Images of static scenes submerged beneath a wavy water surface exhibit severe
non-rigid distortions. The physics of water flow suggests that water surfaces
possess spatio-temporal smoothness and temporal periodicity. Hence they possess
a sparse representation in the 3D discrete Fourier (DFT) basis. Motivated by
this, we pose the task of restoration of such video sequences as a compressed
sensing (CS) problem. We begin by tracking a few salient feature points across
the frames of a video sequence of the submerged scene. Using these point
trajectories, we show that the motion fields at all other (non-tracked) points
can be effectively estimated using a typical CS solver. This by itself is a
novel contribution in the field of non-rigid motion estimation. We show that
this method outperforms state of the art algorithms for underwater image
restoration. We further consider a simple optical flow algorithm based on local
polynomial expansion of the image frames (PEOF). Surprisingly, we demonstrate
that PEOF is more efficient and often outperforms all the state of the art
methods in terms of numerical measures. Finally, we demonstrate that a
two-stage approach consisting of the CS step followed by PEOF much more
accurately preserves the image structure and improves the (visual as well as
numerical) video quality as compared to just the PEOF stage.

通过使用压缩感知和稀疏表示来恢复水下图像，进一步结合基于局部多项式扩展的光流算法，成功提高了图像质量，并在非刚体运动估计领域做出了创新性贡献。

使用压缩感知和局部多项式图像表示相结合的方法恢复非刚性失真水下图像

Restoration of Non-rigidly Distorted Underwater Images using a  Combination of Compressive Sensing and Local Polynomial Image Representations

Human action recognition involves the characterization of human actions
through the automated analysis of video data and is integral in the development
of smart computer vision systems. However, several challenges like dynamic
backgrounds, camera stabilization, complex actions, occlusions etc. make action
recognition in a real time and robust fashion difficult. Several complex
approaches exist but are computationally intensive. This paper presents a novel
approach of using a combination of good features along with iterative optical
flow algorithm to compute feature vectors which are classified using a
multilayer perceptron (MLP) network. The use of multiple features for motion
descriptors enhances the quality of tracking. Resilient backpropagation
algorithm is used for training the feedforward neural network reducing the
learning time. The overall system accuracy is improved by optimizing the
various parameters of the multilayer perceptron network.

本文提出了一种新的方法，使用良好特征和迭代光流算法来计算特征向量，并使用多层感知器（MLP）网络进行分类，最终通过优化多层感知器网络的各种参数来提高系统准确性，从而解决了实时识别与跟踪人类动作的问题。