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检索条件"任意字段=Conference on Statistical and Stochastic Methods for Image Processing"

共 4722 条记录，以下是341-350 订阅

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Optimization of Micro-Object Identification with Factorization and Correction Mechanisms for Distorted image Points

Optimization of Micro-Object Identification with Factorizati...

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International Russian Smart Industry conference (SmartIndustryCon)

作者： Isroil I. Jumanov Rustam A. Safarov Olimjon I. Djumanov Intelligent systems and computer technologies Samarkand state university Samarkand Uzbekistan

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The scientific and methodological foundations for the optimal identification of micro-objects with mechanisms for using information structural components, features, statistical and dynamic characteristics of images have been developed. Mechanisms for tracking, detecting and correcting distorted points, factorizing the structural components of the image, filtering, segmentation, tracking distorted points under conditions of a priori insufficiency, parametric uncertainty, and low reliability of information processing are proposed. methods for converting the initial brightness of a point into homogeneous objects as a function of two variables have been studied and nonlinear optimization functionals have been used. Mechanisms for factorization of the structural components of the image in the form of an indicator of linear connectivity, “neighborhood relationship” of three separate algorithms have been implemented. An alternative image factorization algorithm has been developed. A comparative analysis of effectiveness was carried out. The effectiveness of the mechanisms for detecting and correcting distorted points was studied using the criterion of the probability of undetected errors, the root-mean-square error, according to Shannon's estimates, the average level of intensity of the points, and the total compactness parameter. Rank estimates of integral indicators of image factorization were obtained for 30 images with 100 tests. A software package for visualization, recognition, and classification of images of microobjects has been developed, the implementations of which have been tested using real medical diagnostic data.

关键词： image segmentation Uncertainty Software packages Nonlinear distortion Brightness Noise Information processing Reliability Object recognition Optimization

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image Enhancement using linear filters for Rectal Cancer Diagnosis using MR images

Image Enhancement using linear filters for Rectal Cancer Dia...

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International conference on Biosignals, images and Instrumentation (ICBSII)

作者： Sonal Patreena D’Almeida Surekha Kamath K.V Rajagopal K.N Manjunath Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Radiodiagnosis and Imaging Kasturba Medical College Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798331513863

ISBN: (纸本)9798331513870

Computer-aided diagnosis (CAD) is an efficient tool in analyzing medical images for the diagnosis and staging of rectal cancer. Magnetic Resonance Imaging (MRI) plays a crucial role in the detection and evaluation of rectal cancer due to its high-resolution imaging capabilities. This paper discusses the pre-processing methods for MR images of rectal cancer, which facilitate further image analysis for diagnosing the stage of cancer. Pre-processing is a critical step for MR images, aiding in subsequent steps of segmentation and classification. This paper investigates the impact of widely used pre-processing techniques. Initially, pelvic DICOM images are provided as input. By combining three filters—mean, median, and adaptive median filters—the noise is removed from the image. image enhancement techniques such as Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to enhance the quality of the image. statistical parameters like Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Structural Similarity Index Measure (SSIM) are used to evaluate performance. Results indicate that CLAHE provides superior outcomes.

关键词： image quality Maximum likelihood detection PSNR Magnetic resonance imaging Noise Nonlinear filters Computer aided diagnosis Medical diagnostic imaging image enhancement Cancer

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Research on the Fusion of Superpixels in High-Resolution Remote Sensing images

Research on the Fusion of Superpixels in High-Resolution Rem...

引用

Data Science and Computer Application (ICDSCA), IEEE International conference on

作者： Da Yi Fang Xing Faculty of Science and Technology Kunming University of Science and Technology Oxbridge College Kunming China Scientific and Technological Innovation Team of Spatial Information Integration and Application of Natural Resources in Universities of Yunnan Province Kunming China Yunnan Provincial Appraisal Center for Environmental Engineering Kunming China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

With the development of small UAV remote sensing technology and the gradual improvement of observation system, high-resolution remote sensing images become easy to obtain, and China has entered the era of application of high-resolution remote sensing images. Traditional image segmentation methods treat pixels as processing units, focusing solely on spectral information while neglecting the influence of other image features. This approach fails to meet the accuracy and efficiency requirements for practical applications in high-resolution remote sensing image segmentation. Representing a high-resolution remote sensing image with superpixels aligns better with human visual perception, reduces information redundancy, and decreases data processing volume. Therefore, superpixel-based segmentation methods for high-resolution remote sensing images have become an important research direction. This paper primarily investigates the fusion of superpixels in high-resolution remote sensing images, elaborating on the principles of the fuzzy C-means clustering algorithm and explaining a proposed spatially constrained fuzzy clustering method that integrates spectral differences. Qualitative and quantitative analyses of the experimental results are conducted to validate the effectiveness and reliability of the proposed algorithm.

关键词： Measurement image segmentation Accuracy statistical analysis Redundancy Noise Clustering algorithms Reliability Remote sensing Visual perception

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Study on the Technology of Vehicle Flow Statistics Based on Video images

Study on the Technology of Vehicle Flow Statistics Based on ...

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Digital Analysis and processing, Intelligent Computation (DAPIC), International conference on

作者： Chang Liu Xiaogang Zhang Baiyu Ma Jun Mao School of Traffic and Electrical Engineering Dalian University of Science and Technology Dalian China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

Vehicle traffic flow statistics is a critical research topic in the field of intelligent transportation systems (ITS). Traditional traffic flow statistics methods suffer from issues such as long detection times and insufficient accuracy. This study leverages the OpenCV platform to implement video image-based vehicle detection, tracking, and statistical counting. Vehicle detection is achieved using the background subtraction method, while vehicle tracking combines centroid features with a Kalman filter. Traffic flow counting is realized through the design of counting lines. Experimental results demonstrate that for video images with a resolution of $352 \times 240$ and a frame rate of 25 frames per second (fps), the average accuracy rates for both vehicle detection and counting exceed $\mathbf{9 5 \%}$ , meeting basic real-time requirements.

关键词： Accuracy image resolution Filtering Vehicle detection Working environment noise Streaming media Feature extraction Real-time systems Kalman filters Intelligent transportation systems

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Research on Colour Correction in image Stitching With Equal Depth of Field

Research on Colour Correction in Image Stitching With Equal ...

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Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR), International conference on

作者： Daocheng Lin Zhian Li Guohua Xin Yingjie Xia Jinping Li School of Information Science and Engineering University of Jinan Jinan China

In the process of stitching images with equal depth of field, there are certain differences in brightness and colour between the stitched images, leading to the problem of large differences in brightness and colour at the seams of the stitched images. This affects people's visual effect, which in turn affects some related algorithmic effects on the basis of stitching big picture, so solving the problem of colour inconsistency in equal depth of field image stitching is an important research topic. Some existing methods for processing colour correction of a stitched image include global colour correction methods and local colour correction methods, while the local colour correction methods includes simple local correction methods and colour correction methods that fully considers local features of the image. However, the above methods suffer from inefficiency or poor image correction. In this regard, under the assumption that the colour features around the seams of a stitched large image are similar, a method for eliminating colour inconsistencies in stitched images using local statistical features in the region near the stitch line is proposed. The colour inconsistency in the stitched image is effectively solved by firstly calculating the difference between the colour statistical features in the region near the seams of the stitched images, then using the difference to correct the region near the two sides of the stitch, and finally extending it to the whole image for colour correction. The experimental results show that the method in this paper is simple and effective, which can keep the structure of the original image intact and improve the colour correction effect of the stitched image with better visual effect.

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Quantifying Uncertainty: statistical Speech Signal processing

Quantifying Uncertainty: Statistical Speech Signal Processin...

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Distributed Computing and Optimization Techniques (ICDCOT), International conference on

作者： Sree Harsha Prada Peesapaty Chinchu Joseph Eguri Samson Vineeth Kumar Apurva Naik A. A. Bazil Raj Department of Electrical and Electronics Engineering Dr.Vishwanath Karad MIT World Peace University Pune India Department of Electronics Engineering RF Photonics Lab Defence Institute of Advanced Technology Pune India

One of the most important aspects of any conversation is to maintain the intelligibility and integrity of the message. Assume that the data being transferred is accompanied by an undesired disturbance. In that instance, the intended message tends to be warped and the information to be delivered needs to be clarified. A variety of electrical devices and free space channels are frequently used to transfer different kinds of data, including text, music, and image files. In communication systems, noise frequently taints the data; therefore, eliminating noise is crucial in guaranteeing message clarity. Numerous methods of signal processing guarantee the same thing. This specific work, however, focuses on the statistical aspect of the transmitting signal and attempts to investigate how noisy segments in a signal might be identified and how their presence is reduced by using kurtosis in conjunction with power spectral density.

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Human-Face image Retrieving Based Texture Feature Extraction Method 2

Human-Face Image Retrieving Based Texture Feature Extraction...

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2nd Asian conference on Innovation in Technology, ASIANCON 2022

作者： Hameed Shaker, Shaimaa University of Tecbnology Department Baghdad Iraq

ISBN: (数字)9781665468510

ISBN: (纸本)9781665468510

The face image retrieval solutions are widely studied, Although it deal with different facial features and difficulties to retrieve facial images due to the similarity of these features. This paper introduces a solution of human face-images retrieval based on a combination feature extraction methods where there are some challenges related with human-face image detection and then retrieving. Human face-images retrieving used in various fields like justification, Criminal Evidence and law inspections and robotic intelligence. The research-proposal contributes to conquer on a number of confronts in images of human-face detections then accurate retrieving in acceptable time. So this work deals with getting higher rate of recognition using a combination method of Gray-Level-Co-occurrence-Matrix(GLCM) and Local-Binary-Patterns (LBP) as feature-descriptors and classifiers to develop face-image recognition. First of all some previous processing techniques of image to detect the centre of face image then GLCM calculation method involves process of gray image, after that a number of statistical-texture attributes and 2nd order-attributes are obtained. The LBP technique acts as feature extraction after the representation of a human-face image, and finally the classification. The histograms are finding of blocks of an image of human-face. Then retrieve human-face image based minimum difference between attributes of a strange human-face image with the features of familiar images. All findings of this work were evaluate using MSE, Chi-square test and PSNR. Olivetti Research Laboratory ORL human-face images dataset used in this proposal. The experiments showed that the combination technique detecting the human-faces grows accuracy rate, and effectiveness. The results show increase in recognition exactitude to be 98%. © 2022 IEEE.

关键词： Local binary pattern

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Discriminant and complementary autoencoder network for multi-view learning 2

Discriminant and complementary autoencoder network for multi...

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2nd IEEE Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2021

作者： Changqing, Fu Hanmei, Zhang Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Image Cognition Chongqing China

ISBN: (纸本)9781728190181

In real-world applications, objects can be sampled by different equipment or collected from different angles, which appears a significant and challenging problem for classifying samples from these heterogeneous views. Although effectiveness is achieved, most of the previous methods could not learn a discriminant and complementary representation simultaneously. Differently, in this paper, we propose a novel model termed Discriminant and Complementary Autoencoder Network (DCAEN), which integrates discriminant information between views into a compact representation by the variant of the traditional LDA-like framework. The proposed method has the following merits: (1) our model incorporates supervised information into the common representation subspace, i.e., maximize the between-class distance and minimize the within-class distance from the inter-view and intra-view. (2) our model performs view-specific representation and common representation in a unified framework. The proposed model is solved by the stochastic gradient descent (SGD) algorithm and its performance is far better than many state-of-the-art methods, demonstrating its superiority. © 2021 IEEE.

关键词： stochastic systems

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Investigating Novel Coronavirus Through statistical methods for Biomedical Applications: An Engineering Student Perspective

Investigating Novel Coronavirus Through Statistical Methods ...

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International conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2021

作者： Shakya, Amit Kumar Ramola, Ayushman Vidyarthi, Anurag Department of Electronics and Communication Engineering Sant Longowal Institute of Engineering and Technology Longowal Punjab Sangrur India Uttarakhand Dehradun India

ISBN: (纸本)9789811687730

The World of today is suffering from novel coronavirus (nCOV2). This is a respiratory infectious disease that has affected the entire globe. This respiratory infection is first originated in Wuhan, China. Today, it has many variants like the "United Kingdom (UK) variant called B.1.1.7," "South African variant is called B.1.351," "Brazilian variant is known as P.1," etc. In this research work, we will discuss the Indian scenario to tackle nCOV2. We will also present an engineering student’s perspective to detect changes developed in the patient’s chest suffering from nCOV2 employing statistical methods. Among all the statistical techniques, GLCM-based texture analysis-based technique has gained popularity due to its diverse applications. It is used in many applications like remote sensing, image processing, biomedical applications, seismic data analysis. Thus in this research work, this methodology is used various changes in the before and after images of the patient suffering from the novel coronavirus. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coronavirus

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Method of Structural-statistical Coding of Video Segments in Spectral-Cluster Space 4

Method of Structural-Statistical Coding of Video Segments in...

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4th IEEE International conference on Advanced Trends in Information Theory, ATIT 2022

作者： Krasnorutsky, Andrii Kolesnyk, Vitalii Berchanov, Anatolii Barannik, Volodymyr Kharchenko, Nataliia Malko, Oleksander Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine State Research Institute of Armament and Military Equipment Testing and Certification Chernihiv Ukraine V. N. Karazin Kharkiv National University Kharkiv Ukraine Kharkov National University of Radio Electronics Kharkiv Ukraine National University of Civil Defence of Ukraine Kharkiv Ukraine

ISBN: (纸本)9798350332629

The article provides a general description of the problems of ensuring the necessary quality indicators of remote video service provision. The reason for this is the imbalance between the reliability of the received video information and time delays in the process of delivering video information over wireless communication channels. The destruction of existing wireless infocommunication technologies, which is the cause of disruption of the decision-making process in critical infrastructure systems, is briefly disclosed. It is proposed to solve the imbalance of video information delivery by using a set of technological solutions. The main component here is the use of technologies to reduce the bit rate of the video stream. The disadvantages of existing methods of reducing the bit rate of the video stream are substantiated. Among such disadvantages of the methods is that they have low efficiency: in terms of bit rate reduction and resistance to channel interference. It is proposed to solve the imbalance of reducing the bit intensity of the video stream by coding video data as part of integrated image processing technologies. Such technologies provide: additional consideration of new types of redundancy that are not related to the exclusion of psycho-visual redundancy;the construction of code structures with greater resistance to the negative impact of channel errors on the process of decoding video data. The technology of structural-statistical coding in the spectral-cluster space of the image transform is proposed. This technology is based on the principle of the duality of transformant components. The clustering of image transforms is proposed to be carried out in the binary space of their components. This creates conditions for simplifying the process of evaluating information characteristics and the formation of code values in binary space. As such information characteristics, it is proposed to use the number of series of units in the binary representation of the

关键词： Redundancy

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