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。传统的算法在结构化的小型数据库上工作得最好，但是当数据被缩放或当数据是非结构化的时，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，大型神经网络就会胜过其他算法。这部分是由于神经网络模型具有更多的参数并且具有学习复杂的非线性模式的能力。深度学习算法能够在深度神经网络架构中提取多个级别的信息，并将任何数据映射到相应的输出类中。拟议研究的总体目标是开发各种高效可靠的算法，用于使用传统和深度神经网络处理图像和视频信号。特别是，它建议在未来几年集中在视频监控在朦胧的条件下，基于动作的视频摘要生成在多摄像头网络基础设施。(a)监控中一个非常关键的问题是通过检测和识别来感知场景理解，以便进行有效的决策。在朦胧的环境条件下捕获的图像遭受能见度差、对比度低和偏色问题。一个统一的框架，处理烟雾引起的失真，有效地检测和识别的场景内容是至关重要的。大多数现有的方法不考虑显式的机制来处理在去雾期间的偏色。在执行颜色校正之前，考虑模糊图像是否包含偏色非常重要。它旨在构建一种尖端的人工智能技术，在部署到系统上后，可以自动处理与烟雾相关的问题，旨在进行有效的场景分析，以进行监控和探索。(b)视频监控是当今最需要研究的领域之一，考虑到不断的安全威胁。目前，越来越多的重点放在视频摘要生成，使监控的自动化和智能化。概要的主要目的是通过同时可视化不同时区的多个对象，从长视频中产生短视频摘要。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，多相机网络被广泛使用，其中从多个角度记录相同的场景。单视点视频摘要技术不能直接应用于多视点视频。因此，该建议旨在通过建立多个视图和动作之间的对应关系，使用多摄像机网络的基于动作的视频概要生成。 (c)最后，我们会把录像摘要与建议第（一）部分结合，研究在有雾情况下拍摄的录像摘要问题。

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.