When taking images against strong light sources, the resulting images often contain heterogeneous flare artifacts. These artifacts can importantly affect image visual quality and downstream computer vision tasks. Whil...
When taking images against strong light sources, the resulting images often contain heterogeneous flare artifacts. These artifacts can importantly affect image visual quality and downstream computer vision tasks. While collecting real data pairs of flare-corrupted/flare-free images for training flare removal models is challenging, current methods utilize the direct-add approach to synthesize data. However, these methods do not consider automatic exposure and tone mapping in image signal processing pipeline (ISP), leading to the limited generalization capability of deep models training using such data. Besides, existing methods struggle to handle multiple light sources due to the different sizes, shapes and illuminance of various light sources. In this paper, we propose a solution to improve the performance of lens flare removal by revisiting the ISP and remodeling the principle of automatic exposure in the synthesis pipeline and design a more reliable light sources recovery strategy. The new pipeline approaches realistic imaging by discriminating the local and global illumination through convex combination, avoiding global illumination shifting and local over-saturation. Our strategy for recovering multiple light sources convexly averages the input and output of the neural network based on illuminance levels, thereby avoiding the need for a hard threshold in identifying light sources. We also contribute a new flare removal testing dataset containing the flare-corrupted images captured by ten types of consumer electronics. The dataset facilitates the verification of the generalization capability of flare removal methods. Extensive experiments show that our solution can effectively improve the performance of lens flare removal and push the frontier toward more general situations.
In this paper, a gait data acquisition scheme based on acceleration sensor is introduced, and then the acceleration signal data are preprocessed, feature extraction and analysis are carried out, and the correlation co...
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Programmable toys present interesting tools for the creation and facilitation of learning experiences through gaming and robotics. In order though for educators and game designers to use these tools to their full pote...
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Ultrasound examinations during pregnancy can detect abnormal fetal development, which is a leading cause of perinatal mortality. In multiple pregnancies, the position of the fetuses may change between examinations. Th...
Ultrasound examinations during pregnancy can detect abnormal fetal development, which is a leading cause of perinatal mortality. In multiple pregnancies, the position of the fetuses may change between examinations. The individual fetus cannot be clearly identified. Fetal re-identification may improve diagnostic capabilities by tracing individual fetal changes. This work evaluates the feasibility of fetal re-identification on FETAL_PLANES_DB, a publicly available dataset of singleton pregnancy ultrasound images. Five dataset subsets with 6,491 images from 1,088 pregnant women and two re-identification frameworks (Torchreid, FastReID) are evaluated. FastReID achieves a mean average precision of 68.77% (68.42%) and mean precision at rank 10 score of 89.60% (95.55%) when trained on images showing the fetal brain (abdomen). Visualization with gradient-weighted class activation mapping shows that the classifiers appear to rely on anatomical features. We conclude that fetal re-identification in ultrasound images may be feasible. However, more work on additional datasets, including images from multiple pregnancies and several subsequent examinations, is required to ensure and investigate performance stability and *** relevance— To date, fetuses in multiple pregnancies cannot be distinguished between ultrasound examinations. This work provides the first evidence for feasibility of fetal re-identification in pregnancy ultrasound images. This may improve diagnostic capabilities in clinical practice in the future, such as longitudinal analysis of fetal changes or abnormalities.
Nowadays, studies on unmanned quadrotor transportation system have aroused widespread interests of many researchers. In this paper, a continuous tracking control method based on terminal sliding mode (TSM) is develope...
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ISBN:
(纸本)9781665405362
Nowadays, studies on unmanned quadrotor transportation system have aroused widespread interests of many researchers. In this paper, a continuous tracking control method based on terminal sliding mode (TSM) is developed considering the coupling relationship between the payload swing and quadrotor translation. Finite-time stability of the closed-loop system is guaranteed by Lyapunov techniques. The proposed method can achieve fast convergent rate and high-precision tracking performance under the influence of external disturbances and noises. The validity of the proposed method is supported by simulation results compared with the PD controller and the regular linear quadratic regulator (LQR) control scheme.
This paper presents an introductory yet comprehensive study of the combined signal and geometrical properties of Indoor Positioning Systems (IPSs) based on ultra-wideband (UWB) technology. These IPSs consist of a netw...
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ISBN:
(纸本)9781728157405;9781728157399
This paper presents an introductory yet comprehensive study of the combined signal and geometrical properties of Indoor Positioning Systems (IPSs) based on ultra-wideband (UWB) technology. These IPSs consist of a network of more than three transmitting anchors and a (tagged) single receiving object to be localised. The specific algorithm used in this paper is the Time Difference of Arrival (TDoA) with round-robin scheduling. The analysis is structured in a systematic manner in order to lay the foundations for the optimal number, location and orientation of anchors aiming for maximum precision, and also for maximum size of the working area with a desired prescribed precision.
The proceedings contain 29 papers. The special focus in this conference is on Smart Innovations in Communications and Computational Sciences. The topics include: Multipath routing using improved grey wolf optimizer (i...
ISBN:
(纸本)9789811553448
The proceedings contain 29 papers. The special focus in this conference is on Smart Innovations in Communications and Computational Sciences. The topics include: Multipath routing using improved grey wolf optimizer (igwo)-based ad hoc on-demand distance vector routing (aodv) algorithm on manet;analysis and prediction of fuel oil in a terminal;an android malware detection method based on native libraries;fire early warning system based on precision positioning technology;the construction of learning resource recommendation system based on recognition technology;review of ultra-low-power cmos amplifier for bio-electronic sensor interface;an enhancement of underwater images based on contrast restricted adaptive histogram equalization for image enhancement;hybridization of social spider optimization (sso) algorithm with differential evolution (de) using super-resolution reconstruction of video images;enhanced convolutional neural network (ecnn) for maize leaf diseases identification;facial expression recognition using improved local binary pattern and min-max similarity with nearest neighbor algorithm;visibility improvement of hazy image using fusion of multiple exposure images;comparative analysis of consensus algorithms and issues in integration of blockchain with iot;use of hadoop for sentiment analysis on twitter’s big data;enhanced mobility management model for mobile communications;multi-objective-based lion optimization algorithm for relay selection in wireless cooperative communication networks;embpred30: Assessing 30-days readmission for diabetic patients using categorical embeddings;prediction of different classes of skin disease using machine learning techniques;feature selection and classification based on swarm intelligence approach to detect malware in android platform;preface;trust-based fuzzy bat optimization algorithm for attack detection in manet;transfer learning: Survey and classification.
Uncertainty is an ever present challenge in life. To meet this challenge in data analysis, we propose a method for detecting anomalies in data. This method, based in part on Variational Autoencoder, identifies spiking...
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ISBN:
(纸本)9781728193311
Uncertainty is an ever present challenge in life. To meet this challenge in data analysis, we propose a method for detecting anomalies in data. This method, based in part on Variational Autoencoder, identifies spiking raw data by means of spectrum analysis. Time series data are examined in the frequency domain to enhance the detection of anomalies. In this paper, we have used the standard data sets to validate the proposed method. Experimental results show that the comparison of the frequency domain with the original data for anomaly detection can improve validity and accuracy on all criteria. Therefore, analysis of time series data by combining Variational Autoencoder and frequency domain spectrum methods can effectively detect anomalies. Contribution- We have proposed an anomaly detection method based on the time series data analysis by combining Variational Autoencoder and Spectrum analysis, and have benchmarked the method with reference to recent related research.
This paper surveys the landscape of potential security threats from malicious use of artificial intelligence technologies (MUAI) in Latin America. This study is based on scenario analysis to identify the MUAI-related ...
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ISBN:
(纸本)9781912764747
This paper surveys the landscape of potential security threats from malicious use of artificial intelligence technologies (MUAI) in Latin America. This study is based on scenario analysis to identify the MUAI-related cultural threats for region geopolitical balance. The development of artificial intelligence (AI) and machine learning capabilities stimulates the transition to a new technological order. These technologies with indeed many social applications benefits also transform nations power relations and lead to the new threats and vulnerabilities in the field of international psychological security (IPS), turning artificial intelligence and geopolitics more closer and challenging democracy and national security traditional models. AI-based technologies interact with peoples cognitive assumptions and provide any individual, group, organisation or nation-state to influence on public consciousness as a new kind of weapon in the global system. Research results look at how behavior, self-efficacy and privacy attitude are affected by culture compared to other psychological and demographics variables. It also examines what kind of data people tend to share online and how culture affects these choices. This study presents a multi-cultural view of MUAI for regional balance in Latin America, supports the idea of cultural competency as a mechanism of social influence and sets the distribution of power from the notion of security as a socio-cultural phenomenon. AI is highly likely to have different social impacts for region geopolitical balance depending on people cultural settings traced by customs, values and behaviours. This paper confirms that MUAI elevates threats to IPS to a qualitatively new level, which requires an adequate assessment and reaction from society. The comprehension of cross-cultural new threats of MUAI lead to formulate large-scale strategies to protect the sovereignty and enforce regional roles for building consensus, engagement and international coll
In the past decades, there has been tremendous growth in security-related issues throughout the world. With such a multifold increase in demand for security, video surveillance has become a critical research area. In ...
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ISBN:
(纸本)9781665499897
In the past decades, there has been tremendous growth in security-related issues throughout the world. With such a multifold increase in demand for security, video surveillance has become a critical research area. In the most recent studies, there were many attempts to integrate image processing algorithms. It was also possible to achieve artificial intelligence capabilities using computer vision. Intelligent video surveillance systems can be described as a technique to assist security personnel by providing reliable real-time alerts, thus supporting the investigations with efficient video analysis. This paper discusses a comprehensive and systematic overview of the existing video surveillance techniques and possible enhancements.
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