On-the-Fly Image Classification to Help Blind People

Aljasem, Dalal Khalid; Heeney, Michael; Gritti, Armando Pesenti; Raimondi, Franco

doi:10.1109/ie.2016.33

Cited by 3 publications

References 8 publications

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Low-Cost Assistive Technologies for Disabled People Using Open-Source Hardware and Software: A Systematic Literature Review

Ariza

Pearce

2022

IEEE Access

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Disabled People deal with a series of barriers that limit their inclusion, empowerment, wellbeing, and role in society with a special emphasis in low and medium-income countries. One of these barriers is concerning the accessibility and affordability of assistive technologies (ATs) that help to enhance the quality of life of these persons. In this context, this systematic literature review (SLR) analyzes and describes how free and open-source hardware (OSHW) and open software (OSS) are employed in the design, development, and deployment of low-cost ATs. In the SLR process, different ATs were analyzed for disabilities such as visual, mobility, upper body, prostheses, hearing & speaking, daily living, and participation in society. The ATs were designed with diverse OSHW and OSS technologies such as Arduino, Raspberry Pi, NVidia Jeston, OpenCV, YOLO, MobileNet, EEG and EMG signal conditioning devices, actuators, and sensors such as ultrasonic, LiDar, or flex. 809 studies were collected and analyzed from the database Web of Science, GitHub, and the specialized journals in OSHW HardwareX and the Journal of Open Hardware during the years 2013-2022. In the first part of the SLR, the bibliometric trends and topic clusters regarding the selected studies are described. Secondly, the ATs identified with open source technologies, e.g., sensor-based or computer vision-based, are described along with a complete state-ofart about these based on each disability recognized. Finally, the issues and challenges to this approach are explored including technical factors, documentation, government policies, and the inclusion of disabled people in open source co-creation. The purpose of this study is to inform practitioners, designers, or stakeholders about low-cost (frugal) ATs with OSHW and OSS, and thus promote their development, accessibility, and affordability, contributing to benefit the community of disabled people. INDEX TERMS Assistive technologies, disabled people, persons with disabilities, adaptive aids, open hardware, open-source hardware, open-source software, systematic literature review.

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Low-Cost Assistive Technologies for Disabled People Using Open-Source Hardware and Software: A Systematic Literature Review

Ariza

Pearce

2022

IEEE Access

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Deep learning application: rubbish classification with aid of an android device

2017

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Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment

Cao

Wang

et al. 2019

PLoS ONE

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An image classification algorithm based on adaptive feature weight updating is proposed to address the low classification accuracy of the current single-feature classification algorithms and simple multifeature fusion algorithms. The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. The support vector machine (SVM) classifier is then used to perform parallel training to obtain the optimal SVM classification model, which is then tested. The Pascal VOC 2012, Caltech 256 and SUN databases are adopted to build a massive image library. The speedup, classification accuracy and training time are tested in the experiment, and the results show that a linear growth tendency is present in the speedup of the system in a cluster environment. In consideration of the hardware costs, time, performance and accuracy, the algorithm is superior to mainstream classification algorithms, such as the power mean SVM and convolutional neural network (CNN). As the number and types of images both increase, the classification accuracy rate exceeds 95%. When the number of images reaches 80,000, the training time of the proposed algorithm is only 1/5 that of traditional single-node architecture algorithms. This result reflects the effectiveness of the algorithm, which provides a basis for the effective analysis and processing of image big data.

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On-the-Fly Image Classification to Help Blind People

Cited by 3 publications

References 8 publications

Low-Cost Assistive Technologies for Disabled People Using Open-Source Hardware and Software: A Systematic Literature Review

Low-Cost Assistive Technologies for Disabled People Using Open-Source Hardware and Software: A Systematic Literature Review

Deep learning application: rubbish classification with aid of an android device

Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment

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