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The Applications of Context Awareness Computing and Image Understanding

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 26019

Special Issue Editors


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Co-Guest Editor
The Department of Computer Science, The University of Aizu, Tsuruga, ikki-machi, Aizu-Wakamatsu City, Fukushima 965-80, Japan
Interests: machine learning-based automatic morphing; induction of compact and high-performance awareness agents; brain modeling and awareness science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Context awareness is the ability of a system or system component to gather information about its environment at any given time and adapt behaviors accordingly. Context awareness computing uses software and hardware to collect and analyze data to guide responses automatically. Awareness computing is a state in which a subject perceives the moment when relevant information arrives. It is not only essential for the survival of any living species but also a fundamental ability leading to higher-level intelligence. The objective of context awareness computing is, therefore, to utilize computing technologies to build a system that is aware. More specifically, the goal of awareness computing is to incorporate the latest sensing capabilities of diverse signals with smart computing systems to remain in constant observing and awareness states, such as power-aware, location-aware and context-aware, under a unified computational framework. Image-understanding algorithms often enhance system awareness. Robot vision systems are aimed at analyzing objects from the low-level, iconic processes of early vision to the high-level, symbolic methods of recognition and interpretation. In these applications, image understanding provides an intelligent mechanism for computers to recognize, interpret, and analyze images, which is essential in improving service quality. Many deep learning algorithms are applied in image object recognition. If we can integrate image recognition and context awareness computing, many innovative applications will be achieved.

This Special Issue focuses on related applications based on awareness computing systems and innovative image-understanding technologies. The objective is to bring leading scientists and researchers together and create an interdisciplinary platform of computational theories, methodologies, and techniques.

Prof. Rung-Ching Chen
Prof. Qiangfu Zhao
Guest Editors

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Keywords

  • context awareness
  • location awareness
  • image understanding
  • deep learning
  • pattern recognition

Published Papers (7 papers)

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Research

15 pages, 6427 KiB  
Article
Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation
by Christine Dewi, Rung-Ching Chen, Yan-Ting Liu and Hui Yu
Appl. Sci. 2021, 11(7), 2913; https://doi.org/10.3390/app11072913 - 24 Mar 2021
Cited by 29 | Viewed by 3721
Abstract
A synthetic image is a critical issue for computer vision. Traffic sign images synthesized from standard models are commonly used to build computer recognition algorithms for acquiring more knowledge on various and low-cost research issues. Convolutional Neural Network (CNN) achieves excellent detection and [...] Read more.
A synthetic image is a critical issue for computer vision. Traffic sign images synthesized from standard models are commonly used to build computer recognition algorithms for acquiring more knowledge on various and low-cost research issues. Convolutional Neural Network (CNN) achieves excellent detection and recognition of traffic signs with sufficient annotated training data. The consistency of the entire vision system is dependent on neural networks. However, locating traffic sign datasets from most countries in the world is complicated. This work uses various generative adversarial networks (GAN) models to construct intricate images, such as Least Squares Generative Adversarial Networks (LSGAN), Deep Convolutional Generative Adversarial Networks (DCGAN), and Wasserstein Generative Adversarial Networks (WGAN). This paper also discusses, in particular, the quality of the images produced by various GANs with different parameters. For processing, we use a picture with a specific number and scale. The Structural Similarity Index (SSIM) and Mean Squared Error (MSE) will be used to measure image consistency. Between the generated image and the corresponding real image, the SSIM values will be compared. As a result, the images display a strong similarity to the real image when using more training images. LSGAN outperformed other GAN models in the experiment with maximum SSIM values achieved using 200 images as inputs, 2000 epochs, and size 32 × 32. Full article
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17 pages, 2596 KiB  
Article
Sentence Representation Method Based on Multi-Layer Semantic Network
by Wenfeng Zheng, Xiangjun Liu and Lirong Yin
Appl. Sci. 2021, 11(3), 1316; https://doi.org/10.3390/app11031316 - 1 Feb 2021
Cited by 129 | Viewed by 4515
Abstract
With the development of artificial intelligence, more and more people hope that computers can understand human language through natural language technology, learn to think like human beings, and finally replace human beings to complete the highly difficult tasks with cognitive ability. As the [...] Read more.
With the development of artificial intelligence, more and more people hope that computers can understand human language through natural language technology, learn to think like human beings, and finally replace human beings to complete the highly difficult tasks with cognitive ability. As the key technology of natural language understanding, sentence representation reasoning technology mainly focuses on the sentence representation method and the reasoning model. Although the performance has been improved, there are still some problems such as incomplete sentence semantic expression, lack of depth of reasoning model, and lack of interpretability of the reasoning process. In this paper, a multi-layer semantic representation network is designed for sentence representation. The multi-attention mechanism obtains the semantic information of different levels of a sentence. The word order information of the sentence is also integrated by adding the relative position mask between words to reduce the uncertainty caused by word order. Finally, the method is verified on the task of text implication recognition and emotion classification. The experimental results show that the multi-layer semantic representation network can promote sentence representation’s accuracy and comprehensiveness. Full article
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18 pages, 6105 KiB  
Article
An Intelligent Ubiquitous Learning Environment and Analytics on Images for Contextual Factors Analysis
by Mohammad Nehal Hasnine, Gökhan Akçapınar, Kousuke Mouri and Hiroshi Ueda
Appl. Sci. 2020, 10(24), 8996; https://doi.org/10.3390/app10248996 - 16 Dec 2020
Cited by 7 | Viewed by 2727
Abstract
Contextual factors in which learning occurs are crucial aspects that learning analytics and related disciplines aim to understand for optimizing learning and the environments in which learning occurs. In foreign vocabulary development, taking the notes or memos of learning contexts along with other [...] Read more.
Contextual factors in which learning occurs are crucial aspects that learning analytics and related disciplines aim to understand for optimizing learning and the environments in which learning occurs. In foreign vocabulary development, taking the notes or memos of learning contexts along with other factors, play an essential role in quick memorization and reflection. However, conventional tools fail to automate the learning contexts generation process as learners still need to take memos or e-notes to describe their vocabulary learning contexts. This paper presents the Image Understanding Project (hereafter IUEcosystem) that could produce smartly-generated learning contexts primarily in a learner’s target languages. The IUEcosystem uses visual content analysis of lifelogging images as the sensor data to produce smartly-generated learning contexts that could be used as an alternative to handwritten memos or electronic notes. The IUEcosystem uses applied artificial intelligence to produce smartly-generated learning contexts. This intelligent learning environment collects a learner’s learning satisfaction and interaction data and, later on, analyzes them to produce time-based notifications for enhancing retention. Furthermore, a new learning design is presented that aims to map a learner’s prior vocabulary knowledge with new learning vocabularies to be learned. This learning design would help learners to review and recall prior knowledge while learning new vocabulary. Full article
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16 pages, 3814 KiB  
Article
Deep Learning for Traffic Sign Recognition Based on Spatial Pyramid Pooling with Scale Analysis
by Shao-Kuo Tai, Christine Dewi, Rung-Ching Chen, Yan-Ting Liu, Xiaoyi Jiang and Hui Yu
Appl. Sci. 2020, 10(19), 6997; https://doi.org/10.3390/app10196997 - 7 Oct 2020
Cited by 60 | Viewed by 5729
Abstract
In the area of traffic sign detection (TSD) methods, deep learning has been implemented and achieves outstanding performance. The detection of a traffic sign, as it has a dual function in monitoring and directing the driver, is a big concern for driver support [...] Read more.
In the area of traffic sign detection (TSD) methods, deep learning has been implemented and achieves outstanding performance. The detection of a traffic sign, as it has a dual function in monitoring and directing the driver, is a big concern for driver support systems. A core feature of autonomous vehicle systems is the identification of the traffic sign. This article focuses on the prohibitive sign. The objective is to detect in real-time and reduce processing time considerably. In this study, we implement the spatial pyramid pooling (SPP) principle to boost Yolo V3’s backbone network for the extraction of functionality. Our work uses SPP for more comprehensive learning of multiscale object features. Then, perform a comparative investigation of Yolo V3 and Yolo V3 SPP across various scales to recognize the prohibitory sign. Comparisons with Yolo V3 SPP models reveal that their mean average precision (mAP) is higher than Yolo V3. Furthermore, the test accuracy findings indicate that the Yolo V3 SPP model performs better than Yolo V3 for different sizes. Full article
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17 pages, 8309 KiB  
Article
Learn and Tell: Learning Priors for Image Caption Generation
by Pei Liu, Dezhong Peng and Ming Zhang
Appl. Sci. 2020, 10(19), 6942; https://doi.org/10.3390/app10196942 - 4 Oct 2020
Cited by 2 | Viewed by 3102
Abstract
In this work, we propose a novel priors-based attention neural network (PANN) for image captioning, which aims at incorporating two kinds of priors, i.e., the probabilities being mentioned for local region proposals (PBM priors) and part-of-speech clues for caption words (POS priors), into [...] Read more.
In this work, we propose a novel priors-based attention neural network (PANN) for image captioning, which aims at incorporating two kinds of priors, i.e., the probabilities being mentioned for local region proposals (PBM priors) and part-of-speech clues for caption words (POS priors), into a visual information extraction process at each word prediction. This work was inspired by the intuitions that region proposals have different inherent probabilities for image captioning, and that the POS clues bridge the word class (part-of-speech tag) with the categories of visual features. We propose new methods to extract these two priors, in which the PBM priors are obtained by computing the similarities between the caption feature vector and local feature vectors, while the POS priors are predicated at each step of word generation by taking the hidden state of the decoder as input. After that, these two kinds of priors are further incorporated into the PANN module of the decoder to help the decoder extract more accurate visual information for the current word generation. In our experiments, we qualitatively analyzed the proposed approach and quantitatively evaluated several captioning schemes with our PANN on the MS-COCO dataset. Experimental results demonstrate that our proposed method could achieve better performance as well as the effectiveness of the proposed network for image captioning. Full article
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23 pages, 6146 KiB  
Article
Automatic Salient Object Extraction Based on Locally Adaptive Thresholding to Generate Tactile Graphics
by Akmalbek Abdusalomov, Mukhriddin Mukhiddinov, Oybek Djuraev, Utkir Khamdamov and Taeg Keun Whangbo
Appl. Sci. 2020, 10(10), 3350; https://doi.org/10.3390/app10103350 - 12 May 2020
Cited by 33 | Viewed by 3112
Abstract
Automatic extraction of salient regions is beneficial for various computer vision applications, such as image segmentation and object recognition. The salient visual information across images is very useful and plays a significant role for the visually impaired in identifying tactile information. In this [...] Read more.
Automatic extraction of salient regions is beneficial for various computer vision applications, such as image segmentation and object recognition. The salient visual information across images is very useful and plays a significant role for the visually impaired in identifying tactile information. In this paper, we introduce a novel saliency cuts method using local adaptive thresholding to obtain four regions from a given saliency map. First, we produced four regions for image segmentation using a saliency map as an input image and local adaptive thresholding. Second, the four regions were used to initialize an iterative version of the GrabCuts algorithm and to produce a robust and high-quality binary mask with a full resolution. Finally, salient objects’ outer boundaries and inner edges were detected using the solution from our previous research. Experimental results showed that local adaptive thresholding using integral images can produce a more robust binary mask compared to the results from previous works that make use of global thresholding techniques for salient object segmentation. The proposed method can extract salient objects with a low-quality saliency map, achieving a promising performance compared to existing methods. The proposed method has advantages in extracting salient objects and generating simple, important edges from natural scene images efficiently for delivering visually salient information to the visually impaired. Full article
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13 pages, 5077 KiB  
Article
Contextual Coefficients Excitation Feature: Focal Visual Representation for Relationship Detection
by Yajing Xu, Haitao Yang, Si Li, Xinyi Wang and Mingfei Cheng
Appl. Sci. 2020, 10(3), 1191; https://doi.org/10.3390/app10031191 - 10 Feb 2020
Viewed by 2265
Abstract
Visual relationship detection (VRD), a challenging task in the image understanding, suffers from vague connection between relationship patterns and visual appearance. This issue is caused by the high diversity of relationship-independent visual appearance, where inexplicit and redundant cues may not contribute to the [...] Read more.
Visual relationship detection (VRD), a challenging task in the image understanding, suffers from vague connection between relationship patterns and visual appearance. This issue is caused by the high diversity of relationship-independent visual appearance, where inexplicit and redundant cues may not contribute to the relationship detection, even confuse the detector. Previous relationship detection models have shown remarkable progress in leveraging external textual information or scene-level interaction to complement relationship detection cues. In this work, we propose Contextual Coefficients Excitation Feature (CCEF), a focal visual representation, which is adaptively recalibrated from original visual feature responses by explicitly modeling the interdependencies between features and their contextual coefficients. Specifically, contextual coefficients are obtained by calculation of both the spatial coefficients and generated-label ones. In addition, a conditional Wasserstein Generative Adversarial Network (WGAN) regularized with a relationship classification loss is designed to alleviate inadequate training of generated-label coefficients due to long tail distribution of relationship. Experimental results demonstrate the effective improvements of our method on relationship detection. In particular, our method improves the recall from 8.5% to 23.2% of predicting unseen relationship from zero-shot set. Full article
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