35,754 research outputs found
Smart Content Recognition from Images Using a Mixture of Convolutional Neural Networks
With rapid development of the Internet, web contents become huge. Most of the
websites are publicly available, and anyone can access the contents from
anywhere such as workplace, home and even schools. Nevertheless, not all the
web contents are appropriate for all users, especially children. An example of
these contents is pornography images which should be restricted to certain age
group. Besides, these images are not safe for work (NSFW) in which employees
should not be seen accessing such contents during work. Recently, convolutional
neural networks have been successfully applied to many computer vision
problems. Inspired by these successes, we propose a mixture of convolutional
neural networks for adult content recognition. Unlike other works, our method
is formulated on a weighted sum of multiple deep neural network models. The
weights of each CNN models are expressed as a linear regression problem learned
using Ordinary Least Squares (OLS). Experimental results demonstrate that the
proposed model outperforms both single CNN model and the average sum of CNN
models in adult content recognition.Comment: To be published in LNEE, Code: github.com/mundher/NSF
A Novel Scheme for Intelligent Recognition of Pornographic Images
Harmful contents are rising in internet day by day and this motivates the
essence of more research in fast and reliable obscene and immoral material
filtering. Pornographic image recognition is an important component in each
filtering system. In this paper, a new approach for detecting pornographic
images is introduced. In this approach, two new features are suggested. These
two features in combination with other simple traditional features provide
decent difference between porn and non-porn images. In addition, we applied
fuzzy integral based information fusion to combine MLP (Multi-Layer Perceptron)
and NF (Neuro-Fuzzy) outputs. To test the proposed method, performance of
system was evaluated over 18354 download images from internet. The attained
precision was 93% in TP and 8% in FP on training dataset, and 87% and 5.5% on
test dataset. Achieved results verify the performance of proposed system versus
other related works
Classification of Humans into Ayurvedic Prakruti Types using Computer Vision
Ayurveda, a 5000 years old Indian medical science, believes that the universe and hence humans are made up of five elements namely ether, fire, water, earth, and air. The three Doshas (Tridosha) Vata, Pitta, and Kapha originated from the combinations of these elements. Every person has a unique combination of Tridosha elements contributing to a person’s ‘Prakruti’. Prakruti governs the physiological and psychological tendencies in all living beings as well as the way they interact with the environment. This balance influences their physiological features like the texture and colour of skin, hair, eyes, length of fingers, the shape of the palm, body frame, strength of digestion and many more as well as the psychological features like their nature (introverted, extroverted, calm, excitable, intense, laidback), and their reaction to stress and diseases. All these features are coded in the constituents at the time of a person’s creation and do not change throughout their lifetime. Ayurvedic doctors analyze the Prakruti of a person either by assessing the physical features manually and/or by examining the nature of their heartbeat (pulse). Based on this analysis, they diagnose, prevent and cure the disease in patients by prescribing precision medicine.
This project focuses on identifying Prakruti of a person by analysing his facial features like hair, eyes, nose, lips and skin colour using facial recognition techniques in computer vision. This is the first of its kind research in this problem area that attempts to bring image processing into the domain of Ayurveda
Fair comparison of skin detection approaches on publicly available datasets
Skin detection is the process of discriminating skin and non-skin regions in
a digital image and it is widely used in several applications ranging from hand
gesture analysis to track body parts and face detection. Skin detection is a
challenging problem which has drawn extensive attention from the research
community, nevertheless a fair comparison among approaches is very difficult
due to the lack of a common benchmark and a unified testing protocol. In this
work, we investigate the most recent researches in this field and we propose a
fair comparison among approaches using several different datasets. The major
contributions of this work are an exhaustive literature review of skin color
detection approaches, a framework to evaluate and combine different skin
detector approaches, whose source code is made freely available for future
research, and an extensive experimental comparison among several recent methods
which have also been used to define an ensemble that works well in many
different problems. Experiments are carried out in 10 different datasets
including more than 10000 labelled images: experimental results confirm that
the best method here proposed obtains a very good performance with respect to
other stand-alone approaches, without requiring ad hoc parameter tuning. A
MATLAB version of the framework for testing and of the methods proposed in this
paper will be freely available from https://github.com/LorisNann
A smart vision sensor for detecting risk factors of a toddler's fall in a home environment
This paper presents a smart vision sensor for detecting risk factors of a toddler's fall in an indoor home environment assisting parents' supervision to prevent fall injuries. We identified the risk factors by analyzing real fall injury stories and referring to a related organization's suggestions to prevent falls. In order to detect the risk factors using computer vision, two major image processing methods, clutter detection and toddler tracking, were studied with using only one commercial web-camera. For practical purposes, there is no need for a toddler to wear any sensors or markers. The algorithms for detection have been developed, implemented and tested
Recent Advances and the Potential for Clinical Use of Autofluorescence Detection of Extra-Ophthalmic Tissues
The autofluorescence (AF) characteristics of endogenous fluorophores allow the label-free assessment and visualization of cells and tissues of the human body. While AF imaging (AFI) is well-established in ophthalmology, its clinical applications are steadily expanding to other disciplines. This review summarizes clinical advances of AF techniques published during the past decade. A systematic search of the MEDLINE database and Cochrane Library databases was performed to identify clinical AF studies in extra-ophthalmic tissues. In total, 1097 articles were identified, of which 113 from internal medicine, surgery, oral medicine, and dermatology were reviewed. While comparable technological standards exist in diabetology and cardiology, in all other disciplines, comparability between studies is limited due to the number of differing AF techniques and non-standardized imaging and data analysis. Clear evidence was found for skin AF as a surrogate for blood glucose homeostasis or cardiovascular risk grading. In thyroid surgery, foremost, less experienced surgeons may benefit from the AF-guided intraoperative separation of parathyroid from thyroid tissue. There is a growing interest in AF techniques in clinical disciplines, and promising advances have been made during the past decade. However, further research and development are mandatory to overcome the existing limitations and to maximize the clinical benefits
Histopathological image analysis : a review
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe
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