Feature Matching-based Approaches to Improve the Robustness of Android Visual GUI Testing

2022 ◽  
Vol 31 (2) ◽  
pp. 1-32
Author(s):  
Luca Ardito ◽  
Andrea Bottino ◽  
Riccardo Coppola ◽  
Fabrizio Lamberti ◽  
Francesco Manigrasso ◽  
...  
Keyword(s):  
Computer Vision ◽  
Feature Matching ◽  
State Of The Art ◽  
Design Of Algorithms ◽  
Computational Burden ◽  
Domain Specific ◽  
Gui Testing ◽  
Wide Range ◽  
Full Screen ◽  
Feature Based

In automated Visual GUI Testing (VGT) for Android devices, the available tools often suffer from low robustness to mobile fragmentation, leading to incorrect results when running the same tests on different devices. To soften these issues, we evaluate two feature matching-based approaches for widget detection in VGT scripts, which use, respectively, the complete full-screen snapshot of the application ( Fullscreen ) and the cropped images of its widgets ( Cropped ) as visual locators to match on emulated devices. Our analysis includes validating the portability of different feature-based visual locators over various apps and devices and evaluating their robustness in terms of cross-device portability and correctly executed interactions. We assessed our results through a comparison with two state-of-the-art tools, EyeAutomate and Sikuli. Despite a limited increase in the computational burden, our Fullscreen approach outperformed state-of-the-art tools in terms of correctly identified locators across a wide range of devices and led to a 30% increase in passing tests. Our work shows that VGT tools’ dependability can be improved by bridging the testing and computer vision communities. This connection enables the design of algorithms targeted to domain-specific needs and thus inherently more usable and robust.

Face Recognition Technology

2011 ◽  
pp. 62-100
Author(s):  
Sanjay K. Singh ◽  
Mayank Vatsa ◽  
Richa Singh ◽  
K.K. Shukla ◽  
Lokesh R. Boregowda
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Law Enforcement ◽  
Performance Comparison ◽  
Feature Analysis ◽  
Computer Interfaces ◽  
Human Computer Interfaces ◽  
Wide Range ◽  
Biometric Systems ◽  
Feature Based

Face recognition technology is one of the most widely used problems in computer vision. It is widely used in applications related to security and human-computer interfaces. The two reasons for this are the wide range of commercial and law enforcement applications and the availability of feasible technologies. In this chapter the various biometric systems and the commonly used techniques of face recognition, Feature Based, eigenface based, Line Based Approach and Local Feature Analysis are explained along with the results. A performance comparison of these algorithms is also given.

scholarly journals Sentiment Analysis in Health and Well-Being: Systematic Review (Preprint)

2019 ◽  
Author(s):  
Anastazia Zunic ◽  
Padraig Corcoran ◽  
Irena Spasic
Keyword(s):  
Systematic Review ◽  
Health Care ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Well Being ◽  
The State ◽  
Support Vector ◽  
Domain Specific ◽  
Wide Range ◽  
Health And Well Being

BACKGROUND Sentiment analysis (SA) is a subfield of natural language processing whose aim is to automatically classify the sentiment expressed in a free text. It has found practical applications across a wide range of societal contexts including marketing, economy, and politics. This review focuses specifically on applications related to health, which is defined as “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.” OBJECTIVE This study aimed to establish the state of the art in SA related to health and well-being by conducting a systematic review of the recent literature. To capture the perspective of those individuals whose health and well-being are affected, we focused specifically on spontaneously generated content and not necessarily that of health care professionals. METHODS Our methodology is based on the guidelines for performing systematic reviews. In January 2019, we used PubMed, a multifaceted interface, to perform a literature search against MEDLINE. We identified a total of 86 relevant studies and extracted data about the datasets analyzed, discourse topics, data creators, downstream applications, algorithms used, and their evaluation. RESULTS The majority of data were collected from social networking and Web-based retailing platforms. The primary purpose of online conversations is to exchange information and provide social support online. These communities tend to form around health conditions with high severity and chronicity rates. Different treatments and services discussed include medications, vaccination, surgery, orthodontic services, individual physicians, and health care services in general. We identified 5 roles with respect to health and well-being among the authors of the types of spontaneously generated narratives considered in this review: a sufferer, an addict, a patient, a carer, and a suicide victim. Out of 86 studies considered, only 4 reported the demographic characteristics. A wide range of methods were used to perform SA. Most common choices included support vector machines, naïve Bayesian learning, decision trees, logistic regression, and adaptive boosting. In contrast with general trends in SA research, only 1 study used deep learning. The performance lags behind the state of the art achieved in other domains when measured by F-score, which was found to be below 60% on average. In the context of SA, the domain of health and well-being was found to be resource poor: few domain-specific corpora and lexica are shared publicly for research purposes. CONCLUSIONS SA results in the area of health and well-being lag behind those in other domains. It is yet unclear if this is because of the intrinsic differences between the domains and their respective sublanguages, the size of training datasets, the lack of domain-specific sentiment lexica, or the choice of algorithms.

scholarly journals IFRAD: A Fast Feature Descriptor for Remote Sensing Images

2021 ◽  
Vol 13 (18) ◽  
pp. 3774
Author(s):  
Qinping Feng ◽  
Shuping Tao ◽  
Chunyu Liu ◽  
Hongsong Qu ◽  
Wei Xu
Keyword(s):  
Remote Sensing ◽  
Feature Matching ◽  
State Of The Art ◽  
Image Data ◽  
Feature Descriptor ◽  
Feature Description ◽  
Sensing Applications ◽  
Feature Similarity ◽  
Feature Based ◽  
Remote Sensing Applications

Feature description is a necessary process for implementing feature-based remote sensing applications. Due to the limited resources in satellite platforms and the considerable amount of image data, feature description—which is a process before feature matching—has to be fast and reliable. Currently, the state-of-the-art feature description methods are time-consuming as they need to quantitatively describe the detected features according to the surrounding gradients or pixels. Here, we propose a novel feature descriptor called Inter-Feature Relative Azimuth and Distance (IFRAD), which will describe a feature according to its relation to other features in an image. The IFRAD will be utilized after detecting some FAST-alike features: it first selects some stable features according to criteria, then calculates their relationships, such as their relative distances and azimuths, followed by describing the relationships according to some regulations, making them distinguishable while keeping affine-invariance to some extent. Finally, a special feature-similarity evaluator is designed to match features in two images. Compared with other state-of-the-art algorithms, the proposed method has significant improvements in computational efficiency at the expense of reasonable reductions in scale invariance.

scholarly journals Sentiment Analysis in Health and Well-Being: Systematic Review

10.2196/16023 ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. e16023 ◽  
Author(s):  
Anastazia Zunic ◽  
Padraig Corcoran ◽  
Irena Spasic
Keyword(s):  
Systematic Review ◽  
Health Care ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Well Being ◽  
The State ◽  
Support Vector ◽  
Domain Specific ◽  
Wide Range ◽  
Health And Well Being

Background Sentiment analysis (SA) is a subfield of natural language processing whose aim is to automatically classify the sentiment expressed in a free text. It has found practical applications across a wide range of societal contexts including marketing, economy, and politics. This review focuses specifically on applications related to health, which is defined as “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.” Objective This study aimed to establish the state of the art in SA related to health and well-being by conducting a systematic review of the recent literature. To capture the perspective of those individuals whose health and well-being are affected, we focused specifically on spontaneously generated content and not necessarily that of health care professionals. Methods Our methodology is based on the guidelines for performing systematic reviews. In January 2019, we used PubMed, a multifaceted interface, to perform a literature search against MEDLINE. We identified a total of 86 relevant studies and extracted data about the datasets analyzed, discourse topics, data creators, downstream applications, algorithms used, and their evaluation. Results The majority of data were collected from social networking and Web-based retailing platforms. The primary purpose of online conversations is to exchange information and provide social support online. These communities tend to form around health conditions with high severity and chronicity rates. Different treatments and services discussed include medications, vaccination, surgery, orthodontic services, individual physicians, and health care services in general. We identified 5 roles with respect to health and well-being among the authors of the types of spontaneously generated narratives considered in this review: a sufferer, an addict, a patient, a carer, and a suicide victim. Out of 86 studies considered, only 4 reported the demographic characteristics. A wide range of methods were used to perform SA. Most common choices included support vector machines, naïve Bayesian learning, decision trees, logistic regression, and adaptive boosting. In contrast with general trends in SA research, only 1 study used deep learning. The performance lags behind the state of the art achieved in other domains when measured by F-score, which was found to be below 60% on average. In the context of SA, the domain of health and well-being was found to be resource poor: few domain-specific corpora and lexica are shared publicly for research purposes. Conclusions SA results in the area of health and well-being lag behind those in other domains. It is yet unclear if this is because of the intrinsic differences between the domains and their respective sublanguages, the size of training datasets, the lack of domain-specific sentiment lexica, or the choice of algorithms.

Face Recognition Technology

2008 ◽  
pp. 3968-3999
Author(s):  
Sanjay K. Singh ◽  
Mayank Vatsa ◽  
Richa Singh ◽  
K. K. Shukla
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Law Enforcement ◽  
Performance Comparison ◽  
Feature Analysis ◽  
Computer Interfaces ◽  
Human Computer Interfaces ◽  
Wide Range ◽  
Biometric Systems ◽  
Feature Based

Face recognition technology is one of the most widely used problems in computer vision. It is widely used in applications related to security and human-computer interfaces. The two reasons for this are the wide range of commercial and law enforcement applications and the availability of feasible technologies. In this chapter the various biometric systems and the commonly used techniques of face recognition, Feature Based, eigenface based, Line Based Approach and Local Feature Analysis are explained along with the results. A performance comparison of these algorithms is also given.

Modeling image aesthetics through aesthetics-related attributes

2021 ◽  
Vol 2021 (1) ◽  
pp. 11-15
Author(s):  
Marco Leonardi ◽  
Paolo Napoletano ◽  
Alessandro Rozza ◽  
Raimondo Schettini
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Support Vector Regression ◽  
State Of The Art ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
Automatic Assessment ◽  
Wide Range ◽  
Image Aesthetics ◽  
Multi Level

Automatic assessment of image aesthetics is a challenging task for the computer vision community that has a wide range of applications. The most promising state-of-the-art approaches are based on deep learning methods that jointly predict aesthetics-related attributes and aesthetics score. In this article, we propose a method that learns the aesthetics score on the basis of the prediction of aesthetics-related attributes. To this end, we extract a multi-level spatially pooled (MLSP) features set from a pretrained ImageNet network and then these features are used to train a Multi Layer Perceptron (MLP) to predict image aesthetics-related attributes. A Support Vector Regression machine (SVR) is finally used to estimate the image aesthetics score starting from the aesthetics-related attributes. Experimental results on the ”Aesthetics with Attributes Database” (AADB) demonstrate the effectiveness of our approach that outperforms the state of the art of about 5.5% in terms of Spearman’s Rankorder Correlation Coefficient (SROCC).

scholarly journals Multi-modality Sensor Data Classification with Selective Attention

2018 ◽  
Author(s):  
Xiang Zhang ◽  
Lina Yao ◽  
Chaoran Huang ◽  
Sen Wang ◽  
Mingkui Tan ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Selective Attention ◽  
State Of The Art ◽  
Sensor Data ◽  
Classification Task ◽  
Wearable Sensor ◽  
Domain Specific ◽  
Extra Information ◽  
Learning Scheme ◽  
Wide Range

Multimodel wearable sensor data classificationplays an important role in ubiquitous computingand has a wide range of applications in variousscenarios from healthcare to entertainment. How-ever, most of the existing work in this field em-ploys domain-specific approaches and is thus inef-fective in complex situations where multi-modalitysensor data is collected. Moreover, the wearablesensor data is less informative than the conven-tional data such as texts or images. In this paper,to improve the adaptability of such classificationmethods across different application contexts, weturn this classification task into a game and applya deep reinforcement learning scheme to dynami-cally deal with complex situations. We also intro-duce a selective attention mechanism into the rein-forcement learning scheme to focus on the crucialdimensions of the data. This mechanism helps tocapture extra information from the signal, and canthus significantly improve the discriminative powerof the classifier. We carry out several experimentson three wearable sensor datasets, and demonstratecompetitive performance of the proposed approachcompared to several state-of-the-art baselines.

Recognition of Birds in Blurred and Illumination Images by Local Features

2017 ◽  
Vol 7 (7) ◽  
pp. 243
Author(s):  
Suresha .M ◽  
. Sandeep
Keyword(s):  
Computer Vision ◽  
Time Complexity ◽  
Feature Detection ◽  
Feature Matching ◽  
Fast Algorithms ◽  
Local Features ◽  
Experimental Demonstration ◽  
Harris Corner ◽  
Blurred Image ◽  
Blurred Images

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

CLINICAL AND FORENSIC ASPECTS OF PHARMACOBEZOARS

2020 ◽  
Vol 12 ◽  
Author(s):  
Francisco Basílio ◽  
Ricardo Jorge Dinis-Oliveira
Keyword(s):  
State Of The Art ◽  
Signs And Symptoms ◽  
Immediate Release ◽  
Diagnosis And Treatment ◽  
Blood Concentrations ◽  
Forensic Practice ◽  
Wide Range ◽  
Therapeutic Doses ◽  
Complete History ◽  
Lethal Blood

Background: Pharmacobezoars are specific types of bezoars formed when medicines, such as tablets, suspensions, and/or drug delivery systems, aggregate and may cause death by occluding airways with tenacious material or by eluting drugs resulting in toxic or lethal blood concentrations. Objective: This work aims to fully review the state-of-the-art regarding pathophysiology, diagnosis, treatment and other relevant clinical and forensic features of pharmacobezoars. Results: patients of a wide range of ages and in both sexes present with signs and symptoms of intoxications or more commonly gastrointestinal obstructions. The exact mechanisms of pharmacobezoar formation are unknown but is likely multifactorial. The diagnosis and treatment depend on the gastrointestinal segment affected and should be personalized to the medication and the underlying factor. A good and complete history, physical examination, image tests, upper endoscopy and surgery through laparotomy of the lower tract are useful for diagnosis and treatment. Conclusion: Pharmacobezoars are rarely seen in clinical and forensic practice. They are related to controlled or immediate-release formulations, liquid or non-digestible substances, in normal or altered digestive motility/anatomy tract, and in overdoses or therapeutic doses, and should be suspected in the presence of risk factors or patients taking drugs which may form pharmacobezoars.

Computers in Geology - 25 Years of Progress

1994 ◽  
Keyword(s):  
Computer Modeling ◽  
Quantitative Methods ◽  
State Of The Art ◽  
International Association ◽  
Mathematical Geology ◽  
25Th Anniversary ◽  
The Earth ◽  
Wide Range ◽  
History Of ◽  
Mapping Techniques

This volume vividly demonstrates the importance and increasing breadth of quantitative methods in the earth sciences. With contributions from an international cast of leading practitioners, chapters cover a wide range of state-of-the-art methods and applications, including computer modeling and mapping techniques. Many chapters also contain reviews and extensive bibliographies which serve to make this an invaluable introduction to the entire field. In addition to its detailed presentations, the book includes chapters on the history of geomathematics and on R.G.V. Eigen, the "father" of mathematical geology. Written to commemorate the 25th anniversary of the International Association for Mathematical Geology, the book will be sought after by both practitioners and researchers in all branches of geology.

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