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Highlights • Chlorophyll fluorescence imaging can be used to evaluate chilling stress. • F, Y(Ⅱ), Y(NPQ), Y(NO), qP, qL show good spatial heterogeneity in chilling stress of leaves. • Grayscale histograms and texture features are good indicators for detecting chilling stress. Abstract The effect of chilling injury on leaves was studied by chlorophyll fluorescence imaging, which was mainly investigated from spatial heterogeneity, histogram distribution and texture features of chlorophyll fluorescence grayscale images, as a means to detect the impact of chilling injury. The fluorescence images used in the experiment were the gray images acquired by Imaging-Pam under dark adaptation. Firstly, the images of fluorescence parameters Fo, F, Fm, Fm', Fv/Fm, Y (II), Y (NPQ), Y (NO), NPQ/4, qN, qP, qL and their respective heterogeneity curves were analyzed, in which the chilling injury in the parameter image of F, Y(II), Y(NPQ), Y(NO), qP, qL exhibited good spatial heterogeneity. With the impact of low temperature, F value raised while susceptible to leaf vein, Y (NO) continued to rise but insensitive to the initial stage of chilling injury, Y (NPQ) increased first and then decreased, qP and qL declined rapidly only sensitive to leaf vein, however Y (II) not only fell continuously but was sensitive to initial cold damage and was not affected by leaf veins. Secondly, the histogram of the Y (II) fluorescent grayscale image was analyzed. In the experiment, the Y (II) data shifted to the left, when the leaf suffered cold damage, and the number of peaks changed from one to two. Finally, by calculating the mean and standard deviation of the gray level co-occurrence matrices ASM, ENT, INE, COR, it is found that the energy values and the correlation values on the Y (II) grayscale map of the leaf gradually reduced, and the entropy and inertia expanded. The results of this study indicate that: (i) chlorophyll fluorescence imaging is a useful technique to evaluate cold stress in tomato leaves; (ii) F, Y(Ⅱ), Y(NPQ), Y(NO), qP, qL show good spatial heterogeneity in chilling stress of leaves ; (iii) Histogram and texture features of gray image are good indicators for evaluating chilling damage. Fluorescence imaging can be used for non-destructive diagnosis of plant chilling injury, which has good development prospects. [ABSTRACT FROM AUTHOR]

Recently, tooth decay detection is considered as one of the emerging topics. Many diagnostic techniques have been successfully presented to diagnose the problems. However, the complexity in the tooth decaying diagnosis ascends when the environs are moderately difficult. Thus, this study introduces a novel caries detecting model for the accurate detection of tooth cavities. The model is divided into two phases: feature extraction and classification. Here, the feature extraction is based on multi‐linear principal component analysis (MPCA), and the classification is processed using renowned neural network (NN) classifier. The NN classifier is trained using the adaptive dragonfly algorithm (ADA) algorithm. The proposed MPCA model Non‐linear Programming with ADA (MNP‐ADA) performance is compared with other existing methods and the performance of the approach is analysed in terms of measures such as accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, negative predictive value, false discovery rate, F1‐score, and Mathews correlation coefficient. The performance of the proposed model is analysed in terms of feature analysis and classifier analysis by comparing other models and proves the superiority of the developed caries detection model. [ABSTRACT FROM AUTHOR]

This paper presents a classification system for weeds and vegetables from outdoor crop images. The classifier is based on support vector machine (SVM) with its extension to nonlinear case using radial basis function (RBF) and optimizing its scale parameter σ to smooth the decision boundary. The feature space is the result of principal component analysis (PCA) for 10 texture measurements calculated from gray level co-occurrence matrices (GLCM). The results indicate that classifier performance is above 90%, validated with specificity, sensitivity and precision calculations.

A new framework that uses internet-based images for detecting objects and estimating real world location of the objects via stereo images is proposed. This framework provides a self-learning ability for detecting desired objects in the scene without pre-prepared classifiers by harvesting sample images of the objects from the internet. Histogram and co-occurrence matrices of edge orientation are used as features. The objects are recognized based on likelihood scores and distance in the feature space between every window in the scene and k-nearest prototypes. A local feature matching is used to match the feature points in stereo pair. Disparities from stereo images are used to estimate real world distance and direction of the objects. The experiments on 120 pairs of stereo images from three object classes show the satisfying results in comparison to baseline methods.

To explore a new approach for osteoporosis recognition with images, a texture analysis was made of 27 bone tissue images (16 of which from a SHAM group, and 11 from an OVX group). Texture features were then extracted through a co-occurrence matrix and a run-length matrix, and the texture features with significant differences between the two groups were chosen and used as the features in the classification course based on a Support Vector Machine (SVM). The results show that there are obvious statistic differences between the SHAM group and the OVX group in terms of texture features. Furthermore, the highest recognition accuracy was achieved at 92.59%. A SVM based on a linear function showed the highest accuracy rate and the lowest error rate in recognition. This new approach can be used to recognize osteoporosis effectively and thus can provide a valuable reference to the clinical application in osteoporosis diagnosis with medical images.

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The potential application of image texture processing for non-destructive evaluation of bread staling was investigated by monitoring the changes in gray level co-occurrence matrix (GLCM) features (energy, contrast, homogeneity, correlation, and entropy) of baguette bread over a 5-day storage period. Energy experienced a significant reduction after 2 days of storage and increased significantly afterwards. However, contrast tended to increase dramatically over the entire storage time. While homogeneity and correlation showed a decreasing trend during the storage period, no meaningful trend was observed for changes of entropy. The GLCM texture features contrast, homogeneity, and correlation showed high correlations with the instrumental texture parameters and physicochemical properties of the bread during storage; in fact, the breads with firmer and less springy crumbs and higher crust moisture contents (the stale breads) had noticeably higher contrast and lower homogeneity and correlation values compared to the freshly baked samples. The results suggested that the GLCM texture features are promising indices for non-destructively assessing bread staling progress. [ABSTRACT FROM AUTHOR]

In this work we describe a fast and stable algorithm for the computation of the orthogonal moments of an image. Indeed, orthogonal moments are characterized by a high discriminative power, but some of their possible formulations are characterized by a large computational complexity, which limits their real-time application. This paper describes in detail an approach based on recurrence relations, and proposes an optimized Matlab implementation of the corresponding computational procedure, aiming to solve the above limitations and put at the community’s disposal an efficient and easy to use software. In our experiments we evaluate the effectiveness of the recurrence formulation, as well as its performance for the reconstruction task, in comparison to the closed form representation, often used in the literature. The results show a sensible reduction in the computational complexity, together with a greater accuracy in reconstruction. In order to assess and compare the accuracy of the computed moments in texture analysis, we perform classification experiments on six well-known databases of texture images. Again, the recurrence formulation performs better in classification than the closed form representation. More importantly, if computed from the GLCM of the image using the proposed stable procedure, the orthogonal moments outperform in some situations some of the most diffused state-of-the-art descriptors for texture classification. [ABSTRACT FROM AUTHOR]

In recent years, binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information security. In most state-of-the-art binary image steganographic schemes, they always find out the flippable pixels to minimize the embedding distortions. For this reason, the stego images generated by the previous schemes maintain visual quality and it is hard for steganalyzer to capture the embedding trace in spacial domain. However, the distortion maps can be calculated for cover and stego images and the difference between them is significant. In this paper, a novel binary image steganalytic scheme is proposed, which is based on distortion level co-occurrence matrix. The proposed scheme first generates the corresponding distortion maps for cover and stego images. Then the co-occurrence matrix is constructed on the distortion level maps to represent the features of cover and stego images. Finally, support vector machine, based on the gaussian kernel, is used to classify the features. Compared with the prior steganalytic methods, experimental results demonstrate that the proposed scheme can effectively detect stego images. [ABSTRACT FROM AUTHOR]

Several statistical procedures have been proposed for the analysis of the data from a free sorting task. A straightforward strategy of analysis based on correspondence analysis and cluster analysis performed on the co-occurrence matrix is proposed herein. More specifically, two situations are considered depending on whether the aim is to depict the relationships among the stimuli or to investigate the agreement among the subjects. The approach of analysis is illustrated on the basis of free sorting data characterizing chocolate products. [ABSTRACT FROM AUTHOR]

Highlights • In this paper we introduce a technique based on dynamic speckle patterns, useful for the local evaluation of paint-coating drying process. • We provide a convenient classification of the drying process, looking forward to the constitution of a future framework for industrial applications. • All the presented experimental results were compared with previous proposals and the mathematical models used in the paint-coat drying process. Abstract The paint-coating is part of several industrial processes, including the automotive industry, architectural coatings, machinery and appliances. These paint-coatings must comply with high quality standards, for this reason evaluation techniques from paint-coatings are in constant development. One important factor from the paint-coating process is the drying, as it has influence on the quality of final results. In this work we present an assessment technique based on the optical dynamic speckle interferometry, this technique allows for the temporal activity evaluation of the paint-coating drying process, providing localized information from drying. This localized information is relevant in order to address the drying homogeneity, optimal drying, and quality control. The technique relies in the definition of a new temporal history of the speckle patterns to obtain the local activity; this information is then clustered to provide a convenient indicative of different drying process stages. The experimental results presented were validated using the gravimetric drying curves [ABSTRACT FROM AUTHOR]

The majority of the link prediction measures in heterogeneous complex networks rely on the nodes connectivities while less attention has been paid to the importance of the nodes and paths. In this paper, we propose some new meta-path based statistical similarity measures to properly perform link prediction task. The main idea in the proposed measures is to drive some co-occurrence events in a number of co-occurrence matrices that are occurred between the visited nodes obeying a meta-path. The extracted co-occurrence matrices are analyzed in terms of the energy, inertia, local homogeneity, correlation, and information measure of correlation to determine various information theoretic measures. We evaluate the proposed measures, denoted as link energy, link inertia, link local homogeneity, link correlation, and link information measure of correlation, using a standard DBLP network data set. The results of the AUC score and Precision rate indicate the validity and accuracy of the proposed measures in comparison to the popular meta-path based similarity measures. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach to texture segmentation based on the parametric active contour model (ACM) is proposed. At first, gray-level co-occurrence matrix and subsequently co-occurrence energy of the regions inside and outside of the dynamic contour are calculated. Difference of this energy corresponding to both the regions is used as the external energy of the proposed ACM. The contour stops and converges completely when this difference attains a maximum value. The proposed approach requires only initial contour selection and no object point selection like the other variants of parametric ACM used for texture segmentation. Experiments on a number of synthetic and real-world texture images show that in all cases, we are getting a better segmentation of the object although for few cases the execution time is bit more than that of other existing methods. [ABSTRACT FROM AUTHOR]

Background and Objective The early diagnosis of stomach cancer can be performed by using a proper screening procedure. Chromoendoscopy (CH) is an image-enhanced video endoscopy technique, which is used for inspection of the gastrointestinal-tract by spraying dyes to highlight the gastric mucosal structures. An endoscopy session can end up with generating a large number of video frames. Therefore, inspection of every individual endoscopic-frame is an exhaustive task for the medical experts. In contrast with manual inspection, the automated analysis of gastroenterology images using computer vision based techniques can provide assistance to endoscopist, by finding out abnormal frames from the whole endoscopic sequence. Methods In this paper, we have presented a new feature extraction method named as Gabor-based gray-level co-occurrence matrix (G2LCM) for computer-aided detection of CH abnormal frames. It is a hybrid texture extraction approach which extracts a combination both local and global texture descriptors. Moreover, texture information of a CH image is represented by computing the gray level co-occurrence matrix of Gabor filters responses. Furthermore, the second-order statistics of these co-occurrence matrices are computed to represent images’ texture. Results The obtained results show the possibility to correctly classifying abnormal from normal frames, with sensitivity, specificity, accuracy, and area under the curve as 91%, 82%, 87% and 0.91 respectively, by using a support vector machine classifier and G2LCM texture features. Conclusion It is apparent from results that the proposed system can be used for providing aid to the gastroenterologist in the screening of the gastric tract. Ultimately, the time taken by an endoscopic procedure will be sufficiently reduced. [ABSTRACT FROM AUTHOR]

The change of crowd energy is a fundamental measurement for describing a crowd behavior. In this paper, we present a crowd abnormal detection method based on the change of energy-level distribution. The method can not only reduce the camera perspective effect, but also detect crowd abnormal behavior in time. Pixels in the image are treated as particles, and the optical flow method is adopted to extract the velocities of particles. The qualities of different particles are distributed as different value according to the distance between the particle and the camera to reduce the camera perspective effect. Then a crowd motion segmentation method based on flow field texture representation is utilized to extract the motion foreground, and a linear interpolation calculation is applied to pedestrian’s foreground area to determine their distance to the camera. This contributes to the calculation of the particle qualities in different locations. Finally, the crowd behavior is analyzed according to the change of the consistency, entropy and contrast of the three descriptors for co-occurrence matrix. By calculating a threshold, the timestamp when the crowd abnormal happens is determined. In this paper, multiple sets of videos from three different scenes in UMN dataset are employed in the experiment. The results show that the proposed method is effective in characterizing anomalies in videos. [ABSTRACT FROM AUTHOR]

Our research work uses the dynamic laser speckle technique for the first time to analyze the adulteration of "Dabur Honey" with two golden syrups Dhampur Green Golden Syrup (DGGS) and Solar Golden Syrup (SGS) as adulterate. Identifying 25% adulteration in honey by sweeteners is challenging due to several reasons, such as the complexity of honey composition and the similarity of some sweeteners to honey. A spatially filtered He-Ne laser beam is made to pass through adulterated samples. Forwarding scattered light is captured using a charge-coupled device (CCD) camera and Digital Image Processing is used to analyze the images. Seven textures and six features of images are evaluated. For 20% adulteration in honey by both the adulterates, a significant percentage change of 17%, 16%, 16%, 13%, and 11% is observed in Angular Second Moment, Covariance, Autocorrelation, Inertia, and Kurtosis respectively. Additionally, for a 20% adulteration in honey by DGGS and SGS separately, a notable percentage change of 3.8% and 6.6% respectively in the value of Inertia Moment (IM) is obtained. Thus, it is concluded that the above image processing algorithms can be used to detect 20% adulteration in pure honey. [ABSTRACT FROM AUTHOR]