29 research outputs found

    RS-Net: robust segmentation of green overlapped apples

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    Fruit detection and segmentation will be essential for future agronomic management, with applications in yield estimation, growth monitoring, intelligent picking, disease detection and etc. In order to more accurately and efficiently realize the recognition and segmentation of apples in natural orchards, a robust segmentation net framework specially developed for fruit production is proposed. This model was improved for the more challenging problem which segments the overlapped apples from the monochromatic background regardless of various corruptions. The method extends Mask R-CNN by embedding an attention mechanism for focusing more on the informative pixels but also suppressing the noise caused by adverse factors (occlusions, overlaps, etc.), which could be more suitable and robust for operating in complex natural environment. Specifically, the Gaussian non-local attention mechanism is transplanted into Mask R-CNN for refining the semantic features generated continuously by residual network and feature pyramid network, then the model forward processing based on the balanced feature levels and finally segments the regions where the apples are located. Experimental results verify the hypothesis of current work and show that the proposed method outperforms other start-of-the-art detection and segmentation models, the AP box and AP mask metric values have reached 85.6% and 86.2% in a reasonable run time, respectively, which can meet the precision and robustness of vision system in agronomic managemen

    GEOMETRIC ANALYSIS TOOLS FOR MESH SEGMENTATION

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    Surface segmentation, a process which divides a surface into parts, is the basis for many surface manipulation applications which include model metamorphosis, model simplifica- tion, model retrieval, model alignment and texture mapping. This dissertation discusses novel methods for geometric surface analysis and segmentation and applications for these methods. Novel work within this dissertation includes a new 3D mesh segmentation algo- rithm which is referred to as the ridge-walking algorithm. The main benefit of this algo- rithm is that it can dynamically change the criteria it uses to identify surface parts which allows the algorithm to be adjusted to suit different types of surfaces and different segmen- tation goals. The dynamic segmentation behavior allows users to extract three different types of surface regions: (1) regions delineated by convex ridges, (2) regions delineated by concave valleys, and (3) regions delineated by both concave and convex curves. The ridge walking algorithm is quantitatively evaluated by comparing it with competing algo- rithms and human-generated segmentations. The evaluation is accompanied with a detailed geometrical analysis of a select subset of segmentation results to facilitate a better under- standing of the strengths and weaknesses of this algorithm. The ridge walking algorithm is applied to three domain-specific segmentation prob- lems. The first application uses this algorithm to partition bone fragment surfaces into three semantic parts: (1) the fracture surface, (2) the periosteal surface and (3) the articular surface. Segmentation of bone fragments is an important computational step necessary in developing quantitative methods for bone fracture analysis and for creating computational tools for virtual fracture reconstruction. The second application modifies the 3D ridge walking algorithm so that it can be applied to 2D images. In this case, the 2D image is modeled as a Monge patch and principal curvatures of the intensity surface are computed iv for each image pixel. These principal curvatures are then used by ridge walking algorithm to segment the image into meaningful parts. The third application uses the ridge walking algorithm to facilitate analysis of virtual 3D terrain models. Specifically, the algorithm is integrated as a part of a larger software system designed to enable users to browse, visualize and analyze 3D geometric data generated by NASA’s Mars Exploratory Rovers Spirit and Opportunity. In this context, the ridge walking algorithm is used to identify surface features such as rocks in the terrain models

    Extracting fluorescent reporter time courses of cell lineages from high-throughput microscopy at low temporal resolution

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    Live Cell Imaging and High Throughput Screening are rapidly evolving techniques and have found many applications in recent years. Modern microscopy enables the visualisation of internal changes in the cell through the use of fluorescently tagged proteins which can be targeted to specific cellular components. A system is presented here which is designed to track cells at low temporal resolution within large populations, and to extract fluorescence data which allows relative expression rates of tagged proteins to be monitored. Cell detection and tracking are performed as separate steps, and several methods are evaluated for suitability using timeseries images of Hoechst-stained C2C12 mouse mesenchymal stem cells. The use of Hoechst staining ensures cell nuclei are visible throughout a time-series. Dynamic features, including a characteristic change in Hoechst fluorescence intensity during chromosome condensation, are used to identify cell divisions and resulting daughter cells. The ability to detect cell division is integrated into the tracking, aiding lineage construction. To establish the efficiency of the method, synthetic cell images have been produced and used to evaluate cell detection accuracy. A validation framework is created which allows the accuracy of the automatic segmentation and tracking systems to be measured and compared against existing state of the art software, such as CellProfiler. Basic tracking methods, including nearest-neighbour and cell-overlap, are provided as a baseline to evaluate the performance of more sophisticated methods. The software is demonstrated on a number of biological systems, starting with a study of different control elements of the Msx1 gene, which regulates differentiation of mesenchymal stem cells. Expression is followed through multiple lineages to identify asymmetric divisions which may be due to cell differentiation. The lineage construction methods are applied to Schizosaccharomyces pombe time-series image data, allowing the extraction of generation lengths for individual cells. Finally a study is presented which examines correlations between the circadian and cell cycles. This makes use of the recently developed FUCCI cell cycle markers which, when used in conjunction with a circadian indicator such as Rev-erbα-Venus, allow simultaneous measurements of both cycles

    Representation Learning With Convolutional Neural Networks

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    Deep learning methods have achieved great success in the areas of Computer Vision and Natural Language Processing. Recently, the rapidly developing field of deep learning is concerned with questions surrounding how we can learn meaningful and effective representations of data. This is because the performance of machine learning approaches is heavily dependent on the choice and quality of data representation, and different kinds of representation entangle and hide the different explanatory factors of variation behind the data. In this dissertation, we focus on representation learning with deep neural networks for different data formats including text, 3D polygon shapes, and brain fiber tracts. First, we propose a topic-based word representation learning approach for text classification. The proposed approach takes global semantic relationship between words over the whole corpus into consideration and encodes the relationships into distributed vector representations with continuous Skip-gram model. The learned representations which capture a large number of precise syntactic and semantic word relationships are taken as input of Convolution Neural Networks for classification. Our experimental results show the effectiveness of the proposed method on indexing of biomedical articles, behavior code annotation of clinical text fragments, and classification of news groups. Second, we present a 3D polygon shape representation learning framework for shape segmentation. We propose Directionally Convolutional Network (DCN) that extends convolution operations from images to the polygon mesh surface with rotation-invariant property. Based on the proposed DCN, we learn effective shape representations from raw geometric features and then classify each face of a given polygon into predefined semantic parts. Through extensive experiments, we demonstrate that our framework outperforms the current state-of-the-arts. Third, we propose to learn effective and meaningful representations for brain fiber tracts using deep learning frameworks. We handle the highly unbalanced dataset by introducing asymmetrical loss function for easily classified samples and hard classified ones. The training loss avoids to be dominated by the easy samples and the training step is more efficient. In addition, we learn more effective and meaningful representations by introducing deeper network and metric learning approaches. Furthermore, we propose to improve the interpretability of our framework by inducing attention mechanism. Our experimental results show that our proposed framework outperforms current golden standard significantly on the real-world dataset

    Advancing Land Cover Mapping in Remote Sensing with Deep Learning

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    Automatic mapping of land cover in remote sensing data plays an increasingly significant role in several earth observation (EO) applications, such as sustainable development, autonomous agriculture, and urban planning. Due to the complexity of the real ground surface and environment, accurate classification of land cover types is facing many challenges. This thesis provides novel deep learning-based solutions to land cover mapping challenges such as how to deal with intricate objects and imbalanced classes in multi-spectral and high-spatial resolution remote sensing data. The first work presents a novel model to learn richer multi-scale and global contextual representations in very high-resolution remote sensing images, namely the dense dilated convolutions' merging (DDCM) network. The proposed method is light-weighted, flexible and extendable, so that it can be used as a simple yet effective encoder and decoder module to address different classification and semantic mapping challenges. Intensive experiments on different benchmark remote sensing datasets demonstrate that the proposed method can achieve better performance but consume much fewer computation resources compared with other published methods. Next, a novel graph model is developed for capturing long-range pixel dependencies in remote sensing images to improve land cover mapping. One key component in the method is the self-constructing graph (SCG) module that can effectively construct global context relations (latent graph structure) without requiring prior knowledge graphs. The proposed SCG-based models achieved competitive performance on different representative remote sensing datasets with faster training and lower computational cost compared to strong baseline models. The third work introduces a new framework, namely the multi-view self-constructing graph (MSCG) network, to extend the vanilla SCG model to be able to capture multi-view context representations with rotation invariance to achieve improved segmentation performance. Meanwhile, a novel adaptive class weighting loss function is developed to alleviate the issue of class imbalance commonly found in EO datasets for semantic segmentation. Experiments on benchmark data demonstrate the proposed framework is computationally efficient and robust to produce improved segmentation results for imbalanced classes. To address the key challenges in multi-modal land cover mapping of remote sensing data, namely, 'what', 'how' and 'where' to effectively fuse multi-source features and to efficiently learn optimal joint representations of different modalities, the last work presents a compact and scalable multi-modal deep learning framework (MultiModNet) based on two novel modules: the pyramid attention fusion module and the gated fusion unit. The proposed MultiModNet outperforms the strong baselines on two representative remote sensing datasets with fewer parameters and at a lower computational cost. Extensive ablation studies also validate the effectiveness and flexibility of the framework

    Functional and structural MRI image analysis for brain glial tumors treatment

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    This Ph.D Thesis is the outcome of a close collaboration between the Center for Research in Image Analysis and Medical Informatics (CRAIIM) of the Insubria University and the Operative Unit of Neurosurgery, Neuroradiology and Health Physics of the University Hospital ”Circolo Fondazione Macchi”, Varese. The project aim is to investigate new methodologies by means of whose, develop an integrated framework able to enhance the use of Magnetic Resonance Images, in order to support clinical experts in the treatment of patients with brain Glial tumor. Both the most common uses of MRI technology for non-invasive brain inspection were analyzed. From the Functional point of view, the goal has been to provide tools for an objective reliable and non-presumptive assessment of the brain’s areas locations, to preserve them as much as possible at surgery. From the Structural point of view, methodologies for fully automatic brain segmentation and recognition of the tumoral areas, for evaluating the tumor volume, the spatial distribution and to be able to infer correlation with other clinical data or trace growth trend, have been studied. Each of the proposed methods has been thoroughly assessed both qualitatively and quantitatively. All the Medical Imaging and Pattern Recognition algorithmic solutions studied for this Ph.D. Thesis have been integrated in GliCInE: Glioma Computerized Inspection Environment, which is a MATLAB prototype of an integrated analysis environment that offers, in addition to all the functionality specifically described in this Thesis, a set of tools needed to manage Functional and Structural Magnetic Resonance Volumes and ancillary data related to the acquisition and the patient

    Extracting fluorescent reporter time courses of cell lineages from high-throughput microscopy at low temporal resolution

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    Live Cell Imaging and High Throughput Screening are rapidly evolving techniques and have found many applications in recent years. Modern microscopy enables the visualisation of internal changes in the cell through the use of fluorescently tagged proteins which can be targeted to specific cellular components. A system is presented here which is designed to track cells at low temporal resolution within large populations, and to extract fluorescence data which allows relative expression rates of tagged proteins to be monitored. Cell detection and tracking are performed as separate steps, and several methods are evaluated for suitability using timeseries images of Hoechst-stained C2C12 mouse mesenchymal stem cells. The use of Hoechst staining ensures cell nuclei are visible throughout a time-series. Dynamic features, including a characteristic change in Hoechst fluorescence intensity during chromosome condensation, are used to identify cell divisions and resulting daughter cells. The ability to detect cell division is integrated into the tracking, aiding lineage construction. To establish the efficiency of the method, synthetic cell images have been produced and used to evaluate cell detection accuracy. A validation framework is created which allows the accuracy of the automatic segmentation and tracking systems to be measured and compared against existing state of the art software, such as CellProfiler. Basic tracking methods, including nearest-neighbour and cell-overlap, are provided as a baseline to evaluate the performance of more sophisticated methods. The software is demonstrated on a number of biological systems, starting with a study of different control elements of the Msx1 gene, which regulates differentiation of mesenchymal stem cells. Expression is followed through multiple lineages to identify asymmetric divisions which may be due to cell differentiation. The lineage construction methods are applied to Schizosaccharomyces pombe time-series image data, allowing the extraction of generation lengths for individual cells. Finally a study is presented which examines correlations between the circadian and cell cycles. This makes use of the recently developed FUCCI cell cycle markers which, when used in conjunction with a circadian indicator such as Rev-erbα-Venus, allow simultaneous measurements of both cycles.EThOS - Electronic Theses Online ServiceEngineering and Physical Sciences Research Council (EPSRC)GBUnited Kingdo

    NASA SBIR abstracts of 1990 phase 1 projects

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    The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number

    Sparse Coding for Event Tracking and Image Retrieval

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    Comparing regions of images is a fundamental task in both similarity based object tracking as well as retrieval of images from image datasets, where an exemplar image is used as the query. In this thesis, we focus on the task of creating a method of comparison for images produced by NASA’s Solar Dynamic Observatory mission. This mission has been in operation for several years and produces almost 700 Gigabytes of data per day from the Atmospheric Imaging Assembly instrument alone. This has created a massive repository of high-quality solar images to analyze and categorize. To this end, we are concerned with the creation of image region descriptors that are selective enough to differentiate between highly similar images yet compact enough to be compared in an efficient manner, while also being indexable with current indexing technology. We produce such descriptors by pooling sparse coding vectors produced by spanning learned basis dictionaries. Various pooled vectors are used to describe regions of images in event tracking, entire image descriptors for image comparison in content based image retrieval, and as region descriptors to be used in a content based image retrieval system on the SDO AIA image pipeline

    Developing new techniques to analyse and classify EEG signals

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    A massive amount of biomedical time series data such as Electroencephalograph (EEG), electrocardiography (ECG), Electromyography (EMG) signals are recorded daily to monitor human performance and diagnose different brain diseases. Effectively and accurately analysing these biomedical records is considered a challenge for researchers. Developing new techniques to analyse and classify these signals can help manage, inspect and diagnose these signals. In this thesis novel methods are proposed for EEG signals classification and analysis based on complex networks, a statistical model and spectral graph wavelet transform. Different complex networks attributes were employed and studied in this thesis to investigate the main relationship between behaviours of EEG signals and changes in networks attributes. Three types of EEG signals were investigated and analysed; sleep stages, epileptic and anaesthesia. The obtained results demonstrated the effectiveness of the proposed methods for analysing these three EEG signals types. The methods developed were applied to score sleep stages EEG signals, and to analyse epileptic, as well as anaesthesia EEG signals. The outcomes of the project will help support experts in the relevant medical fields and decrease the cost of diagnosing brain diseases
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