Scene Character Research Articles

The Tactful Chorus: Euripides,Alcestis326–327,Hecuba846–849

This article suggests that the brief choral comments that intervene between the speeches of two characters in Euripidean dialogue scenes may sometimes be more dramatically significant and interesting than is usually supposed. In Alcestis 326–7 and Hecuba 846–9 the chorus-leaders’ comments are directed obliquely toward (respectively) Admetus and Agamemnon, and are designed in a suitably tactful manner to influence their responses to the appeals of Alcestis and Hecuba. The problematic text of Hecuba 846–849 can be explained accordingly, and is so explained in a little-noticed comment by a Byzantine scholiast.

Dataset of Urduud1k from Natural Scenes

In latest years research has drawn attention on text analysis in natural scenes. Databases play a significant part in the efficiency assessment of the algorithm for text recognition. A data set of natural scene text images in six distinct languages have recently been released in an International Conference on Document Analysis and Recognition (ICDAR).This dataset is for multi-languages except Urdu. In the natural images of the Urdu scene, there is an absence of a conventional Urdu text database. This research therefore mainly aims to build a database for Urdu text in natural scenes. The dataset is very large because there are 10 distinct cameras with distinct resolution, distinct angles and distinct range requirements for each picture captured by distinct light zone. The dataset comprises of Urdu words, ligatures and characters in natural scenes. The dataset contains 16k images of words, 32k ligatures and characters images. This dataset contains 1kimagesincluding signboard, a name of the store, banners and so on. In addition, the Urdu dataset is contrasted with the current data set including ICRAR 2003, ARASTI, Chars 74k, etc. The dataset includes many images from the natural scene so it can be used in natural environments to identify Urdu text.

“My Favorite Book!”: Voicing Mary Douglas for Twenty‐First‐Century Conversations

“My Favorite Book!”: Voicing Mary Douglas for Twenty‐First‐Century Conversations

Atypical Visual Processing but Comparable Levels of Emotion Recognition in Adults with Autism During the Processing of Social Scenes.

Understanding the underlying visual scanning patterns of individuals with autism spectrum disorder (ASD) during the processing of complex emotional scenes remains limited. This study compared the complex emotion recognition performance of adults with ASD (n = 23) and matched neurotypical participants (n = 25) using the Reading the Mind in Films Task. Behaviourally, both groups exhibited similar emotion recognition accuracy. Visual fixation time towards key social regions of each stimuli was examined via eye tracking. Individuals with ASD demonstrated significantly longer fixation time towards the non-social areas. No group differences were evident for the facial and body regions of all characters in the social scenes. The findings provide evidence of the heterogeneity associated with complex emotion processing in individuals with ASD.

Boosting scene character recognition by learning canonical forms of glyphs

As one of the fundamental problems in document analysis, scene character recognition has attracted considerable interests in recent years. But the problem is still considered to be extremely challenging due to many uncontrollable factors including glyph transformation, blur, noisy background, uneven illumination, etc. In this paper, we propose a novel methodology for boosting scene character recognition by learning canonical forms of glyphs, based on the fact that characters appearing in scene images are all derived from their corresponding canonical forms. Our key observation is that more discriminative features can be learned by solving specially designed generative tasks compared to traditional classification-based feature learning frameworks. Specifically, we design a GAN-based model to make the learned deep feature of a given scene character capable of reconstructing corresponding glyphs in a number of standard font styles. In this manner, we obtain deep features for scene characters that are more discriminative in recognition and less sensitive against the above-mentioned factors. Our experiments conducted on several publicly available databases demonstrate the superiority of our method compared to the state of the art.

Bilateral Convolutional Activations Encoded with Fisher Vectors for Scene Character Recognition

Bilateral Convolutional Activations Encoded with Fisher Vectors for Scene Character Recognition

Consecutive Convolutional Activations for Scene Character Recognition

Driven by the rapid growth of communication technologies and the wide applications of intelligent mobile terminals, the scene character recognition has become a significant yet very challenging task in people’s lives. In this paper, we design a novel feature representation scheme termed consecutive convolutional activations (CCA) for character recognition in natural scenes. The proposed CCA could integrate both the low-level and the high-level patterns into the global decision by learning character representations from several successive convolutional layers. Concretely, one shallow convolutional layer is first selected for extracting the convolutional activation features, and then, the next consecutive deep convolutional layers are utilized to learn weight matrices for these convolutional activation features. Finally, the Fisher vectors are employed to encode the CCA features so as to obtain the image-level representations. Extensive experiments are conducted on two English scene character databases (ICDAR2003 and Chars74K) and one Chinese scene character database (“Pan+ChiPhoto”), and the experimental data indicate that the proposed method achieves a superior performance than the previous algorithms.

Deep Contextual Stroke Pooling for Scene Character Recognition

Characters, as a kind of symbols carrying rich semantic information, are composed of strokes arranged in a certain structure and are of great significance in our daily life. In this paper, we are concerned with the problem of scene character recognition, and study the problem from the perspective of feature representation. We propose a novel pooling method termed deep contextual stroke pooling (DCSP) for scene character recognition. The proposed DCSP discovers the most prominent stroke information by using stroke detectors and captures the spatial context of discriminative strokes by learning contextual factor. Specifically, we first utilize the convolutional summing map in one convolutional layer to select discriminative strokes and use the convolutional activation features of discriminative strokes to train stroke detectors. Then, we propose the contextual factor to represent the co-occurrence probability of the stroke and its location. Finally, in the response regions, we incorporate the contextual factor into the detector scores and obtain the deep contextual confidence vectors of scene characters. Extensive experiments are conducted on three databases, i.e., ICDAR2003, Chars74k, and SVHN, and the experimental results demonstrate that our method achieves higher accuracies than the state-of-the-art methods.

As concepções do conselheiro real Polônio em quatro adaptações de Hamlet para os quadrinhos

Resumo Centrando-se na figura do conselheiro real da peça Hamlet, de Shakespeare, o estudo apresenta as variações nas versões de quatro HQs na reelaboração da personagem Polônio: a adaptação de rant e Mandrake (1990), influenciada pela escola de Brandywine; a adaptação da Equipe ast Press (2013), um mangá que demonstra um cuidado com a distribuição das personagens mudas em cena para afirmar uma pesquisa consistente do texto shakespeariano; a adaptação de Vieceli (2011), um mangá que se centra no vestuário para adotar uma atmosfera tecnológico-futurística, e a adaptação de Srbek e Shibao (2013), com foco maior nas posições corpóreas em cena.

ILLOCUTIONARY ACTS OF MAIN CHARACTERS IN DEAD POET SOCIETY AND FREEDOM WRITERS

Abstract: This study analyzed speech acts contextualized in Dead Poet Society and Freedom Writers. This study focused on the use of illocutionary acts used by main characters in both movies. In analyzing the data, this study included content analysis. In this regard, three research questions are addressed: 1) What dominant types and functions of illocutionary acts are uttered by the main characters in certain contextual scenes in both movies? 2) What are the intended meanings of those illocutionary acts?, and 3) How frequently are the illocutionary acts used in both movies? The collected data included 97 utterances of the main character in the Dead Poet Society and 84 utterances in the Freedom Writers which contain illocutionary acts in certain scene contexts. The findings revealed that the kinds of illocutionary acts on both films consisted of five kinds: assertive (representative), directive, commissive, expressive, and declarative. Moreover, the second result of the study showed that the most frequently illocutionary acts used by the main characters in both movies were suitable with its movie scene contexts. Thus, the intended meaning of the main characters are well described and understandable by the viewers. The third result showed the different percentage of the use of illocutionary acts classification expressed by the main characters in both movies. The most frequently used illocutionary in Dead Poet Society is representative (47.06%) which emphasized on informing (30.59%), and directive (45.87%) in Freedom Writers which emphasized on asking (21.10%). Pedagogically, the findings above recommend that the lecturers develop their creativity in teaching pragmatics (sociolinguistics) to improve teaching and learning process by adapting other relevant sources such as movies. Key words: Illocutionary acts, Main Character, Movie

Multi-order co-occurrence activations encoded with Fisher Vector for scene character recognition

Scene character recognition remains a challenging task due to many interference factors. Considering that characters are composed of a series of parts arranged in certain structures, in this paper, we propose a novel representation termed multi-order co-occurrence activations (MCA) encoded with Fisher Vector (FV), namely MCA-FV. It implicitly models the co-occurrence information of discriminative character parts at different orders to boost the recognition performance. We first extract convolutional activations as local descriptors of character parts from convolutional neural networks (CNNs). Then, we introduce MCA features to capture the multi-order co-occurrence cues among different discriminative character parts. Finally, we apply FV to encode co-occurrence features of each order and obtain a global representation of MCA-FV. The proposed method is evaluated on four scene character datasets including English and Chinese datasets. Experiment results demonstrate the effectiveness of the proposed method for scene character recognition.

Archétypes et iconicité dans la nouvelle fantastique belge contemporaine. À propos de La Madone de Nedermunster d’Hubert Lampo et du Retour des Chasseurs de Gabriel Deblander

In this paper, fantastic short stories by a Flemish writer, Hubert Lampo, and a Walloon author, Gabriel Deblander, are compared. Some of the stories include allusions to aspects of works by early Flemish painters. The iconic function which can be attributed to the confrontation with paintings or pictorial motifs is linked to the iconicity concept, emphasizing the archetypal character of the scenes we discuss as a means to enable a contrastive interpretation of the stories.

Fisher vector for scene character recognition: A comprehensive evaluation

Fisher vector (FV), which could be seen as a bag of visual words (BOW) that encodes not only word counts but also higher-order statistics, works well with linear classifiers and has shown promising performance for image categorization. For character recognition, although standard BOW has been applied, the results are still not satisfactory. In this paper, we apply Fisher vector derived from Gaussian Mixture Models (GMM) based visual vocabularies on character recognition and integrate spatial information as well. We give a comprehensive evaluation of Fisher vector with linear classifier on a series of challenging English and digits character recognition datasets, including both the handwritten and scene character recognition ones. Moreover, we also collect two Chinese scene character recognition datasets to evaluate the suitability of Fisher vector to represent Chinese characters. Through extensive experiments we make three contributions: (1) we demonstrate that FV with linear classifier could outperform most of the state-of-the-art methods for character recognition, even the CNN based ones and the superiority is more obvious when training samples are insufficient to train the networks; (2) we show that additional spatial information is very useful for character representation, especially for Chinese ones, which have more complex structures; and (3) the results also imply the potential of FV to represent new unseen categories, which is quite inspiring since it is quite difficult to collect enough training samples for large-category Chinese scene characters.

Scene Character Recognition Using Coupled Spatial Learning

Scene Character Recognition Using Coupled Spatial Learning

Good initialization model with constrained body structure for scene text recognition

Scene text recognition has gained significant attention in the computer vision community. Character detection and recognition are the promise of text recognition and affect the overall performance to a large extent. We proposed a good initialization model for scene character recognition from cropped text regions. We use constrained character’s body structures with deformable part-based models to detect and recognize characters in various backgrounds. The character’s body structures are achieved by an unsupervised discriminative clustering approach followed by a statistical model and a self-build minimum spanning tree model. Our method utilizes part appearance and location information, and combines character detection and recognition in cropped text region together. The evaluation results on the benchmark datasets demonstrate that our proposed scheme outperforms the state-of-the-art methods both on scene character recognition and word recognition aspects.

The influence of television on childhood domestic violence in modern Russia

The article is focused on the influence of television as a factor of the problem of children’s exposure to domestic violence in modern Russia. A review of the main theoretical approaches is undertaken. The problem’s analysis is based on theories of violence by Sitikh E. L., Tabatadze G. S., Safonova T. Y., Tsymbal E. I. and also based on communication theories by McCombs M., Shaw D., Lasswel H., Gerbner G., Brayant D., Tomson S. The author also presents her own methodology in the article. It involves a conceptual model, sample and method. TV violence and the influence mechanism are key understandings of the conceptual model. The research model involves such violent actions towards the children as physical and psychological violence. The article presents the author’s pilot survey results characterizing the role of TV content in the lives of modern parents in 2013. The level of violent TV content of different levels is measured. The TV watching involvement level of modern parents is valued and the number of violent scenes in movies and TV series is reported. Moreover, the author determines the display of different violence types by parents over the children in correlation with recently watched violence on TV and values the character of violent scenes’ influence on the formation different types of children’s exposure to domestic physical and psychological violence. Refs 42. Tables 4.

Scene text localization using edge analysis and feature pool

Due to the rapid development of machine learning and data mining in nowadays, how to acquire information from images becomes more and more important. The direct information of an image is the texts inside. However, detecting such texts in images is always a challenging problem in computer vision area.Edge is one of the most important clues in scene character detection task. However, many edge based text detection methods usually had trouble with sticky edges and did not fully utilize characteristic of texts. In this paper, we proposed a method for detecting and localizing texts in natural scene images, by edge recombining, edge filtering and multi-channel processing. In order to segment texts from backgrounds, edges are firstly over-segmented into edge segments during edge analysis. These edge segments are then recombined to candidate characters and an edge filter is used to filter out most of background edges. The left candidate character edges are linked up to candidate text lines. We use two different classifiers to filter out non-text lines. To classify more accurately, extracted edge-based and region-based features are firstly stored in feature pools. Then we use liner SVM to select the most effective features from the feature pool to train classifiers. Finally, multi-channel is used to ensure the recall and a modified non-maximal suppress is applied to eliminate duplicate results. Experimental results on the ICDAR 2011 competition dataset and SVT database demonstrate the effectiveness of our method.

Stroke Detector and Structure Based Models for Character Recognition: A Comparative Study.

Characters, which are man-made symbols composed of strokes arranged in a certain structure, could provide semantic information and play an indispensable role in our daily life. In this paper, we try to make use of the intrinsic characteristics of characters and explore the stroke and structure-based methods for character recognition. First, we introduce two existing part-based models to recognize characters by detecting the elastic strokelike parts. In order to utilize strokes of various scales, we propose to learn the discriminative multi-scale stroke detector-based representation (DMSDR) for characters. However, the part-based models and DMSDR need to manually label the parts or key points for training. In order to learn the discriminative stroke detectors automatically, we further propose the discriminative spatiality embedded dictionary learning-based representation (DSEDR) for character recognition. We make a comparative study of the performance of the tree-structured model (TSM), mixtures-of-parts TSM, DMSDR, and DSEDR for character recognition on three challenging scene character recognition (SCR) data sets as well as two handwritten digits recognition data sets. A series of experiments is done on these data sets with various experimental setup. The experimental results demonstrate the suitability of stroke detector-based models for recognizing characters with deformations and distortions, especially in the case of limited training samples.

Multilingual scene character recognition with co-occurrence of histogram of oriented gradients

Automatic machine reading of texts in scenes is largely restricted by the poor character recognition accuracy. In this paper, we extend the Histogram of Oriented Gradient (HOG) and propose two new feature descriptors: Co-occurrence HOG (Co-HOG) and Convolutional Co-HOG (ConvCo-HOG) for accurate recognition of scene texts of different languages. Compared with HOG which counts orientation frequency of each single pixel, the Co-HOG encodes more spatial contextual information by capturing the co-occurrence of orientation pairs of neighboring pixels. Additionally, ConvCo-HOG exhaustively extracts Co-HOG features from every possible image patches within a character image for more spatial information. The two features have been evaluated extensively on five scene character datasets of three different languages including three sets in English, one set in Chinese and one set in Bengali. Experiments show that the proposed techniques provide superior scene character recognition accuracy and are capable of recognizing scene texts of different scripts and languages.

A Study of the Costumes and Make-up in the Movie "Anna Karenina"

The purpose of this study is to establish how main characters' mental states are expressed through costumes and makeup by understanding and analyzing the relationship between the costumes and the makeup in the movie "Anna Karenina" and to compare the 19th century Russian costumes. The study methods are a literature review and an empirical case study. The results are as follows. First, the costumes in "Anna Karenina" are a mixture of late crinoline and early bustle style in general. However, the costumes were altered after the actress's movements and acting were considered. Regarding colors, Anna wears a lavender robe as the virtuous wife, a wine-colored robe for love and death, and a white robe for innocence, thus reflecting her mental states; through the colors, her changes of mental state are expressed. Second, the costumes of Alexei Vronsky, are similar in style and colors to those of mid-late 19th century Russian military uniforms. White, blue and black costume colors are used to describe his situations and emotional changes. Third, Alexei Karenin wears 19th century costumes of dark and achromatic colors without decorations, representing his conservative, authoritative character. However, he showshis rage with a wine and red-colored costume at the end of the movie. Fourth, regarding the makeup in the movie, Anna wears makeup of a soft pink-gold color influenced by 19th century naturalism. Male characters express their characteristics with their beards and hair styles. Vronsky's Hollywood mustaches and wavy blonde hair show his charm, and Karenin's chin curtain beard and bald head reveal his conservative character. The costumes of the three main characters in the movie are not very different from 19th century style in general, but mental states and situations are expressed through accessories, and colors maximize all of the effects. Through this research, it is illustrated that the costumes and makeup in a movie can not only express characters' emotional changes but also show the relationships between the characters in different scenes.