Eurasip Journal on Image and Video Processing最新文献

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.

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.

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.

Binarization plays an important role in document analysis and recognition (DAR) systems. In this paper, we present our winning algorithm in ICFHR 2018 competition on handwritten document image binarization (H-DIBCO 2018), which is based on background estimation and energy minimization. First, we adopt mathematical morphological operations to estimate and compensate the document background. It uses a disk-shaped structuring element, whose radius is computed by the minimum entropy-based stroke width transform (SWT). Second, we perform Laplacian energy-based segmentation on the compensated document images. Finally, we implement post-processing to preserve text stroke connectivity and eliminate isolated noise. Experimental results indicate that the proposed method outperforms other state-of-the-art techniques on several public available benchmark datasets.

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