63 research outputs found
FIGARO, Hair Detection and Segmentation in the Wild
Hair is one of the elements that mostly characterize people appearance. Being able to detect hair in images can be useful in many applications, such as face recognition, gender classification, and video surveillance. To this purpose we propose a novel multi-class image database for hair detection in the wild, called Figaro. We tackle the problem of hair detection without relying on a-priori information related to head shape and location. Without using any human-body part classifier, we first classify image patches into hair vs. non-hair by relying on Histogram of Gradients (HOG) and Linear Ternary Pattern (LTP) texture features in a random forest scheme. Then we obtain results at pixel level by refining classified patches by a graph-based multiple segmentation method. Achieved segmentation accuracy (85%) is comparable to state-of-the-art on less challenging databases
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
Diagnosis of Scalp Disorders using Machine Learning and Deep Learning Approach -- A Review
The morbidity of scalp diseases is minuscule compared to other diseases, but
the impact on the patient's life is enormous. It is common for people to
experience scalp problems that include Dandruff, Psoriasis, Tinea-Capitis,
Alopecia and Atopic-Dermatitis. In accordance with WHO research, approximately
70% of adults have problems with their scalp. It has been demonstrated in
descriptive research that hair quality is impaired by impaired scalp, but these
impacts are reversible with early diagnosis and treatment. Deep Learning
advances have demonstrated the effectiveness of CNN paired with FCN in
diagnosing scalp and skin disorders. In one proposed Deep-Learning-based scalp
inspection and diagnosis system, an imaging microscope and a trained model are
combined with an app that classifies scalp disorders accurately with an average
precision of 97.41%- 99.09%. Another research dealt with classifying the
Psoriasis using the CNN with an accuracy of 82.9%. As part of another study, an
ML based algorithm was also employed. It accurately classified the healthy
scalp and alopecia areata with 91.4% and 88.9% accuracy with SVM and KNN
algorithms. Using deep learning models to diagnose scalp related diseases has
improved due to advancements i computation capabilities and computer vision,
but there remains a wide horizon for further improvements
Artificial intelligence and sensory assessment of hair assembly features: a combined approach
An explorative comparison of the sensitivity of human perception with that of classification algorithms for machine learning when applied to human hair tresses
Tag-based annotation creates better avatars
Avatar creation from human images allows users to customize their digital
figures in different styles. Existing rendering systems like Bitmoji,
MetaHuman, and Google Cartoonset provide expressive rendering systems that
serve as excellent design tools for users. However, twenty-plus parameters,
some including hundreds of options, must be tuned to achieve ideal results.
Thus it is challenging for users to create the perfect avatar. A machine
learning model could be trained to predict avatars from images, however the
annotators who label pairwise training data have the same difficulty as users,
causing high label noise. In addition, each new rendering system or version
update requires thousands of new training pairs. In this paper, we propose a
Tag-based annotation method for avatar creation. Compared to direct annotation
of labels, the proposed method: produces higher annotator agreements, causes
machine learning to generates more consistent predictions, and only requires a
marginal cost to add new rendering systems.Comment: 15 pages, 7 figures, 4 table
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