Efficient Algorithms for Large-Scale Image Analysis

This work develops highly efficient algorithms for analyzing large images. Applications include object-based change detection and screening. The algorithms are 10-100 times as fast as existing software, sometimes even outperforming FGPA/GPU hardware, because they are designed to suit the computer architecture. This thesis describes the implementation details and the underlying algorithm engineering methodology, so that both may also be applied to other applications

Multisource and Multitemporal Data Fusion in Remote Sensing

The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary datasets, however, opens up the possibility of utilizing multimodal datasets in a joint manner to further improve the performance of the processing approaches with respect to the application at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several spaceborne sensors, the integration of the temporal information with the spatial and/or spectral/backscattering information of the remotely sensed data is possible and helps to move from a representation of 2D/3D data to 4D data structures, where the time variable adds new information as well as challenges for the information extraction algorithms. There are a huge number of research works dedicated to multisource and multitemporal data fusion, but the methods for the fusion of different modalities have expanded in different paths according to each research community. This paper brings together the advances of multisource and multitemporal data fusion approaches with respect to different research communities and provides a thorough and discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to conduct novel investigations on this challenging topic by supplying sufficient detail and references

Advances in Image Processing, Analysis and Recognition Technology

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

Multisource and multitemporal data fusion in remote sensing:A comprehensive review of the state of the art

The recent, sharp increase in the availability of data captured by different sensors, combined with their considerable heterogeneity, poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary data sets, however, opens up the possibility of utilizing multimodal data sets in a joint manner to further improve the performance of the processing approaches with respect to applications at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several

A Data Fusion and Visualisation Platform for Multi-Phase Flow by Electrical Tomography

Electrical tomography, e.g. electrical resistance tomography (ERT) and electrical capacitance tomography (ECT), has been successfully applied to many industries for measuring and visualising multiphase flow. This research aims to investigate the data fusion and visualisation technologies with electrical tomography as the key data processing tools of a platform for multiphase flow characterisation. Gas-oil-water flow is a common flow in the gas and oil industries but still presents challenges in understanding its complex dynamics. This research systematically studied the data fusion and visualisation technologies using dual-modality electrical tomography (ERT-ECT). Based on a general framework, two data fusion methods, namely threshold and fuzzy logic with decision tree, were developed to quantify and qualify the flow. The experimental results illustrated the feasibility of the methods integrated with the framework to visualise and measure flows in six typical common flow regimes, including stratified, wavy stratified, slug, plug, annular, and bubble flow. In addition, the performance of ERT-ECT was also evaluated. A 3D visualisation approach, namely Bubble Mapping, was proposed to transform concentration distribution to individual bubbles. With a bubble-based lookup table and enhanced isosurface algorithms, the approach overcomes the limits of the conventional concentration tomograms in visualisation of bubbles with sharp boundaries between gas and liquid, providing sophisticated flow dynamic information. The experiments proved that Bubble Mapping is able to visualise typical flow regimes in different pipeline orientations. Two sensing methods were proposed, namely asymmetrical sensing and imaging (ASI) and regional imaging with limited measurement (RILM), to improve the precision of the velocity profile derived from the cross-correlation method by enhancing ERT sensing speed, which is particularly helpful for industrial flows that their disperse phase velocity is very high, e.g. 20 m/s of the gas phase. It is expected that the outcome of this study will significantly move electrical tomography for multiphase flow applications beyond its current challenges in both quantification and qualification

Advancements in PCB Components Recognition Using WaferCaps: A Data Fusion and Deep Learning Approach

Data Availability Statement: Data supporting the findings of this study are unavailable due to privacy restrictions.Microelectronics and electronic products are integral to our increasingly connected world, facing constant challenges in terms of quality, security, and provenance. As technology advances and becomes more complex, the demand for automated solutions to verify the quality and origin of components assembled on printed circuit boards (PCBs) is skyrocketing. This paper proposes an innovative approach to detecting and classifying microelectronic components with impressive accuracy and reliability, paving the way for a more efficient and safer electronics industry. Our approach introduces significant advancements by integrating optical and X-ray imaging, overcoming the limitations of traditional methods that rely on a single imaging modality. This method uses a novel data fusion technique that enhances feature visibility and detectability across various component types, crucial for densely packed PCBs. By leveraging the WaferCaps capsule network, our system improves spatial hierarchy and dynamic routing capabilities, leading to robust and accurate classifications. We employ decision-level fusion across multiple classifiers trained on different representations—optical, X-ray, and fused images—enhancing accuracy by synergistically combining their predictive strengths. This comprehensive method directly addresses challenges surrounding concurrency, reliability, availability, and resolution in component identification. Through extensive experiments, we demonstrate that our approach not only significantly improves classification metrics but also enhances the learning and identification processes of PCB components, achieving a remarkable total accuracy of 95.2%. Our findings offer a substantial contribution to the ongoing development of reliable and accurate automatic inspection solutions in the electronics manufacturing sector.This research received no external funding

Spatiotemporal Features and Deep Learning Methods for Video Classification

Classification of human actions from real-world video data is one of the most important topics in computer vision and it has been an interesting and challenging research topic in recent decades. It is commonly used in many applications such as video retrieval, video surveillance, human-computer interaction, robotics, and health care. Therefore, robust, fast, and accurate action recognition systems are highly demanded. Deep learning techniques developed for action recognition from the image domain can be extended to the video domain. Nonetheless, deep learning solutions for two-dimensional image data cannot be directly applicable for the video domain because of the larger scale and temporal nature of the video. Specifically, each frame involves spatial information, while the sequence of frames carries temporal information. Therefore, this study focused on both spatial and temporal features, aiming to improve the accuracy of human action recognition from videos by making use of spatiotemporal information. In this thesis, several deep learning architectures were proposed to model both spatial and temporal components. Firstly, a novel deep neural network was developed for video classification by combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Secondly, an action template-based keyframe extraction method was proposed and temporal clues between action regions were used to extract more informative keyframes. Thirdly, a novel decision-level fusion rule was proposed to better combine spatial and temporal aspects of videos in two-stream networks. Finally, an extensive investigation was conducted to find out how to combine various information from feature and decision fusion to improve the video classification performance in multi-stream neural networks. Extensive experiments were conducted using the proposed methods and the results highlighted that using both spatial and temporal information is required in video classification architectures and employing temporal information effectively in multi-stream deep neural networks is crucial to improve video classification accuracy