Data-guided statistical sparse measurements modeling for compressive sensing

Digital image acquisition can be a time consuming process for situations where high spatial resolution is required. As such, optimizing the acquisition mechanism is of high importance for many measurement applications. Acquiring such data through a dynamically small subset of measurement locations can address this problem. In such a case, the measured information can be regarded as incomplete, which necessitates the application of special reconstruction tools to recover the original data set. The reconstruction can be performed based on the concept of sparse signal representation. Recovering signals and images from their sub-Nyquist measurements forms the core idea of compressive sensing (CS). In this work, a CS-based data-guided statistical sparse measurements method is presented, implemented and evaluated. This method significantly improves image reconstruction from sparse measurements. In the data-guided statistical sparse measurements approach, signal sampling distribution is optimized for improving image reconstruction performance. The sampling distribution is based on underlying data rather than the commonly used uniform random distribution. The optimal sampling pattern probability is accomplished by learning process through two methods - direct and indirect. The direct method is implemented for learning a nonparametric probability density function directly from the dataset. The indirect learning method is implemented for cases where a mapping between extracted features and the probability density function is required. The unified model is implemented for different representation domains, including frequency domain and spatial domain. Experiments were performed for multiple applications such as optical coherence tomography, bridge structure vibration, robotic vision, 3D laser range measurements and fluorescence microscopy. Results show that the data-guided statistical sparse measurements method significantly outperforms the conventional CS reconstruction performance. Data-guided statistical sparse measurements method achieves much higher reconstruction signal-to-noise ratio for the same compression rate as the conventional CS. Alternatively, Data-guided statistical sparse measurements method achieves similar reconstruction signal-to-noise ratio as the conventional CS with significantly fewer samples

Optimization of ROI-based LiDAR sampling in on-road environment for autonomous driving

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. Hyuk-Jae Lee.Light detection and ranging (LiDAR) 센서는 최근 로보틱스와 자율 주행을 비롯한 여러 분야에서 사용되고 있다. 이런 LiDAR 센서는 다른 센서보다 낮은 해상도가 특징으로, 효과적인 샘플링 알고리즘을 설계하는 것이 필수적이다. 자율 주행에 적용되는 LiDAR 샘플링 알고리즘의 경우 도로의 복잡한 환경에서도 강인하게 높은 품질로 reconstruction을 하는 것이 목표이다. 이를 위해 현행 ROI 기반 샘플링 알고리즘은 시멘틱 정보를 이용하고 있다. 하지만, 객체, 도로, 배경 등에 따른 sampling rate는 지금까지 충분히 논의되지 않았고, 이로 인해 종합적인 reconstruction 품질이 저하될 수 있다. 이러한 문제를 해결하기 위해, 본 논문에서는 객체, 도로, 배경에 따른 sampling budget ratio를 도출할 수 있는 방법을 제안한다. 이 방법은 객체, 도로, 배경의 특성이 샘플링 이전에 선행 지식으로 주어져 있다는 가정을 이용한다. 제안하는 sampling budget을 적용한 결과, 현행 알고리즘보다 객체에 대한 mean-absolute-error (MAE)는 최대 45.92% 감소하였을 뿐만 아니라 전반적인 MAE 또한 3.36% 감소하였고, 도로에 대한 MAE는 오직 54.18% 감소하였다.In recent years, light detection and ranging (LiDAR) sensors have been applied in several situations, including robotics and autonomous driving. However, LiDAR sensors have relatively low resolutions. Therefore, it is imperative to design an effective sampling algorithm for LiDAR sensors. To manage complex on-road environments, conventional ROI-based LiDAR sampling algorithm utilizes semantic information to achieve robust and high reconstruction quality. However, the ratio between sampling rates of objects, roads, and background areas is not thoroughly investigated. Therefore, the overall reconstruction quality may be degraded. To address this problem, this study presents a proposed method to examine the sampling budget ratio between objects, roads, and background areas, under the assumption that characteristics of objects, roads, and background areas are known prior to sampling. Experimental results depict a significant reduction in the mean-absolute-error (MAE) of the object region, road region and overall region by up to 45.92%, 54.18% and 3.36% under the proposed method, respectively, compared to the conventional method.Chapter 1. Introduction １ 1.1. Overview １ 1.2. Light detection and ranging sensor LiDAR sampling １ Chapter 2. Background ４ 2.1. Definition of a sampling problem ４ 2.2. Oracle Random Sampling ４ 2.2.1 Sampling Model ４ 2.2.2 Oracle Random Scheme ５ 2.3. ROI-based LiDAR sampling algorithm ６ Chapter 3. Proposed method ８ 3.1. Analytical method ８ Chapter 4. Experimental results １５ 4.1. Dataset １５ 4.2 Quantitative evaluation １６ Chapter 5. Conclusion ２０ Appendix ２１ References ３１ 초 록(Abstract in Korean) ３２Maste

듀얼미러 라이다 이미징을 위한 타이밍이 고려된 샘플링 알고리즘

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2019. 2. Lee, Hyuk-Jae.In recent years, active sensor technologies such as light detection and ranging (LIDAR) have been intensively studied in theory and widely adopted in many applications, i.e., self-driving cars, robotics and sensing. Generally, the spatial resolution of a depth-acquisition device, such as a LiDAR sensor, is limited because of a slow acquisition speed. To accurately reconstruct a depth image from a limited spatial resolution, a two-stage sampling process has been widely used. However, two-stage sampling uses an irregular sampling pattern for the sampling operation, which requires a large amount of computation for reconstruction. A mathematical formulation of a LiDAR system demonstrates that the existing two-stage sampling does not satisfy its timing constraint for practical use. Therefore, designing a LiDAR system with an efficient sampling algorithm is a significant technological challenge. Firstly, this thesis addresses the problem of adopting the state-of-art laser marking system of a dual-mirror deflection scanner when creating a high-definition LIDAR system. Galvanometer scanners are modeled and parameterized based on concepts of their controllers and the well-known raster scanning method. The scanning strategy is then modeled and analyzed considering the physical scanning movement and the minimum spanning tree. From this analysis, the link between the quality of the captured image of a field of view (FOV) and the scanning speed is revealed. Furthermore, sufficient conditions are derived to indicate that the acquired image fully covers the FOV and that the captured objects are well aligned under a specific frame rate. Finally, a sample LIDAR system is developed to illustrate the proposed concepts. Secondly, to overcome the drawbacks of two-stage sampling, we propose a new sampling method that reduces the computational complexity and memory requirements by generating the optimal representatives of a sampling pattern in down-sample data. A sampling pattern is derived from a k-NN expanding operation from the downsampled representatives. The proposed algorithm is designed to preserve the object boundary by restricting the expansion-operation only to the object boundary or complex texture. In addition, the proposed algorithm runs in linear-time complexity and reduces the memory requirements using a down-sampling ratio. Experimental results with Middlebury datasets and Brown laser-range datasets are presented. Thirdly, state-of-the-art adaptive methods such as two-step sampling are highly effective while addressing indoor, less complex scenes at a moderately low sampling rate. However, their performance is relatively low in complex on-road environments, particularly when the sampling rate of the measuring equipment is low. To address this problem, this thesis proposes a region-of-interest-(ROI)-based sampling algorithm in on-road environments for autonomous driving. With the aid of fast and accurate road and object detection algorithms, particularly those based on convolutional neural networks (CNNs), the proposed sampling algorithm utilizes the semantic information and effectively distributes samples in road, object, and background areas. Experimental results with KITTI datasets are presented.최근 LIDAR (light detection and ranging)와 같은 능동적 센서 기술은 이론적으로도 집중적으로 연구되었고, 자율주행차, 로봇, 센싱 등 다양한 응용 분야에 널리 사용되고 있다. 일반적으로 LiDAR 센서와 같은 심도측정장치는 느린 속도 때문에 공간적 해상도가 제한된다. 제한된 공간적 해상도로부터 심도 이미지를 정확하게 재구성하기 위해서 2단계 샘플링 방법이 널리 사용되고 있다. 그러나 2단계 샘플링은 불규칙적인 샘플링 패턴으로 샘플링을 하기 때문에, 재구성 과정에 많은 양의 연산이 필요하다. LiDAR 시스템을 수학적인 모델을 사용하여 분석하였을 때, 기존의 2단계 샘플링은 실용적으로 사용되기 위한 타이밍 제약 조건을 만족하지 못함을 확인하였다. 따라서 효율적인 샘플링 알고리즘을 사용하는 LiDAR 시스템을 설계하는 것은 중요한 기술적 과제이다. 첫째, 본 논문은 최신의 레이저 마킹 시스템을 dual-mirror 스캐너에 적용하여 고해상도 LiDAR 시스템을 만드는 문제를 다룬다. Galvanometer 스캐너 컨트롤러와 잘 알려진 래스터 스캔 방법에 기초하여 Galvanometer 스캐너를 모델링, 매개변수화한다. 그리고 물리적인 스캐닝 움직임과 최소 신장 트리를 고려하여 스캐닝 방법을 모델링하고 분석한다. 분석으로부터 원하는 FOV (field of view)로 캡쳐된 이미지의 품질과 스캐닝 속도 사이의 관계를 밝혔다. 또한 획득된 이미지가 FOV를 완전히 표현하며, 캡쳐된 object들이 특정 프레임 레이트에서 잘 정렬됨을 나타내는 충분조건을 유도하였다. 마지막으로 제안된 개념을 확인하기 위해 샘플 LIDAR 시스템을 개발하였다. 둘째, 2단계 샘플링의 단점을 극복하기 위해, 다운 샘플 데이터에서 샘플링 패턴의 최적 표현을 생성함으로써 연산 복잡도와 메모리 요구량을 줄일 수 있는 새로운 샘플링 방법을 제안한다. 샘플링 패턴은 다운 샘플된 표현의 k-NN 확장 연산으로부터 도출된다. 제안된 방법은 물체 경계 또는 복잡한 텍스처에 한해서 확장연산을 수행함으로써 물체 경계를 보존하도록 설계되었다. 또한 제안하는 방법은 선형적인 시간 복잡도로 동작하며 다운 샘플링 비율을 이용하여 메모리 요구량을 줄인다. Middlebury 데이터셋과 Brown laser-range 데이터셋을 사용한 실험 결과가 제시된다. 셋째, 2단계 샘플링과 같은 최신의 적응적 방법들은 비교적 낮은 샘플링 레이트로 실내의 복잡하지 않은 장면들을 처리하는 데 매우 효과적이다. 그러나 복잡한 도로 환경에서는, 특히 측정 장비의 샘플링 레이트가 낮은 경우에, 해당 방법들의 성능이 상대적으로 떨어진다. 이 문제를 해결하기 위해 본 논문은 자율주행을 위한 도로 환경에서의 ROI (region-of-interest) 기반 샘플링 알고리즘을 제안한다. 제안된 샘플링 알고리즘은 CNN (convolutional neural network) 기반의 빠르고 정확한 도로 및 물체 감지 알고리즘을 사용하여, semantic 정보를 활용하고 도로, 물체, 배경 영역에 샘플들을 효과적으로 분배한다. KITTI 데이터셋을 사용한 실험 결과가 제시된다.Abstract i Table of Contents iii List of Figures vii List of Tables xi Chapter 1: Introduction １ 1.1. Overview １ 1.2. Scope and contributions ２ 1.3. Thesis Outlines ３ Chapter 2: Related work ４ 2.1. LiDAR sensors ４ 2.2. Sampling ６ 2.2.1. Sampling problem definition ６ 2.2.2. Sampling model ７ 2.2.3. Oracle Random sampling (Gradient-based sampling) ８ 2.3. Reconstruction ９ Chapter 3: Dual-Mirror LiDAR １１ 3.1. Introduction １１ 3.1.1. Related work １２ 3.2. Modelling a controller of dual-mirror scanners １３ 3.2.1. Dual-mirror scanners １３ 3.2.2. Controller Model １５ 3.2.2.1. FOV representation １５ 3.2.2.2. Timing constraints １６ 3.2.2.3. Maximum Speed of LiDAR scanners １７ 3.3. LiDAR scanning optimization problem １８ 3.3.1. Scanning Problem １９ 3.3.2. Optimal scanning pattern ２０ 3.3.2.1. Grid-graph representation of Field of View ２０ 3.3.2.2. Optimal scanning pattern ２１ 3.3.2.3. Combining an optimal sampling pattern with timing constraints ２4 3.4. LiDAR system Prototype ３０ 3.4.1. System overview ３０ 3.4.2. Speed evaluation ３２ 3.4.3. Subjective Evaluation ３３ 3.4.4. Accuracy Evaluation ３６ Chapter 4: Sampling for Dual-Mirror LiDAR: Sampling Model and Algorithm ３８ 4.1. Introduction ３８ 4.2. Sampling Model for Dual-Mirror LiDAR ４１ 4.2.1. Timing constraint ４１ 4.2.2. Memory-space constraint ４５ 4.2.3. New sampling problem with constraints ４７ 4.3. Proposed sampling Algorithm and Its Properties ４８ 4.3.1. Downsampling and k-NN expanding operator ４８ 4.3.2. Proposed Sampling Algorithm with k-NN Expanding ５２ 4.3.3. Example with Synthetic Data ５７ 4.3.4. Proposed sampling algorithm with interpolation ５９ 4.3.5. Timing and memory constraints ６２ 4.3.5.1. Timing constraint ６２ 4.3.5.2. Memory constraint ６３ 4.4. Experimental results ６４ 4.4.1. Comparison on the conventional sampling problem ６５ 4.4.1.1. Subjective comparison ６５ 4.4.1.2. Quantitative comparison ６５ 4.4.2. Comparison on the new sampling problem for LiDAR ６９ 4.4.2.1. Compression ratios ６９ 4.4.2.2. Quantitative evaluation with Peak-signal-to-noise-ratio ７０ 4.4.2.3. Quantitative evaluation with Percentages of bad pixels ７２ 4.4.3. Subjective evaluation ７７ 4.4.4. Proposed grid sampling and grid sampling method ７９ 4.4.4.1. Middlebury datasets ７９ 4.4.4.2. Brown Laser range datasets ８０ Chapter 5: ROI-based LiDAR Sampling in On-Road Environment for Autonomous Driving ８４ 5.1. Introduction ８４ 5.2. Proposed ROI-based sampling algorithm ８７ 5.2.1. Motivating example ８７ 5.2.2. ROI-based Sampling Problem ９１ 5.2.3. Proposed ROI-based sampling algorithm ９３ 5.2.4. Practical considerations ９４ 5.2.5. Distortion optimization problem ９５ 5.3. Experimental results ９６ 5.3.1. Datasets ９６ 5.3.2. Evaluation with different parameters ９９ 5.3.3. Object and quantitative comparisons １０２ Chapter 6: Implementation Issues １０８ 6.1. Implementation of gradient-based sampling １０８ 6.2. System overview １１１ Chapter 7: Conclusion １１３ References １１５ 초록 １２４Docto

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

Advances in Intelligent Robotics and Collaborative Automation

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them.The contributions of the different authors have been grouped into four main sections:• Robots• Control and Intelligence• Sensing• Collaborative automationThe chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area

Advances in Intelligent Robotics and Collaborative Automation

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them.The contributions of the different authors have been grouped into four main sections:• Robots• Control and Intelligence• Sensing• Collaborative automationThe chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas