Eurasip Journal on Image and Video Processing最新文献_第9页

Single image super resolution based on multi-scale structure and non-local smoothing 基于多尺度结构和非局部平滑的单幅图像超分辨率

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2021-05-17 DOI: 10.1186/s13640-021-00552-8

Wenyi Wang, Jun Hu, Xiaohong Liu, Jiying Zhao, Jianwen Chen

{"title":"Single image super resolution based on multi-scale structure and non-local smoothing","authors":"Wenyi Wang, Jun Hu, Xiaohong Liu, Jiying Zhao, Jianwen Chen","doi":"10.1186/s13640-021-00552-8","DOIUrl":"https://doi.org/10.1186/s13640-021-00552-8","url":null,"abstract":"In this paper, we propose a hybrid super-resolution method by combining global and local dictionary training in the sparse domain. In order to present and differentiate the feature mapping in different scales, a global dictionary set is trained in multiple structure scales, and a non-linear function is used to choose the appropriate dictionary to initially reconstruct the HR image. In addition, we introduce the Gaussian blur to the LR images to eliminate a widely used but inappropriate assumption that the low resolution (LR) images are generated by bicubic interpolation from high-resolution (HR) images. In order to deal with Gaussian blur, a local dictionary is generated and iteratively updated by K-means principal component analysis (K-PCA) and gradient decent (GD) to model the blur effect during the down-sampling. Compared with the state-of-the-art SR algorithms, the experimental results reveal that the proposed method can produce sharper boundaries and suppress undesired artifacts with the present of Gaussian blur. It implies that our method could be more effect in real applications and that the HR-LR mapping relation is more complicated than bicubic interpolation.","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Quaternion fractional-order color orthogonal moment-based image representation and recognition 基于四元数分数阶彩色正交矩的图像表示与识别

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2021-05-17 DOI: 10.1186/s13640-021-00553-7

Bing He, Jun Liu, Tengfei Yang, Bin Xiao, Yanguo Peng

{"title":"Quaternion fractional-order color orthogonal moment-based image representation and recognition","authors":"Bing He, Jun Liu, Tengfei Yang, Bin Xiao, Yanguo Peng","doi":"10.1186/s13640-021-00553-7","DOIUrl":"https://doi.org/10.1186/s13640-021-00553-7","url":null,"abstract":"Inspired by quaternion algebra and the idea of fractional-order transformation, we propose a new set of quaternion fractional-order generalized Laguerre orthogonal moments (QFr-GLMs) based on fractional-order generalized Laguerre polynomials. Firstly, the proposed QFr-GLMs are directly constructed in Cartesian coordinate space, avoiding the need for conversion between Cartesian and polar coordinates; therefore, they are better image descriptors than circularly orthogonal moments constructed in polar coordinates. Moreover, unlike the latest Zernike moments based on quaternion and fractional-order transformations, which extract only the global features from color images, our proposed QFr-GLMs can extract both the global and local color features. This paper also derives a new set of invariant color-image descriptors by QFr-GLMs, enabling geometric-invariant pattern recognition in color images. Finally, the performances of our proposed QFr-GLMs and moment invariants were evaluated in simulation experiments of correlated color images. Both theoretical analysis and experimental results demonstrate the value of the proposed QFr-GLMs and their geometric invariants in the representation and recognition of color images.","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Approximate calculation of 8-point DCT for various scenarios of practical applications 8点DCT的近似计算适用于各种实际应用场景

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2021-05-17 DOI: 10.1186/s13640-021-00557-3

D. Puchala

引用次数: 8

OSDDY: embedded system-based object surveillance detection system with small drone using deep YOLO OSDDY:基于嵌入式系统的目标监视检测系统，小型无人机使用深度YOLO

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2021-05-17 DOI: 10.1186/s13640-021-00559-1

K. Madasamy, V. Shanmuganathan, Vijayalakshmi Kandasamy, Mi Young Lee, M. Thangadurai

引用次数: 21

Non-frontal facial expression recognition based on salient facial patches 基于显著面部补丁的非正面面部表情识别

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2021-05-12 DOI: 10.1186/s13640-021-00555-5

Bin Jiang, Qiuwen Zhang, Zuhe Li, Qinggang Wu, Huanlong Zhang

引用次数: 2

A probabilistic segmentation and entropy-rank correlation-based feature selection approach for the recognition of fruit diseases 基于概率分割和熵秩相关的水果病害特征选择方法

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2021-05-10 DOI: 10.1186/s13640-021-00558-2

Muhammad Attique Khan, Tallha Akram, Muhammad Sharif, Majed Alhaisoni, Tanzila Saba, Nadia Nawaz

{"title":"A probabilistic segmentation and entropy-rank correlation-based feature selection approach for the recognition of fruit diseases","authors":"Muhammad Attique Khan, Tallha Akram, Muhammad Sharif, Majed Alhaisoni, Tanzila Saba, Nadia Nawaz","doi":"10.1186/s13640-021-00558-2","DOIUrl":"https://doi.org/10.1186/s13640-021-00558-2","url":null,"abstract":"Agriculture plays a critical role in the economy of several countries, by providing the main sources of income, employment, and food to their rural population. However, in recent years, it has been observed that plants and fruits are widely damaged by different diseases which cause a huge loss to the farmers, although this loss can be minimized by detecting plants’ diseases at their earlier stages using pattern recognition (PR) and machine learning (ML) techniques. In this article, an automated system is proposed for the identification and recognition of fruit diseases. Our approach is distinctive in a way, it overcomes the challenges like convex edges, inconsistency between colors, irregularity, visibility, scale, and origin. The proposed approach incorporates five primary steps including preprocessing,Standard instruction requires city and country for affiliations. Hence, please check if the provided information for each affiliation with missing data is correct and amend if deemed necessary. disease identification through segmentation, feature extraction and fusion, feature selection, and classification. The infection regions are extracted using the proposed adaptive and quartile deviation-based segmentation approach and fused resultant binary images by employing the weighted coefficient of correlation (CoC). Then the most appropriate features are selected using a novel framework of entropy and rank-based correlation (EaRbC). Finally, selected features are classified using multi-class support vector machine (MC-SCM). A PlantVillage dataset is utilized for the evaluation of the proposed system to achieving an average segmentation and classification accuracy of 93.74% and 97.7%, respectively. From the set of statistical measure, we sincerely believe that our proposed method outperforms existing method with greater accuracy.","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

An early CU partition mode decision algorithm in VVC based on variogram for virtual reality 360 degree videos 基于方差函数的虚拟现实360度视频VVC早期CU划分模式决策算法

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2021-05-10 DOI: 10.1186/s13640-023-00605-0

Mengmeng Zhang, Yan-yan Hou, Zhi Liu

引用次数: 1

An enhanced binarization framework for degraded historical document images 退化历史文档图像的增强二值化框架

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2021-05-10 DOI: 10.1186/s13640-021-00556-4

Wei Xiong, Lei Zhou, Ling Yue, Lirong Li, Song Wang

引用次数: 31

Performance enhancement method for multiple license plate recognition in challenging environments 挑战性环境下多车牌识别的性能增强方法

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2021-05-05 DOI: 10.21203/RS.3.RS-449680/V1

Khurram Khan, A. Imran, H. A. U. Rehman, A. Fazil, M. Zakwan, Zahid Mehmood

{"title":"Performance enhancement method for multiple license plate recognition in challenging environments","authors":"Khurram Khan, A. Imran, H. A. U. Rehman, A. Fazil, M. Zakwan, Zahid Mehmood","doi":"10.21203/RS.3.RS-449680/V1","DOIUrl":"https://doi.org/10.21203/RS.3.RS-449680/V1","url":null,"abstract":"Multiple-license plate recognition is gaining popularity in the Intelligent Transport System (ITS) applications for security monitoring and surveillance. Advancements in acquisition devices have increased the availability of high definition (HD) images, which can capture images of multiple vehicles. Since license plate (LP) occupies a relatively small portion of an image, therefore, detection of LP in an image is considered a challenging task. Moreover, the overall performance deteriorates when the aforementioned factor combines with varying illumination conditions, such as night, dusk, and rainy. As it is difficult to locate a small object in an entire image, this paper proposes a two-step approach for plate localization in challenging conditions. In the first step, the Faster-Region-based Convolutional Neural Network algorithm (Faster R-CNN) is used to detect all the vehicles in an image, which results in scaled information to locate plates. In the second step, morphological operations are employed to reduce non-plate regions. Meanwhile, geometric properties are used to localize plates in the HSI color space. This approach increases accuracy and reduces processing time. For character recognition, the look-up table (LUT) classifier using adaptive boosting with modified census transform (MCT) as a feature extractor is used. Both proposed plate detection and character recognition methods have significantly outperformed conventional approaches in terms of precision and recall for multiple plate recognition.","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2021-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49222339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Segmentation of epithelial human type 2 cell images for the indirect immune fluorescence based on modified quantum entropy 基于改进量子熵的间接免疫荧光分割人上皮2型细胞图像

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2021-04-23 DOI: 10.1186/s13640-021-00554-6

Abu-Zinadah Hanaa, Abdel Azim Gamil

{"title":"Segmentation of epithelial human type 2 cell images for the indirect immune fluorescence based on modified quantum entropy","authors":"Abu-Zinadah Hanaa, Abdel Azim Gamil","doi":"10.1186/s13640-021-00554-6","DOIUrl":"https://doi.org/10.1186/s13640-021-00554-6","url":null,"abstract":"The autoimmune disorders such as rheumatoid, arthritis, and scleroderma are connective tissue diseases (CTD). Autoimmune diseases are generally diagnosed using the antinuclear antibody (ANA) blood test. This test uses indirect immune fluorescence (IIf) image analysis to detect the presence of liquid substance antibodies at intervals the blood, which is responsible for CTDs. Typically human alveolar epithelial cells type 2 (HEp2) are utilized as the substrate for the microscope slides. The various fluorescence antibody patterns on HEp-2 cells permits the differential designation-diagnosis. The segmentation of HEp-2 cells of IIf images is therefore a crucial step in the ANA test. However, not only this task is extremely challenging, but physicians also often have a considerable number of IIf images to examine.In this study, we propose a new methodology for HEp2 segmentation from IIf images by maximum modified quantum entropy. Besides, we have used a new criterion with a flexible representation of the quantum image(FRQI). The proposed methodology determines the optimum threshold based on the quantum entropy measure, by maximizing the measure of class separability for the obtained classes over all the gray levels. We tested the suggested algorithm over all images of the MIVIA HEp 2 image data set.To objectively assess the proposed methodology, segmentation accuracy (SA), Jaccard similarity (JS), the F1-measure,the Matthews correlation coefficient(MCC), and the peak signal-to-noise ratio (PSNR) were used to evaluate performance. We have compared the proposed methodology with quantum entropy, Kapur and Otsu algorithms, respectively.The results show that the proposed algorithm is better than quantum entropy and Kapur methods. In addition, it overcomes the limitations of the Otsu method concerning the images which has positive skew histogram.This study can contribute to create a computer-aided decision (CAD) framework for the diagnosis of immune system diseases","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2