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Significance of Contrast and Structure Features for an Improved Color image Classification System 5

Significance of Contrast and Structure Features for an Impro...

引用

IEEE International conference on signal and image processing applications (ICSIPA)

作者： Sowmya, V. Govind, D. Soman, K. P. Amrita Univ Amrita Vishwa Vidyapeetham Amrita Sch Engn Coimbatore Ctr Computat Engn & Networking CEN Coimbatore Tamil Nadu India

ISBN: (纸本)9781509055593

In general, the three main modules of color image classification systems are: color-to-grayscale image conversion, feature extraction and classification. The color-to-grayscale image conversion is the important pre-processing step which must incorporate the significant and discriminative contrast and structure information in the converted grayscale images as in the original color image. All the existing techniques for color-to-grayscale image conversion preserves the significant contrast and structure information in the converted grayscale images in different manners. Hence, the present work is to analyze the significant and discriminative contrast and structure information preserved in the converted grayscale images using two different decolorization techniques called rgb2gray and singular value decomposition based color-to-grayscale image conversion (SVD) applied in the color image classification systems using the three different proposed features. The three different features for color image classification systems are proposed based on the combination of the existing dense SIFT features and the contrast & structure content computed using color-to-gray structure similarity index (C2G-SSIM) metric.

关键词： Color Gray-scale Feature extraction image color analysis conferences Wheels

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An Extended Architecture to Optimize Execution Time of 3D image processing Deflectometry Algorithm Using FPGA 5

An Extended Architecture to Optimize Execution Time of 3D Im...

引用

IEEE International conference on signal and image processing applications (ICSIPA)

作者： Bhatti, Faraz Greiner, Thomas Heizmann, Michael Ziebarth, Mathias Pforzheim Univ Inst Smart Syst & Serv Pforzheim Germany Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9781509055593

The use of image processing is being accelerated over the past years in areas, including artificial intelligence, medical field, remote sensing and microscopic imaging. For 3D reconstruction of the objects, deflectometry is used to collect topographic information of surfaces. Due to computationally intensive nature of the algorithm, the execution time is one of the challenges faced by the deflectometry. In this paper, an extended FPGA based architecture is proposed to execute and improve the performance of deflectometry algorithm. The whole process consists of several stages, including initialization, acquisition and processing of data. The main idea is to utilize the optimizations e.g., pipelining, parallelization, provided by an FPGA to improve the performance of the algorithm. However, the advantage of parallelization can only be utilized if the associated algorithm contains the number of tasks, which can run independent of each other. For this reason, the deflectometry algorithm is adapted to the architecture of an FPGA to improve the performance. After successful realization of proposed architecture, the results have shown that performance is significantly improved in terms of execution time. Moreover, a rapid design development methodology is employed to decrease the prototyping time.

关键词： image processing Field programmable gate arrays Surface treatment Computer architecture Cameras Three-dimensional displays

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x-RAY COMPUTED TOMOGRAPHY SIMULTANEOUS image RECONSTRUCTION AND CONTOUR DETECTION USING A HIERARCHICAL MARKOVIAN MODEL

X-RAY COMPUTED TOMOGRAPHY SIMULTANEOUS IMAGE RECONSTRUCTION ...

引用

IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Wang, Li Mohammad-Djafari, Ali Gac, Nicolas CentraleSupelec Lab Signaux & Syst Gif Sur Yvette France

ISBN: (纸本)9781509041176

The 3D x-ray Computed Tomography (CT) is used in many domains. In medical imaging and industrial Non Destructive Testing (NDT) applications, this technique becomes of great interest. In these applications, very often, we need not only to reconstruct the image, but also to detect the contours between the homogeneous regions of the piecewise continuous image. Generally, contours are obtained by a post processing from the reconstructed image. In this paper, we propose a method to estimate image and contour simultaneously. For this we use the Bayesian approach with a prior model in which the relationship between the image and its contour is considered by using a hierarchical Markovian model, and use a sparsity enforcing prior model for the contours. This proposed method can be used for reconstructions when the image is piecewise continuous. The simulation results are compared with some state of the art methods, and they show the efficiency of simultaneously reconstructing and edge detecting by using proposed method.

关键词： Computed Tomography Non Destructive Testing Simultaneous reconstruction and contour detection Markovian Model and Generalized Student-t distribution

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Tumor detection and whole slide classification of H&E lymph node images using convolutional neural network 5

Tumor detection and whole slide classification of H&E lymph ...

引用

IEEE International conference on signal and image processing applications (ICSIPA)

作者： Jamaluddin, M. F. Fauzi, M. F. A. Abas, F. S. Multimedia Univ Fac Engn Cyberjaya 63000 Selangor Malaysia Multimedia Univ Fac Engn & Technol Bukit Beruang 75450 Melaka Malaysia

ISBN: (纸本)9781509055593

Histopathological analysis of tissues has been gaining a lot of interests recently, from developing computer algorithms to assist pathologists from cell detection and counting, to tissue classification and cancer grading. With the advent of whole slide imaging, the field of digital pathology has gained enormous popularity, and is currently regarded as one of the most promising avenues of diagnostic medicine. Deep learning advancement on image set today has successfully evolved as many models has been proposed and produced state-of-the-art object classifying results. This is not limited to large database such as imagenet but also has seen applications in other medical image analysis related areas. In this paper we have carefully constructed and expanded the deep model network to classify normal and tumor slides in histology images of lymph nodes tissue. We have proposed our own deep learning model based on convolutional neural network with smaller requirement using 64x64x3 input image with 12 convolutional layer with max pooling and ReLU activation function. Our method has better AUC result at 0.94 than the winner of Camelyon16 Challenge with AUC of 0.925.

关键词： Tumors Feature extraction image color analysis Training Heating systems Histograms

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Analysis of Q-learning on ANNs for Robot Control using Live Video Feed 5

Analysis of Q-learning on ANNs for Robot Control using Live ...

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IEEE International conference on signal and image processing applications (ICSIPA)

作者： Murali, Nihal Gupta, Kunal Bhanot, Surekha BITS Pilani Dept Elect & Elect Engn Pilani Campus Pilani 333031 Rajasthan India

ISBN: (纸本)9781509055593

Training of artificial neural networks (ANNs) using reinforcement learning (RL) techniques is being widely discussed in the robot learning literature. The high model complexity of ANNs along with the model-free nature of RL algorithms provides a desirable combination for many robotics applications. There is a huge need for algorithms that generalize using raw sensory inputs, such as vision, without any hand-engineered features or domain heuristics. In this paper, the standard control problem of line following robot was used as a test-bed, and an ANN controller for the robot was trained on images from a live video feed using Q-learning. A virtual agent was first trained in simulation environment and then deployed onto a robot's hardware. The robot successfully learns to traverse a wide range of curves and displays excellent generalization ability. Qualitative analysis of the evolution of policies, performance and weights of the network provide insights into the nature and convergence of the learning algorithm.

关键词： Artificial neural networks Hardware implementation Q-learning Raw image inputs Reinforcement learning Robot learning

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Deep-learning: A Potential Method for Tuberculosis Detection using Chest Radiography 5

Deep-learning: A Potential Method for Tuberculosis Detection...

引用

IEEE International conference on signal and image processing applications (ICSIPA)

作者： Hooda, Rahul Sofat, Sanjeev Kaur, Simranpreet Mittal, Ajay Meriaudeau, Fabrice PEC Univ Technol Dept CSE Chandigarh India Panjab Univ UIET Dept CSE Chandigarh India Univ Teknol Petronas Ctr Intelligent Signal & Imaging Res Elect & Elect Engn Dept Seri Iskandar Malaysia

ISBN: (纸本)9781509055593

Tuberculosis (TB) is a major health threat in the developing countries. Many patients die every year due to lack of treatment and error in diagnosis. Developing a computer-aided diagnosis (CAD) system for TB detection can help in early diagnosis and containing the disease. Most of the current CAD systems use handcrafted features, however, lately there is a shift towards deep-learning-based automatic feature extractors. In this paper, we present a potential method for tuberculosis detection using deep-learning which classifies CxR images into two categories, that is, normal and abnormal. We have used CNN architecture with 7 convolutional layers and 3 fully connected layers. The performance of three different optimizers has been compared. Out of these, Adam optimizer with an overall accuracy of 94.73% and validation accuracy of 82.09% performed best amongst them. All the results are obtained using Montgomery and Shenzhen datasets which are available in public domain.

关键词： Deep learning Tuberculosis Medical imaging Chest x-rays

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Tamper Localization of the Medical images Based on Fragile Watermarking 25

Tamper Localization of the Medical Images Based on Fragile W...

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25th signal processing and Communications applications conference (SIU)

作者： Ustubioglu, Arda Ulutas, Guzin Ustubioglu, Beste Karadeniz Tech Univ Bilgisayar Muhendisligi Trabzon Turkey

ISBN: (纸本)9781509064946

Flexible usage and easy acquisition of image processing software has made "tamper localization in medical images" a popular area. Medical image Watermarking (MIW), which is one of the recommended methods in this area, relies on embedding additional information into the original image. In general, the medical image is separated into two parts called ROI - Region of Interest and RONI - Region of NonInterest. In the proposed method, image is separated into non-overlapping 8x8 blocks and then the BTLV (Block Tamper Localization Vector) of the corresponding block is calculated and embedded to tamper localization on the ROI. Similar works in the literature determined small size in ROI but it can reach up %80 of the image in the proposed method. Experimental results also indicate that proposed method gives higher PSNR for watermarked images and Normalize Correlation (NC) value is also better than the similar works.

关键词： Medical image Watermarking Tamper Localization Discrete Cosine Transform

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Subaperture image Segmentation for Lossless Compression 7

Subaperture Image Segmentation for Lossless Compression

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7th International conference on image processing Theory, Tools and applications (IPTA)

作者： Schiopu, Ionut Gabbouj, Moncef Iosifidis, Alexandros Zeng, Bing Liu, Shuaicheng Tampere Univ Technol Signal Proc Lab FIN-33101 Tampere Finland Univ Elect Sci & Technol China Inst Image Proc Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9781538618424

The paper proposes an image segmentation method for lossless compression of plenoptic images. Each light-field image captured by the plenoptic camera is processed to obtain a stack of subaperture images. Each subaperture image is encoded by using a gradient-base detector which classifies the image edges and designs refined contexts for an improved prediction and segmentation. The paper's main contribution is a new segmentation method which generates a preliminary segmentation, either by scaling the intensity differences or by using a quantum cut based algorithm, and merges it with an edge ranking-based segmentation. The results show around 2% improved performance compared to the state-of-the-art for a dataset of 118 plenoptic images.

关键词： Lossless compression image segmentation plenoptic image quantum cut segmentation

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Cooperative Non-orthogonal Multiple Access using Two-way Relay 5

Cooperative Non-orthogonal Multiple Access using Two-way Rel...

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IEEE International conference on signal and image processing applications (ICSIPA)

作者： Ho, Chun Yeen Leow, Chee Yen Univ Teknol Malaysia Wireless Commun Ctr Johor Baharu Malaysia

ISBN: (纸本)9781509055593

The existing work on cooperative non-orthogonal multiple access (NOMA) considers one-way relaying which consumes extra channel resources for the relaying operation. The use of extra channel resource results in degradation in spectral efficiency. This paper proposes two-way relaying in cooperative NOMA to enhance the spectral efficiency. The proposed scheme enable two-way information exchange between base station and users without consuming extra channel resource. In additional, the NOMA power allocation region is proposed to achieve a better rate compared to orthogonal multiple access scheme. Based on Monte Carlo simulation, the proposed scheme is shown to achieve better sum rate compared to OMA scheme and conventional cooperative NOMA.

关键词： Relays NOMA signal to noise ratio Base stations Uplink Resource management Downlink

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image Cryptography based on the Imitation of Gene Fusion and Horizontal Gene Transfer 7

Image Cryptography based on the Imitation of Gene Fusion and...

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7th International conference on image processing Theory, Tools and applications (IPTA)

作者： Hamici, Zoubir Al Zaytoonah Univ Jordan Dept Elect Engn Signal Proc ZUJ Amman Jordan

ISBN: (纸本)9781538618424

In this paper a novel signal processing algorithm for secure image communication is proposed. It is a genetic algorithm that combines both gene fusion and Horizontal Gene Transfer (HGT) inspired from the spread of antibiotic resistance in bacteria. The symmetric encryption key playing the role of a pathogen is generated by controlled multi-layer random sequences. The principal key is then absorbed by the organism genome represented by the image. The process of encryption starts by a full uptake of the key-agent or pathogen which has the size of the whole image;the image and the principal key are merged together in a gene fusion process. A second phase of encryption is an obfuscation process produced by an HGT where the genes are pixels and the chromosomes are the rows and columns of the image. The obfuscation process as a second layer of encryption is realized by two sub-keys from which the principal key matrix is constructed. The whole process is repeated recursively L rounds. A Salt extracted from the image hash-value is used against chosen-plaintext cryptanalysis, therefore, even a modification of one pixel will generate different encryption keys adding a stealthy-key feature to the cipher. The key generation process with the two layers of encryption of the genetic algorithm are fully described. Results of the signal processing algorithm based on gene fusion and HGT show a great promise in genetic inspired data security.

关键词： cryptography genetic algorithm horizontal gene transfer gene fusion obfuscation

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