Accepted Papers
- Gender classification based on self-organizing neural network clustering
Shirin Yazdan Panah and Dr Vahid Rostami,Azad University of Qazvin,Iran.
ABSTRACT
Face is one of the most important biometric of human and contains lots of useful information such as gender, age, race and identity. Gender classification is very easy for human but it considers a challenge for computers. Gender classification through face images has recently been considered so much. Gender recognition can be useful in interaction between human and computer like identifying individual's identity. It is also applicable in TV networks in order to study the rate of viewers. Various algorithms have been designed for this issue and each of them has unraveled that to some extent. The last obtained rate to identify gender was through article written by Mozaffari who obtained mean rate of 83% for identification. It is the proposed method of the present study which has brought identification rate to 92.5. in this method we draw out face features based on Gabor filters and local binary patterns. These features are resistant against noise and they select proper features against bottleneck of images. In order to obtain a proper classification, we use self-organized map (SOM) (type of artificial neural network). This neural network finds the proper weights for each gender with very little error. Obtained results are compared with existing datasets and therefore, superiority of the proposed method would be evident.
- Analog Signal Processing Solution for Image Alignment
Nihar Athreyas1, Zhiguo Lai2, Jai Gupta2 and Dev Gupta2, 1University of Massachusetts, USA and 2Newlans Inc., USA
ABSTRACT
Imaging and Image sensors is a field that is continuously evolving. There are new products coming into the market every day. Some of these have very severe Size, Weight and Power constraints whereas other devices have to handle very high computational loads. Some require both these conditions to be met simultaneously. Current imaging architectures and digital image processing solutions will not be able to meet these ever increasing demands. There is a need to develop novel imaging architectures and image processing solutions to address these requirements. In this work we propose analog signal processing as a solution to this problem. The analog processor is not suggested as a replacement to a digital processor but it will be used as an augmentation device which works in parallel with the digital processor, making the system faster and more efficient. In order to show the merits of analog processing the highly computational Normalized Cross Correlation algorithm is implemented. We propose two novel modifications to the algorithm and a new imaging architecture which, significantly reduces the computation time..
- A New Hybrid Watermarking Technique using DCT and DWT based on Scaling Factor
U.S.N. Raju, Kamalakanta Sethi, Sunaina Choudhary and Priyanka Jain ,National Institute of Technology,India.
ABSTRACT
The Digital Watermarking can be used to hide an image over a cover image and also for copyright protection. In particular, digital image watermarking algorithms based on DCT and DWT are most preferable than others. In this paper a hybrid watermarking technique has been introduced which combines DCT and DWT to produce the watermarked image. The proposedmethod allows us to get visible as well as the invisible watermarked image by changing the value of the scaling factor. Here in this
paper, the perceptibility is measured using statistical parameters such as PSNR and MSE on different values of the scaling factor.
Also, the effectiveness of this technique is measured for different types of attacks on the basis of BCR..
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