18 research outputs found

    Visibility in underwater robotics: Benchmarking and single image dehazing

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    Dealing with underwater visibility is one of the most important challenges in autonomous underwater robotics. The light transmission in the water medium degrades images making the interpretation of the scene difficult and consequently compromising the whole intervention. This thesis contributes by analysing the impact of the underwater image degradation in commonly used vision algorithms through benchmarking. An online framework for underwater research that makes possible to analyse results under different conditions is presented. Finally, motivated by the results of experimentation with the developed framework, a deep learning solution is proposed capable of dehazing a degraded image in real time restoring the original colors of the image.Una de las dificultades más grandes de la robótica autónoma submarina es lidiar con la falta de visibilidad en imágenes submarinas. La transmisión de la luz en el agua degrada las imágenes dificultando el reconocimiento de objetos y en consecuencia la intervención. Ésta tesis se centra en el análisis del impacto de la degradación de las imágenes submarinas en algoritmos de visión a través de benchmarking, desarrollando un entorno de trabajo en la nube que permite analizar los resultados bajo diferentes condiciones. Teniendo en cuenta los resultados obtenidos con este entorno, se proponen métodos basados en técnicas de aprendizaje profundo para mitigar el impacto de la degradación de las imágenes en tiempo real introduciendo un paso previo que permita recuperar los colores originales

    Intelligent Transportation Related Complex Systems and Sensors

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    Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

    水中環境における光学画像の画質改善に関する研究

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    Since the 1960s, autonomous underwater vehicles (AUVs) and unmanned underwater vehicles (UUVs) have been used for deep-sea exploration. Sonar sensors also have been extensively used to detect and recognize objects in oceans. Although sonar sensors are suitable for long-range distance imaging, due to the principles of acoustic imaging, sonar images are low signal to noise ratio, low resolution and no colors. In order to acquire more detail information of underwater object, a short-range imaging system is required. In this situation, a photo vision sensor is used reasonably.However, the low contrast and color distortion of underwater images are still the major issues for practical applications. Therefore, this thesis will concentrate on the underwater optical images quality improvement.Although the underwater optical imaging technology has made a great progress, the recognition of underwater objects is still a challenging subject nowadays. Different from the normal images, underwater images suffer from poor visibility due to the medium scattering and light distortion. First of all, capturing good quality images in underwater circumstance is difficult, mostly due to attenuation caused by light that is reflected from a surface and is deflected and scattered by particles. Secondly, absorption substantially reduces the light energy. The random attenuation of the light mainly causes the haze appearance along with the part of the light scattered back from the water. In particular, an underwater object which 10 meters away from camera lens is almost indistinguishable because of light absorption. Furthermore, when the artificial light is employed, it can cause a distinctive footprint on the seafloor.In order to obtain high quality underwater images that can be adapted to the traditional image identification algorithms, this work aimed to construct an underwater image processing framework. Due to the special characteristic of underwater images,segment the image to several parts before directly perform a subject identification is thought an efficient way. And for obtaining a good underwater image segment result, the work to improve the quality of the image is necessary. Such work contains image enhancement, color correction and noise reduction, etc. The experiments demonstrate that the proposed methods produced visually pleasing results, and the numerical image quality assessment also proved the effectiveness of this proposal. The organization of this thesis is as follows.Chapter 1 briefly reviews the characteristics and types of acoustic imaging and optical imaging technologies in ocean. The traditional underwater imaging models and the issues of recent underwater imaging systems are also introduced.Chapter 2 describes a novel underwater image enhancement method. The transmission is estimated by the proposed dual-channel prior. Then a robust locally adaptive filter algorithm for enhancing underwater images is used. In addition, theartificial light removal method is also proposed. Compared with the traditional methods, the proposed method obtains better images.Chapter 3 presents a color correction method to recover the distorted image colors. In the experiments, the proposed method recovers the distorted colors in real-time. The color corrected images have a reasonable noise level in their dark regions, and the global contrast is also well improved.Chapter 4 describes two methods for image segmentation. The first one is the automatic clustering Weighted Fuzzy C Means (WFCM) based segmentation method. It automatically obtains a reasonable clustering result for the underwater images with simple texture. The second method is fast Active Contour Model (ACM) based image segmentation method, which dramatically improves the calculation speed. Compare with the traditional methods, the processing speed is improved by over 10 times.Chapter 5 presents the conclusions of this work, and points out some future researchdirections.九州工業大学博士学位論文 学位記番号:工博甲第398号 学位授与年月日:平成27年9月25日1 INTRODUCTION|2 IMAGE ENHANCEMENT|3 COLOR CORRECTION|4 IMAGE SEGMENTATION|5 CONCLUSIONS九州工業大学平成27年

    RGB-D And Thermal Sensor Fusion: A Systematic Literature Review

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    In the last decade, the computer vision field has seen significant progress in multimodal data fusion and learning, where multiple sensors, including depth, infrared, and visual, are used to capture the environment across diverse spectral ranges. Despite these advancements, there has been no systematic and comprehensive evaluation of fusing RGB-D and thermal modalities to date. While autonomous driving using LiDAR, radar, RGB, and other sensors has garnered substantial research interest, along with the fusion of RGB and depth modalities, the integration of thermal cameras and, specifically, the fusion of RGB-D and thermal data, has received comparatively less attention. This might be partly due to the limited number of publicly available datasets for such applications. This paper provides a comprehensive review of both, state-of-the-art and traditional methods used in fusing RGB-D and thermal camera data for various applications, such as site inspection, human tracking, fault detection, and others. The reviewed literature has been categorised into technical areas, such as 3D reconstruction, segmentation, object detection, available datasets, and other related topics. Following a brief introduction and an overview of the methodology, the study delves into calibration and registration techniques, then examines thermal visualisation and 3D reconstruction, before discussing the application of classic feature-based techniques as well as modern deep learning approaches. The paper concludes with a discourse on current limitations and potential future research directions. It is hoped that this survey will serve as a valuable reference for researchers looking to familiarise themselves with the latest advancements and contribute to the RGB-DT research field.Comment: 33 pages, 20 figure

    Synthetic image generation and the use of virtual environments for image enhancement tasks

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    Deep learning networks are often difficult to train if there are insufficient image samples. Gathering real-world images tailored for a specific job takes a lot of work to perform. This dissertation explores techniques for synthetic image generation and virtual environments for various image enhancement/ correction/restoration tasks, specifically distortion correction, dehazing, shadow removal, and intrinsic image decomposition. First, given various image formation equations, such as those used in distortion correction and dehazing, synthetic image samples can be produced, provided that the equation is well-posed. Second, using virtual environments to train various image models is applicable for simulating real-world effects that are otherwise difficult to gather or replicate, such as dehazing and shadow removal. Given synthetic images, one cannot train a network directly on it as there is a possible gap between the synthetic and real domains. We have devised several techniques for generating synthetic images and formulated domain adaptation methods where our trained deep-learning networks perform competitively in distortion correction, dehazing, and shadow removal. Additional studies and directions are provided for the intrinsic image decomposition problem and the exploration of procedural content generation, where a virtual Philippine city was created as an initial prototype. Keywords: image generation, image correction, image dehazing, shadow removal, intrinsic image decomposition, computer graphics, rendering, machine learning, neural networks, domain adaptation, procedural content generation

    On-the-fly dense 3D surface reconstruction for geometry-aware augmented reality.

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    Augmented Reality (AR) is an emerging technology that makes seamless connections between virtual space and the real world by superimposing computer-generated information onto the real-world environment. AR can provide additional information in a more intuitive and natural way than any other information-delivery method that a human has ever in- vented. Camera tracking is the enabling technology for AR and has been well studied for the last few decades. Apart from the tracking problems, sensing and perception of the surrounding environment are also very important and challenging problems. Although there are existing hardware solutions such as Microsoft Kinect and HoloLens that can sense and build the environmental structure, they are either too bulky or too expensive for AR. In this thesis, the challenging real-time dense 3D surface reconstruction technologies are studied and reformulated for the reinvention of basic position-aware AR towards geometry-aware and the outlook of context- aware AR. We initially propose to reconstruct the dense environmental surface using the sparse point from Simultaneous Localisation and Map- ping (SLAM), but this approach is prone to fail in challenging Minimally Invasive Surgery (MIS) scenes such as the presence of deformation and surgical smoke. We subsequently adopt stereo vision with SLAM for more accurate and robust results. With the success of deep learning technology in recent years, we present learning based single image re- construction and achieve the state-of-the-art results. Moreover, we pro- posed context-aware AR, one step further from purely geometry-aware AR towards the high-level conceptual interaction modelling in complex AR environment for enhanced user experience. Finally, a learning-based smoke removal method is proposed to ensure an accurate and robust reconstruction under extreme conditions such as the presence of surgical smoke

    Advances in Object and Activity Detection in Remote Sensing Imagery

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    The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms

    Text Similarity Between Concepts Extracted from Source Code and Documentation

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    Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p

    Advances in Image Processing, Analysis and Recognition Technology

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    For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches
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