Reviewerly/exHarmony huggingface dataset:
A Methodological Study of Document Layout Analysis

Document layout analysis is an important part of document information processing systems, which is essential for many applications such as optical character recognition (OCR) systems, machine translation, information retrieval, and document structured data extraction, as well as for digitizing paper documents and classifying and identifying document image regions. Document-like images contain a wealth of information, and in order to automatically extract and classify regions of interest in document images, the document images are programmed to analyze the layout content for subsequent OCR and automatic transcription. However, the proposed algorithms still have more limitations due to various document layouts and variations of block positions, inter-class and within-class variations, and background noise. This paper first summarizes the traditional learning algorithms based on tour smoothing and segmentation projection, deep learning algorithms using recurrent convolutional neural networks and twin networks, and algorithms combining traditional learning and deep learning proposed in recent years. The current mainstream algorithms and common datasets in experiments for deep learning and their access are highlighted. As well as the comparison of some algorithms on benchmark datasets, and some experimental results with good robustness are given. Finally, the future research areas are prospected for further development.
A Skeleton Posture Transfer Method from Kinect Capture

At present, 3D models are applied to many fields, and the action control of 3D model needs be studied further. In order to control a 3D model using user-acceptable devices, this paper proposes a method to transfer the posture of the skeleton captured by Kinect to a skeleton of the Soldier model, which is a sample in Microsoft's DirectX SDK. First, using the Kinect to collect the user's skeleton information, combined with the initial posture of the Soldier skeleton, through node matching, inverse mirror transformation, transformation from absolute to relative, coordinate space transformation, to generate a skeleton with the same structure as the Soldier skeleton, which can be applied to the Soldier model. And in a virtual reality scene, the corresponding actions of users can drive the Soldier model. Experiments show that the method is feasible and effective. The proposed method can also be referenced and applied to a target skeleton with other structures.
TransNeRF: Multi-View Optimization for General Neural Radiance Fields Across Scenes

Novel view synthesis by neural radiance fields has achieved great improvement with the development of deep learning. However, how to make the method generic across scenes has always been a challenging task. A good idea is to introduce 2D features of the single-view image of the scene as prior knowledge for adaptive modeling. In this paper, we are dedicated to exploring ways to better integrate multi-view image features and model this prior knowledge with inquired novel target views. Our framework innovatively adopts a transformer encoder to fuse multi-view features as global memory, and this global memory will be further input into the transformer decoder to get the more effective features conditioned on the target view as a query. This feature acts as prior knowledge to guide the model to become a general neural radiation field. Extensive experiments are carried out both on category-specific and category-agnostic benchmarks. The results show that TransNeRF achieves state-of-the-art performance and is superior to the earlier novel view synthesis methods, whether single-view input or multi-view input.
Tomb Mural Image Enhancement based on Improved CycleGAN

For the problem of fading or discoloration, the E-CycleGAN model is proposed to realize the digital restoration of tomb murals in color. Specifically, first, StyleGAN3 network is used to generate the tomb mural face; then use empty convolution to replace the original convolution in CycleGAN, to expand the detail information of the mural face, and finally, based on CycleGAN Loss, add Identity Loss to ensure that the content of the original image does not change. Repair was performed on the self-built tomb chamber mural face dataset, and the experimental results showed that the NIQE index decreased by 1.72% on average. It proves that the network has obtained better restoration results in the color restoration of the tomb murals.
Study on Soil Water and Salt Information Model of Digital Farmland

Farmland water-salt dynamics is an important part of crop environmental information, and it is also important information of digital farmland. The research history and achievements of soil water and salt transport models are summarized[1]. According to the wide application of soil water and salt transport model, the HYDRUS model, the SWAP (Soil-Water-Atmosphere-Plant) model, and PSWE (Progressive salt-water embedding neural network) model are analyzed emphatically. The solution and application of the model are introduced, aiming at the problem of the spatial variability of model parameters, it is considered that the combination of GIS technology or the use of the PSWE model[2] will produce better simulation results. The development direction of the intelligent solution of soil water and salt transport model is prospected, which provides a reference for the application of the model in digital farmland.
Person Detection Based Adaptive Video Synthesis: A Fusion Framework for Ultra HD Panorama and Green Screen Videos

The past two years have witnessed the increasing prevalence of metaverse, while the COVID-19 pandemic has accelerated the formation of a non-contact culture. Under this circumstance, virtual reality once again attracts the public attention. Panorama video, as one of the most important forms of virtual reality, provides users with excellent immersion experience. This paper has proposed a fusion framework for Ultra HD panorama videos and green screen videos. In this framework, panorama videos are set as the virtual background layer on which the user-defined real foreground layer of portrait obtained by green screen matting is superimposed. During video fusion process, the portrait size is adaptively determined by parameters provided by a person detection algorithm running on panorama videos. Therefore, a more natural video synthesis result can be achieved, and presented on a head-mounted display or a flat screen device to provide an indistinguishable visual experience to the users.
Inter- and Intra-sentence Interaction Network for Multi-choice Reading Comprehension

Multiple choice reading comprehension is a challenging task in natural language processing, which aims to select the correct answer from a set of candidate options when given a passage and a question. When modeling the relationship between passages and questions, previous approaches usually focus only on the features between passages and questions and ignore the changes in the information within the passages after the interaction between passages and questions. For example, grammatical features and phrase structure can change. In this work, we propose a Inter- and Intra-sentence Interaction Network, which first performs passage-question and passage-option inter-sentence interactions, focusing on the features among passage, question and option sentences. Then the passages obtained from the inter-sentence interactions are subjected to intra-sentence interactions through an attention mechanism, focusing on the information inside the passages. Finally, the vector representation of question-aware passage and the vector representation of option-aware passage are fused by the gating mechanism to dynamically select the vector of passages containing the answers to the questions. The experimental results show that excellent results are achieved on both RACE and MCTest datasets, confirming that intra-sentence interaction can effectively improve the model's performance.
A Method of Text Detection and Script Identification in Natural Scene

In the process of multilingual optical character recognition(OCR), script identification directly affects the efficiency of text recognition. Image-level identification is usually performed on segmented images, and there are fewer studies on end-to-end model. In this study, we implemented the pixel-level script identification by adding a functional branch to the improved detection model. In addition, the enhanced receptive field is used by improving the structure of the feature extraction network, and finally an end-to-end joint text detection and script identification model is proposed. Evaluations from MLE2E and ICDAR MLT 2017 datasets show that the improved model achieves with F1 value of 83% and 64.69% respectively, which outperforms existing methods.
An Integrated Graph Convolutional Neural Network Approach to Educational Object Reordering

In recent years, Graph Convolutional Neural Networks (GCNs) have been widely used in tasks such as node semi-supervised classification, connection prediction and image clustering by constructing connection relations between nodes for information transfer of node features and achieving feature aggregation between nodes. To address the problem that the inter-relationship between image features is ignored in the task of educational object retrieval, the re-ranking task is transformed into the classification and ranking problem of educational object image nodes by utilizing graph convolutional neural network, and we realize the feature transfer between images by constructing the connection relationship of image nodes, so as to pull closer the distance of the same educational object image nodes and push away the distance of different educational object image nodes.
Low-light Image Enhancement with Domain Adaptation

In recent years, deep learning-based algorithms for low-light images have achieved SOTA performance. Traditional unsupervised learning methods typically use image-to-image transformation models to solve low-light image enhancement problems. However they do not perform well on real images and scenes because of the over-reliance on paired real and synthetic image datasets that do not suppress the noise in existing scenes. This is mainly due to domain migration. To address the effects of domain migration, we propose an unsupervised domain adaptation paradigm. This includes a different image translation module, an image illumination enhancement module and a noise suppression module. In order to reduce the impact of domain migration, we align two domains through a two-way translation network. We use the images before and after translation and join the network for training at the same time to maintain a consistency loss between the same group of images. At this stage, we incorporate real low-light images into the enhancement network training using the properties of well-illuminated images (Retinex theory) to further improve domain adaptation. Through ablation and comparative experiments, our proposed method outperforms recent approaches under various metrics about visual quality and subjective feelings.
Medical Intention Recognition Based on MCBERT-TextCNN Model

The existing static vector representation methods such as Word2Vec cannot solve the problem that medical text has a long tail concept. In this paper, combining MCBERT medical pre-training model and TextCNN convolutional neural network, a special model for medical text intention recognition, McBert-TextCNN, was proposed. The MCBERT pre-training model uses different mask generation procedures to mask the scope of the token, and introduces two mask strategies: entity mask and whole span mask, which can learn complex terms and various combinations of phrases in the medical field. TextCNN is added to the MCBERT model to extract semantic information features at different levels of abstraction to achieve more accurate text classification effect. The performance of the algorithm is tested on the Chinese medical intention recognition dataset. Experimental results show that the comprehensive evaluation accuracy of the model reaches 0.95, the recall rate reaches 0.94, and the F1 value reaches 0.94, which can effectively improve the effect of medical intention recognition.
Discovering the Structure Information of the Classroom from A Single Image

Discovering the structure information of a class-room automatically can greatly improve the effectiveness of analyzing the student's behavior, which is very useful for evaluating the study effect of a student. Existing methods were either based on the radio frequency system to detect the seats or adopted a simple image processing method to find the seats. None of the existed can discover the rows and columns structure in a classroom, which can provide much more information for detecting students. In this paper, a novel method for discovering the structure of classroom is proposed. Yolo is firstly employed to find the positions of desks and tables in an image. Then, these positions are reorganized to form a set of potential structure points. Based on these structure points, the rows and columns are discovered by iteratively clustering them into groups and finding the lines from a group of structure points. Experiments are carried out on different classroom images to show the validity of the proposed method.
Wavelets-Based Digital Watermarking Scheme for Medical Image Data

Recently, with the wide application of image processing in medical research, researchers need to produce or apply datasets containing considerable numbers of images. Since image data are easy to reproduce and preserve, the abuse issues on copyright and authorship protection regarding medical image datasets continue to arise. Therefore, various watermarking techniques have been developed to protect copyright and prevent illegal copies of the image dataset. This paper proposed a watermarking scheme for medical image data based on discrete wavelet transform. We introduce the Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) to construct our watermarking scheme to its similarity to the human visual system in processing spatial information. In addition to the DWT and SVD, we adopted the Arnold transformation in the implantation and extraction process for our watermark to provide better protection than the state-of-the-art. We applied the proposed method on CHASE_DB1 dataset and ref. [10]. The experimental results demonstrate the proposed method can embed a watermark in medical images without degrading the visual quality of the original images and has high robustness to image processing attacks.
3D animation face modeling and feature extraction based on computer graphics and image processing technology

After long-term efforts, people have created the era of computer 3D vision technology. In the field of new media, the application of graphics technology in animation industry is very extensive[1]. Through the analysis of computer graphics and image processing technology, this paper accurately expounds the specific process and related algorithms of 3D animation face modeling technology. On this basis, this paper also explains the specific algorithm of feature extraction in 3D design. On the basis of improving animation design, follow the design requirements of realism.
Improved SIFT image feature matching algorithm

Image matching technology is one of the important research problems in the field of computer vision. Scale invariant feature transform (SIFT) is a widely used algorithm in image matching, but the SIFT algorithm has problems such as long matching time and incorrect image matching. In order to improve the image matching accuracy, this paper proposes an improved SIFT image feature matching algorithm. First, for the detection of image feature points, the original image is subjected to grayscale conversion and binarization processing, and the image contour point information is extracted to remove background interference; secondly, the Even-odd algorithm is used to extract the point information from the original image and the target. The feature point sets extracted from the images are compared to screen out unnecessary background feature point sets, thereby reducing the number of matching feature point sets and reducing the probability of misidentification. The experimental results show that the algorithm can effectively reduce the interference caused by the image background, thereby improving the accuracy of image matching.
Local image management system in a multi-user environment based on Docker

Virtualization technology has evolved from traditional hardware virtualization to operating system virtualization technology represented by Docker. The hierarchical storage of Docker images greatly reduces the storage pressure on the host, but there are still problems with local image management. Too many containers lead to too many images, and inappropriate use of containers causes invalid images to occupy disk space[1]. Therefore, this paper proposes an image management system that dynamically cleans invalid images in real-time. First, a security management module is established to set attribute value verification for the image to ensure misoperation by other users. Second, establish a monitoring system to dynamically monitor container status, calculate container availability, and image layer availability, and implement storage space reduction strategies. Finally, users can dynamically adjust monitoring policies according to the container type, which is more user-friendly and more targeted. In this paper, the design scheme of the system has given and experiments are carried out to prove that the system has better security and more efficient disk space management.
Image Retrieval Method Based on Improved Frequency-turned Algorithm

In order to solve the problem that the salient area is not prominent enough and redundant information exists in the image retrieval algorithm based on salient target detection, this paper proposes an improved FT algorithm using the single-threshold segmentation method and the multi-threshold segmentation method. This method can remove noise information, improve the saliency map effect, and enhance the weight of salient regions. The experimental results show that the method proposed in this paper can improve the accuracy of image retrieval, and has certain theoretical and practical significance.
Real-time detection of formation head vehicles based on improved YOLOv5s network

With the development of military modernization in various countries, mechanized troops are working towards unmanned combat. The control and command of formation operations is an important part of unmanned operations. How to achieve stable formation autonomously following people in a complex battlefield environment has great technical difficulties. In this paper, the monocular vision sensor is used for acquisition, and the YOLOv5s neural network structure is modified to realize the rapid identification of the target vehicle. The simulation experiment is used to simplify the model, and the local path planning is designed for the complex environment. Better experimental results were obtained. In this paper, the network structure is optimized based on the existing YOLOv5s, which reduces the computational difficulty, improves the network solution efficiency, and improves the network real-time detection.
Diagnosis of Facial Skin Problems with the Forward Chaining Reasoning Method and Tracing Certainty Factor Beliefs

Most people's dream is to have white, healthy, clean and well-maintained facial skin. However, the treatment does not pay attention to skin type, causing new problems such as acne, dry skin and others. Before carrying out skin care, determining the type of facial skin is very necessary because the determination of skin care must be adjusted to the type of facial skin. The role of a skin specialist is highly expected in determining the type of facial skin care according to skin type. The limited number of dermatologists , doctor's office hours , and the very long queue process and long travel distances result in obstacles that are often experienced. In order to make it easier for the public to recognize skin problems on the face, we need a system that can assist doctors in the initial diagnosis of facial skin problems. In this study, the forward chaining method and certainty factor were used in diagnosing facial skin problems to calculate the accuracy of the types of problems experienced based on the symptoms felt by the user. From the test results obtained in dealing with facial skin problems with an accuracy rate of 99.99 % . The resulting expert system can assist patients in consulting to deal with facial skin problems
Multi-Stream Adaptive GCN with Dropgraph for Skeleton-Based Action Recognition

Graph convolution networks (GCNs) have performed excellent results in skeleton-based action recognition. Regrettably, traditional GCN-based methods only learn from the original skeleton data, that is a fixed graph topology may not be suitable for the diversity of samples and the hierarchy of the GCN models. Another common problem with GCNs is overfitting. In the work, we propose a new multi-stream adaptive GCN with dropgraph (MS-ADGCN). On the input data the graph topology of the model learns both uniformly and individually, which increases the versatility of the model. Moreover, the spatial- temporal-channel (STC) attention module helps the model pay more attention to important features. DropGraph can discard features of related vertexes to enhance the regularization effect. Additionally, we train the joints and bones information with their motion information to further improve the model performance. We conduct massive experiments on the NTU RGB+D and Kinetics-Skeleton datasets and outperform the state-of-the-art methods.
A Creativity Survey of Generative Adversarial Networks Applied to Images

In recent years, generative adversarial networks have been widely used in various image-processing tasks and have shown good performance. Scholars at home and abroad have studied the algorithms and application fields of generative adversarial networks for specific image tasks. How to quickly find the breakthrough point of research according to various image-processing problems is also one of the problems that scholars pay extensive attention to. In this paper, generative adversarial methods for a series of image processing problems are studied. Firstly, we summarize the research status. Secondly, we classify the research objects, research methods, and measurement methods of the target literature. Finally, we summarize the future research directions and innovations.
ELERP: An ellipsoidal linear interpolation for ellipsoidal graph visualization

The Digital Earth (DE) is a digital twin of the real earth, whose shape is approximated by a 3D reference ellipsoid. Abstract relationship among objects in the world can be represented by graph visualization on the DE. However, because the diversity of curvature on ellipsoid, it is generally difficult to construct accurate and concise curvature-fitting edges for 3D graph visualization. Therefore, a curvature-fitting ellipsoidal graph visualization algorithm (CFE) is proposed to address the gap. The core work is a novel ellipsoidal linear interpolation (ELERP) with closed form. ELERP is derived by the combination of spherical linear interpolation (SLERP) and composite transformation W. W accomplishes the scaled transformation from general ellipsoid into unit sphere and implements the rotation from 3D inclined plane into 2D XY-plane. In W, Householder matrix H is from the Hadamard product generated from the geometric composite information of ellipsoid and plane. H has concise computation stemmed from the excellent properties that its inverse and transpose are itself. Lastly, using ELERP and Bezier interpolation to construct a new beret-style and curvature-fitting edge. The experimental results verify the validity of CFE. Compared with classical complex geodesic algorithm, CFE is faster and more concise. Generally, CFE provides an ellipsoidal graph visualization, which will be applied on ellipsoid-based celestial bodies in the vast universe, such as, earth, moon and Mars.