基于对高分辨率水下视觉调查的需求，本研究表明，现有的烟囱II自主水下车辆（AUV）适应完全悬停的AUV完全能够进行自主，近​​距离成像调查任务。本文重点介绍了AUV机动能力的增强（实现了改进的机动控制），实现了最新推进器分配算法的状态（允许最佳推进器分配和推进器冗余），以及在控制器之后的升级路径的开发以便于精确开发高分辨率成像任务所需的精致运动。为了便于车辆适应，开发了一种动态模型。提出了使用良好接受的公式，通过计算流体动力学和实际海上实验获得最初获得的动态模型系数的校准过程。还提出了耐压成像系统的房屋开发。该系统包括立体声相机和高功率闪电闪光灯，并作为专用AUV有效载荷装配。最后，在实际海床视觉调查任务中证明了平台的性能。

Neural style transfer is a deep learning technique that produces an unprecedentedly rich style transfer from a style image to a content image and is particularly impressive when it comes to transferring style from a painting to an image. It was originally achieved by solving an optimization problem to match the global style statistics of the style image while preserving the local geometric features of the content image. The two main drawbacks of this original approach is that it is computationally expensive and that the resolution of the output images is limited by high GPU memory requirements. Many solutions have been proposed to both accelerate neural style transfer and increase its resolution, but they all compromise the quality of the produced images. Indeed, transferring the style of a painting is a complex task involving features at different scales, from the color palette and compositional style to the fine brushstrokes and texture of the canvas. This paper provides a solution to solve the original global optimization for ultra-high resolution images, enabling multiscale style transfer at unprecedented image sizes. This is achieved by spatially localizing the computation of each forward and backward passes through the VGG network. Extensive qualitative and quantitative comparisons show that our method produces a style transfer of unmatched quality for such high resolution painting styles.

Neural networks trained on large datasets by minimizing a loss have become the state-of-the-art approach for resolving data science problems, particularly in computer vision, image processing and natural language processing. In spite of their striking results, our theoretical understanding about how neural networks operate is limited. In particular, what are the interpolation capabilities of trained neural networks? In this paper we discuss a theorem of Domingos stating that "every machine learned by continuous gradient descent is approximately a kernel machine". According to Domingos, this fact leads to conclude that all machines trained on data are mere kernel machines. We first extend Domingo's result in the discrete case and to networks with vector-valued output. We then study its relevance and significance on simple examples. We find that in simple cases, the "neural tangent kernel" arising in Domingos' theorem does provide understanding of the networks' predictions. Furthermore, when the task given to the network grows in complexity, the interpolation capability of the network can be effectively explained by Domingos' theorem, and therefore is limited. We illustrate this fact on a classic perception theory problem: recovering a shape from its boundary.

标准化流量（NF）是基于可能性的强大生成模型，能够在表达性和拖延性之间进行折衷，以模拟复杂的密度。现已建立的研究途径利用了最佳运输（OT），并寻找Monge地图，即源和目标分布之间的努力最小的模型。本文介绍了一种基于Brenier的极性分解定理的方法，该方法将任何受过训练的NF转换为更高效率的版本而不改变最终密度。我们通过学习源（高斯）分布的重新排列来最大程度地减少源和最终密度之间的OT成本。由于Euler的方程式，我们进一步限制了导致估计的Monge图的路径，将估计的Monge地图放在量化量的差异方程的空间中。所提出的方法导致几种现有模型的OT成本降低的平滑流动，而不会影响模型性能。

极化成像已应用于越来越多的机器人视觉应用中（例如，水下导航，眩光去除，脱落，对象分类和深度估计）。可以在市场RGB极化摄像机上找到可以在单个快照中捕获颜色和偏振状态的摄像头。由于传感器的特性分散和镜头的使用，至关重要的是校准这些类型的相机以获得正确的极化测量。到目前为止开发的校准方法要么不适合这种类型的相机，要么需要在严格的设置中进行复杂的设备和耗时的实验。在本文中，我们提出了一种新方法来克服对复杂的光学系统有效校准这些相机的需求。我们表明，所提出的校准方法具有多个优点，例如任何用户都可以使用统一的线性极化光源轻松校准相机，而无需任何先验地了解其偏振状态，并且收购数量有限。我们将公开提供校准代码。

任何相机的光学元件都会降低照片的清晰度，这是关键的视觉质量标准。该降解的特征是点传播函数（PSF），该函数取决于光的波长，并且在整个成像场中都是可变的。在本文中，我们提出了一个两步方案，以纠正单个RAW或JPEG图像中的光学畸变，即没有相机或镜头上任何事先信息。首先，我们估计当地的高斯模糊内核，以重叠斑块，并通过非盲脱毛技术锐化它们。基于数十个透镜的PSF的测量值，这些模糊内核被建模为由七个参数定义的RGB高斯人。其次，我们使用卷积神经网络去除其余的侧向色差（第一步中未考虑），该网络被训练，可将红色/绿色和蓝色/绿色残留图像最小化。关于合成图像和真实图像的实验表明，这两个阶段的组合产生了一种快速的最新盲目畸变补偿技术，该技术与商业非盲算法竞争。