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The procedure of procurement the original image from the degraded image assumed the knowledge of the debasing factors is called as Image Restoration. The corrupted image will be considered as input and provided to the soft computing method for decreasing the noisy information from the input image in our suggested method. Nonetheless, the established output image obtained from the soft computing method will seem to be blurry with less superiority in the contrast level. This blurred information damages the function of image reconstruction. Thus, into overawed this disadvantage, we deed an Extended Kalman filter method to provide great quality reconstructed the image, in that an optimization algorithm Grey Wolf Optimization (GWO) will be used for a greater reconstructed image. The main contribution of the proposed work is to improve the image quality of reconstruction image by means of optimal Extended Kalman filter. For proving the function of our suggested method, the reconstructed image quality will be associated with the conventional methods. The restoration method was tested with different image modalities such as MRI, CT and also Ultra Sound images of the Human abdomen. The method will be applied to the functioning platform of MATLAB. The implemented restoration technique achieves the maximum Peak Signal to Noise Ratio (PSNR) and structural similarity index (SSIM) value for liver CT image are 35.10db and 0.999 and minimum Mean square error (MSE) and Mean absolute error (MAE) value for liver CT image are 7.90E-05 and 1.1281 respectively. [ABSTRACT FROM AUTHOR]

The recognition frameworks are highly vulnerable to spoofing attacks and this vulnerability generates an effective security concerned issues in biometric domain. Moreover, some of the earlier proposed approaches have attained attractive results with intra test (i.e. by training and testing the system on same database) evaluation done to detect the face spoofing attack. Consequently, most of these techniques generate incorrect decision on the recognition of genuine faces with unseen attacks in case of inter test evaluation (i.e. the system is trained on one database and then tested on another database). However, this impact is considered as a major difficulty in the highly focused biometric anti-spoofing research domain. In this work, we propose a multimodal biometric framework for the accurate recognition of fake face from genuine face. Initially, face image features which are coupled to the color spaces HSV and YCbCr are extracted with EDGHM-SURF (Enhanced Discrete Gaussian-Hermite Moment based Speed-up Robust Feature) descriptor. Then, a newly developed method of feature-level fusion using OGWO (FLFO) is used to fuse these extracted features. This method utilized the OGWO (Oppositional Gray Wolf Optimization) algorithm due to its excellent exploitation and exploration behavior in the identification of optimal weight score from the solution space, without allowing the solutions to stick in the local optimum. Finally, the fused features are fed into the Layered k-SVM (k-support vector machine) classifier for the recognition of fake face. The experimental results of our proposed approach are evaluated on three traditional benchmark face spoofing databases, namely the Replay-Attack, the CASIA Face Anti-Spoofing, and the MSU Mobile Face Spoof database. The outcome of our proposed approach exhibited steady and robust performance across all the three datasets. More commonly, our proposed approach executes well in the inter database tests and yields high performance, even though when only operated with minimized training data. [ABSTRACT FROM AUTHOR]

In visual tracking topic, developing a robust tracking method is very challenging, seen that there are many issues to look at, particularly, fast motion, target appearance changing, background clutter and camera motion. To override these problems, we present a new object tracking method with the fusion of interacting multiple models (IMM) and the particle filter (PF). First, the IMM is applied with a bank of parallel H∞ filter to estimate the global motion, the target motion is efficiently represented using only two parametric single models, and an adaptive strategy is preformed to adjust automatically the parameters of the two sub models at each recursive time step. Second, the particle filter is performed to estimate the local motion, we fuse the color and texture features to describe the appearance of the tracked object, we use the alpha Gaussian mixture model (α-GMM) to model the color feature distribution, the parameter α allows the probability function to possesses a flatter distribution, and the texture feature is represented by the distinctive uniform local binary pattern histogram (DULBP) based on the uniform local binary pattern (ULBP) operator; we fuse then the two features to represent the target’s appearance under the particle filter framework. We conduct quantitative and qualitative experiments on a variety of challenging public sequences; the results show that our method performs robustly and demonstrates strong accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a method to capture human pose from individual real-world RGB images using a deep learning technique. The current works on estimating human pose by deep learning are designed in a detection or a regression framework, and in a part-based manner. As a new perspective, we introduce a classification scheme for this problem, which reasons the pose holistically. To the best of our knowledge, this is the first work for holistic human pose classification task that owes its feasibility to the great power of convolutional neural networks in feature learning. After training a convolutional neural network to classify the input image to one of the KeyPoses, the final pose is computed as a linear combination of several KeyPoses. In this new holistic classification attitude, the vast and high degree of freedom human pose space is divided into a finite number of subspaces and the convolutional neural network shows promising results in learning the features of each subspace. Empirical results (PCP and PCK rates) demonstrate that the proposed scheme is successfully able to understand human pose (i.e., predict a valid, true and coarse pose) in real-world unconstrained images with challenges like severe occlusion, high articulation, low quality and cluttered background. Furthermore, using the proposed method, the need for defining a complex model (such as appearance model or joints pairwise relations) is relieved. We have also verified a potential application of our proposed method in semantic image retrieval based on human pose. [ABSTRACT FROM AUTHOR]

In the present paper, an advanced encryption technique commonly known as Elliptic Curve Cryptography (ECC) is used to embed a binary image as a watermark in five grayscale host images in a semi-blind manner. The ECC algorithm is a fast encryption technique which successfully encrypts the subject with significantly less number of bits as compared to other popular encryption algorithms such as Rivest-Shamir-Adleman (RSA) and Direct Selling Association (DSA). In the proposed watermarking scheme, embedding in the grayscale host images is carried out in DWT-SVD domain. First, entropy based Human Visual System (HVS) parameters are computed block wise to identify the most appropriate blocks in spatial domain. First level DWT is computed for these selected blocks and watermark embedding is carried out by using the calculated Singular Value Decomposition (SVD) parameters. Preliminary results of this work show that proposed scheme outperforms the other similar schemes carried out in DCT-SVD domain without using any encryption method. It is concluded that the use of DWT-SVD hybrid architecture along with the fast encryption technique ECC is responsible for better performance in present case. In the second part of this simulation, an established HVS model working in DCT domain is implemented and compared with the entropy based HVS model implemented in transform domain to embed the ECC encrypted binary watermark in images. In this case also, proposed scheme performs better both in terms of visual imperceptibility and robustness as compared to other scheme. It is concluded that HVS parameters - Luminance, Contrast and Edge Sensitivity are better placed in comparison to entropy parameters to examine image features and characteristics for watermarking purpose. [ABSTRACT FROM AUTHOR]