An Assistive Object Recognition System for Enhancing Seniors Quality of Life

Elhariri, Esraa; El-Bendary, Nashwa; Hassanien, Aboul Ella; Snášel, Václav

doi:10.1016/j.procs.2015.09.013

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Here we will use the Histograms of oriented gradients (HOG) algorithm to extract traits that extracting traits from the image is one of the most important stages necessary for image processing and used the Histogram of Directed Gradient (HOG) algorithm to extract important traits from the image to benefit from them in the classification process and reduce the size of the data and thus reduce the time required to make classification [27,28]. Basic stages of the HOG algorithm [29]:…”

Section: Feature Extractionmentioning

confidence: 99%

Palmprint Identification Using Dolphin Optimization

Al-Taie,

Khaleel

2024

Period. Polytech. Elec. Eng. Comp. Sci.

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Palm print recognition is a rapidly evolving area in the field of biometrics, providing a high level of security for various applications Advances in scanning technology and software have led to faster and more accurate palm print analysis, we have proposed in this paper a system for palm recognition using the Dolphin Optimization Algorithm (DOA) as a computational technique inspired by nature aimed at solving complex improvement problems. We reduced the number of image features using the Histograms of oriented gradients (HOG) algorithm, we named this method as (DOA) and we also proposed a hybrid method by integrating the DOA algorithm with the Support Vector Machine (SVM) model to improve prediction accuracy by combining DSA's ability to search for global optimal solutions with the effective classification capabilities of SVM, this allows The hybrid approach creates a robust and the proposed hybrid method was named (SVM-DOA), and we also proposed a hybrid method by integrating the DOA algorithm with fuzzy c – mean (FCM) and we named the proposed hybrid method (DOA-fuzzy membership), we verified the validity of the proposed method on public database images of palm print. experiment show that the average accuracy rate of the dolphin swarm algorithm (DSO) is (96.8%), while the average accuracy rate of the proposed hybrid algorithm (DSO-SVM) is (97.8%), and the average accuracy of the proposed hybrid algorithm (DSO-fuzzy membership) is (98.1%).

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Section: Feature Extractionmentioning

confidence: 99%

Palmprint Identification Using Dolphin Optimization

Al-Taie,

Khaleel

2024

Period. Polytech. Elec. Eng. Comp. Sci.

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“…There are some related works on elderly object recognition assist system that used standard benchmark dataset to identify objects. In [1] the authors present an indoor object recognition system to classify different indoor objects in order to improve the life quality of the elderlies. The datasets used for these experiments consist of 347 images of eight different indoor objects.…”

Section: Data Descriptionmentioning

confidence: 99%

MYNursingHome: A fully-labelled image dataset for indoor object classification.

et al. 2020

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A fully labelled image dataset serves as a valuable tool for reproducible research inquiries and data processing in various computational areas, such as machine learning, computer vision, artificial intelligence and deep learning. Today's research on ageing is intended to increase awareness on research results and their applications to assist public and private sectors in selecting the right equipments for the elderlies. Many researches related to development of support devices and care equipment had been done to improve the elderly's quality of life. Indoor object detection and classification for autonomous systems require large annotated indoor images for training and testing of smart computer vision applications. This dataset entitled MYNursingHome is an image dataset for commonly used objects surrounding the elderlies in their home cares. Researchers may use this data to build up a recognition aid for the elderlies. This dataset was collected from several nursing homes in Malaysia comprises 37,500 digital images from 25 different indoor object categories including basket bin, bed, bench, cabinet and others.

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