scholarly journals Style Transformation Method of Stage Background Images by Emotion Words of Lyrics

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1831
Author(s):  
Hyewon Yoon ◽  
Shuyu Li ◽  
Yunsick Sung
Keyword(s):  
Probability Distribution ◽  
Total Variation ◽  
Transformation Method ◽  
Input Image ◽  
Design Stage ◽  
Edge Information ◽  
Emotion Words ◽  
Emotion Word ◽  
Song Lyrics ◽  
Background Image

Recently, with the development of computer technology, deep learning has expanded to the field of art, which requires creativity, which is a unique ability of humans, and an understanding of the human emotions expressed in art to process them as data. The field of art is integrating with various industrial fields, among which artificial intelligence (AI) is being used in stage art, to create visual images. As it is difficult for a computer to process emotions expressed in songs as data, existing stage background images for song performances are human designed. Recently, research has been conducted to enable AI to design stage background images on behalf of humans. However, there is no research on reflecting emotions contained in song lyrics to stage background images. This paper proposes a style transformation method to reflect emotions in stage background images. First, multiple verses and choruses are derived from song lyrics, one at a time, and emotion words included in each verse and chorus are extracted. Second, the probability distribution of the emotion words is calculated for each verse and chorus, and the image with the most similar probability distribution from an image dataset with emotion word tags in advance is selected for each verse and chorus. Finally, for each verse and chorus, the stage background images with the transferred style are outputted. Through an experiment, the similarity between the stage background and the image transferred to the style of the image with similar emotion words probability distribution was 38%, and the similarity between the stage background image and the image transferred to the style of the image with completely different emotion word probability distribution was 8%. The proposed method reduced the total variation loss of change from 1.0777 to 0.1597. The total variation loss is the sum of content loss and style loss based on weights. This shows that the style transferred image is close to edge information about the content of the input image, and the style is close to the target style image.

Emotion Word Use by Adolescents Who Stutter: An Exploratory Study

2013 ◽  
Vol 23 (1) ◽  
pp. 6-14
Author(s):  
Corrin G. Richels ◽  
Rogge Jessica
Keyword(s):  
Positive Emotion ◽  
Negative Emotion ◽  
Conversational Speech ◽  
Word Count ◽  
Word Use ◽  
Emotion Words ◽  
Emotion Word ◽  
Linguistic Inquiry ◽  
Positive Valence ◽  
Analyze Data

Purpose: Deficits in the ability to use emotion vocabulary may result in difficulties for adolescents who stutter (AWS) and may contribute to disfluencies and stuttering. In this project, we aimed to describe the emotion words used during conversational speech by AWS. Methods: Participants were 26 AWS between the ages of 12 years, 5 months and 15 years, 11 months-old (n=4 females, n=22 males). We drew personal narrative samples from the UCLASS database. We used Linguistic Inquiry and Word Count (LIWC) software to analyze data samples for numbers of emotion words. Results: Results indicated that the AWS produced significantly higher numbers of emotion words with a positive valence. AWS tended to use the same few positive emotion words to the near exclusion of words with negative emotion valence. Conclusion: A lack of diversity in emotion vocabulary may make it difficult for AWS to engage in meaningful discourse about negative aspects of being a person who stutters

scholarly journals Research on the Extraction of Image Edge Information in Convolutional Neural Networks

2021 ◽  
Vol 2083 (3) ◽  
pp. 032015
Author(s):  
Guanru Zou ◽  
Yulin Luo ◽  
Zefeng Feng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Input Image ◽  
Edge Weight ◽  
Edge Information ◽  
Image Dimension ◽  
Original Dataset ◽  
Image Edge ◽  
Accuracy Rates ◽  
Object Move

Abstract Convolutional neural network is an important neural network model in deep learning and a common algorithm in computer vision problems. From the perspective of practical application scenarios, this paper studies whether padding in convolutional neural network convolution layer weakens the image edge information. In order to eliminate the background factor, this paper select MNIST dataset as the research object, move the 0-9 digital image to the specified image edge by clearing the white area pixels in the specified direction, and use OpenCV to realize bilinear interpolation to scale the image to ensure that the image dimension is 28×28. The convolution neural network is built to train the original dataset and the processed dataset, and the accuracy rates are 0.9892 and 0.1082 respectively. In the comparative experiment, padding cannot solve the problem of weakening the image edge weight well. In the actual digital recognition scene, it is necessary to consider whether the core recognition area in the input image is at the edge of the image.

Construction of Shadow Model by Robust Features to Illumination Changes

2013 ◽  
Vol 1 (4) ◽  
pp. 45-55 ◽  
Author(s):  
Shuya Ishida ◽  
Shinji Fukui ◽  
Yuji Iwahori ◽  
M. K. Bhuyan ◽  
Robert J. Woodham
Keyword(s):  
Detection Method ◽  
Harmful Effect ◽  
Color Space ◽  
Shadow Detection ◽  
Edge Information ◽  
Correlation Image ◽  
Illumination Changes ◽  
Sign Correlation ◽  
The Difference ◽  
Background Image

Methods in the field of computer vision need a shadow detection because shadows often have a harmful effect on a result. A new shadow detection method is proposed in this paper. The proposed method is based on the shadow model. The model is constructed by robust features to illumination changes. The proposed method uses the difference of chrominance (UV) components of luma chrominance (YUV) color space between the background image and the observed image, Normalized Vector Distance, Peripheral Increment Sign Correlation image and edge information. These features remove shadow effects in part. The proposed method can construct the effective shadow model by using the features. In addition, the result is improved by the region based method and the shadow model is updated. The proposed method can extract shadows accurately. Results are demonstrated by the experiments using the real videos.

scholarly journals A SAR Image Despeckling Method Based on an Extended Adaptive Wiener Filter and Extended Guided Filter

2020 ◽  
Vol 12 (15) ◽  
pp. 2371 ◽  
Author(s):  
Hadi Salehi ◽  
Javad Vahidi ◽  
Thabet Abdeljawad ◽  
Aziz Khan ◽  
Seyed Yaser Bozorgi Rad
Keyword(s):  
Additive Noise ◽  
Weighted Least Squares ◽  
Wiener Filter ◽  
Speckle Noise ◽  
Input Image ◽  
Logarithmic Transformation ◽  
Guided Filter ◽  
Sar Image ◽  
Edge Information ◽  
Image Despeckling

The elimination of multiplicative speckle noise is the main issue in synthetic aperture radar (SAR) images. In this study, a SAR image despeckling filter based on a proposed extended adaptive Wiener filter (EAWF), extended guided filter (EGF), and weighted least squares (WLS) filter is proposed. The proposed EAWF and EGF have been developed from the adaptive Wiener filter (AWF) and guided Filter (GF), respectively. The proposed EAWF can be applied to the SAR image, without the need for logarithmic transformation, considering the fact that the denoising performance of EAWF is better than AWF. The proposed EGF can remove the additive noise and preserve the edges’ information more efficiently than GF. First, the EAWF is applied to the input image. Then, a logarithmic transformation is applied to the resulting EAWF image in order to convert multiplicative noise into additive noise. Next, EGF is employed to remove the additive noise and preserve edge information. In order to remove unwanted spots on the image that is filtered by EGF, it is applied twice with different parameters. Finally, the WLS filter is applied in the homogeneous region. Results show that the proposed algorithm has a better performance in comparison with the other existing filters.

scholarly journals ANGER, LOVE, SADNESS: DO EMOTION WORDS HAVE THE SAME SEMANTIC MEANING ACROSS AGE-GROUPS?

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S841-S842
Author(s):  
Madeline J Nichols ◽  
Jennifer A Bellingtier ◽  
Frances Buttelmann
Keyword(s):  
Older Adults ◽  
Age Differences ◽  
Language Use ◽  
Age Groups ◽  
Past Research ◽  
Semantic Meaning ◽  
Younger Adults ◽  
Emotion Words ◽  
Emotion Word ◽  
Age Related

Abstract Every day we use emotion words to describe our experiences, but past research finds that the meanings of these words can vary. Furthermore, historical shifts in language use and experiential knowledge of the emotions may contribute to age-differences in what these emotion words convey. We examined age-related differences in the valence, arousal, and expression connoted by the words anger, love, and sadness. We predicted age-related differences in the semantic meanings of the words would emerge such that older adults would more clearly differentiate the positivity/negativity of the words, whereas younger adults would report higher endorsement for the conveyed arousal and expression. Participants included American and German older adults (N=61; mean age=68.98) and younger adults (N=77; mean age=20.77). Using the GRID instrument (Swiss Center for Affective Sciences, 2013), they rated each emotion word for its valence, arousal, and expression when used by a speaker of the participant’s native language. Across emotions and dimensions, older adults were generally more moderate in their understanding of emotion words. For example, German older adults rated anger and sadness as suggesting the speaker felt less bad and more good than the younger adults. American older adults rated love as connoting the speaker felt more bad and less good than younger adults. Arousal ratings were higher for German younger, as opposed to older, adults. Cultural differences were most pronounced for sadness such that German participants gave more moderate answers than American participants. Overall, our research suggests that there are age-related differences in the understanding of emotion words.

Image-Searching for Office Equipment Using Bag-of-Keypoints and AdaBoost

2011 ◽  
Vol 23 (6) ◽  
pp. 1080-1090 ◽  
Author(s):  
Seiji Aoyagi ◽  
◽  
Atsushi Kohama ◽  
Yuki Inaura ◽  
Masato Suzuki ◽  
...  
Keyword(s):  
Input Image ◽  
Image Features ◽  
Scale Invariant ◽  
Edge Information ◽  
Localization And Mapping ◽  
Template Image ◽  
Specific Object ◽  
Monocular Image ◽  
Scale Invariant Feature ◽  
Office Equipment

For an indoor mobile robot’s Simultaneous Localization And Mapping (SLAM), a method of processing only one monocular image (640×480 pixel) of the environment is proposed. This method imitates a human’s ability to grasp at a glance the overall situation of a room, i.e., its layout and any objects or obstacles in it. Specific object recognition of a desk through the use of several camera angles is dealt with as one example. The proposed method has the following steps. 1) The bag-of-keypoints method is applied to the image to detect the existence of the object in the input image. 2) If the existence of the object is verified, the angle of the object is further detected using the bag-ofkeypoints method. 3) The candidates for the projection from template image to input image are obtained using Scale Invariant Feature Transform (SIFT) or edge information. Whether or not the projected area correctly corresponds to the object is checked using the AdaBoost classifier, based on various image features such as Haar-like features. Through these steps, the desk is eventually extractedwith angle information if it exists in the image.

DETECTING GHOST AND LEFT OBJECTS IN SURVEILLANCE VIDEO

2009 ◽  
Vol 23 (07) ◽  
pp. 1503-1525 ◽  
Author(s):  
SIJUN LU ◽  
JIAN ZHANG ◽  
DAVID DAGAN FENG
Keyword(s):  
Object Tracking ◽  
Shape Matching ◽  
Detection Method ◽  
Input Image ◽  
Second Step ◽  
Surveillance Systems ◽  
Surveillance Video ◽  
Stationary Objects ◽  
Continuous Object ◽  
Background Image

This paper proposes an efficient method for detecting ghost and left objects in surveillance video, which, if not identified, may lead to errors or wasted computational power in background modeling and object tracking in video surveillance systems. This method contains two main steps: the first one is to detect stationary objects, which narrows down the evaluation targets to a very small number of regions in the input image; the second step is to discriminate the candidates between ghost and left objects. For the first step, we introduce a novel stationary object detection method based on continuous object tracking and shape matching. For the second step, we propose a fast and robust inpainting method to differentiate between ghost and left objects by reconstructing the real background using the candidate's corresponding regions in the current input and background image. The effectiveness of our method has been validated by experiments over a variety of video sequences and comparisons with existing state-of-art methods.

scholarly journals Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status Updates (Preprint)

2017 ◽  
Author(s):  
Elizabeth M Seabrook ◽  
Margaret L Kern ◽  
Ben D Fulcher ◽  
Nikki S Rickard
Keyword(s):  
Social Media ◽  
Negative Emotion ◽  
Depression Severity ◽  
Emotion Words ◽  
Emotion Word ◽  
The Social ◽  
Average Proportion ◽  
Media Platform ◽  
Twitter Users ◽  
Dynamic Measures

BACKGROUND Frequent expression of negative emotion words on social media has been linked to depression. However, metrics have relied on average values, not dynamic measures of emotional volatility. OBJECTIVE The aim of this study was to report on the associations between depression severity and the variability (time-unstructured) and instability (time-structured) in emotion word expression on Facebook and Twitter across status updates. METHODS Status updates and depression severity ratings of 29 Facebook users and 49 Twitter users were collected through the app MoodPrism. The average proportion of positive and negative emotion words used, within-person variability, and instability were computed. RESULTS Negative emotion word instability was a significant predictor of greater depression severity on Facebook (rs(29)=.44, P=.02, 95% CI 0.09-0.69), even after controlling for the average proportion of negative emotion words used (partial rs(26)=.51, P=.006) and within-person variability (partial rs(26)=.49, P=.009). A different pattern emerged on Twitter where greater negative emotion word variability indicated lower depression severity (rs(49)=−.34, P=.01, 95% CI −0.58 to 0.09). Differences between Facebook and Twitter users in their emotion word patterns and psychological characteristics were also explored. CONCLUSIONS The findings suggest that negative emotion word instability may be a simple yet sensitive measure of time-structured variability, useful when screening for depression through social media, though its usefulness may depend on the social media platform.

Emotion word comprehension in children aged 4–7 years

2019 ◽  
Vol 36 (2) ◽  
pp. 82-87 ◽  
Author(s):  
Christelle Declercq ◽  
Pauline Marlé ◽  
Régis Pochon
Keyword(s):  
Social Relationships ◽  
Word Comprehension ◽  
Lexical Development ◽  
Emotion Words ◽  
Emotion Word ◽  
Abstract Words ◽  
Emotional Knowledge ◽  
Emotional Lexicon ◽  
Regular Training ◽  
Concrete Words

AbstractDespite its importance for furthering social relationships, the development of the emotional lexicon has seldom been studied. Recent research suggests that during childhood, emotion words are acquired less rapidly than concrete words, but more rapidly than abstract words. The present study directly compared the comprehension of emotion words with the comprehension of concrete and abstract words in children aged 4–7 years. Children were shown 48 sets of four pictures and for each set had to point to the picture corresponding to a word that had just been pronounced. Words referred to concrete (16), abstract (16), or emotional (16) concepts. Results showed that concrete words were better understood than either emotion or abstract words, and emotion words were better understood than abstract ones. This finding emphasises the importance of the emotional lexicon in lexical development, and suggests that emotion word comprehension should be enhanced through regular training. This would increase children’s emotional knowledge, improve their communication skills, and promote their socialisation.

Construction of Pump-Turbine Characteristics at Any Specific Speed by Domain-Partitioned Transformation

2014 ◽  
Vol 137 (3) ◽  
Author(s):  
Wei Zeng ◽  
Jiandong Yang ◽  
Yongguang Cheng
Keyword(s):  
Transformation Method ◽  
Design Stage ◽  
Characteristic Surface ◽  
Pump Turbine ◽  
Characteristic Curves ◽  
Preliminary Design Stage ◽  
Measured Characteristic ◽  
Specific Speed ◽  
Surface Meshes ◽  
Pumped Storage

Pump-turbine characteristics are important for designing pumped-storage plants and indispensable for simulating hydraulic transients, but are often not available in the preliminary design stage. Therefore, constructing a set of pump-turbine characteristics is necessary, when no suitable characteristics at the same specific speed can be used for substitution. In this paper, we propose a new method for pump-turbine characterization at any specific speed using a database of 25 available sets of pump-turbine characteristics. The intersecting curves, defined by the intersections of the characteristic curves with a coordinate axis, are formularized to prepare for the characterization primarily. Next is an introduction of a transformation method for characteristic curves base on domain partition, through which the curves are transformed into eight characteristic surface meshes in eight separate domains. Then, we present the construction procedures in each domain, which include merging the transformed surface meshes for all the sets of collected characteristic curves into a cube mesh, constructing a super surface by interpolation to construct the regular characteristic surface meshes for an arbitrary specific speed, and transforming the constructed meshes reversely to get the conventional characteristic curves. This method is verified by comparing them to measured characteristic curves with reasonable accuracies.

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