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Adaptive threshold vein enhancement method based on illuminance estimation

Adaptive threshold vein enhancement method based on illumina...

引用

IEEE advanced Information Technology, Electronic and Automation Control conference (IAEAC)

作者： Hailan Kuang Fuying Guan Xiaolin Ma Xinhua Liu School of Information Engineering Wuhan University of Technology Wuhan China Key Laboratory of Fiber Optic Sensing Technology and Information Processing Wuhan University of Technology Ministry of Education Wuhan China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

Infrared vein images often contain a lot of noise due to the performance defects of the infrared imaging system, and these images have the characteristics of low contrast, low signal-to-noise ratio, and blurred target details. In order to improve this situation, an adaptive threshold enhancement algorithm based on guided filter illuminance estimation (ATEBIE) is proposed to enhance the detailed information of the vein infrared image in this paper, especially the vein pattern, which is of great significance for applications such as vein display and vein recognition. Firstly, the infrared image of the vein is converted to the LAB color space representation to obtain the L-component of the image; Secondly, the multi-scale Gaussian blurring is performed on the component, and the noise is removed at the same time to obtain the initial illuminance estimation; then the guided filtering is used for accuracy illumination estimation; Finally, according to the illuminance image, an adaptive threshold is adopted to dynamically adjust the image to achieve the purpose of vein image enhancement. The experimental results show that the proposed algorithm not only suppresses the image noise very well, but also effectively enhances the vein texture details, and has a good application prospect.

关键词： image color analysis Veins Estimation Filtering algorithms Information filters image enhancement signal to noise ratio

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Bidirectional Backpropagation for High-Capacity Blocking Networks

Bidirectional Backpropagation for High-Capacity Blocking Net...

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international conference on Machine Learning and Applications (ICMLA)

作者： Olaoluwa Adigun Bart Kosko Signal and Image Processing Institute University of Southern California Los Angeles CA

ISBN: (纸本)9781665443388

The new bidirectional backpropagation algorithm helps blocking networks learn and recall large numbers of image patterns. Bidirectional backpropagation exploits backward-pass learning that ordinary unidirectional backpropagation ignores. The backward pass reveals a hidden regressor in classifiers since the input neurons are identity units. Blocking networks allow deep classifiers to learn and accurately recognize more patterns than the older classifiers that use softmax neurons at the output classification layer. Blocking networks use logistic neurons at the output layer of a block. They use random bipolar coding from the vertices of a hypercube rather than from the vertices of the simplex embedded in it as with l-in-K encoding. Bidirectional deep sweeps improved classification accuracy on the CIFAR-100 image data base and did so at little extra computational cost.

关键词： Backpropagation Codes Machine learning algorithms conferences Neurons Machine learning Hypercubes

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An Intelligent image processing-based Approach to Optimize Vehicle Headlamp Aiming

An Intelligent Image Processing-based Approach to Optimize V...

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IEEE SSD international Multi-conference on Systems, signals and Devices

作者： Javad Navaei Mohammad Babakmehr Rajeev Kalamdani Yu-Ning Liu Shanshan Qiu J. Farhan Jasper Blackful Global Data Insight and Analytics Ford Motor Company Dearborn MI USA Global Advanced Manufacturing Ford Motor Company Dearborn MI USA

ISBN: (纸本)9781665414937;9781665430579

In this paper, a novel image processing-based approach is proposed to optimize vehicle headlamps aiming. Currently, most aiming devices rely on numerical derivative oriented methods to find the essential focal features, also called reference points, to perform the aiming process. However, these approaches are not robust, and minor changes in isocurves' smoothness may result in finding inaccurate aiming focal features. To address the associated robustness issue, a statistical signal processing-based approach named penalized contrast for changepoint detection is proposed. Experimental results indicate a high accuracy level of the proposed method concerning the master-tuned ground truth case studies while suggesting a robust mathematical process. Furthermore, it provides an extra level of flexibility for defining alternative focal features by calculating several points along isocurves, each of which can be considered as an axillary reference point for enhancing the aiming process.

关键词： Performance evaluation Robustness

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Improved black-box attack based on query and perturbation distribution

Improved black-box attack based on query and perturbation di...

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international Workshop on advanced Computational Intelligence (IWACI)

作者： Weiwei Zhao Zhigang Zeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China

Adversarial examples cause the deep neural network prediction error, which is a great threat to the deep neural network. How to generate more natural adversarial examples and improve the robustness of deep neural networks has received attention. In this paper, we propose an improved blackbox attack (IBBA) algorithm based on query and perturbation distribution. This algorithm only needs the top-l label of the attacked model to generate the adversarial examples. Based on the existing black-box attacks, we optimize the performance of the algorithm from two aspects: query distribution and perturbation distribution. In the aspect of query distribution, we adopt different strategies for nontargeted attack and targeted attack; in the aspect of perturbation distribution, we choose different low-frequency noise according to the difference between the targeted attack and nontargeted attack. The experimental results on imageNet show that the proposed algorithm is better than the existing algorithms in low query number, and the targeted attack is better in each specified query number.

关键词： Perturbation methods Neural networks Prediction algorithms Robustness Low-frequency noise Discrete cosine transforms Computational intelligence

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Which one is Kaphrao? Identify Thai Herbs with Similar Leaf Structure using Transfer Learning of Deep Convolutional Neural Networks

Which one is Kaphrao? Identify Thai Herbs with Similar Leaf ...

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IEEE Region 10 international conference TENCON

作者： Sila Temsiririrkkul Prarinya Siritanawan Rungravi Temsiririrkkul Division of Computing Science and Digital Technology Faculty of Science and Technology Huachiew Chalermprakiet University Samutprakarn Thailand School of Information Science Japan Advanced Institute of Science and Technology (JAIST) Ishikawa Japan Faculty of Pharmacy Thammasat University Pathumthani Thailand

ISBN: (纸本)9781665495332

The recent Deep Learning algorithms have demonstrated incredible performance in many fields of research, especially an object detection task where algorithm advances are at a level that can be applied to real-world problems. Classification of plants by leaf image is one of the problems that has been of interest among computer vision researchers for a long time because of its challenges due to a large number of species and their complex features. There are many approaches presented and show promising performance for the task. However, in the tropical area where the diversity of plants is rich, examples of the physical similarity of the leaves in different species can be found easily. Moreover, it is reported that many accidents of misidentifying plants and their usages can be life-threatening. Thus, specimens of plants with similar leaf structures are interested in this research. We present a method to identify plants that are similar in morphological characters of leaf. Using image processing and deep learning techniques and transfer learning of several deep convolutional neural network architectures: VGG-16, ResNet-50, and InceptionV3, the proposed method can identify seven Lamiaceae plants yielded high accuracy prediction of 98.71%, 91.32%, and 98.17%, respectively.

关键词： Deep learning image processing conferences Transfer learning Computer architecture Object detection Classification algorithms

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Evaluation of Bit Manipulation Instructions in Optimization of Size and Speed in RISC-V

Evaluation of Bit Manipulation Instructions in Optimization ...

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international conference on VLSI Design

作者： P S Babu Snehashri Sivaraman Deepa N Sarma Tripti S Warrier Cochin University of Science and Technology Kochi Kerala India Meenakshi Sundararajan Engineering College Chennai India Indian Institute of Technology Madras Chennai India

With an ever-increasing usage of electronic controllers in various disciplines that could be attributed to Industry 4.0, Internet of Things (IoT) and quick shift in computational paradigms, a demand for high code density and faster controllers are expected at the diversified nodes that improve energy efficiency without performance penalty. RISC-V is an open-source Instruction Set Architecture (ISA) which is designed with modularized extensions, that enables to design processors with a provision of individual extension evaluation helping in the design of low-power and secure embedded controllers. Bit manipulation is one of the key operations performed in domains such as Cryptography, Communication and Networking protocols, Digital signal processing, Bioinformatics etc., which are currently implemented using RISC-V standard instruction set. This paper implements the `B' extension of RISC-V that hosts instructions specific to operate at bit-level manipulations, which is absent in ratified unprivileged ISA manual. A quantitative analysis is performed to assess the impact of Bit Manipulative Instructions (BMI) in size and speed improvements using the Embench ™ benchmarks against the standard instruction set `IMAC' under the RV32 configuration. The results show significant improvements, with some programs achieving a speedup of 28 % and size reduction of 20 %.

关键词： image coding Protocols Statistical analysis Instruction sets signal processing algorithms Benchmark testing Very large scale integration

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Hybrid Compression Method for Hyper Spectral images using 3-Way SVD Tensor Decomposition and Discrete Wavelet Transform

Hybrid Compression Method for Hyper Spectral Images using 3-...

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Intelligent Systems, Smart and Green Technologies (ICISSGT), IEEE international conference on

作者： B. Sucharitha K.Anitha Sheela Electronics and Communication Engineering Jawaharlal Nehru Technological University Hyderabad India

ISBN: (纸本)9781665447263

Hyper Spectral image compression has attracted a lot of concentration in recent years due to massive data volumes. Thousands of gigabytes make up hyper spectral datasets. The amount of important information included in these hyper spectral images is quite limited due to redundancy. The collected HSI data sets are dominated by spatial and spectral correlations. Many transform coding approaches are available for compressing hyper spectral images by reducing spatial dimensions. These algorithms were originally designed for grayscale or color images and have been extended to hyper spectral images. Tensor decomposition methods are developed to minimize the size of an N-dimensional function. The spatial and spectral dimensions of a hyper spectral image can be reduced by modeling it as a 3rd order tensor. In this proposed hybrid method, a Discrete Wavelet Transform is used to process the hyper spectral image, which is then decomposed into Core Tensor and Component Matrices using the 3 Way Singular Value Decomposition Tucker Decomposition method. Four datasets are used to evaluate the proposed method and observed SSIM, PCC, and PSNR performance factors are good with high compression ratio for datasets over the existing techniques.

关键词： Visualization image coding Tensors Redundancy Transform coding signal processing algorithms Transforms

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image Stitching Algorithm Based on Region Division for Underwater Dam Crack image

Image Stitching Algorithm Based on Region Division for Under...

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Symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Yuanbo Huang Zhuo Zhang Xiaolong Xu The College of IoT Engineering Hohai University Changzhou China

ISBN: (纸本)9781665426565

The surface crack of underwater dam is one of the important indexes to evaluate the normal operation of the dam. Complete crack image is an important means to improve the accuracy of evaluation. In view of the limitations of traditional algorithms in underwater crack image stitching, we propose an underwater dam surface crack image stitching algorithm based on region division(ISA-RD). First of all, an image enhancement algorithm aiming at increasing the number of feature points is used. Secondly, we simplify the process of feature point selection and matching by relying on the features of multiple regions in the local crack image, and improve the matching accuracy by mining the close relationship between the matching of feature points and different regions. Finally, the high matching feature point pairs are used for image fusion. We take the crack image of the real scene as the research object. Compared with the classical image stitching algorithm, the feature point matching algorithm proposed in this paper improves the accuracy of feature point matching. Obviously, the image quality after stitching is improved.

关键词： image quality Dams Transforms Feature extraction Indexes Surface cracks Informatics

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A kNN Based Voyage’s Containers’ Entering Time Distribution Prediction System

A kNN Based Voyage’s Containers’ Entering Time Distributio...

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Symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Shitong Shen Jian Cao Yinyue Yang Yameng Guo Shanghai Jiao Tong University Shanghai China Shangdong Container Terminal Branch Shanghai International Port (Group) Co.Ltd Shanghai China

ISBN: (纸本)9781665426565

Compared with the air transportation and land transportation, water transportation has many advantages such as larger loading capacity, lower unit transportation cost, lower construction investment and so on. What’s more, water transportation has played an important role in the economical development of China, especially in the aspect of international trade. Therefore, the improvement in the efficiency of water transportation will be of great significance. In this paper, we designed a system to predict the containers’ entering time distribution of a given voyage at a specific port by using machine learning algorithms and statistical methods. Using Shanghai Yangshan Port phase IV automated terminal’s data, we perform some experiments, and the result shows that our system can provide valid predictions.

关键词： international trade Machine learning algorithms Costs Statistical analysis Loading Land transportation Containers

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External feature based quality evaluation of Tomato using K-means clustering and support vector classification

External feature based quality evaluation of Tomato using K-...

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international conference on Computing Methodologies and Communication (ICCMC)

作者： S. Deulkar Shweta Sunita S. Barve School of Computer Engineering and Technology MIT Academy of Engineering Pune India

ISBN: (纸本)9781665403603;9781665447751

The exponential growth of the human population and consumer demand of ready to eat food products has encouraged food manufacturers to adapt new strategies, tools and techniques to automate food processing. Among many contributing digital technologies in the fourth industrial revolution is the advanced human-machine interfaces. Fruit classification is an important task to be executed during the post-harvesting and production process. This research work proposes a new image processing application using supervised classifier for quality identification of Tomato fruit. The current post-harvesting process is inadequate in terms of efficiency, accuracy, speed, and cost. The proposed system is an integration of thresholding, clustering and suppervised classifiers. It helps to reduce manual efforts and increases the accuracy in terms of grading. The system goes under three processing step i.e image binarization, image clustering and classification. image binarization is performed using Otsu thresholding algorithm which helps to calculate optimal threshold and binarize the image. In the next step, k-means clustering partitions the image into three different clusters. These clusters are represented using various colors like red, green and blue. The clustering also helps to extract the features such as color, intensity, image quality, image size, and clusters. These features are stored as support vectors which benefits classifier for training and testing. Support vector machine is trained with already grouped 60 different Tomato images. Experimental results proves that high classification accuracy and fast computation time, makes our method appropriate for the real time quality evaluation of fruits.

关键词： Training Thresholding (Imaging) image color analysis Clustering algorithms Support vector machine classification Tools Feature extraction

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