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检索条件"任意字段=2021 International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2021"

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An Improved Neural Network Based on UNet for Surface Defect Segmentation 1

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2nd international conference on 3D Imaging Technologies-Multidimensional signal processing and Deep Learning, 3D IT-MSPandDL 2020

作者： Wang, Daolei Liu, Yiteng Shanghai University of Electric Power Shanghai200240 China

ISBN: (数字)9789811631801

ISBN: (纸本)9789811631795

Surface defect defection is a critical task in product quality assurance in industrial production lines. The defect characteristics of surface are easily affected by light. A surface defect detection model based on improved UNet-based neural network, surface defect UNet (S-UNet), is proposed to address the limitations of traditional detection algorithms in surface defect detection, as well as the problems of low accuracy, low precision, and cumbersome detection process. S-UNet uses atrous spatial pyramid pooling (ASPP) to acquire different receptive fields, overcoming the limitations of the UNet model with a single receptive field and improving the ability to segment targets of different sizes. S-UNet utilizes features at different levels to achieve complementary information and improve the coherence and accuracy of segmented areas. The model performs well on small industrial commutator datasets and steel surface defect dataset, reaching the optimal level in terms of precision, recall, and F-score. Compared to UNet and other models, as well as traditional methods, the proposed method achieves better results. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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A Neural Network Based Technique for Staircase Detection using Smart Phone images

A Neural Network Based Technique for Staircase Detection usi...

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international conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Anurag Ramteke Bhagath Parabattina Pradip K. Das IIT Guwahati Assam India Lakireddy Bali Reddy College of Engineering Mylavaram Andhra Pradesh India

The problem of stair case detection is important for various applications that include accident avoidance for elderly people and visually impaired persons. This also has significance in autonomous robotic systems that requires a stairs detection module. The proposed system addresses the problem of stair case detection using handheld mobile devices. The algorithm works using images captured in a mobile phone. It uses both image and geo-spatial features. The performance of individual features in the context of stair detection is studied using a neural network based system. The obtained results with the proposed algorithm is explained in the paper.

关键词： Wireless communication Performance evaluation image edge detection Neural networks Senior citizens signal processing algorithms Stairs

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Comparison Between Different Machine Learning algorithms

Comparison Between Different Machine Learning Algorithms

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international conference on image Information processing

作者： Souvik Sarkar Sidhant Singh Rohit Kumar Debraj Chatterjee Techno Internaional New Town Kolkata India

ISBN: (纸本)9781665433624

Machine learning is a major application of AI it is a phenomena by which system automatically learn and improve from real world experience in form of data on which it is trained or by observing the surroundings. Machine learning is changing the planet by transforming all segments including healthcare services, education, transport, food, entertainment, and different production line and lots of more. Machine learning plays a great role in changing our lives as well as industries in sector like housing and applications, cars, shopping, food ordering, etc. One of the application of ML is character recognition. Character recognition is used to convert handwritten or printed documents or image characters of document to computer codes. The basic process involves examining the text of a document and translating the characters into code that can be used for data processing. Hardware, like an optical scanner or specialized circuit card is employed to repeat or read text while software typically handles the advanced processing. Software can also take advantage of machine learning algorithms to implement more advanced methods of intelligent character recognition, like identifying languages or styles of handwriting. In this era there are various machine Learning algorithms like a linear regression, Convolutional Neural Network(CNN), kNN, K-Means, Random Forest etc. In this paper we are going to compare the results between between linear regression and Convolution Neural Network(CNN) over a same data-set of 0-9 digits.

关键词： Convolutional codes Training Analytical models Machine learning algorithms image analysis Convolution Computational modeling

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image Dehazing via Multilayer Detail Enhancement and Saturation Compensation

Image Dehazing via Multilayer Detail Enhancement and Saturat...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Ziye Zhang Tian Pu Ping Zhang School of Optoelectronic Science and Engineering UESTC Chengdu China School of Information and Communication Engineering UEST Chengdu China

images taken in hazy conditions often suffer from severe quality degradation, such as detail loss, low contrast, and color cast. To solve these problems, this paper proposed a naturalness preserved dehazing method. First, a hue histogram distribution prior is used to determine if the input image is color- casted, and a simple color correction method is provided to restore colors. Second, a multilayer detail enhancement unit is applied to sharpen the intensity channel, and an adaptive local gamma correction is used to compensate saturation. Dehazed results can be achieved by converting back to RGB. Extensive experimental results indicate that the proposed method can effectively improve the visibility of hazy images, bring out the details without undesirable halo effects, and reproduce vivid color. The assessment results demonstrate that our method is capable of achieving outstanding dehazing performance, compared with some state-of-the-art methods.

关键词： Degradation Histograms image color analysis signal processing algorithms signal processing Nonhomogeneous media Attenuation

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An adaptive imaging method for ultrasound coherent plane-wave compounding based on the polar coherence factor

An adaptive imaging method for ultrasound coherent plane-wav...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Mengjia Chang Zhenkun Lu School of Artificial Intelligence Guangxi University for Nationalities Guangxi China College of Electronic Information Guangxi University for Nationalities Guangxi China

ISBN: (纸本)9781665447300

Coherent Plane-Wave Compounding (CPWC) is a compromise between the frame rate and image quality after a single plane wave imaging, which emits plane waves at different angles to form a backscattered signal through the target, and then forms an image by superimposing it. Although this technology effectively improves the imaging quality, the unfocused nature of the plane waves leads to a strong coherence, especially when the two angles are similar, the artifacts in the image become more obvious. Therefore, this paper proposes a method of coherent plane-wave compounding based on the polar coherence factor (PCF), which takes the imaging results of plane waves at different angles as the weighting object, and then calculates the PCF factor. Finally, multiply the PCF factor and the result is the final weighted pixel value. The experimental results show that the PCF-CPWC algorithm greatly improves the resolution and contrast of the image, and it has better imaging quality.

关键词： image quality image resolution Ultrasonic imaging Imaging signal processing algorithms Coherence Transforms

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international conference on Computational Methods and Data Engineering, ICMDE 2020

International Conference on Computational Methods and Data E...

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international conference on Computational Methods and Data Engineering, ICMDE 2020

ISBN: (纸本)9789811568756

The proceedings contain 50 papers. The special focus in this conference is on Computational Methods and Data Engineering. The topics include: On roman domination of graphs using a genetic algorithm;general variable neighborhood search for the minimum stretch spanning tree problem;tabu-embedded simulated annealing algorithm for profile minimization problem;deep learning-based asset prognostics;evaluation of two feature extraction techniques for age-invariant face recognition;XGBoost: 2D-object recognition using shape descriptors and extreme gradient boosting classifier;Comparison of principle component analysis and stacked autoencoder on NSL-KDD dataset;maintainability configuration for component-based systems using fuzzy approach;development of petri net-based design model for energy efficiency in wireless sensor networks;Hybrid ANFIS-GA and ANFIS-PSO based models for prediction of type 2 diabetes mellitus;lifting wavelet and discrete cosine transform-based super-resolution for satellite image fusion;biologically inspired intelligent machine and its correlation to free will;weather status prediction of Dhaka City using machine learning;image processing: What, how and future;a study of efficient methods for selecting quasi-identifier for privacy-preserving data mining;day-ahead wind power forecasting using machine learning algorithms;query relational databases in Punjabi language;machine learning algorithms for big data analytics;fault classification using support vectors for unmanned helicopters;EEG signal analysis and emotion classification using bispectrum;social network analysis of youtube: A case study on content diversity and genre recommendation;Slack feedback analyzer (SFbA);a review of tools and techniques for preprocessing of textual data;A U-shaped printed UWB antenna with three band rejection;model for predicting academic performance through artificial intelligence.

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Color Endoscopic image Enhancement Technology Based on Nonlinear Unsharp Mask and CLAHE

Color Endoscopic Image Enhancement Technology Based on Nonli...

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IEEE international conference on signal and image processing (ICSIP)

作者： Shanshan Fang Chao Xu Bo Feng Yuzhe Zhu AnHui Engineering laboratory of Agro-Ecological Big Data School of Electronics and Information Engineering AnHui University HeFei China

ISBN: (纸本)9781665446006

Since the existing endoscopic image enhancement algorithms cannot take into account the problems of detail enhancement, brightness preservation and color preservation, this paper proposes an endoscopic image enhancement based on an improved unsharp mask and CLAHE algorithm. First, perform local brightness adjustment and saturation correction on the input image, and secondly, it is also the key point to perform contrast enhancement, which is divided into low-frequency background enhancement and high-frequency detail enhancement. CLAHE is used to balance the background intensity of low frequency components, and then the high-frequency parts are nonlinearly sharpened. The improved nonlinear transfer function proposed is used, and the high-frequency texture part is emphasized in combination with canny edge detection. Finally, the objective and subjective quality assessment of the image is carried out. The results show that the proposed nonlinear method enriches the texture details such as blood vessels. The peak signal-to-noise ratio and Tenengrad gradient are better than those of the compared algorithm. And the algorithms in this paper are all enhanced in the brightness channel, which has the function of color preservation, so as to achieve the natural enhancement effect as much as possible.

关键词： PSNR Minimally invasive surgery image color analysis Brightness Transfer functions Blood vessels Quality assessment

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Star image Simulation for Starlight Refraction Sensor

Star Image Simulation for Starlight Refraction Sensor

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IEEE international conference on signal and image processing (ICSIP)

作者： Qing Xu Chunhui Zhao Xiao Li Beijing Institute of Control Engineering Space Optoelectronic Measurement and Perception Lab Beijing China

ISBN: (纸本)9781665446006

The indirectly sensing horizon technique of starlight refraction utilizes star sensors to detect refracted starlight under the Earth's atmospheric limb background to achieve high precision attitude and position measurement of satellites. As star images usually can't be obtained in real time, software star image simulation is a quick and easy to implement approach for star identification algorithm validation. This paper firstly introduces the basic theory of starlight atmospheric refraction navigation. Then the atmospheric limb background radiation model and stellar radiation model of starlight refraction sensor are established. On this basis, the process of refraction star image simulation is discussed in detail. According to the given star sensor parameters and simulated radiation data, an example of refraction star image simulation is realized and the offset image coordinates of the refracted stars are analyzed for verification.

关键词： image sensors Satellites Atmospheric measurements Atmospheric modeling Software algorithms Position measurement Satellite navigation systems

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Numerical DWI phantoms to optimize accuracy and precision of quantitative parametric maps for non-Gaussian diffusion

Numerical DWI phantoms to optimize accuracy and precision of...

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Medical Imaging conference - image processing

作者： Malyarenko, Dariya, I Pang, Yuxi Amouzandeh, Ghoncheh Chenevert, Thomas L. Univ Michigan Radiol 1500 E Med Ctr Dr Ann Arbor MI 48109 USA

ISBN: (纸本)9781510633940

Clinical applications of quantitative diffusion-weighted imaging (qDWI) require confidence intervals for derived diffusion parameters to aid differentiation of technical errors from tissue characteristics. This study outlines practical procedures to evaluate precision (uncertainty) and accuracy (bias) of parametric maps derived for non-Gaussian diffusion models using numerical DWI phantoms (digital reference objects (DROs)) generated for advanced qDWI clinical trial protocols. The generated DROs include simulated acquisition noise, DICOM scaling and clinically-relevant qDWI parameter ranges for perfusion-fraction intra-voxel incoherent motion, kurtosis and stretched exponential diffusion models. Evaluation of fit accuracy and precision is illustrated for unsupervised linear least-squares (LLS) versus nonlinear minimization (NLM) algorithms ignorant of noise. DRO application examples are shown for calibration of noise-induced uncertainty with a physical DWI phantom, retrospective model fidelity analysis for brain tumor data and prospective error mapping in renal tissue. Physical DWI phantom analysis confirms adequate DRO noise model and realistic predicted fit errors. Detection of step-like bias activation in fit parameter space, consistent with model-dependent signal truncation at the noise floor, is proposed based on differences in NLM versus LLS fit maps. For all diffusion models, noise-induced bias in diffusivity is anti-correlated to bias in the model-specific (non-diffusivity) parameter. For confident assessment of fit parameter precision, bias minimization through b-range constraints and b-dependent averaging is advised prior to fit uncertainty mapping. For low bias, higher precision is achieved by LLS versus NLM, and for diffusivity versus model parameter. The described procedures allow efficient qDWI protocol optimization toward reduced acquisition and model-dependent errors.

关键词： digital reference object diffusion weighted imaging (DWI) non-Gaussian diffusion two-parameter diffusion Rician noise digital image communication in medicine (DICOM)

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A context-based image segmentation using multiverse optimization and joint entropy

A context-based image segmentation using multiverse optimiza...

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Control, Automation, Power and signal processing (CAPS), 2021 international conference on Control, Automation, Power and signal processing (CAPS)

作者： Mausam Chouksey Rajib Kumar Jha Indian Institute of Technology Patna Patna India

ISBN: (纸本)9781665445788

One of the most commonly used approaches for image segmentation is multilevel thresholding. Histogram segmentation is the most often used approach in image segmentation. While histogram-based techniques only examine intensity frequency and ignore spatial information. Contextual knowledge helps improve the segmented image by helping users see how vital each pixel is and comprehend the context of other pixels. Spatial information is built into a curve with the same characteristics as a histogram. This work proposes Joint entropy and a multilevel energy curve for segmenting colour images. Multiverse optimization is employed as an optimization algorithm to find out the threshold. The energy curve based method is compared with a histogram-based method and variational mode decomposition-based method. The numerical metrics used to evaluate the proposed algorithm's output include structural similarity index, feature similarity index, peak signal to noise ratio, uniformity and a quality index based on local variance and computing time. Experiments show that the proposed algorithm produces more consistent results than existing techniques. The proposed algorithm delivers more consistent results than the other two techniques, according to the experiments.

关键词： Measurement image segmentation Histograms PSNR image color analysis System performance signal processing algorithms

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