Image and Video Signal Processing with Applications

信号处理理论和算法在图像、视频、生物医学和语音处理等领域有着广泛的应用。传统算法在结构化的小型数据库上工作得最好，但当数据被扩展或非结构化时，容易产生错误的结果。在小数据集上，传统算法做得很好，但一旦数据规模大幅增长，大型神经网络的表现就会优于其他神经网络。这在一定程度上是因为神经网络模型具有更多的参数，并具有学习复杂非线性模式的能力。深度学习算法能够在深度神经网络体系结构中从多个层次提取信息，并将任何数据映射到相应的输出类中。本研究的总体目标是利用传统神经网络和深度神经网络开发各种高效、可靠的图像和视频信号处理算法。特别是，建议在今后几年集中精力在雾霾条件下进行视频监控，并在多摄像机网络基础设施中生成基于行动的视频摘要。(A)监视中的一个非常关键的问题是通过检测和识别来了解可感知的场景，以便进行有效的决策。在雾霾环境中拍摄的图像存在能见度低、对比度低和偏色等问题。为了有效地检测和识别场景内容，处理雾霾引起的失真的统一框架至关重要。现有的大多数方法都没有考虑显式机制来处理去污过程中的偏色问题。在执行颜色校正之前，重要的是要考虑模糊图像是否包含偏色。它旨在建立一种尖端的人工智能技术，一旦部署到系统上，就可以自动处理与雾霾相关的问题，旨在为监视和探索提供有效的场景分析。(B)考虑到持续不断的安全威胁，视频监控是当今最苛刻的研究领域之一。目前，视频摘要的生成越来越受到重视，以实现监控的自动化和智能化。摘要的主要目标是通过同时可视化不同时区的多个对象，从较长的视频中生成简短的摘要。过去，视频监控大多局限于室内环境中的单个静态摄像机。目前，多摄像机网络被广泛使用，从多个角度记录同一场景。单视点视频摘要技术不能直接应用于多视点视频。因此，该方案旨在通过建立多个视点和动作之间的对应关系来使用多摄像机网络来生成基于动作的视频摘要。(C)最后，当局会把录影摘要与建议的第(一)部分结合起来，以研究在雾霾情况下拍摄的录影摘要的问题。

Signal processing theory and algorithms have a wide range of applications such as in image, video biomedical and speech processing. Traditional algorithms work best over structured small size databases, but are prone to erroneous results when data is scaled or when it is unstructured. On a small dataset, traditional algorithms do a great job, but once the data scale grows enormously, the large neural nets outperform others. This is partially due to the fact that a neural network model has many more parameters and has the ability to learn complicated nonlinear patterns. Deep learning algorithms have the ability to extract information at multiple levels in deep neural network architecture and map any data into a corresponding output class. Overall objectives of the proposed research are to develop a variety of efficient & reliable algorithms for processing image and video signals, using traditional as well as deep neural networks. In particular, it is proposed to concentrate over the next few years on video surveillance under hazy conditions and action-based video synopsis generation in a multi-camera network infrastructure. (a) A very critical issue in surveillance is perceptible scene understanding through detection and recognition for effective decision-making. Images captured in hazy environmental conditions suffer from poor visibility, low contrast and color cast issues. A unified framework that handles haze-induced distortions for effective detection and recognition of scene content is of utmost importance. Most of the existing approaches do not consider explicit mechanisms to handle the color cast during dehazing. It is important to consider whether a hazy image contains color cast before performing color correction. It is intended to build a cutting-edge AI technology, which upon deploying onto the system can automatically handle haze-related issues, aiming at effective scene analytics for surveillance and exploration. (b) Video surveillance is one of the most demanding areas of research in present day considering the unceasing security threats. Presently, more emphasis is being laid on video synopsis generation to make the surveillance automated and intelligent. The primary objective of synopsis is to produce a short summery of the video from a long one by simultaneous visualization of multiple objects in different time zones. In the past, video surveillance has been mostly limited to single static camera confined to indoor environment. Presently, multi-camera networks are used extensively, where the same scene is recorded from multiple angles. Mono-view video synopsis technologies cannot directly be applied to multi-view videos. Hence, this proposal aims at an action-based video synopsis generation using multi-camera network by establishing correspondence among multiple views and actions. (c) Finally, video synopsis would be combined with part (a) of the proposal to study the problem of synopsis of videos taken under hazy conditions.