Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Digital Coding of Waveforms

Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world images. It is thus unclear how these algorithms would perform on images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater image enhancement using large-scale real-world images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater images, 890 of which have the corresponding reference images. We treat the rest 60 underwater images which cannot obtain satisfactory reference images as challenging data. Using this dataset, we conduct a comprehensive study of the state-of-the-art underwater image enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater image enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark evaluations and the proposed Water-Net demonstrate the performance and limitations of state-of-the-art algorithms, which shed light on future research in underwater image enhancement. The dataset and code are available at https://li-chongyi.github.io/proj_benchmark.html .

An Underwater Image Enhancement Benchmark Dataset and Beyond

Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real-world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new super-resolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.

/pdf/super-resolution-a-comprehensive-survey-xjl42yt9nt.pdf

Super-resolution: a comprehensive survey

Deep learning on image denoising: An overview.

In block motion estimation, a search pattern with a different shape or size has a very important impact on search speed and distortion performance. A square-shaped search pattern is adopted in many popular fast algorithms. Recently, a diamond-shaped search pattern was introduced in fast block motion estimation and has exhibited a faster search speed. Based on an in-depth examination of the influence of the search pattern on speed performance, we propose a novel algorithm using a hexagon-based search pattern to achieve further improvement. The hexagon-based search pattern is investigated in comparison with diamond search pattern and demonstrates significant speedup gain over the diamond-based search. Analysis shows that a speed improvement rate of the hexagon-based search (HEXBS) algorithm over the diamond search (DS) algorithm can be over 80% for locating some motion vectors in certain scenarios. In short, the proposed HEXBS algorithm can find the same motion vector with fewer search points than the DS algorithm. Generally speaking, the larger the motion vector, the more search points the. HEXBS algorithm can save, which is further justified by experimental results.

Hexagon-based search pattern for fast block motion estimation

The three-step search algorithm has been widely used in block matching motion estimation due to its simplicity and effectiveness. The sparsely distributed checking points pattern in the first step is very suitable for searching large motion. However, for stationary or quasistationary blocks it will easily lead the search to be trapped into a local minimum. In this paper we propose a modification on the three-step search algorithm which employs a small diamond pattern in the first step, and the unrestricted search step is used to search the center area. Experimental results show that the new efficient three-step search performs better than new three-step search in terms of MSE and requires less computation by up to 15% on average.

An efficient three-step search algorithm for block motion estimation

The elderly population is increasing rapidly all over the world. One major risk for elderly people is fall accidents, especially for those living alone. In this paper, we propose a robust fall detection approach by analyzing the tracked key joints of the human body using a single depth camera. Compared to the rivals that rely on the RGB inputs, the proposed scheme is independent of illumination of the lights and can work even in a dark room. In our scheme, a pose-invariant randomized decision tree algorithm is proposed for the key joint extraction, which requires low computational cost during the training and test. Then, the support vector machine classifier is employed to determine whether a fall motion occurs, whose input is the 3-D trajectory of the head joint. The experimental results demonstrate that the proposed fall detection method is more accurate and robust compared with the state-of-the-art methods.

Fall Detection Based on Body Part Tracking Using a Depth Camera

Fast block motion estimation normally consists of low-resolution coarse search and the following fine-resolution inner search. Most motion estimation algorithms developed attempt to speed up the coarse search without considering accelerating the focused inner search. On top of the hexagonal search method recently developed, an enhanced hexagonal search algorithm is proposed to further improve the performance in terms of reducing number of search points and distortion, where a novel fast inner search is employed by exploiting the distortion information of the evaluated points. Our experimental results substantially justify the merits of the proposed algorithm.

/pdf/enhanced-hexagonal-search-for-fast-block-motion-estimation-1sntiozao2.pdf

Enhanced hexagonal search for fast block motion estimation

This paper proposes a new algorithm to integrate image registration into image super-resolution (SR) Image SR is a process to reconstruct a high-resolution (HR) image by fusing multiple low-resolution (LR) images A critical step in image SR is accurate registration of the LR images or, in other words, effective estimation of motion parameters Conventional SR algorithms assume either the estimated motion parameters by existing registration methods to be error-free or the motion parameters are known a priori This assumption, however, is impractical in many applications, as most existing registration algorithms still experience various degrees of errors, and the motion parameters among the LR images are generally unknown a priori In view of this, this paper presents a new framework that performs simultaneous image registration and HR image reconstruction As opposed to other current methods that treat image registration and HR reconstruction as disjoint processes, the new framework enables image registration and HR reconstruction to be estimated simultaneously and improved progressively Further, unlike most algorithms that focus on the translational motion model, the proposed method adopts a more generic motion model that includes both translation as well as rotation An iterative scheme is developed to solve the arising nonlinear least squares problem Experimental results show that the proposed method is effective in performing image registration and SR for simulated as well as real-life images

Lap-Pui Chau

Papers

Hexagon-based search pattern for fast block motion estimation

An efficient three-step search algorithm for block motion estimation

Fall Detection Based on Body Part Tracking Using a Depth Camera

Enhanced hexagonal search for fast block motion estimation

A Nonlinear Least Square Technique for Simultaneous Image Registration and Super-Resolution