A Study of the Negative Influence of English Loan Words from Chinese Origin on the Image of Chinese People——Taking “Tuhao”“Dama” and “Guanxi” for examples

With the increasing Chinese influence on the international language English, words like "tuhao" and "dama" have been included into the Oxford Dictionary. Some loan words from China have undermined the image of Chinese people. This paper, by taking"tuhao","dama"and"guanxi"for examples, analyzes the impact of these new loan words in English on the image of Chinese people. The result shows these words have negative influence on the image of Chinese people. Therefore, countermeasures should be taken to strengthen the spread of China's positive image.

Coverless Information Hiding Based on the Molecular Structure Images of Material

The traditional information hiding methods embed the secret information by modifying the carrier,which will inevitably leave traces of modification on the carrier.In this way,it is hard to resist the detection of steganalysis algorithm.To address this problem,the concept of coverless information hiding was proposed.Coverless information hiding can effectively resist steganalysis algorithm,since it uses unmodified natural stego-carriers to represent and convey confidential information.However,the state-of-the-arts method has a low hidden capacity,which makes it less appealing.Because the pixel values of different regions of the molecular structure images of material(MSIM)are usually different,this paper proposes a novel coverless information hiding method based on MSIM,which utilizes the average value of sub-image’s pixels to represent the secret information,according to the mapping between pixel value intervals and secret information.In addition,we employ a pseudo-random label sequence that is used to determine the position of sub-images to improve the security of the method.And the histogram of the Bag of words model(BOW)is used to determine the number of subimages in the image that convey secret information.Moreover,to improve the retrieval efficiency,we built a multi-level inverted index structure.Furthermore,the proposed method can also be used for other natural images.Compared with the state-of-the-arts,experimental results and analysis manifest that our method has better performance in anti-steganalysis,security and capacity.

Visual processing of word and image in the fusiform gyrus

BACKGROUND： The role of the left midfusiform gyrus as a target for visual word processing has been a topic of discussion. Numerous studies have utilized alphabetic writing for subject matter. However, few have addressed visual processing of Chinese characters in the left midfusiform gyrus. OBJECTIVE： To verify visual processing of Chinese characters and images in the left midfusiform gyrus using functional magnetic resonance imaging. DESIGN, TIME AND SETTING： A blocked design paradigm study. Experiments were performed at the Room of Magnetic Resonance, Guangdong Provincial Second People＇s Hospital, China from May to June 2009. PARTICIPANTS： A total of eight undergraduate students were recruited from Guangzhou University of China, comprising two females and six males, aged 20-23 years. The subjects were right-handed which was determined by a Chinese standard questionnaire. None of the subjects had a history of psychoneurosis, familial disease, color blindness, or color weakness. METHODS： A total of eight undergraduates were enrolled as subjects. Picture-naming and verb generation tasks were employed through the use of functional magnetic resonance imaging. Analysis of Functional Neurolmages software was used to process the data. MAIN OUTCOME MEASURES： Visual processing of Chinese characters and images in the left midfusiform gyrus was measured. RESULTS： Picture-naming and verb generation tasks were shown to significantly activate the bilateral midfusiform gyrus. Activation occurred in the visual word form area of the left midfusiform gyrus. CONCLUSION： The left midfusiform gyrus plays a general role in visual processing of Chinese characters and images.

Word-Image Intertwining:William Blake’s Illuminated Poetry and the Aesthetics of Compositeness

Bowen Wang Trinity College Dublin,Dublin,From ut pictura poesis to intermediality,the close,interactive,and complicated relationship between poetry and painting has been an inevitable subject under discussion throughout the historiography of literature and fine art.Different approaches and interpretations to/on their sisterhood could come to a broad spectrum of interartistic ideas and practices.William Blake,an English Romantic poet,painter,and printmaker,links word and image by his invention of relief etching,or more understandably,the illuminated painting which juxtaposes his verse with in-text illustrations simultaneously on the same page.His technical strategy has already implied a dynamic and dialectical interrelation between word and image,like the treatment of earlier medieval manuscripts,yet in a more innovative rather than decorative manner.This article,therefore,through a comparative analysis of both Blake’s verbal and visual representations,will first attempt to clarify two distinct modes of combining poetic language with visualisation vis-à-vis form and content,and then critically investigate how this word-image interaction can be used to reflect the poet’s Romantic thought about sociocultural changes and provide new possibilities of reading,interpretation,and aesthetic complexity in that specific epoch.

基于图像Bag-of-Words模型的无载体信息隐藏

介绍一种基于bag-of-words(BOW)模型的无载体信息隐藏方法.该方法使用BOW模型提取图像的视觉关键词(visual words,VW)以表达待隐藏的文本信息,从而实现文本信息在图像中的隐藏.首先使用BOW模型提取图像集中每幅图像的VW,构建文本信息的关键词和VW的映射关系库;然后把每幅图像分为若干子图像,统计每一幅子图像的VW频数直方图,选择频数最高的VW表示该子图像;最后根据构建的文本关键词和子图像VW的映射关系库,搜索出与待隐藏文本信息存在映射关系的子图像序列,将含有这些子图像的图像作为含密图像进行传递.实验结果和分析表明,该隐藏算法在抗隐写分析、鲁棒性和安全性方面均有良好的表现.

基于Bag of Words的干果图像分类研究

针对干果图像信息量大、分类精度低和耗时多的特点,提出利用Bag of Words模型提取图片的代表特征,并采用朴素贝叶斯分类器指导特征矩阵分类。结果表明,图像分类精度能达到80%,分类处理时间约为2 s。通过增加学习样本来进一步提高分类精度,将Bag of Words应用于干果图像识别和分类是可行的。

基于Bag-of-words和Hash编码的近似重复图像检测算法

针对近似重复图像检测的传统算法存在检测效率和准确率不够高的缺点,提出了基于Bag-of-words和哈希编码的近似重复图像检测算法。该算法首先利用Bag-of-words把一幅图像表示成一个500维的特征向量;然后,利用主成分分析(PCA)和尺度不变特征转换(SIFT)进行特征降维,并利用Hash编码技术对特征进行编码;最后,利用动态距离度量技术实现近似重复图像的检测。实验结果表明,利用该算法进行近似重复图像检测是完全可行的,在准确度和查全率之间做到了较好的平衡,查准率可达90%～95%,查全率可达70%～80%。

Cultural Image of Animal Words

This paper,after introducing the definition and forms of cultural image,focuses on the detailed comparison and analysis of cultural image of animal words both in English and in Chinese from four aspects,that is,same animal word,same cultural image;same animal word,different cultural images;different animal words,same cultural image;different animal words,different cultural images.

A Fast Recognition System for Isolated Printed Characters Using Center of Gravity and Principal Axis

The purpose of this paper is to propose a new multi stage algorithm for the recognition of isolated characters. It was similar work done before using only the center of gravity (This paper is extended version of “A fast recognition system for isolated printed characters using center of gravity”, LAP LAMBERT Academic Publishing 2011, ISBN: 978-38465-0002-6), but here we add using principal axis in order to make the algorithm rotation invariant. In my previous work which is published in LAP LAMBERT, I face a big problem that when the character is rotated I can’t recognize the character. So this adds constrain on the document to be well oriented but here I use the principal axis in order to unify the orientation of the character set and the characters in the scanned document. The algorithm can be applied for any isolated character such as Latin, Chinese, Japanese, and Arabic characters but it has been applied in this paper for Arabic characters. The approach uses normalized and isolated characters of the same size and extracts an image signature based on the center of gravity of the character after making the character principal axis vertical, and then the system compares these values to a set of signatures for typical characters of the set. The system then provides the closeness of match to all other characters in the set.

Image Classification Based on the Fusion of Complementary Features

Image classification based on bag-of-words（BOW）has a broad application prospect in pattern recognition field but the shortcomings such as single feature and low classification accuracy are apparent.To deal with this problem,this paper proposes to combine two ingredients：（i）Three features with functions of mutual complementation are adopted to describe the images,including pyramid histogram of words（PHOW）,pyramid histogram of color（PHOC）and pyramid histogram of orientated gradients（PHOG）.（ii）An adaptive feature-weight adjusted image categorization algorithm based on the SVM and the decision level fusion of multiple features are employed.Experiments are carried out on the Caltech101 database,which confirms the validity of the proposed approach.The experimental results show that the classification accuracy rate of the proposed method is improved by 7%-14%higher than that of the traditional BOW methods.With full utilization of global,local and spatial information,the algorithm is much more complete and flexible to describe the feature information of the image through the multi-feature fusion and the pyramid structure composed by image spatial multi-resolution decomposition.Significant improvements to the classification accuracy are achieved as the result.

基于Bag of words模型的图像检索系统的设计与实现

该系统将Bag of words模型用于大批量图像检索,基于OpenCV C语言库提取图像的SIFT特征,然后使用Kmeans算法进行聚类,再将其表示成Bag of words矢量并进行归一化,实现大批量图像检索,并用caltech256数据集进行实验。实验表明,该系统该系统采用的方法是有效的。

The Common Traits of Good English Poems

What are the essential elements of good poems? Through observation and analysis,it can be said that a good poem has excellentword choice and photographic pictures.A good poem can also provide another way to see the world and contains multiple meanings.Last-ly,generally speaking,a good poem is the outflow of strong inner feelings and can touch the readers heart deeply.

Research on the Recovery of the Transported Static Image with Channel Errors

A discussion is presented to gain the relationship between the original image and the reconstructed image while errors are mixed with the DCT data of the original image for the transmission. We can find the wrong reconstructed blocks, locate blocks, which are most likely to contain errors and eliminate errors. The method needs no channel error protection, needs no verifiable bits, and needs no extra bandwidth. Experimental results are provided in the end.

Algorithm of the Real-Time Extraction Image for Vehicle

An algorithm applied to a real-time extraction image of vehicle is introduced. The algorithm include an image processing with a binarzation method, image grab for a vehicle with high speed, character isolator one by one, and neural network algorithm. The techniques include vehicles sensing, image garb control, vehicle license location, lighting and optic character recognition. The system is much more robust and faster than the traditional thresholding method.

Intertextuality Based on Image Memory and Association in Shrek

This paper offers an analysis of the intertextuality based on image memory and association in Shrek.The author summarizes the significance and value of this analysis,finds out the mutual benefit of intertextuality and image memory and association,and can apply this mutual benefit in various markets.

Enhanced Image Captioning Using Features Concatenation and Efficient Pre-Trained Word Embedding

One of the issues in Computer Vision is the automatic development of descriptions for images,sometimes known as image captioning.Deep Learning techniques have made significant progress in this area.The typical architecture of image captioning systems consists mainly of an image feature extractor subsystem followed by a caption generation lingual subsystem.This paper aims to find optimized models for these two subsystems.For the image feature extraction subsystem,the research tested eight different concatenations of pairs of vision models to get among them the most expressive extracted feature vector of the image.For the caption generation lingual subsystem,this paper tested three different pre-trained language embedding models:Glove(Global Vectors for Word Representation),BERT(Bidirectional Encoder Representations from Transformers),and TaCL(Token-aware Contrastive Learning),to select from them the most accurate pre-trained language embedding model.Our experiments showed that building an image captioning system that uses a concatenation of the two Transformer based models SWIN(Shiftedwindow)and PVT(PyramidVision Transformer)as an image feature extractor,combined with the TaCL language embedding model is the best result among the other combinations.

基于BoF和迹变换多特征融合的图像纹理分类研究

针对BoF模型缺少几何特征、结构特征的表达,对纹理图像特征描述不充分等问题,提出一种基于BoF和迹变换多特征融合的图像纹理分类方法。首先通过关键点检测的方法获取纹理图像的碎片化图像,然后提取碎片化图像的迹变换特征和SIFT特征,通过特征交叉编码的方式和动态鉴别能量的方法,获取迹变换特征和SIFT特征的融合特征并进行特征单词优选,再以BoF模型进行特征编码,最后输入到支持向量机(SVM)中进行训练、预测和分类。实验在OutexTC10/TC12000和KTHTIPS纹理数据集上分别取得了100%、99.87%和97.6%的识别精度,结果表明该设计方法对具有几何特征、结构特征的纹理图像可以获得较好的分类效果,有效地提高了纹理分类的识别性能。

Sora:从技术哲学到文艺学的思考(笔谈)

哲学学者吴冠军从技术哲学的视角提出:从ChatGPT、DALL·E到Sora,基于转化器的AIGC大模型并不只改变了内容创作,还改变了“世界”在最字面上的意义。世界化成的过程,越来越多被以有损压缩-重新生成为发生学机制的AIGC之创造性活动所介入,这就导致“宇宙”(我们眼里的“物理宇宙”)与“元宇宙”不断深层次地叠加,“自然进程”与“人工创造”不断深层次地交织以至进入无可区分之域。这就意味着,我们所熟悉的那个“世界”,将一去不复返:当AIGC已“深”到参与世界化成本身之中时,讨论“深伪”将越来越没有意义。文艺学学者赵宪章则从“语图关系学”的视角提出:Sora的横空出世及其未来演进,不仅不是图像时代符号危机的解除,更不是语图关系研究的终结。恰恰相反,Sora加深了这一危机,语图关系研究也延宕出新的面向。

“父亲”形象的互文书写——《王蒙自传》与《活动变人形》对照解读

《王蒙自传》与《活动变人形》中的“父亲”形象,构成了显著的互文现象。作为两种创作路向,自传和小说相互提供由文外看文内的参照性史料。《王蒙自传》为《活动变人形》“父亲”形象倪吾诚的虚构,提供了政治与心理动机的说明;以《王蒙自传》父亲王锦第为原型,在倪吾诚形象的生成中,见出作家艺术表达中重抒情想象的情感审美,重政治意义的逻辑起点和真诚勇敢的心理线索;受小说人物影响,王锦第与倪吾诚同构,因而在史实、史评、史思三个层面偏离了传记人物的全面性、客观性和思想性。由此可见,关注传记与其他文类编织的史料网络,可弥补文学史偏于虚构文学而致的结构性缺失,赋予文学研究以历史维度和思想质感。

苏轼诗词书法作品接受中的语图关系变化

欣赏诗词书法作品可以欣赏书法艺术,也可以识读诗词内容,接受过程中表现出语图关系的变化。大多数接受者对苏轼诗词书法作品的欣赏分为三个过程:首先被书法书像所吸引,进入图像欣赏中,沉醉于视觉效果;其次对所书诗词内容进行识读,了解所写诗词的内容;再次将两者放在一起去欣赏,书法与诗歌在迭代中达到更高的融合,欣赏者获得双重的审美享受。