA implementation was deployed. The accuracy of the system was also tested and able to reach an accuracy of 96.67% for near target distance. For far target distance, the accuracy degraded to 88.33% but the system has managed to maintain its specificity value despite the noise becoming dominant for smaller region occupied by the target.

scholarly journals Design of an Automatic English Pronunciation Error Correction System Based on Radio Magnetic Pronunciation Recording Devices

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhang Shufang
Keyword(s):  
Speech Recognition ◽  
Error Correction ◽  
Block Diagram ◽  
Linear Mapping ◽  
Recording Device ◽  
Endpoint Detection ◽  
English Pronunciation ◽  
Data Driven Approach ◽  
Correction System ◽  
Pronunciation Dictionary

In this paper, a system for automatic detection and correction of mispronunciation of native Chinese learners of English by speech recognition technology is designed with the help of radiomagnetic pronunciation recording devices and computer-aided software. This paper extends the standard pronunciation dictionary by predicting the phoneme confusion rules in the language learner’s pronunciation that may lead to mispronunciation and generates an extended pronunciation dictionary containing the standard pronunciation of each word and the possible mispronunciation variations, and automatic speech recognition uses the extended pronunciation dictionary to detect and diagnose the learner’s mispronunciation of phonemes and provides real-time feedback. It is generated by systematic crosslinguistic phonological comparative analysis of the differences in phoneme pronunciation with each other, and a data-driven approach is used to do automatic phoneme recognition of learner speech and analyze the mapping relationship between the resulting mispronunciation and the corresponding standard pronunciation to automatically generate additional phoneme confusion rules. In this paper, we investigate various aspects of several issues related to the automatic correction of English pronunciation errors based on radiomagnetic pronunciation recording devices; design the general block diagram of the system, etc.; and discuss some key techniques and issues, including endpoint detection, feature extraction, and the system’s study of pronunciation standard algorithms, analyzing their respective characteristics. Finally, we design and implement a model of an automatic English pronunciation error correction system based on a radiomagnetic pronunciation recording device. Based on the characteristics of English pronunciation, the correction algorithm implemented in this system uses the similarity and pronunciation duration ratings based on the log posterior probability, which combines the scores of both, and standardizes this system scoring through linear mapping. This system can achieve the purpose of automatic recognition of English mispronunciation correction and, at the same time, improve the user’s spoken English pronunciation to a certain extent.

An Image Reconstruction System Applied to High Voltage Microscopy

1975 ◽  
Vol 33 ◽  
pp. 292-293
Author(s):  
D. E. Johnson
Keyword(s):  
High Voltage ◽  
Prior Information ◽  
Block Diagram ◽  
Three Dimensional ◽  
Real Space ◽  
Two Dimensional ◽  
Efficient Manner ◽  
Fourier Methods ◽  
Tilt Series ◽  
Methods Of Reconstruction

Increased specimen penetration; the principle advantage of high voltage microscopy, is accompanied by an increased need to utilize information on three dimensional specimen structure available in the form of two dimensional projections (i.e. micrographs). We are engaged in a program to develop methods which allow the maximum use of information contained in a through tilt series of micrographs to determine three dimensional speciman structure.In general, we are dealing with structures lacking in symmetry and with projections available from only a limited span of angles (±60°). For these reasons, we must make maximum use of any prior information available about the specimen. To do this in the most efficient manner, we have concentrated on iterative, real space methods rather than Fourier methods of reconstruction. The particular iterative algorithm we have developed is given in detail in ref. 3. A block diagram of the complete reconstruction system is shown in fig. 1.

On-line alignment and astigmatism correction using a TV and personal computer

1993 ◽  
Vol 51 ◽  
pp. 202-203
Author(s):  
F. Hosokawa ◽  
Y. Kondo ◽  
T. Honda ◽  
Y. Ishida ◽  
M. Kersker
Keyword(s):  
High Resolution ◽  
Personal Computer ◽  
Block Diagram ◽  
Incident Beam ◽  
Ccd Camera ◽  
Alignment Procedure ◽  
Astigmatism Correction ◽  
Transmission Electron ◽  
Alignment System ◽  
On Line

High-resolution transmission electron microscopy must attain utmost accuracy in the alignment of incident beam direction and in astigmatism correction, and that, in the shortest possible time. As a method to eliminate this troublesome work, an automatic alignment system using the Slow-Scan CCD camera has been introduced recently. In this method, diffractograms of amorphous images are calculated and analyzed to detect misalignment and astigmatism automatically. In the present study, we also examined diffractogram analysis using a personal computer and digitized TV images, and found that TV images provided enough quality for the on-line alignment procedure of high-resolution work in TEM. Fig. 1 shows a block diagram of our system. The averaged image is digitized by a TV board and is transported to a computer memory, then a diffractogram is calculated using an FFT board, and the feedback parameters which are determined by diffractogram analysis are sent to the microscope(JEM- 2010) through the RS232C interface. The on-line correction system has the following three modes.

The codevelopment of group relationships: The role of individual group member’s and other group members’ mutual influence and shared group environment.

2019 ◽  
Vol 66 (5) ◽  
pp. 640-649 ◽  
Author(s):  
Gianluca Lo Coco ◽  
Salvatore Gullo ◽  
Gabriele Profita ◽  
Chiara Pazzagli ◽  
Claudia Mazzeschi ◽  
...  
Keyword(s):  
Mutual Influence ◽  
Individual Group ◽  
Group Environment ◽  
Group Members ◽  
Group Relationships
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