In this paper, we propose a new unsupervised feature learning framework, namely Deep Sparse Coding (DeepSC), that extends sparse coding to a multi-layer architecture for visual object recognition tasks. The main innovation of the framework is that it connects the sparse-encoders from different layers by a sparse-to-dense module. The sparse-to-dense module is a composition of a local spatial poo...

This paper proposes a simple spatial-temporal smoothness based method for solving dense non-rigid structure-frommotion (NRSfM). First, we revisit the temporal smoothness and demonstrate that it can be extended to dense case directly. Second, we propose to exploit the spatial smoothness by resorting to the Laplacian of the 3D non-rigid shape. Third, to handle real world noise and outliers in mea...

Due to the complicated structure of microwave radiometric imaging system and the massive amount of data collection in one snapshot, it is difficult to achieve the high spatial resolution image by conventional microwave radiation imaging method based on the Nyquist sampling. In this paper, according to the priori information of the compressible multi-structural information of microwave radiation...

This paper is concerned with reconstructing the metric geometry of a scene imaged with a single camera and a scanning laser. Our aim is to assign each image pixel with a range value using both image appearance and sparse laser data. We pose the problem as an optimisation of a cost function encapsulating a spatially varying smoothness cost and measurement compatibility. In particular we introduc...

It is often the case that images generated by image synthesis algorithms are judged by visual examination. The user resorts to an iterative refinement process of inspection and rendering until a satisfactory image is obtained. In this paper we propose quantitative metrics to compare images that arise from an image synthesis algorithm. The intent is to be able to guide the refinement process inh...

About twenty years ago the measure of smoothness ω φ (f, t) was introduced and related to the rate of polynomial approximation. In this article we survey developments about this and related concepts since that time. MSC: 41A10, 41A17, 41A25, 41A27, 41A30, 41A36, 41A40, 41A50, 41A63, 26A15, 26B35, 26B05, 42C05, 26A51, 26A33, 46E35 keywords: Moduli of smoothness, K-functionals, realization functi...

About twenty years ago the measure of smoothness ω φ (f, t) was introduced and related to the rate of polynomial approximation. In this article we survey developments about this and related concepts since that time. MSC: 41A10, 41A17, 41A25, 41A27, 41A30, 41A36, 41A40, 41A50, 41A63, 26A15, 26B35, 26B05, 42C05, 26A51, 26A33, 46E35 keywords: Moduli of smoothness, K-functionals, realization functi...

| Tikhonov regularization with a modiied total variation regularization functional is used to recover an image from noisy, blurred data. This approach is appropriate for image processing in that it does not place a priori smoothness conditions on the solution image. An eecient algorithm is presented for the discretized problem which combines a xed point iteration to handle nonlinearity with an ...

This paper introduces an Advanced Artificial Potential Field (AAPF) controller which is used to control the robot's motion in cluttered environments. The proposed approach gives less computation and increases the reaction speed of the robot at obstacle avoidance situations. The increasing of robot's reaction speed doesn't affect on the smoothness of its path due to the use of Gen...