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Understanding multimedia content uses on different context specially to achieve smart and easy usages in daily life is one of the most useful researching topics currently. We have proposed an effective machine learning method to analyze multimedia content addressing gesture event detection and recognition. Our machine learning method has training robust mechanisms as based on well-studied techniques such that Procrustes Analysis, Combination of Local and Global Representations, Linear Shape Model, and application to SMART TV Virtual Keyboard. The spatial configuration of a predefined set of points is important for Geometry in gesture event detection. Generally, increasing these points have improved robustness of algorithm with a linear incremental computational complexity. We consider the parameterization of composition of local deformation that accounts for the differences between shape across identities and global transformation that accounts for the overall placement of particular shape as fingers and hands. In this research, we address gesture event detection specially fingertip gesture detection to get smart and advanced usage of technology. Our modern vision keyboard could be a good next generation replacement of SMART TV remote control. It can be more economical as we don’t need physical object like traditional keyboard, remote control and their energy resources like batteries.
Rise of deep structured learning (deep learning) technique is taking much attention in recent years for artificial intelligence (AI). Auto encoding technique (Autoencoder) is the one of the promising approach especially with deep structured architecture, typically for the purpose of dimensional reduction. As example driverless car is the best usage of it. Naturally, our human vision has the similar process on our perception system. Here we proposed the general technique for it. Specially, non-flat (curved) and noisy tackling non-linear machine learning method is taken into account in our proposed method with Neuroscience motivations. From Neuroscience, today, we have the important results about human perception that is how do our eyes work with brain especially under flux conditions to perciept the real world. In Neuroscience, it is discovered as when our eyes transfer the signal to brain, the brain has the special process that is the dimensional reduction processing. In the future, machine will make advances in touch, sight, hearing, taste, and smell. The ultimate goal of Computer Vision is for computers to have capability of human eyes and brains-or even to surpass and assist the human in certain ways. It leads us to systematically study the differential geometry such that differential manifold theory. In detail, to accomplish it, first our mathematical model must consider the general manifold learning as curved (non-flat) manifold case and real world data as noise handling model are taken the most attention. We have proposed the fusion mathematic model which solved the curvature data, outlier detection, cost optimization, automatic parameter choosing, and out-of-sample extension to apply it for machine.
Practical application of outlier detection is widely ranged as mechanical faults, system behavior, human error, natural deviations in populations, big data, and high dimensional data as depth point clouds and non-linear (manifold) learning, etc. We mainly focused on non-linear learning application as dimensional reduction due to natural requirement to recognize noise in order to preserve the meaningful main data. Without this technique, dimensional reduction approaches is impossible to gain correct result on noise data. In the natural structure of Riemannian space, it is already considered the high dimensional data, which means working in more practical case, such as 3D point cloud data that is very important to have outset mechanism. | en_US |