An Assistive Object Recognition System for Enhancing Seniors Quality of Life

AbstractThis paper presents an indoor object recognition system based on the histogram of oriented gradient and Machine Learning (ML) algorithms; such as Support Vector Machines (SVMs), Random Forests (RF) and Linear Discriminant Analysis (LDA) algorithms, for classifying different indoor objects to improve quality of elderly people's life. The proposed approach consists of three phases; namely segmentation, feature extraction, and classification phases. Datasets used for these experiments, are totally consisted of 347 images with different eight indoor objects used for both training and testing datasets. Training dataset is divided into eight classes representing the different eight indoor objects. Experimental results showed that RF classification algorithm outperformed both SVMs and LDA algorithms, where RF achieved 80.12%, SVMs and LDA achieved 77.81% and 78.76% respectively

Food Object Recognition Using a Mobile Device: State of the Art

In this paper nine mobile food recognition systems are described based on their system architecture and their core properties (the core properties and experimental results are shown on the last page). While the mobile hardware increased its power through the years (2009 - 2013) and the food detection algorithms got optimized, still there was no uniform approach to the question of food detection. Also, some system used additional information for better detection, like voice data, OCR and bounding boxes. Three systems included a volume estimation feature. First five systems were implemented on a client-server architecture, while the last three took advantage of the available hardware in later years and proposed a client only based architecture

Food Object Recognition Using a Mobile Device: State of the Art

In this paper nine mobile food recognition systems are described based on their system architecture and their core properties (the core properties and experimental results are shown on the last page). While the mobile hardware increased its power through the years (2009 - 2013) and the food detection algorithms got optimized, still there was no uniform approach to the question of food detection. Also, some system used additional information for better detection, like voice data, OCR and bounding boxes. Three systems included a volume estimation feature. First five systems were implemented on a client-server architecture, while the last three took advantage of the available hardware in later years and proposed a client only based architecture