Scene Character Research Articles

A new XR-based human‐robot collaboration assembly system based on industrial metaverse

The industrial metaverse is an emerging paradigm of integrated information technologies. A human-robot collaboration assembly system is developed using extended reality (XR) and blockchain technologies. This system includes the physical, virtual reality (VR), augmented reality (AR) supporting, and blockchain systems. Virtual avatars represent physical operators and mobile robots in the XR environment. The VR system emulates real-time physical operations, facilitating pre-planning, online task optimization, and converting strategic decisions into executable instructions for physical robots. The AR system enhances the cognitive experience of intelligent operators, contributing to decision-making and VR system control through multi-modal interactions. The integration of the VR and AR systems in perception-decision-control enables real-time process optimization and the reverse control of physical operations. The system is implemented using a decentralized blockchain and end-edge-cloud collaborative network architecture to regulate interaction permissions for each scene characters, ensuring systematic functionality, robust communication, and comprehensive data management. A case study of a gearbox assembly is used to demonstrate the system’s development and efficacy. The results indicate the VR system's comprehensive perception and the AR system's detailed insights, yielding reliable information on global and local levels. The integration of artificial intelligence (AI) algorithms and spatiotemporal visualization during VR decision-making provides computational advantages. Incorporating human input into the AR system enables human-in-the-loop operational decision-making, providing optimal instructions to improve human-robot collaboration performance. Notably, the fusion of blockchain and end-edge-cloud architectures augments inter-system data exchange by efficiently managing and processing human-robot interactions and their diverse avatars. This system offers an innovative approach to human-robot collaboration assembly in manufacturing, with implications for analogous contexts.

The Value of Character Education in Wayang Beber Pacitan Ninth Scene

Wayang beber Pacitan is a type of wayang that has existed since the Majapahit era. This type of wayang is a painting which is divided into 24 scenes. Each scene has a main character and supporting characters. Each character possesses different characteristics. The purpose of this study is to find out and describe the main characters in the ninth scene of wayang beber Pacitan, namely, Prabu Brawijaya and Prabu Klana. This study follows the qualitative descriptive research methodology. Several data collection techniques were used, for example, observation, interviews, and document analysis. Purposive sampling was used as a sampling technique. Interactive analysis methods, with Roland Barthes’s semiotic theory, namely, through denotative, connotative, and mythical stages, were used for data analysis. Moreover, triangulation was used for data validation. The research concludes that the characters in the ninth scene can be divided into two, namely, Prabu Brawijaya, as the protagonist, and Prabu Klana, as the antagonist. Prabu Brawijaya, as the protagonist, has the character of being careful, wise, and alert, while Prabu Klana, as the antagonist, has the character of being easily angry, self-willed, and arrogant. The ninth scene of wayang beber Pacitan teaches us about the value of character education so one should always be careful in acting and wise in making decisions and should not be arrogant and selfish as this will have a negative impact on oneself and others. Apart from being an entertainment medium, wayang beber performances can also be used as a medium to convey moral messages. Through stories and understanding the characters in each scene, it can be used as a medium of character education. Keywords: antagonist, character, protagonist, scene, wayang beber

Text localization and recognition of Chinese characters in natural scenes based on improved faster R-CNN

Text detection and recognition are widely used in daily life. Although it is a very rich market, it has very difficult challenges in practical application. The complex and changeable natural scenes lead to the complex background of the text in the image, which also reflects the research value of text information recognition and extraction in natural scenes. To solve the many problems faced by the recognition of Chinese characters, such as complex shapes and diverse structures, this paper uses VGG16 to extract features and introduces a two-layer bidirectional LSTM network. It improves Faster R-CNN by using a RPN to extract candidate boxes and adjust the position of candidate regions. In this paper, the improved model Faster BLSTM-CNN is tested, and the effectiveness experiment of feature extraction, the difference comparison before and after the improvement of the algorithm, and the comparison experiment with the traditional recognition algorithm are carried out respectively. And it finally carried out an experimental comparison of the combination of text recognition and positioning, and obtained the results. The algorithm Faster BLSTM-CNN in this paper is better in the localization and recognition of Chinese characters in the dataset. In the natural scene, the recognition rate of Faster BLSTM-CNN in this paper is 81.54%, the positioning accuracy is 88.14%, and the detection speed is 86 ms, which has improved performance. Therefore, the improvement of Faster R-CNN is effective. It can effectively locate and recognize Chinese characters in natural scenes.

The Help of God and Fear of Man: Comparing Anxiety and Religiosity in Fury (2014) and Merah-Putih (2009)

This research aims at investigating the influence of religiosity on anxiety during the war. This research compares two contrasting military leader characters to identify the influence on the way that these characters cope with war-induced anxiety. The main comparative objects are the characters Don from the movie Fury (2014) and Amir in Merah-Putih (2009). Both characters show similar anxiety and religiosity but differ in how they cope with anxiety. The general assumption is that religiosity helps to cope with anxiety. This research uses Freud’s psychoanalysis theory to study the characters. By comparing these characters, we may find the relation of religiosity towards anxiety, and we may also find other influences such as the character’s motivation and backstory. The five dimensions of religiosity, as explained by Glock Stark and Freud’s psychoanalytic theory on anxiety, determine religiosity and anxiety. The variables are expressed through practice, discourse, and symbols by both characters in scenes and dialogues. The findings are listed in three items: 1) The coping mechanism for anxiety works not only to repress but also to create new anxiety. 2) The new anxiety can be so much worse than the initial anxiety. 3) Religiosity is not a necessary variable in dealing with anxiety. Additionally, the founding of new anxiety contributes to previous theory on anxiety.

A New Lightweight Script Independent Scene Text Style Transfer Network

Scene text style transfer without a language barrier is an open challenge for the video and scene text recognition community because this plays a vital role in poster, web design, augmenting character images, and editing characters to improve scene text recognition performance and usability. This work presents a new model, called Script Independent Scene Text Style Transfer Network (SISTSTNet), for extracting scene characters and transferring text style simultaneously. The SISTSTNet performs mapping in language-independent feature space for transferring style. It is designed based on a Style Parameter Network and Target Encoder Network through lightweight MobileNetv3 convolutional and residual blocks to capture the style and shape to generate target characters. Similarly, a generative model is explored through the Visual Geometry Group (VGG) network for character replacement. The SISTSTNet is flexible and works on different languages and arbitrary examples in a neat and unified fashion. The experimental results on images in various languages, namely, English, Chinese, Hindi, Russian, Japanese, Arabic, Greek, and Bengali and cross-language validation demonstrate the effectiveness of the proposed method. The performance of the method is superior compared to the state-of-the-art methods in terms of quality measures, language independence, shape-preserving, and efficiency. The code and dataset will be released to the public to support reproducibility.

A SEMIOTIC ANALYSIS OF ALADDIN MOVIE BY USING ROLAND BARTHES THEORY

Movie is a picture or life event that is brought to the screen as a means of communication and entertainment. Messages or signs contained in the movie can be analyzed using semiotics. Semiotics is the study of signs. The aims of this research is to determine the types and meanings in semiotics by using Roland Barthes Theory. Roland Barthes developed the theory of semiotics into three stages, namely, denotation, connotation and myth. This research uses descriptive qualitative method. The data taken from scenes and script of Aladdin movie 2019. The researcher collect data by watching movie, observe main characters in scene and movie script, make take note and categorize sentence of semiotics by Roland Barthes theory and analyzes the results data. The types of signs found are denotation, connotation and myth. The researcher found 38 types and meanings of semiotics by Roland Barthes consisting of 21 types and meanings of denotation, 13 types and meanings of connotation and 4 types and meanings of myth. From the explanation above, the types and meanings of denotation is more dominant and the least is myth. The results of this study state that the type of semiotics by Roland Barthes can be used to explain the meaning of semiotics contained in it. The type of Semiotics by Roland Barthes in this movie also has different meanings in describing something according to the reference to the word or phrase used in the movie scene.

Improved Alexnet-Based Character Detection Method

Extracting text information in complex images is a hot spot in pattern recognition research with broad application prospects. Natural scene door numbers produce serious distortion due to blurred images, uneven illumination and low light illumination, which makes it difficult to achieve ideal results in character recognition, and recognizing characters of arbitrary length is even more of a challenge. In this paper, we adopt the improved Niblack’s local threshold segmentation method to segment the images, and mark the connected areas of the segmented images to highlight the important features, and finally input the above pre-processed images to the improved AlexNet network for target detection on SVHN (Street View House Number) to realize the detection of characters in real scenes. The experimental results show that the improved Alexet-based target detection method is able to complete the detection task of streetscape door number characters well with 92.89% correct recognition rate.

Does beautiful nature motivate to work? Outlining an alternative pathway to nature-induced cognitive performance benefits

Ample research shows that spending time in natural (vs. urban) environments, or merely watching nature scenes, can seemingly replenish depleted cognitive resources and thereby improve cognitive functioning. While such findings are traditionally explained by referring to Attention Restoration Theory (ART), in the present research, we outline and test a potential alternative explanation for nature-related performance benefits. Our account centers on the notion that the aesthetically pleasing character of nature scenes simply fosters individuals' willingness to work, including working on the cognitive tasks used in attention restoration research. After outlining our theoretical proposal, we report the results of a preregistered showcase study in which we asked participants (N = 219) to watch a slideshow of fifteen photos of natural scenes/elements. The beauty of the slideshow images was manipulated by presenting either aesthetically attractive nature images (beautiful nature condition) or nature images where the aesthetic qualities had been removed through pixilation (pixelated nature condition). We subsequently tested the effect of this manipulation on participants' performance on a simple and mindless “clicking task”, consisting of freely clicking radio buttons. We also varied the number of clicking tasks participants had to perform (between-subjects); while all participants had to click buttons after the nature slideshow, a subset of them also had to do the clicking task before watching the slideshow images. Results show that participants who only had to do the clicking task once (i.e., after the slideshow) and who had watched beautiful nature, clicked more buttons, and reported to feel more motivated to click buttons compared to participants who had watched relatively unappealing pixelated nature images. This general nature-related performance enhancement could account for the general pattern of positive nature effects on cognitive tasks without requiring ART's additional assumptions of resource replenishment and resource specificity.

Superhero Shakespeare in Golden Age Comics

Albert Lewis Kanter launched Classic Comics in 1941, a series of comic books that retold classic literature for a young audience. Five of Shakespeare’s celebrated plays appear in the collection. The popularity of Classics Illustrated encouraged Seaboard Publishing to issue a competitive brand, Stories by Famous Authors Illustrated (1949-51), which retold three Shakespearean dramas. Although both these enterprises aimed to reinforce a humanist perspective of education based on Western literature, the classic comics belie a Posthuman aesthetic by presenting Shakespearean characters in scenes and postures that recall Golden Age superheroes. By examining the Shakespearean covers of Classic Illustrated and Stories by Famous Authors, this essay explores how Shakespearean characters are reimagined as Superhuman in strength and power.

The reflection of the pandemic experience in contagion (an operant conditioning theory criticism)

This study aims to analyze the reflection on the pandemic circumstances and on the attempts to solve such turmoil as portrayed in the film entitled Contagion. As a sort of literary criticism, this study is qualitative in design. Data collection deals with the use of transcript of the speeches and actions of the characters in particular scenes and certain minutes. For data analysis, this study applies a kind of psychological approach especially Skinner’s Operant Conditioning Theory as means of doing the analytical criticism on the film. The analysis results in the findings such as Contagion illustrates the occurrence of the pandemic as indicated by the presence of the pandemic, its victim and spread. Further, the study also reveals that solution for the pandemic can be performed by the government, society, and individual. This study seems to be significant to inspire and encourage solution for the current pandemic condition.

Synthetic Scene Character Generator and Ensemble Scheme with the Random Image Feature Method for Japanese and Chinese Scene Character Recognition

Synthetic Scene Character Generator and Ensemble Scheme with the Random Image Feature Method for Japanese and Chinese Scene Character Recognition

An efficient indoor scene character recognition using Bayesian interactive search algorithm-based adaboost-CNN classifier

An efficient indoor scene character recognition using Bayesian interactive search algorithm-based adaboost-CNN classifier

AksharaNet: A GPU Accelerated Modified Depth-Wise Separable Convolution for Kannada Text Classification

For content-based indexing and retrieval applications, text characters embedded in images are a rich source of information. Owing to their different shapes, grayscale values, and dynamic backgrounds, these text characters in scene images are difficult to detect and classify. The complexity increases when the text involved is a vernacular language like Kannada. Despite advances in deep learning neural networks (DLNN), there is a dearth of fast and effective models to classify scene text images and the availability of a large-scale Kannada scene character dataset to train them. In this paper, two key contributions are proposed, AksharaNet, a graphical processing unit (GPU) accelerated modified convolution neural network architecture consisting of linearly inverted depth-wise separable convolutions and a Kannada Scene Individual Character (KSIC) dataset which is grounds-up curated consisting of 46,800 images. From results it is observed AksharaNet outperforms four other well-established models by 1.5% on CPU and 1.9% on GPU. The result can be directly attributed to the quality of the developed KSIC dataset. Early stopping decisions at 25% and 50% epoch with good and bad accuracies for complex and light models are discussed. Also, useful findings concerning learning rate drop factor and its ideal application period for application are enumerated.

QuickLook: Movie summarization using scene-based leading characters with psychological cues fusion

Due to recent advances in the film industry, the production of movies has grown exponentially, which has led to challenges in what is referred to as discoverability: given the overwhelming number of choices, choosing which film to watch has become a tedious task for audiences. Movie summarization (MS) could help, as it presents the central theme of the movie in a compact format and makes browsing more efficient for the audience. In this paper, we present an automatic MS framework coined as ‘QuickLook’, which identifies the leading characters and fuses multiple cues extracted from a movie. Firstly, the movie data is preprocessed for its division into scenes, followed by shot segmentation. Secondly, the leading characters in each segmented scene are determined. Next, four visual cues that capture the film's scenic beauty, memorability, informativeness and emotional resonance are extracted from shots containing the leading characters. These extracted features are then intelligently fused based on the assignment of different weights; shots with a fusion score above a certain threshold are selected for the final summary. The proposed MS framework is assessed by comparison with official trailers from ten Hollywood movies, providing a novel baseline for future fair comparison in the MS literature. The proposed framework is shown to outperform other state-of-the-art MS methods in terms of enjoyability and informativeness.

Improved firefly algorithm-based optimized convolution neural network for scene character recognition

The most common and challenging issues in image recognition are scene character recognition from the street view image, and the scene character consists of both text and number. Recently, the researchers were introduced a lot of scene character recognition methods, but the performance of the methods often degraded due to complexity. So, we proposed the improved firefly algorithm for local trapping problem (IFLT) utilizing convolutional neural network (CNN) for the extraction of features from the scene character. The IFLT approach is the improved version of the firefly optimization algorithm to solve local trapping problems. During feature extraction, the hyperparameters on CNN are tuned with the help of the IFLT approach. The alignment and multilayer perceptron layers are used on CNN. Subsequently, the support vector machine approach is used to classify the relevant class of scene characters from the street view image. Experimentally, we use six scene character dataset SVHN, ISN, IIIT5K-words, SVT, ICDAR 2003, and ICDAR 2013 dataset. The performance of the proposed IFLT approach is evaluated with standard deviation, mean, average computational time, and most excellent minimum (MEmin) parameters. The experimental results demonstrate that the proposed IFLT-CNN is well suitable for scene character recognition.

Who Wants Money? Radical Performance and Experimental Urbanism in the Heart of São Paulo

In August 2017, the mayor of Sao Paulo, Joao Doria, mediated an arbitration between billionaire TV presenter Silvio Santos and legendary theater director Ze Celso, aiming to settle a decades-long dispute over Santos’s proposal to build luxury high-rises in the property that surrounds Ze Celso’s historic Teatro Oficina. At stake in this meeting, however, was much more than the future of one block in the largest city in the Americas. Each in their own way, the three characters in this peculiar scene are enmeshed with the history and the present of the city of Sao Paulo. This paper presents a close reading of the epistemological clash that this meeting represents, from the six decades of protagonism of Silvio Santos and Ze Celso to the tensions inherent in Brazil’s own version of the global turn toward the far right. This dispute over Teatro Oficina thus serves as a particularly charged stage to reveal competing visions for the city of Sao Paulo, both in the past and in the present.

Journey of scene text components recognition: Progress and open issues

In computer vision, scene text component recognition is an important problem in end-to-end scene text reading systems. It involves two major sub-problems - segmentation of such components into scene characters and classification of segmented characters into known character classes. Significant attention and increasingly focused research efforts are being put forth and reasonable progress in this field has already been made, though a diversity of challenges like background complexity, variety of text appearances, noise, blur, distortion and various other degradation and deformation issues are still left to address. In this paper, we present (i) a detail survey of scene component segmentation and/or recognition methods reported so far in literature, (ii) related datasets available for quantitative evaluation and benchmarking segmentation and/or recognition performance, (iii) comparative results and analysis over the reported methods, and (iv) discussion on open areas to be looked into in order to achieve the desired goal of end-to-end scene text recognition. Moreover, this paper provides an acceptable reference for researcher in the area of scene text components segmentation and recognition.

LCSegNet: An Efficient Semantic Segmentation Network for Large-Scale Complex Chinese Character Recognition

Complex scene character recognition is a challenging yet important task in machine learning, especially for languages with large character sets, such as Chinese, which is composed of hieroglyphics with large-scale categories and similar glyphs. Recently, state-of-the-art methods based on semantic segmentation have achieved great success in scene parsing and have been applied in scene text recognition. However, because of limitations in terms of memory and computation, they are only applied in the small category recognition tasks, such as tasks involving English alphabets and digits. In this paper, we propose an efficient semantic segmentation model based on label coding (LC), called LCSegNet, to recognize large-scale Chinese characters. First, to reduce the number of labels, we design a new label coding method based on the Wubi Chinese characters code, called Wubi-CRF. In this method, glyphs and structure information of Chinese characters are encoded into 140-bit labels. Second, we employ an efficient semantic segmentation model for pixel-wise prediction and utilize a conditional random field (CRF) module to learn the constraint rules of Wubi-like coding. Finally, experiments are conducted on three benchmarks: a large Chinese text dataset in the wild (CTW), ICDAR2019-ReCTS, and HIT-OR3C dataset. Results show that the proposed method achieves state-of-the-art performances in both complex scene and handwritten character recognition tasks.

This must be the place: Venues and urban space in underground music scenes

Art subcultures, and music scenes in particular, have featured prominently in academic discourse on gentrification in the neo-liberal city. Although scholarly accounts have done much to clarify the process through which music scenes become implicated and entangled within wider patterns of urban transformation and redevelopment, these studies often leave us with a flattened and undertheorized picture of the scenes themselves. Departing from David Ley’s conception of the ‘cultural field of gentrification’, I sketch out an analytical framework for understanding the heterogenous and contested character of music scenes in the face of urban change, focusing on a case study of the underground music scene in Rome’s Pigneto neighbourhood in 2017. As variegated waves of scene participants drift into new spaces, scenes coalesce into distinct territories, administered by venues and delineated by ‘scene ideologies’ – matrices of ethical and aesthetic values and judgements constituting a collective scene habitus. This complicates any facile conception of artistic communities as either unwitting agents of gentrification or isolated underground enclaves; rather, premised on collective rituals of aesthetic judgement and differentiation, music scenes constitute a continuum of cultural production whose spatial practices both generate and subvert conditions for their eventual appropriation by market forces.

Radical analysis network for learning hierarchies of Chinese characters

Chinese characters have a valuable property, this is, numerous Chinese characters are composed of a compact set of fundamental and structural radicals. This paper introduces a radical analysis network (RAN) that makes full use of this valuable property to implement radical-based Chinese character recognition. The proposed RAN employs an attention mechanism to extract radicals from Chinese characters and to detect spatial structures among the radicals. Then, the decoder in RAN generates a hierarchical composition of Chinese characters based on the knowledge of the extracted radicals and their internal structures. The method of treating a Chinese character as a composition of radicals rather than as a single character category is a human-like method that can reduce the size of the vocabulary, ignore redundant information among similar characters and enable the system to recognize unseen Chinese character categories, i.e., zero-shot learning. Through experiments, we assess the practicality of RAN for recognizing Chinese characters in natural scenes. Furthermore, a RAN framework can be proposed for scene text recognition with the extension of a dense recurrent neural network (denseRNN) encoder, a multihead coverage attention model and HSV representations. The proposed approach achieved the best performance in the ICPR MTWI 2018 competition.