Motion detection is a basic operation in the selection of significant segments of the video signals. For an effective Human Computer Intelligent Interaction, the computer needs to recognize the motion and track the moving object. Here an efficient neural network system is proposed for motion detection from the static background. This method mainly consists of four parts like Frame Separation, R...

A technique for designing frames to use with vector selection algorithms, for example Matching Pursuits (MP), is presented. The design algorithm is iterative and requires a training set of signal vectors. An MP algorithm chooses frame vectors to approximate each training vector. Each vector in the frame is then adjusted by using the residuals for the training vectors which used that particular ...

A new class of Support Vector Machine (SVM) which is applicable to sequential-pattern recognition is developed by incorporating an idea of non-linear time alignment into the kernel. Since time-alignment operation of sequential pattern is embedded in the kernel evaluation, same algorithms with the original SVM for training and classification can be employed without modifications. Furthermore, fr...

In this research paper, we propose the regression-based packet loss concealment (PLC) for digital speech transmission by using deep neural networks (DNNs) with a multiple-layer deep architecture. For the DNN training, log-power spectra and phases are employed as features in the input layer for the large training set, which ensures non-linear mapping the frames from the last correctly received f...

GiSHEO platform [1] providing on-demand services for training and high education in Earth Observation is developed, in the frame of an ESA funded project through its PECS programme, to respond to the needs of powerful education resources in remote sensing field. It intends to be a Grid-based platform of which potential for experimentation and extensibility are the key benefits compared with a d...

We solve the problem of how to densely align actions in videos at frame level, with only order occurring available, save time-consuming efforts accurately annotate temporal boundaries each action. propose three task-specific innovations under this scenario: (1) To encode fine-grained spatiotemporal local features and long-range patterns simultaneously, we test popular backbones compare their ac...

Frame semantics is a linguistic theory that has been instantiated for English in the FrameNet lexicon. We solve the problem of frame-semantic parsing using a two-stage statistical model that takes lexical targets (i.e., content words and phrases) in their sentential contexts and predicts frame-semantic structures. Given a target in context, the first stage disambiguates it to a semantic frame. ...

We explore discriminative training of HMM parameters that directly minimizes the expected error rate. In discriminative training one is interested in training a system to minimize a desired error function, like word error rate, phone error rate, or frame error rate. We review a recent method (McAllester, Hazan and Keshet, 2010), which introduces an analytic expression for the gradient of the ex...

In this work, we give parallel transport frame of a curve and we introduce the relations between the frame and Frenet frame of the curve in 4-dimensional Euclidean space. The relation which is well known in Euclidean 3-space is generalized for the …rst time in 4-dimensional Euclidean space. Then we obtain the condition for spherical curves using the parallel transport frame of them. The conditi...