Adaptive Methods for Point Cloud and Mesh Processing

Point clouds and 3D meshes are widely used in numerous applications ranging from games to virtual reality to autonomous vehicles. This dissertation proposes several approaches for noise removal and calibration of noisy point cloud data and 3D mesh sharpening methods. Order statistic filters have been proven to be very successful in image processing and other domains as well. Different variations of order statistics filters originally proposed for image processing are extended to point cloud filtering in this dissertation. A brand-new adaptive vector median is proposed in this dissertation for removing noise and outliers from noisy point cloud data. The major contributions of this research lie in four aspects: 1) Four order statistic algorithms are extended, and one adaptive filtering method is proposed for the noisy point cloud with improved results such as preserving significant features. These methods are applied to standard models as well as synthetic models, and real scenes, 2) A hardware acceleration of the proposed method using Microsoft parallel pattern library for filtering point clouds is implemented using multicore processors, 3) A new method for aerial LIDAR data filtering is proposed. The objective is to develop a method to enable automatic extraction of ground points from aerial LIDAR data with minimal human intervention, and 4) A novel method for mesh color sharpening using the discrete Laplace-Beltrami operator is proposed. Median and order statistics-based filters are widely used in signal processing and image processing because they can easily remove outlier noise and preserve important features. This dissertation demonstrates a wide range of results with median filter, vector median filter, fuzzy vector median filter, adaptive mean, adaptive median, and adaptive vector median filter on point cloud data. The experiments show that large-scale noise is removed while preserving important features of the point cloud with reasonable computation time. Quantitative criteria (e.g., complexity, Hausdorff distance, and the root mean squared error (RMSE)), as well as qualitative criteria (e.g., the perceived visual quality of the processed point cloud), are employed to assess the performance of the filters in various cases corrupted by different noisy models. The adaptive vector median is further optimized for denoising or ground filtering aerial LIDAR data point cloud. The adaptive vector median is also accelerated on multi-core CPUs using Microsoft Parallel Patterns Library. In addition, this dissertation presents a new method for mesh color sharpening using the discrete Laplace-Beltrami operator, which is an approximation of second order derivatives on irregular 3D meshes. The one-ring neighborhood is utilized to compute the Laplace-Beltrami operator. The color for each vertex is updated by adding the Laplace-Beltrami operator of the vertex color weighted by a factor to its original value. Different discretizations of the Laplace-Beltrami operator have been proposed for geometrical processing of 3D meshes. This work utilizes several discretizations of the Laplace-Beltrami operator for sharpening 3D mesh colors and compares their performance. Experimental results demonstrated the effectiveness of the proposed algorithms

Texture-based Deep Neural Network for Histopathology Cancer Whole Slide Image (WSI) Classification

Automatic histopathological Whole Slide Image (WSI) analysis for cancer classification has been highlighted along with the advancements in microscopic imaging techniques. However, manual examination and diagnosis with WSIs is time-consuming and tiresome. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. In this paper, we propose a novel cancer texture-based deep neural network (CAT-Net) that learns scalable texture features from histopathological WSIs. The innovation of CAT-Net is twofold: (1) capturing invariant spatial patterns by dilated convolutional layers and (2) Reducing model complexity while improving performance. Moreover, CAT-Net can provide discriminative texture patterns formed on cancerous regions of histopathological images compared to normal regions. The proposed method outperformed the current state-of-the-art benchmark methods on accuracy, precision, recall, and F1 score

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics

Mapping landscape patterns and dynamics is essential to various scientific domains and many practical applications. The availability of large-scale and high-resolution light detection and ranging (LiDAR) remote sensing data provides tremendous opportunities to unveil complex landscape patterns and better understand landscape dynamics from a 3D perspective. LiDAR data have been applied to diverse remote sensing applications where large-scale landscape mapping is among the most important topics. While researchers have used LiDAR for understanding landscape patterns and dynamics in many fields, to fully reap the benefits and potential of LiDAR is increasingly dependent on advanced cyberGIS and deep learning approaches. In this context, the central goal of this dissertation is to develop a suite of innovative cyberGIS-enabled deep-learning frameworks for combining LiDAR and optical remote sensing data to analyze landscape patterns and dynamics with four interrelated studies. The first study demonstrates a high-accuracy land-cover mapping method by integrating 3D information from LiDAR with multi-temporal remote sensing data using a 3D deep-learning model. The second study combines a point-based classification algorithm and an object-oriented change detection strategy for urban building change detection using deep learning. The third study develops a deep learning model for accurate hydrological streamline detection using LiDAR, which has paved a new way of harnessing LiDAR data to map landscape patterns and dynamics at unprecedented computational and spatiotemporal scales. The fourth study resolves computational challenges in handling remote sensing big data and deep learning of landscape feature extraction and classification through a cutting-edge cyberGIS approach

LiDAR based multi-sensor fusion for localization, mapping, and tracking

Viimeisen vuosikymmenen aikana täysin itseohjautuvien ajoneuvojen kehitys on herättänyt laajaa kiinnostusta niin teollisuudessa kuin tiedemaailmassakin, mikä on merkittävästi edistänyt tilannetietoisuuden ja anturiteknologian kehitystä. Erityisesti LiDAR-anturit ovat nousseet keskeiseen rooliin monissa havainnointijärjestelmissä niiden tarjoaman pitkän kantaman havaintokyvyn, tarkan 3D-etäisyystiedon ja luotettavan suorituskyvyn ansiosta. LiDAR-teknologian kehittyminen on mahdollistanut entistä luotettavampien ja kustannustehokkaampien antureiden käytön, mikä puolestaan on osoittanut suurta potentiaalia parantaa laajasti käytettyjen kuluttajatuotteiden tilannetietoisuutta. Uusien LiDAR-antureiden hyödyntäminen tarjoaa tutkijoille monipuolisen valikoiman tehokkaita työkaluja, joiden avulla voidaan ratkaista paikannuksen, kartoituksen ja seurannan haasteita nykyisissä havaintojärjestelmissä. Tässä väitöskirjassa tutkitaan LiDAR-pohjaisia sensorifuusioalgoritmeja. Tutkimuksen pääpaino on tiheässä kartoituksessa ja globaalissa paikan-nuksessa erilaisten LiDAR-anturien avulla. Tutkimuksessa luodaan kattava tietokanta uusien LiDAR-, IMU- ja kamera-antureiden tuottamasta datasta. Tietokanta on välttämätön kehittyneiden anturifuusioalgoritmien ja yleiskäyttöisten paikannus- ja kartoitusalgoritmien kehittämiseksi. Tämän lisäksi väitöskirjassa esitellään innovatiivisia menetelmiä globaaliin paikannukseen erilaisissa ympäristöissä. Esitellyt menetelmät kartoituksen tarkkuuden ja tilannetietoisuuden parantamiseksi ovat muun muassa modulaarinen monen LiDAR-anturin odometria ja kartoitus, toimintavarma multimodaalinen LiDAR-inertiamittau-sjärjestelmä ja tiheä kartoituskehys. Tutkimus integroi myös kiinteät LiDAR -anturit kamerapohjaisiin syväoppimismenetelmiin kohteiden seurantaa varten parantaen kartoituksen tarkkuutta dynaamisissa ympäristöissä. Näiden edistysaskeleiden avulla autonomisten järjestelmien luotettavuutta ja tehokkuutta voidaan merkittävästi parantaa todellisissa käyttöympäristöissä. Väitöskirja alkaa esittelemällä innovatiiviset anturit ja tiedonkeruualustan. Tämän jälkeen esitellään avoin tietokanta, jonka avulla voidaan arvioida kehittyneitä paikannus- ja kartoitusalgoritmeja hyödyntäen ainutlaatuista perustotuuden kehittämismenetelmää. Työssä käsitellään myös kahta haastavaa paikannusympäristöä: metsä- ja kaupunkiympäristöä. Lisäksi tarkastellaan kohteen seurantatehtäviä sekä kameraettä LiDAR-tekniikoilla ihmisten ja pienten droonien seurannassa. ---------------------- The development of fully autonomous driving vehicles has become a key focus for both industry and academia over the past decade, fostering significant progress in situational awareness abilities and sensor technology. Among various types of sensors, the LiDAR sensor has emerged as a pivotal component in many perception systems due to its long-range detection capabilities, precise 3D range information, and reliable performance in diverse environments. With advancements in LiDAR technology, more reliable and cost-effective sensors have shown great potential for improving situational awareness abilities in widely used consumer products. By leveraging these novel LiDAR sensors, researchers now have a diverse set of powerful tools to effectively tackle the persistent challenges in localization, mapping, and tracking within existing perception systems. This thesis explores LiDAR-based sensor fusion algorithms to address perception challenges in autonomous systems, with a primary focus on dense mapping and global localization using diverse LiDAR sensors. The research involves the integration of novel LiDARs, IMU, and camera sensors to create a comprehensive dataset essential for developing advanced sensor fusion and general-purpose localization and mapping algorithms. Innovative methodologies for global localization across varied environments are introduced. These methodologies include a robust multi-modal LiDAR inertial odometry and a dense mapping framework, which enhance mapping precision and situational awareness. The study also integrates solid-state LiDARs with camera-based deep-learning techniques for object tracking, refining mapping accuracy in dynamic environments. These advancements significantly enhance the reliability and efficiency of autonomous systems in real-world scenarios. The thesis commences with an introduction to innovative sensors and a data collection platform. It proceeds by presenting an open-source dataset designed for the evaluation of advanced SLAM algorithms, utilizing a unique ground-truth generation method. Subsequently, the study tackles two localization challenges in forest and urban environments. Furthermore, it highlights the MM-LOAM dense mapping framework. Additionally, the research explores object-tracking tasks, employing both camera and LiDAR technologies for human and micro UAV tracking

Review on Active and Passive Remote Sensing Techniques for Road Extraction

Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe

Multimodal Data Fusion and Quantitative Analysis for Medical Applications

Medical big data is not only enormous in its size, but also heterogeneous and complex in its data structure, which makes conventional systems or algorithms difficult to process. These heterogeneous medical data include imaging data (e.g., Positron Emission Tomography (PET), Computerized Tomography (CT), Magnetic Resonance Imaging (MRI)), and non-imaging data (e.g., laboratory biomarkers, electronic medical records, and hand-written doctor notes). Multimodal data fusion is an emerging vital field to address this urgent challenge, aiming to process and analyze the complex, diverse and heterogeneous multimodal data. The fusion algorithms bring great potential in medical data analysis, by 1) taking advantage of complementary information from different sources (such as functional-structural complementarity of PET/CT images) and 2) exploiting consensus information that reflects the intrinsic essence (such as the genetic essence underlying medical imaging and clinical symptoms). Thus, multimodal data fusion benefits a wide range of quantitative medical applications, including personalized patient care, more optimal medical operation plan, and preventive public health. Though there has been extensive research on computational approaches for multimodal fusion, there are three major challenges of multimodal data fusion in quantitative medical applications, which are summarized as feature-level fusion, information-level fusion and knowledge-level fusion: • Feature-level fusion. The first challenge is to mine multimodal biomarkers from high-dimensional small-sample multimodal medical datasets, which hinders the effective discovery of informative multimodal biomarkers. Specifically, efficient dimension reduction algorithms are required to alleviate "curse of dimensionality" problem and address the criteria for discovering interpretable, relevant, non-redundant and generalizable multimodal biomarkers. • Information-level fusion. The second challenge is to exploit and interpret inter-modal and intra-modal information for precise clinical decisions. Although radiomics and multi-branch deep learning have been used for implicit information fusion guided with supervision of the labels, there is a lack of methods to explicitly explore inter-modal relationships in medical applications. Unsupervised multimodal learning is able to mine inter-modal relationship as well as reduce the usage of labor-intensive data and explore potential undiscovered biomarkers; however, mining discriminative information without label supervision is an upcoming challenge. Furthermore, the interpretation of complex non-linear cross-modal associations, especially in deep multimodal learning, is another critical challenge in information-level fusion, which hinders the exploration of multimodal interaction in disease mechanism. • Knowledge-level fusion. The third challenge is quantitative knowledge distillation from multi-focus regions on medical imaging. Although characterizing imaging features from single lesions using either feature engineering or deep learning methods have been investigated in recent years, both methods neglect the importance of inter-region spatial relationships. Thus, a topological profiling tool for multi-focus regions is in high demand, which is yet missing in current feature engineering and deep learning methods. Furthermore, incorporating domain knowledge with distilled knowledge from multi-focus regions is another challenge in knowledge-level fusion. To address the three challenges in multimodal data fusion, this thesis provides a multi-level fusion framework for multimodal biomarker mining, multimodal deep learning, and knowledge distillation from multi-focus regions. Specifically, our major contributions in this thesis include: • To address the challenges in feature-level fusion, we propose an Integrative Multimodal Biomarker Mining framework to select interpretable, relevant, non-redundant and generalizable multimodal biomarkers from high-dimensional small-sample imaging and non-imaging data for diagnostic and prognostic applications. The feature selection criteria including representativeness, robustness, discriminability, and non-redundancy are exploited by consensus clustering, Wilcoxon filter, sequential forward selection, and correlation analysis, respectively. SHapley Additive exPlanations (SHAP) method and nomogram are employed to further enhance feature interpretability in machine learning models. • To address the challenges in information-level fusion, we propose an Interpretable Deep Correlational Fusion framework, based on canonical correlation analysis (CCA) for 1) cohesive multimodal fusion of medical imaging and non-imaging data, and 2) interpretation of complex non-linear cross-modal associations. Specifically, two novel loss functions are proposed to optimize the discovery of informative multimodal representations in both supervised and unsupervised deep learning, by jointly learning inter-modal consensus and intra-modal discriminative information. An interpretation module is proposed to decipher the complex non-linear cross-modal association by leveraging interpretation methods in both deep learning and multimodal consensus learning. • To address the challenges in knowledge-level fusion, we proposed a Dynamic Topological Analysis framework, based on persistent homology, for knowledge distillation from inter-connected multi-focus regions in medical imaging and incorporation of domain knowledge. Different from conventional feature engineering and deep learning, our DTA framework is able to explicitly quantify inter-region topological relationships, including global-level geometric structure and community-level clusters. K-simplex Community Graph is proposed to construct the dynamic community graph for representing community-level multi-scale graph structure. The constructed dynamic graph is subsequently tracked with a novel Decomposed Persistence algorithm. Domain knowledge is incorporated into the Adaptive Community Profile, summarizing the tracked multi-scale community topology with additional customizable clinically important factors

Deep Learning for Detection and Segmentation in High-Content Microscopy Images

High-content microscopy led to many advances in biology and medicine. This fast emerging technology is transforming cell biology into a big data driven science. Computer vision methods are used to automate the analysis of microscopy image data. In recent years, deep learning became popular and had major success in computer vision. Most of the available methods are developed to process natural images. Compared to natural images, microscopy images pose domain specific challenges such as small training datasets, clustered objects, and class imbalance. In this thesis, new deep learning methods for object detection and cell segmentation in microscopy images are introduced. For particle detection in fluorescence microscopy images, a deep learning method based on a domain-adapted Deconvolution Network is presented. In addition, a method for mitotic cell detection in heterogeneous histopathology images is proposed, which combines a deep residual network with Hough voting. The method is used for grading of whole-slide histology images of breast carcinoma. Moreover, a method for both particle detection and cell detection based on object centroids is introduced, which is trainable end-to-end. It comprises a novel Centroid Proposal Network, a layer for ensembling detection hypotheses over image scales and anchors, an anchor regularization scheme which favours prior anchors over regressed locations, and an improved algorithm for Non-Maximum Suppression. Furthermore, a novel loss function based on Normalized Mutual Information is proposed which can cope with strong class imbalance and is derived within a Bayesian framework. For cell segmentation, a deep neural network with increased receptive field to capture rich semantic information is introduced. Moreover, a deep neural network which combines both paradigms of multi-scale feature aggregation of Convolutional Neural Networks and iterative refinement of Recurrent Neural Networks is proposed. To increase the robustness of the training and improve segmentation, a novel focal loss function is presented. In addition, a framework for black-box hyperparameter optimization for biomedical image analysis pipelines is proposed. The framework has a modular architecture that separates hyperparameter sampling and hyperparameter optimization. A visualization of the loss function based on infimum projections is suggested to obtain further insights into the optimization problem. Also, a transfer learning approach is presented, which uses only one color channel for pre-training and performs fine-tuning on more color channels. Furthermore, an approach for unsupervised domain adaptation for histopathological slides is presented. Finally, Galaxy Image Analysis is presented, a platform for web-based microscopy image analysis. Galaxy Image Analysis workflows for cell segmentation in cell cultures, particle detection in mice brain tissue, and MALDI/H&E image registration have been developed. The proposed methods were applied to challenging synthetic as well as real microscopy image data from various microscopy modalities. It turned out that the proposed methods yield state-of-the-art or improved results. The methods were benchmarked in international image analysis challenges and used in various cooperation projects with biomedical researchers