Retracted: English Web-Based Teaching Supervision Based on Intelligent Face Image Perception and Processing for IoT

In order to solve the problem that all levels of civil aviation meteorological centers are unable to provide all the possible meteorological information services that may reach the low-altitude airspace for general aviation flight, this paper proposes a real-time low-altitude meteorological intelligence image collection and processing method. Based on this method, a real-time low-altitude meteorological intelligence image collection system can be designed and implemented. The real-time low-altitude meteorological intelligence image collection system is an information system that provides real-time low-altitude airspace visibility and weather condition information services for image-based flight photography, transmission, processing, and meteorological information dissemination. It includes the image collection subsystem and image processing subsystem, meteorological information publishing Web Server. Among them: The image collection subsystem is mainly used for automatically taking images of the surrounding environment at regular intervals, and transmitting the photographs and their time and place information to the image processing system installed in the low-altitude navigational information service department through the wireless communication device; the image processing subsystem collects images and related information for further processing; the meteorological information publication Web server mainly provides general airline users with services such as consultation, inquiry, and submission of low-altitude meteorological intelligence information of relevant airspace related to images.

Due to the increasing health risks posed by the COVID-19, the organizing committee decided to change the 2020 2nd International Conference on Intelligent Medicine and Image Processing (IMIP 2020) into online meeting. Finally, IMIP 2020 was held successfully during April 23-26, 2020 under the support from Tiangong University. IMIP provided the communication platform for international researchers, engineers, academicians as well as industrial professionals who are interested in Intelligent Medicine and Image Processing and its current applications.

Facial image processing is a promising tool for consumer electronics and social infrastructure systems. In recent years, digital processing of a facial image can easily be performed with the spread of digital image apparatus, such as a digital camera and a mobile phone, by improvement of throughput of a computer. The performance of the face detection that is basic of the facial image processing improves drastically, and the computational cost has also decreased. It is the reason why that has expanded the application to various appliances. This paper introduces our group’s facial image processing algorithm as an example and trends of various applications using facial image processing in consumer electronics field and social infrastructure systems.KeywordsFace RecognitionLocal Binary PatternFace DetectionGesture RecognitionIndependent Component AnalysisThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

As the principal place for currency flow nowadays, the banking system plays a special role in society and its security has always been a focus. The current bank branches' security systems usually use video surveillance system, which is basically used to provide evidence after incidents, and it can not do real-time warning. With the rapid development of computer vision, video and intelligent recognition, real-time alarm analysis of surveillance system has become possible. Based on the technologies of human body detection, motion tracking and behavior judgement, this article designs a bank intelligent video image processing and monitoring control system which is based on OpenCV, and the system not only realizes real-time monitoring and alarm within monitoring zone effectively, but also brings innovations to the traditional video surveillance system, providing strong protection of safe operation for banks.

The application of computer vision in face processing remains an important research field. The aim of this chapter is to provide an up-to-date review of research efforts of computer vision scientist in facial image processing, especially in the areas of entertainment industry, surveillance, and other human computer interaction applications. To be more specific, this chapter reviews and demonstrates the techniques of visible facial analysis, regardless of specific application areas. First, the chapter makes a thorough survey and comparison of face detection techniques. It provides some demonstrations on the effect of computer vision algorithms and colour segmentation on face images. Then, it reviews the facial expression recognition from the psychological aspect (Facial Action Coding System, FACS) and from the computer animation aspect (MPEG-4 Standard). The chapter also discusses two popular existing facial feature detection techniques: Gabor feature based boosted classifiers and Active Appearance Models, and demonstrate the performance on our in-house dataset. Finally, the chapter concludes with the future challenges and future research direction of facial image processing.

Empowering displays with intelligent functions enables their application in intelligent image processing and intelligent interactive displays, which is the combination of future display and artificial intelligence technologies. However, existing display technologies face significant processing and transmission burdens due to their conventional hardware separation architecture, which separates memory from the processor and display module from the processing module. To address these challenges, a highly integrated memory‐ processing‐display intelligent light‐emitting hardware (MPDIH) capable of information generation, memory, processing, and visualization is proposed. The MPDIH, based on an organic light‐sensitive layer and activated by ultraviolet light irradiation, exhibits unique photoinhibition behavior attributed to the reverse light‐induced electric field formed by the directional arrangement of photo‐generated excitons. This behavior enables dynamic regulation and autonomous learning during the device training process. Leveraging this phenomenon, intelligent image processing is successfully demonstrated, achieving a significant contrast improvement with a maximum enhancement of 261% compared to unprocessed raw signals. Furthermore, the memory‐processing‐display intelligent image processing scheme enables fashion MNIST image recognition using an artificial neural network, and achieves noticeably higher recognition accuracy (>89%) compared to the original fuzzy images (<59%). Consequently, the proposed memory‐processing‐display intelligent light‐emitting hardware holds promise for future applications in integrated photonic systems and interactive artificial intelligence displays.

We have designed and constructed a column parallel vision (CPV) system to realize an intelligent and general purpose image processing system with higher frame rate within 1 millisecond. The system consists of an original designed photo detector array (PDA) with high frame rate, a parallel processing unit with fully parallel processing elements (PEs), and a controller for PDA and PEs. The column parallel architecture enables the PDA 1 millisecond frame rate with 256 analog levels which is required for industrial image processing and measurements. The parallel processing unit has been fabricated by using FPGAs and has 128 X 128 PEs to perform the fully parallel image processing. The PEs have the S<SUP>3</SUP>PE architecture which has SIMD type parallel processing flow, and was constructed only 500 transistors suitable for integration in future. Since the PEs are operated by control signal from the controller, we can achieve desired image processing task to the system by changing the software. We have demonstrated that the system can be worked as a tracking system. The experimental results show the system was realized high speed feedback loop as 1000 frame/s including the tracking operation with noise reduction, matching (self-window algorithm) and moment calculation.

Over the past few years, the healthcare industry has seen a dramatic increase in the use of intelligent automation enabled by AI technology. These developments are made to better the standard of medical decision making and the standard of treatment given to patients. Fuzzy boundaries, shifting sizes, and aberrations like hair or ruler lines all provide difficulties for automatic detection of skin lesions in dermoscopic images, slowing down the otherwise efficient process of diagnosing skin cancer. However, these difficulties may be conquered by employing image processing software. To address these issues, the authors of this paper provide a novel IMLT-DL model for intelligent dermoscopic image processing. Multi-level thresholding and deep learning are brought together in this model. Top hat filtering and inpainting have been included into IMLT-DL for use in image processing. In addition, Mayfly Optimization has been used in tandem with multilayer Kapur's thresholding to identify specific trouble spots. For further investigation, it uses an Inception v3-based feature extractor, and for data classification, it makes use of gradient boosting trees (GBTs). On the ISIC dataset, this model was shown to outperform state-of-the-art alternatives by a margin of 0.992% over the duration of trial iterations. These advances are a major step forwards in the quest for faster and more accurate skin lesion detection.

In this paper we report on the technical developments of the head worn display (HWD) for DARPA’s SCENICC program. The goal of the SCENICC program is to provide the warfighter with vision capabilities exceeding normal human vision. This is being achieved with an advanced imaging system that is able to capture the surrounding scene with superior visual acuity, contrast sensitivity, and wavelength sensitivity. With this increased visual information density, intelligent image processing provides imagery to the wearer’s eyes via an advanced HWD. The goal of this HWD is to provide digital visual information at the limits of human perception over a field of view near the human peripheral vision limits. This represents a tremendous amount of information requiring novel concepts in order to achieve such ambitious goals. One important concept is the use of imaging optics located directly on the eye, moving with the eye as it changes its gaze angle. A second concept is the use of demagnification optics to convert a large, low spatial resolution image into a smaller, high spatial resolution image. This is done in conjunction with image processing that is constantly modifying the image presented based on real-time pupil tracking. In addition to enabling a high performance optical system, integrating the imaging optical components into contact lenses eliminates much of the bulky imaging optics from the HWD itself creating a high performance wearable display in a standard protective eyewear form factor. The resulting quantum advance in HWD performance will enable HWDs to expand well beyond their current limited roles.

Nowadays, the world suffers from a terrible problem with COVID-19. Some researchers worked for a facemask detection biometric system by using Residual Networks such as Resnet50 or Resnet100. This research focuses on depreciating the transmission of Corona Virus Disease (COVID) from person to person. To wear a facemask plays an excellent role in not spreading COVID to the community. Moreover, to enhance social life, automatic facemask detection and facemask recognition systems are needed. Additionally, this system is helpful for security enhancement through face recognition. Here, the research for developing and implementing a biometric system for banks, offices, universities, and other public areas. This system helps in detecting the facemask for wearing by the human With Artificial Intelligence (AI), Deep Learning (DL), and Digital Image Processing technologies, this system allows the person to enter inside or stay outside because of not wearing a facemask. Besides designing and implementing a new model, the authors will discuss pre-defined the VGG16 deep learning model of CNN (Convolution Neural Network). Further, conclude the performance of both CNN models. This paper proposes a Facemask Detection model for solving the problem of manual care. This proposed model is a combination of most trending technologies such as AI, DL, and Image processing. In the future, describe this newly implemented model by appending Gaussian noise with images and make the system more reliable for actual use.KeywordsCOVID-19FacemaskFace detectionBiometric systemArtificial Intelligence (AI)

Intelligent Computer Vision and Image Processing

The book puts special stress on the contemporary techniques for reasoning-based image processing and analysis: learning based image representation and advanced video coding; intelligent image processing and analysis in medical vision systems; similarity learning models for image reconstruction; visual perception for mobile robot motion control, simulation of human brain activity in the analysis of video sequences; shape-based invariant features extraction; essential of paraconsistent neural networks, creativity and intelligent representation in computational systems.KeywordsVideo CodecAdvanced Video CodeVisual Attention ModelPublic Service BroadcasterIntelligent RepresentationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Face recognition is an important identification technology in daily life and one of the research hotspots in the field of computer vision and intelligent image processing. However, many algorithms based on Pulse Coupled Neural Network (PCNN) do not adequately describe features, same parameters are used to process the entire face image, ignoring the difference between the local recognition rate and gray distribution of each part of the face image. This paper proposes a block face recognition algorithm based on the weighted intensity PCNN model, we have refined the description of the features through parameter adaptation, partitioned the face according to the degree of contribution in the recognition process, and set the corresponding weight parameters through the improved grid search algorithm, which can effectively improve the PCNN model’s ability to recognize faces. Experiments show that in different face databases, the performance of this model is greatly improved compared with common methods such as PCA and ICA.

This paper presents the results of a feasibility study on intelligent image processing for flood monitoring on board satellites. The ability to detect temporal changes in images is one of the most important functions in intelligent image processing systems for hazard and disaster monitoring applications. An automatic change detection system is proposed, the purpose of which is to monitor particular areas on earth for flooding events and give warnings to the authorities accordingly. A novel solution to flood detection based on the combined use of optical multispectral imagery and global positioning system reflectometry data is introduced. A fuzzy inference engine is used in the decision-making process, which generates control signals to other subsystems on board the satellite.

We are researching about an age and gender estimation based on wrinkle texture and color of facial images. A preliminary questionnaire (enquete) examination on how effectively the facial images could be used for gender and age estimations was executed by using 300 different faces and 21 examinees. Wrinkles appearing in the face and the shape and size of the facial parts are selected to model the age and gender estimation in this research basing on this enquete. Basing on this preliminary consideration, an image-processing algorithm for wrinkle modeling, was proposed. In addition, a method for making relationships between facial images and their keywords was proposed by using the latent semantic indexing. An efficient interface for displaying the relationships among keywords and facial images has been introduced with 2D/3D-feature space defined by the indexing.