Acute respiratory distress syndrome (ARDS) can occur in people with or without previous lung disease. Analysis of aeration in artificial ventilation for ARDS is one of the major applications of Computed Tomography (CT...
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ISBN:
(纸本)9781424407071
Acute respiratory distress syndrome (ARDS) can occur in people with or without previous lung disease. Analysis of aeration in artificial ventilation for ARDS is one of the major applications of Computed Tomography (CT) lung density examination. A movie of an affected rabbit lung over the respiratory cycle was produced by dynamic CT with a cine loop technique. This technique can produce thousands of CT images for analysis with a single experiment. A fully automated algorithm based on the capability of wavelet transformation to detect edges in the image is proposed. This method accurately and consistently segments the lung in pulmonary CT images. The speed and accuracy of this technique allows it to outperform other methods when dealing with the large number of images created by dynamic Computed Tomography.
Color quantization is a critical task, frequently involved in imageprocessing that reduces the number of distinct colors used in an image while retaining as much of the original representation capabilities. The key a...
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ISBN:
(纸本)9781467352062;9781467352055
Color quantization is a critical task, frequently involved in imageprocessing that reduces the number of distinct colors used in an image while retaining as much of the original representation capabilities. The key aspect here is to find the optimal palette and evaluate against unprocessed target images. The purpose of this paper is to compare the effectiveness of three well known unsupervised vector quantization algorithms (Neural Gas, Growing Neural Gas and Instantaneous Topological Map) in the field of color abstraction. Evaluation data for L*a*b* and L*u*v* uniform color spaces and a number of quality indices, exhibiting the performance in terms of overall quality, are presented.
This paper proposes an algorithm based on convolutional neural networks for the estimation of the quality level of voice signals transmitted through cellular communication systems. The objective is to take advantage o...
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ISBN:
(纸本)9781728114910
This paper proposes an algorithm based on convolutional neural networks for the estimation of the quality level of voice signals transmitted through cellular communication systems. The objective is to take advantage of artificial intelligence methods to estimate the MOS parameter and obtain a similar accuracy to that obtained by methods and procedures established in the international norms and international licensed standards. The proposed algorithm uses the MOS results obtained by the method detailed in the ITU-T P.862 standard. The values were obtained for different signals acquired at different reception points. With this information we proceeded to design and train a convolutional neuronal network of 4 layers, achieving very satisfactory results. For the validation, the mean square error was used to measure the degree of similarity of the MOS values obtained by ITU-T P.862 and by the proposed algorithm. The results show a mean square error of 0.00007 for the proposed algorithm.
Discrete relaxation techniques have proven useful in solving a wide range of problems in digital signal and digital imageprocessing, artificial intelligence, operations research, and machine vision. Much work has bee...
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Discrete relaxation techniques have proven useful in solving a wide range of problems in digital signal and digital imageprocessing, artificial intelligence, operations research, and machine vision. Much work has been devoted to finding efficient hardware architectures. This paper shows that a conventional hardware design for a Discrete Relaxation Algorithm (DRA) suffers from O(n2m3) time complexity and O(n2m2) space complexity. By reformulating DRA into a parallel computational tree and using a multiple tree-root pipelining scheme, time complexity is reduced to O(nm), while the space complexity is reduced by a factor of 2. For certain relaxation processing, the space complexity can even be decreased to O(nm). Furthermore, a technique for dynamic configuring an architectural wavefront is used which leads to an O(n) time highly concurrent DRA3 architecture.","doi":"10.1109/TPAMI.1987.4767988","publicationTitle":"ieee Transactions on Pattern Analysis and Machine intelligence","startPage":"816","endPage":"831","rightsLink":"http://***/AppDispatchServlet?publisherName=ieee&publication=0162-8828&title=A+Parallel+Architecture+for+Discrete+Relaxation+Algorithm&isbn=&publicationDate=Nov.+1987&author=Jun+Gu&ContentID=10.1109/TPAMI.1987.4767988&orderBeanReset=true&startPage=816&endPage=831&volumeNum=PAMI-9&issueNum=6","displayPublicationTitle":"ieee Transactions on Pattern Analysis and Machine intelligence","pdfPath":"/iel5/34/4767975/***","keywords":[{"type":"ieee Keywords","kwd":["Parallel architectures","Hardware","signalprocessing","Digital images","Artificial intelligence","Operations research","Machine vision","Computer architecture","Algorithm design and analysis","Concurrent computing"]},{"type":"Author Keywords ","kwd":["VLSI","Algorithm-configured dynamic architectural wave-front system","associative circular pipelining","Discrete Relaxation Algorithm (DRA)","interleaved processing","multiprocessor architecture","recursive systolic computat
The ability to explain the reasons for one's decisions to others is an important aspect of being human intelligence. We will look at the explainability aspects of the deep learning models, which are most frequentl...
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ISBN:
(纸本)9798350398823
The ability to explain the reasons for one's decisions to others is an important aspect of being human intelligence. We will look at the explainability aspects of the deep learning models, which are most frequently used in medical imageprocessing tasks. The Explainability of machine learning models in medicine is essential for understanding how the particular ML model works and how it solves the problems it was designed for. The work presented in this paper focuses on the classification of lung CT scans for the detection of COVID-19 patients. We used CNN and DenseNet models for the classification and explored the application of selected visual explainability techniques to provide insight into how the model works when processing the images.
Breast cancer is the second leading cause of cancer death in women according to World Health Organization (WHO). Development of computer aided diagnostic (CAD) systems has great importance as a secondary reader system...
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ISBN:
(纸本)9781509039098
Breast cancer is the second leading cause of cancer death in women according to World Health Organization (WHO). Development of computer aided diagnostic (CAD) systems has great importance as a secondary reader systems for a correct diagnosis and treatment process. In this paper, a deep learning based feature extraction method by convolutional neural network (CNN) is proposed for automated mitosis detection for cancer diagnosis and grading by histopathological images. The proposed framework is tested on the MITOS data set provided for a contest on mitosis detection in breast cancer histological images released for research purposes in International Conference on Pattern Recognition (ICPR' 2014). By using provided histopathological images, cellular structures are initially found by combined clustering based segmentation and blob analysis after preprocessing step. Then, obtained cellular image patches are cropped automatically from the histopathological images for feature extraction stage. CNN, which is a prominent deep learning method on imageprocessing tasks, is utilized for extracting discriminative features. Due to the high dimensional output of the CNN, combination of PCA and LDA dimension reduction methods are performed respectively for regularization and dimension reduction process. Afterwards, a robust kernel based classifier, support vector machine (SVM), is used for final classification of mitotic and non-mitotic cells. The test results on MITOS data set prove that the proposed framework achieved promising results for mitosis detection on histopathological images.
Music transcription could be defined as an act of listening to a piece of music and writing down music notation for the piece. In this paper we describe our work on automatic music transcription of single instrument p...
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ISBN:
(纸本)9781538646403
Music transcription could be defined as an act of listening to a piece of music and writing down music notation for the piece. In this paper we describe our work on automatic music transcription of single instrument polyphonic music. We developed a software tool that can be used to address all the phases of automatic music transcription process.
In this paper we show a method for edge detection in discretized range images. Heavy quantization results in low number of layers present in range images. These values suffer from systematic error and must not be trea...
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ISBN:
(纸本)9781479953370
In this paper we show a method for edge detection in discretized range images. Heavy quantization results in low number of layers present in range images. These values suffer from systematic error and must not be treated as random additive noise. In our method we utilize morphologic operations for noise noise reduction and skeleton extraction for each layer. Based on the extracted features such as layer skeletons we present an algorithm to identify and classify edges in range images. We present edge detection results of simulated and captured real range images.
Superpixel algorithms are becoming a widely used method for many computer vision applications, and it could be used as a basis of image segmentation for digital microscopy images of HE stained tissue samples. Research...
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ISBN:
(纸本)9781479901944;9781479901975
Superpixel algorithms are becoming a widely used method for many computer vision applications, and it could be used as a basis of image segmentation for digital microscopy images of HE stained tissue samples. Research results show that among the many superpixel methods SLIC yields the best results when it comes to boundary adherence accuracy for normal images. In an effort to find out if it can be used for segmenting tissue images we have devised a benchmark to measure the performance of SLIC and tried improving the performance by careful tuning of the parameters to better fit SLIC to our special imageprocessing needs.
Global warming induced drastic climate changes have increased the frequency of natural disasters such as flooding, worldwide. Flooding is a constant threat to humanity and reliable systems for flood monitoring and ana...
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ISBN:
(纸本)9781509006120
Global warming induced drastic climate changes have increased the frequency of natural disasters such as flooding, worldwide. Flooding is a constant threat to humanity and reliable systems for flood monitoring and analysis need to be developed. Flood hazard assessment needs to take into account physical characteristics such as flood depth, flow velocity and the duration of flooding. This paper provides the researchers with a detailed compilation of the methods that can be used for the estimation of flood water depth. A comparative study has been done between the water depth estimation techniques based on imageprocessing and those which does not involve imageprocessing. The comparison is based on various attributes such as implementation methods, advantages, accuracy and cost. imageprocessing methods are classified based on various algorithms such as character recognition, feature extraction, region of interest (ROI), FIR filter etc. Similarly, non-imageprocessing methods are classified based on hardware used such as sensors, level indicators, etc., and other signal based techniques. This study can be used to identify the best method for flood water depth estimation.
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