This work proposes a pre-informed Chan vese based level sets algorithm. Pre information includes objects colour, texture and shape fused features. The aim is to use this algorithm to segment flower images and extract ...
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This work proposes a pre-informed Chan vese based level sets algorithm. Pre information includes objects colour, texture and shape fused features. The aim is to use this algorithm to segment flower images and extract meaningful features that will help is classification of floral content. Shape pre-information modelling is handled manually using advance image processing tools. local binary patterns features makeup texture pre-information and Red, Green and Blue colour channels of the object provide colour pre-information. All pre-defined object information is fused together to for high dimension subspace defining object characteristics. Testing of the algorithm on flower images datasets show a jump in information content in the resulting segmentation output compared to other models in the category. Segmentation of flowers is important for recognition, classification and quality assessment to ever increasing volumes in floral markets.
Human age estimation is very interesting challenge today also. The prediction of age based on different techniques and by human, itself is very similar now. The major challenges for machine based age estimation are st...
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ISBN:
(纸本)9781467393393
Human age estimation is very interesting challenge today also. The prediction of age based on different techniques and by human, itself is very similar now. The major challenges for machine based age estimation are still alive. Challenges are uncontrolled environment, different light settings, variant posing and expression, ageing effects and life style. Human age estimation started with craniofacial growth has now reached to the condition where challenges are changing into opportunities for developing of applications. Applications include Biometrics, forensic investigation during crime, Relationship management over e-commerce, security control, Monitoring and surveillance, entertainment, cosmetology. Computer-based age estimation via facial images includes stages such as pre-processing, feature extraction, feature reduction and lastly classification or regression. Pre-processing provides invariability against different light settings and environment. Feature extraction deals with extraction of features like facial landmarks, shape and texture for further analysis. Feature reduction is considered for faster analysis of feature vectors. Lastly, regression, classification or combination is used for estimation of the age.
Digital images are widely used nowadays and there are powerful and convenient software tools for various types of image processing and enhancement. Unfortunately, these tools also facilitate for easy digital image for...
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ISBN:
(纸本)9781509040872
Digital images are widely used nowadays and there are powerful and convenient software tools for various types of image processing and enhancement. Unfortunately, these tools also facilitate for easy digital image forgery. In this paper we present an algorithm for digital image forgery detection that deals with the situation when some object, together with its shadow, is copied and pasted to some other location in the same or different image. Algorithm is based on the property that shadows do not change the texture of the underlaying surface. Our algorithms uses local binary patterns from shadow and adjacent non-shadow regions and features extracted from their histograms where energy and entropy proved to be the most discriminative. The proposed method was tested on some benchmark forged images and compared with other approaches from literature where it proved to be successful in detection of this type of forgery.
Facial recognition is the fast growing and challenging field in biometric applications. Variety of improvements has been suggested considering the feature extraction and matching techniques for accurate and efficient ...
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Facial recognition is the fast growing and challenging field in biometric applications. Variety of improvements has been suggested considering the feature extraction and matching techniques for accurate and efficient facial recognition. In this paper we proposed an approach which uses facial recognition methodologies to implement a computational efficient technique for facial biometric application to help visually impaired people. In this system we used LBP for feature extraction to classify image either valid or invalid person using SVM classification so that it helps visual impaired people in improving their lifestyle and safety. The architecture has been verified with both in a real environment Actual users and printed images have achieved very good results.
This paper proposed a facial expression recognition approach based on Gabor wavelet transform. Gabor wavelet filter is first used as pre-processing stage for extraction of the feature vector representation. Dimensiona...
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ISBN:
(纸本)9781479948734
This paper proposed a facial expression recognition approach based on Gabor wavelet transform. Gabor wavelet filter is first used as pre-processing stage for extraction of the feature vector representation. Dimensionality of the feature vector is reduced using Principal Component Analysis (PCA) and localbinary pattern (LBP) algorithms. Experiments were carried out of using Japanese female facial expression (JAFFE) database. In all experiments conducted using JAFFE database, results obtained reveal that GW+LBP has outperformed other approaches in this paper with an average recognition rate of 90% under the same experimental setting.
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