166 research outputs found

    Site Characterization Using Integrated Imaging Analysis Methods on Satellite Data of the Islamabad, Pakistan, Region

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    We develop an integrated digital imaging analysis approach to produce a first-approximation site characterization map for Islamabad, Pakistan, based on remote-sensing data. We apply both pixel-based and object-oriented digital imaging analysis methods to characterize detailed (1:50,000) geomorphology and geology from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. We use stereo-correlated relative digital elevation models (rDEMs) derived from ASTER data, as well as spectra in the visible near-infrared (VNIR) to thermal infrared (TIR) domains. The resulting geomorphic units in the study area are classified as mountain (including the Margala Hills and the Khairi Murat Ridge), piedmont, and basin terrain units. The local geologic units are classified as limestone in the Margala Hills and the Khairi Murat Ridge and sandstone rock types for the piedmonts and basins. Shear-wave velocities for these units are assigned in ranges based on established correlations in California. These ranges include Vs30-values to be greater than 500 m/sec for mountain units, 200–600 m/sec for piedmont units, and less than 300 m/sec for basin units. While the resulting map provides the basis for incorporating site response in an assessment of seismic hazard for Islamabad, it also demonstrates the potential use of remote-sensing data for site characterization in regions where only limited conventional mapping has been done

    NEW CHANGE DETECTION MODELS FOR OBJECT-BASED ENCODING OF PATIENT MONITORING VIDEO

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    The goal of this thesis is to find a highly efficient algorithm to compress patient monitoring video. This type of video mainly contains local motions and a large percentage of idle periods. To specifically utilize these features, we present an object-based approach, which decomposes input video into three objects representing background, slow-motion foreground and fast-motion foreground. Encoding these three video objects with different temporal scalabilities significantly improves the coding efficiency in terms of bitrate vs. visual quality. The video decomposition is built upon change detection which identifies content changes between video frames. To improve the robustness of capturing small changes, we contribute two new change detection models. The model built upon Markov random theory discriminates foreground containing the patient being monitored. The other model, called covariance test method, identifies constantly changing content by exploiting temporal correlation in multiple video frames. Both models show great effectiveness in constructing the defined video objects. We present detailed algorithms of video object construction, as well as experimental results on the object-based coding of patient monitoring video

    From Manual to Automated Design of Biomedical Semantic Segmentation Methods

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    Digital imaging plays an increasingly important role in clinical practice. With the number of images that are routinely acquired on the rise, the number of experts devoted to analyzing them is by far not increasing as rapidly. This alarming disparity calls for automated image analysis methods to ease the burden on the experts and prevent a degradation of the quality of care. Semantic segmentation plays a central role in extracting clinically relevant information from images, either all by themselves or as part of more elaborate pipelines, and constitutes one of the most active fields of research in medical image analysis. Thereby, the diversity of datasets is mirrored by an equally diverse number of segmentation methods, each being optimized for the datasets they are addressing. The resulting diversity of methods does not come without downsides: The specialized nature of these segmentation methods causes a dataset dependency which makes them unable to be transferred to other segmentation problems. Not only does this result in issues with out-of-the-box applicability, but it also adversely affects future method development: Improvements over baselines that are demonstrated on one dataset rarely transfer to another, testifying a lack of reproducibility and causing a frustrating literature landscape in which it is difficult to discern veritable and long lasting methodological advances from noise. We study three different segmentation tasks in depth with the goal of understanding what makes a good segmentation model and which of the recently proposed methods are truly required to obtain competitive segmentation performance. To this end, we design state of the art segmentation models for brain tumor segmentation, cardiac substructure segmentation and kidney and kidney tumor segmentation. Each of our methods is evaluated in the context of international competitions, ensuring objective performance comparison with other methods. We obtained the third place in BraTS 2017, the second place in BraTS 2018, the first place in ACDC and the first place in the highly competitive KiTS challenge. Our analysis of the four segmentation methods reveals that competitive segmentation performance for all of these tasks can be achieved with a standard, but well-tuned U-Net architecture, which is surprising given the recent focus in the literature on finding better network architectures. Furthermore, we identify certain similarities between our segmentation pipelines and notice that their dissimilarities merely reflect well-structured adaptations in response to certain dataset properties. This leads to the hypothesis that we can identify a direct relation between the properties of a dataset and the design choices that lead to a good segmentation model for it. Based on this hypothesis we develop nnU-Net, the first method that breaks the dataset dependency of traditional segmentation methods. Traditional segmentation methods must be developed by experts, going through an iterative trial-and-error process until they have identified a good segmentation pipeline for a given dataset. This process ultimately results in a fixed pipeline configuration which may be incompatible with other datasets, requiring extensive re-optimization. In contrast, nnU-Net makes use of a generalizing method template that is dynamically and automatically adapted to each dataset it is applied to. This is achieved by condensing domain knowledge about the design of segmentation methods into inductive biases. Specifically, we identify certain pipeline hyperparameters that do not need to be adapted and for which a good default value can be set for all datasets (called blueprint parameters). They are complemented with a comprehensible set of heuristic rules, which explicitly encode how the segmentation pipeline and the network architecture that is used along with it must be adapted for each dataset (inferred parameters). Finally, a limited number of design choices is determined through empirical evaluation (empirical parameters). Following the analysis of our previously designed specialized pipelines, the basic network architecture type used is the standard U-Net, coining the name of our method: nnU-Net (”No New Net”). We apply nnU-Net to 19 diverse datasets originating from segmentation competitions in the biomedical domain. Despite being applied without manual intervention, nnU-Net sets a new state of the art in 29 out of the 49 different segmentation tasks encountered in these datasets. This is remarkable considering that nnU-Net competed against specialized manually tuned algorithms on each of them. nnU-Net is the first out-of-the-box tool that makes state of the art semantic segmentation methods accessible to non-experts. As a framework, it catalyzes future method development: new design concepts can be implemented into nnU-Net and leverage its dynamic nature to be evaluated across a wide variety of datasets without the need for manual re-tuning. In conclusion, the thesis presented here exposed critical weaknesses in the current way of segmentation method development. The dataset dependency of segmentation methods impedes scientific progress by confining researchers to a subset of datasets available in the domain, causing noisy evaluation and in turn a literature landscape in which results are difficult to reproduce and true methodological advances are difficult to discern. Additionally, non-experts were barred access to state of the art segmentation for their custom datasets because method development is a time consuming trial-and-error process that needs expertise to be done correctly. We propose to address this situation with nnU-Net, a segmentation method that automatically and dynamically adapts itself to arbitrary datasets, not only making out-of-the-box segmentation available for everyone but also enabling more robust decision making in the development of segmentation methods by enabling easy and convenient evaluation across multiple datasets

    Off-line Thai handwriting recognition in legal amount

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    Thai handwriting in legal amounts is a challenging problem and a new field in the area of handwriting recognition research. The focus of this thesis is to implement Thai handwriting recognition system. A preliminary data set of Thai handwriting in legal amounts is designed. The samples in the data set are characters and words of the Thai legal amounts and a set of legal amounts phrases collected from a number of native Thai volunteers. At the preprocessing and recognition process, techniques are introduced to improve the characters recognition rates. The characters are divided into two smaller subgroups by their writing levels named body and high groups. The recognition rates of both groups are increased based on their distinguished features. The writing level separation algorithms are implemented using the size and position of characters. Empirical experiments are set to test the best combination of the feature to increase the recognition rates. Traditional recognition systems are modified to give the accumulative top-3 ranked answers to cover the possible character classes. At the postprocessing process level, the lexicon matching algorithms are implemented to match the ranked characters with the legal amount words. These matched words are joined together to form possible choices of amounts. These amounts will have their syntax checked in the last stage. Several syntax violations are caused by consequence faulty character segmentation and recognition resulting from connecting or broken characters. The anomaly in handwriting caused by these characters are mainly detected by their size and shape. During the recovery process, the possible word boundary patterns can be pre-defined and used to segment the hypothesis words. These words are identified by the word recognition and the results are joined with previously matched words to form the full amounts and checked by the syntax rules again. From 154 amounts written by 10 writers, the rejection rate is 14.9 percent with the recovery processes. The recognition rate for the accepted amount is 100 percent

    Computer visualization of petroleum reservoir characterization data

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    A Robust Morphological Approach for Semantic Segmentation of Very High Resolution Images

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    State-of-the-art methods for semantic segmentation of images involve computationally intensive neural network architectures. Most of these methods are not adaptable to high-resolution image segmentation due to memory and other computational issues. Typical approaches in literature involve design of neural network architectures that can fuse global information from low-resolution images and local information from the high-resolution counterparts. However, architectures designed for processing high resolution images are unnecessarily complex and involve a lot of hyper parameters that can be difficult to tune. Also, most of these architectures require ground truth annotations of the high resolution images to train, which can be hard to obtain. In this article, we develop a robust pipeline based on mathematical morphological (MM) operators that can seamlessly extend any existing semantic segmentation algorithm to high resolution images. Our method does not require the ground truth annotations of the high resolution images. It is based on efficiently utilizing information from the low-resolution counterparts, and gradient information on the high-resolution images. We obtain high quality seeds from the inferred labels on low-resolution images using traditional morphological operators and propagate seed labels using a random walker to refine the semantic labels at the boundaries. We show that the semantic segmentation results obtained by our method beat the existing state-of-the-art algorithms on high-resolution images. We empirically prove the robustness of our approach to the hyper parameters used in our pipeline. Further, we characterize some necessary conditions under which our pipeline is applicable and provide an in-depth analysis of the proposed approach.Comment: Under review at IEEE Transactions on Image Processin

    Robust Multi-Seasonal Ice Classification from High Resolution X-Band SAR

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    Automated solutions for sea ice type classification from synthetic aperture (SAR) imagery offer an opportunity to monitor sea ice, unimpeded by cloud cover or the arctic night. However, there is a common struggle to obtain accurate classifications year round; particularly in the melt and freeze-up seasons. During these seasons, the radar backscatter signal is affected by wet snow cover, obscuring information about underlying ice types. By using additional spatiotemporal contextual data and a combination of convolutional neural networks and a dense conditional random field, we can mitigate these problems and obtain a single classifier which is able to classify accurately at 3.5 m spatial resolution for five different classes of sea ice surface from October to May. During the near year-long drift of the MOSAiC expedition we collected satellite scenes of the same patch of Arctic pack ice with X-Band SAR with a revisit-time of less than a day on average. Combined with in-situ observations of the local ice properties this offers up the unprecedented opportunity to perform a detailed and quantitative assessment of the robustness of our classifier for level, deformed and heavily deformed ice. For these three classes, we can perform accurate classification with a probability > 95% and calculate a lower bound for the robustness between 85% and 88%

    (SEMI)-AUTOMATED ANALYSIS OF MELANOCYTIC LESIONS

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    Melanoma is a very aggressive form of skin cancer whose incidence has constantly grown in the last 50 years. To increase the survival rate, an early diagnosis followed by a prompt excision is crucial and requires an accurate and periodic analysis of the patient's melanocytic lesions. We have developed an hardware and software solution named Mole Mapper to assist the dermatologists during the diagnostic process. The goal is to increase the accuracy of the diagnosis, accelerating the entire process at the same time. This is achieved through an automated analysis of the dermatoscopic images which computes and highlights the proper information to the dermatologist. In this thesis we present the 3 main algorithms that have been implemented into the Mole Mapper: A robust segmentation of the melanocytic lesion, which is the starting point for any other image processing algorithm and which allows the extraction of useful information about the lesion's shape and size. It outperforms the speed and quality of other state-of-the-art methods, with a precision that meets a Senior Dermatologist's standard and an execution time that allows for real-time video processing; A virtual shaving algorithm, which increases the precision and robustness of the other computer vision algorithms and provides the dermatologist with a hair-free image to be used during the evaluation process. It matches the quality of state-of-the-art methods but requires only a fraction of the computational time, allowing for computation on a mobile device in a time-frame compatible with an interactive GUI; A registration algorithm through which to study the evolution of the lesion over time, highlighting any unexpected anomalies and variations. Since a standard approach to this problem has not yet been proposed, we define the scope and constraints of the problem; we analyze the results and issues of standard registration techniques; and finally, we propose an algorithm with a speed compatible with Mole Mapper's constraints and with an accuracy comparable to the registration performed by a human operator
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