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Статті в журналах з теми "Image moment"

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Автор: Grafiati

Опубліковано: 12 червня 2021

Оновлено: 1 лютого 2022

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1

Hui, Fan, Hai Feng Wang, and Jin Jiang Li. "Image Registration Based on Feature Points Krawtchouk Moments." Applied Mechanics and Materials 40-41 (November 2010): 584–89. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.584.

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An image registration based on feature points Krawtchouk moments is proposed. Moments are the shape descriptors based on region. Krawtchouk moments are a set of discrete orthogonal moments and are more suitable for describing two-dimensional images compared to Zemike, Legendre moments. In the image registration based on feature points Krawtchouk moments, Krawtchouk moment invariants of the feature points neighborhood that have been extracted are solved, and then these Krawtchouk moment invariants constitute feature vectors used to describe the feature points, finally feature points are matched by calculating the Euclidean distance of feature vectors. The results of experiments show that Krawtchouk moment is simple and effective to describe image and is independent of rotation, scaling, and translation of the image.

2

Wang, Mei, E. Ye Wang, and Guo Hua Pan. "Image Quality Assessment Based on Invariant Moments Similarity." Advanced Materials Research 546-547 (July 2012): 565–69. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.565.

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To resolve the problems of the image quality assessment issue and the algorithm adaptability for different image size and deformation, this paper proposes a image quality assessment algorithm based on Invariant Moments Similarity. Firstly, Hu invariant moments values of original image and evaluated image are computed. Secondly the invariant moments distance is completed between original image and evaluated image. At last, the method assess the restoration image quality depend on the invariant moment distance. The experimental result shows that the algorithm result is better than MSE, PSNR, SSIM for the same-size images. And the algorithm based on invariant moment similarity can evaluate different image-size and deformation images with low computing-complexity. The assessment experimental result for difference actual images certifies the algorithm effectiveness.

3

Zhang, Chao Xin, and Ping Xi. "Analysis of Gaussian-Hermite Moment Invariants on Image Geometric Transformation." Applied Mechanics and Materials 519-520 (February 2014): 557–61. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.557.

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Gaussian-Hermite moments and their invariants have been widely used in image processing and pattern recognition. The moments are strictly invariant for the continuous function. However, the digital images are discrete. The image function and the moment imvariants may change during image geometric transformation. To address this problem, an analysis with respect to the fluctuation of moment invariants on image geometric transformation is presented. The guidance is provided as well to minimizing the fluctuation of the Gaussian-Hermite moments.

4

Yang, Jianwei, Ming Li, Zirun Chen, and Yunjie Chen. "Cutting Affine Moment Invariants." Mathematical Problems in Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/928161.

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The extraction of affine invariant features plays an important role in many fields of image processing. In this paper, the original image is transformed into new images to extract more affine invariant features. To construct new images, the original image is cut in two areas by a closed curve, which is called general contour (GC). GC is obtained by performing projections along lines with different polar angles. New image is obtained by changing gray value of pixels in inside area. The traditional affine moment invariants (AMIs) method is applied to the new image. Consequently, cutting affine moment invariants (CAMIs) are derived. Several experiments have been conducted to evaluate the proposed method. Experimental results show that CAMIs can be used in object classification tasks.

5

Hameed, Vazeerudeen Abdul. "Orthogonal Moment Invariant Function for Image Processing." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3400–3403. http://dx.doi.org/10.1166/jctn.2019.8299.

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Orthogonal moments are of great importance in image processing due to their high discriminatory capability. Orthogonal moment invariant functions like Legendre moments and Complex Zernike moments are known for high computational complexity and/or they are complex valued. This paper presents a new orthogonal moment function that is real valued. The formulation is appraised to prove that it is computationally less complex when compared to the existing moment functions. The proposed orthogonal moment functions are appraised over their reversible nature to obtain the original data. The new moment functions are also appraised for their discriminating ability through derivations and experiments. Invariance properties such as scaling, translation and rotational invariance are studied over the new formulation to demonstrate the use of the functions over image processing applications that involve invariance to image transformations.

6

Wan, Li. "Image Classification Combined with Fusion Gaussian–Hermite Moments Feature and Improved Nonlinear SVM Classifier." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 6 (October 20, 2018): 875–82. http://dx.doi.org/10.20965/jaciii.2018.p0875.

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With the development of computer technology, data mining, artificial intelligence, and image-processing technology have been applied to medical diagnosis. Image classification is one of the main technologies of medical image processing, which can be used to determine whether a patient suffers from breast cancer according to x-ray images of the breast. To achieve reliable classification of breast images, an image classification method combined with a fusion Gaussian–Hermite moments feature and improved nonlinear support vector machine (SVM) classifier is proposed. The proposed fusion Gaussian–Hermite moments features can improve the robustness and distinguish the ability of features by constructing Gaussian–Hermite invariant moments according to invariant moment theory and constructing a Gaussian–Hermite Fisher moment according to Fisher’s idea. The proposed improved nonlinear SVM classifier can improve the efficiency and accuracy of the classifier through eigen decomposition and sample learning. Experimental results demonstrate that the proposed method has a high accuracy rate for breast x-ray image classification.

7

Zhang, Chao Xin, Ping Xi, and Mo Dai. "Gaussian-Geometric Moments and its Application in Feature Matching." Advanced Materials Research 718-720 (July 2013): 2113–19. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2113.

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Since the 7 famous Hus invariants had been introduced in 1960s, the moment invariants play an important role in image analysis and pattern recognition. In this paper, we propose a new moment called Gaussian-Geometric moment, and derived their translation and rotation invariants. One significant conclusion drawn is that the rotation invariants of Gaussian-Geometric moments have the identical forms to those of geometric moments.The Gaussian-Geometric moments and the geometric moments both can represent the image information, difference is that the Gaussian-Geometric moment can represent the center information of an image and the geometric moments represent the edge information of an imageonly. This is particularly evident in the performance of high order ones. Another important property of Gaussian-Geometric moments is that it has a scale parameter which allows choosing the best scale to represent the interest region of an image. A detailed comparison has been made to test the feature matching capability between the proposed moments and the geometric moments. The results show that the proposed moments perform much better than the geometric ones.

8

Bing, He. "Geometrically Robust Image Watermarking Based on Krawtchouk Invariant Moments." Advanced Materials Research 998-999 (July 2014): 951–56. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.951.

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In this paper an image watermarking based on krawtchouk moment invariants is proposed. krawtchouk moments are selected for image watermarking because image reconstruction with these moments is better than other orthogonal moments like Legendre, Zernike and Tchebichef. Watermarking is composed of the mean of several function of the first and second krawtchouk moment invariants order designed to be invariant to translation, scaling and rotation. The watermarked image is a linear combination of the original image and a weighted nonlinear transformation of original. The weight is computed such that the mean of the watermarked image invariants is a predefined number. Watermark detection is as simple as computing the moment invariants of received image. The experiment results demonstrate the proposed method can obtain better visual effect, meanwhile, it is also robust enough to some image degradation process such as adding noise, cropping, filtering and JPEG compression.

9

Liang, Chen Hua, and Qing Chang. "Weighted Modified Hu Moment in Human Behavior Recognition." Advanced Materials Research 765-767 (September 2013): 2603–7. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2603.

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t has been shown that the traditional seven Hu invariant moment does not have scaling invariance with low recognition rate in human behavior recognition. In order to improve the recognition rate, a human behavior recognition method will be put forward in this paper based on weighted modified Hu moments. Firstly, the traditional seven Hu moments will be extended to ten Hu moments to get more image details. Then, the extended Hu moments will be modified to make the Hu moments has the feature of scaling invariance. Lastly, the weighted modified Hu moment will be obtained through least squares method based on minimum variance criterion. The simulation of the sequence images shows that the weighted modified Hu moment can improve the recognition rate effectively.

10

Pham, Nam, Jong-Weon Lee, Goo-Rak Kwon, and Chun-Su Park. "Hybrid Image-Retrieval Method for Image-Splicing Validation." Symmetry 11, no. 1 (January 14, 2019): 83. http://dx.doi.org/10.3390/sym11010083.

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Recently, the task of validating the authenticity of images and the localization of tampered regions has been actively studied. In this paper, we go one step further by providing solid evidence for image manipulation. If a certain image is proved to be the spliced image, we try to retrieve the original authentic images that were used to generate the spliced image. Especially for the image retrieval of spliced images, we propose a hybrid image-retrieval method exploiting Zernike moment and Scale Invariant Feature Transform (SIFT) features. Due to the symmetry and antisymmetry properties of the Zernike moment, the scaling invariant property of SIFT and their common rotation invariant property, the proposed hybrid image-retrieval method is efficient in matching regions with different manipulation operations. Our simulation shows that the proposed method significantly increases the retrieval accuracy of the spliced images.

11

Sucharitha, G., and Ranjan K. Senapati. "Shape Based Image Retrieval using Lower Order Zernike Moments." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 3 (June 1, 2017): 1651. http://dx.doi.org/10.11591/ijece.v7i3.pp1651-1660.

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Shape is one of the significant features of Content Based Image Retrieval (CBIR). This paper proposes a strong and successful shape feature, which is based on a set of orthogonal complex moments of images known as Zernike moments. For shape classification Zernike moment (ZM) is the dominant solution. The radial polynomial of Zernike moment produces the number of concentric circles based on the order. As the order increases number of circles will increases, due to this the local information of an image will be ignored. In this paper, we introduced a novel method for radial polynomial where local information of an image given importance. We succeeded to extract the local features and shape features at very a low order of polynomial compared to the state of traditional ZM.The proposed method gives an advantage of a lower order, less complex, and lower dimension feature vector.For more similar images we find that simple Euclidian distance approximately zero. Proposed method tested on a MPEG-7 CE-1 shape database, Coil-100 databases. Experiments demonstrated that it is outperforming in identifying the shape of an object in the image and reduced the retrieving time and complexity of calculations.

12

Wang, Wenbing, Yan Li, and Shengli Liu. "A Polar Complex Exponential Transform-Based Zero-Watermarking for Multiple Medical Images with High Discrimination." Security and Communication Networks 2021 (March 17, 2021): 1–13. http://dx.doi.org/10.1155/2021/6615678.

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Zero-watermarking is one of the solutions for image copyright protection without tampering with images, and thus it is suitable for medical images, which commonly do not allow any distortion. Moment-based zero-watermarking is robust against both image processing and geometric attacks, but the discrimination of watermarks is often ignored by researchers, resulting in the high possibility that host images and fake host images cannot be distinguished by verifier. To this end, this paper proposes a PCET- (polar complex exponential transform-) based zero-watermarking scheme based on the stability of the relationships between moment magnitudes of the same order and stability of the relationships between moment magnitudes of the same repetition, which can handle multiple medical images simultaneously. The scheme first calculates the PCET moment magnitudes for each image in an image group. Then, the magnitudes of the same order and the magnitudes of the same repetition are compared to obtain the content-related features. All the image features are added together to obtain the features for the image group. Finally, the scheme extracts a robust feature vector with the chaos system and takes the bitwise XOR of the robust feature and a scrambled watermark to generate a zero-watermark. The scheme produces robust features with both resistance to various attacks and low similarity among different images. In addition, the one-to-many mapping between magnitudes and robust feature bits reduces the number of moments involved, which not only reduces the computation time but also further improves the robustness. The experimental results show that the proposed scheme meets the performance requirements of zero-watermarking on the robustness, discrimination, and capacity, and it outperforms the state-of-the-art methods in terms of robustness, discrimination, and computational time under the same payloads.

13

Agrawal, Vikram, and Dilipsinh Bheda. "Optimized Image Mosaicing with Moment Invariants and SIFT features." Oriental journal of computer science and technology 10, no. 1 (February 11, 2017): 66–75. http://dx.doi.org/10.13005/ojcst/10.01.09.

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In the field of Image mosaicing, much research has been done to fulfil the two major challenges, time complexity and quality improvement. Proposed method is a pre-processing step before actual image stitching carried out. The method aims to find out the overlapping regions in two images. Thus features can be extracted from these overlapping regions and not from the whole images, which result into reduction of computation time. For detecting overlapping portion, gradient based edge extraction method and invariant moments are used. In the deduced region, SIFT features are extraction to determine the matching features. The registration process carried out by RANSAC algorithm and final output mosaic will obtained by warping the images. An optimized approach to calculate the moment difference values is presented to improve time efficiency and quality.

14

Kaur, Manjit, and Vijay Kumar. "Fourier–Mellin moment-based intertwining map for image encryption." Modern Physics Letters B 32, no. 09 (March 30, 2018): 1850115. http://dx.doi.org/10.1142/s0217984918501154.

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In this paper, a robust image encryption technique that utilizes Fourier–Mellin moments and intertwining logistic map is proposed. Fourier–Mellin moment-based intertwining logistic map has been designed to overcome the issue of low sensitivity of an input image. Multi-objective Non-Dominated Sorting Genetic Algorithm (NSGA-II) based on Reinforcement Learning (MNSGA-RL) has been used to optimize the required parameters of intertwining logistic map. Fourier–Mellin moments are used to make the secret keys more secure. Thereafter, permutation and diffusion operations are carried out on input image using secret keys. The performance of proposed image encryption technique has been evaluated on five well-known benchmark images and also compared with seven well-known existing encryption techniques. The experimental results reveal that the proposed technique outperforms others in terms of entropy, correlation analysis, a unified average changing intensity and the number of changing pixel rate. The simulation results reveal that the proposed technique provides high level of security and robustness against various types of attacks.

15

Goh, Jia Yin, and Tsung Fei Khang. "On the classification of simple and complex biological images using Krawtchouk moments and Generalized pseudo-Zernike moments: a case study with fly wing images and breast cancer mammograms." PeerJ Computer Science 7 (September 9, 2021): e698. http://dx.doi.org/10.7717/peerj-cs.698.

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In image analysis, orthogonal moments are useful mathematical transformations for creating new features from digital images. Moreover, orthogonal moment invariants produce image features that are resistant to translation, rotation, and scaling operations. Here, we show the result of a case study in biological image analysis to help researchers judge the potential efficacy of image features derived from orthogonal moments in a machine learning context. In taxonomic classification of forensically important flies from the Sarcophagidae and the Calliphoridae family (n = 74), we found the GUIDE random forests model was able to completely classify samples from 15 different species correctly based on Krawtchouk moment invariant features generated from fly wing images, with zero out-of-bag error probability. For the more challenging problem of classifying breast masses based solely on digital mammograms from the CBIS-DDSM database (n = 1,151), we found that image features generated from the Generalized pseudo-Zernike moments and the Krawtchouk moments only enabled the GUIDE kernel model to achieve modest classification performance. However, using the predicted probability of malignancy from GUIDE as a feature together with five expert features resulted in a reasonably good model that has mean sensitivity of 85%, mean specificity of 61%, and mean accuracy of 70%. We conclude that orthogonal moments have high potential as informative image features in taxonomic classification problems where the patterns of biological variations are not overly complex. For more complicated and heterogeneous patterns of biological variations such as those present in medical images, relying on orthogonal moments alone to reach strong classification performance is unrealistic, but integrating prediction result using them with carefully selected expert features may still produce reasonably good prediction models.

16

Li, Wan Bing, Hong Wei Quan, and Xia Fei Huang. "Feature Extraction Method Based on Moment Invariants." Advanced Materials Research 936 (June 2014): 2263–66. http://dx.doi.org/10.4028/www.scientific.net/amr.936.2263.

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To match two or more images originated from the same scenario, a new fast automatic registration algorithm based on sparse feature point extraction is proposed. At the first step, the improved Harris corner detection algorithm is used to get two sets of feature points from the reference image and registration image. Second, a group of sparse feature points are selected from the reference image set as initial control points. Then, the corresponding matching points in the registration image set are searched based on local moment invariant similarity detection. Experimental results demonstrate that this method is fast and efficient.

17

Premaratne, Prashan, and Malin Premaratne. "Image matching using moment invariants." Neurocomputing 137 (August 2014): 65–70. http://dx.doi.org/10.1016/j.neucom.2013.02.058.

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18

Gholizadeh, Mahdieh, Mohammad Hossein Gholizadeh, Hossein Ghayoumi Zadeh, and Mostafa Danaeian. "The noise reduction of medical radiography images using fractional moments." Medical Journal of Tabriz University of Medical Sciences and Health Services 42, no. 6 (February 24, 2021): 649–58. http://dx.doi.org/10.34172/mj.2021.005.

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Background: This paper presents a method to improve medical radiography images based on the use of statistical signal moments. Methods: In this paper, the image with noise is considered as a statistical signal, and the noise reduction is performed by using fractional moments. The fractional moment’s method, on the one hand, has a speed similar to the moment method, and, on the other hand, has not the limitations of the moment method, which sometimes achieves inaccurate results. The proposed method is ultimately examined on radiographic images (CT). Results: The information obtained from the fractional moments of the received signal is a criterion to estimate the noise parameters and the gray scales of the main image. One of the limitations of the proposed method is that the image should be sent several times, because in statistical discussions, we cannot make a decision with only one sample. The error of the proposed noise reduction method in terms of the number of times the original image was sent, is about 0.009, 0.0009, 0.0002, and 0.0001, for n = 3, n = 6, n = 9 and n = 14, respectively. Conclusion: The simulation results show that the proposed method is more effective than the most conventional noise reduction methods, both in the low signal to noise ratio and in terms of image quality, and is more powerful than the most notable noise removal methods in restoring the subtleties and image details.

19

Sukafona, I. Made, and Emmy Febriani Thalib. "CONTENT BASED IMAGE RETRIEVAL DENGAN METODE COLOR MOMENT DAN K-MEANS." Jurnal RESISTOR (Rekayasa Sistem Komputer) 1, no. 2 (October 28, 2018): 73–78. http://dx.doi.org/10.31598/jurnalresistor.v1i2.322.

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Content Based Image Retrieval (CBIR) is a research cluster that is very important to overcome problems related to the image search process. The development of internet technology and data communication has caused the number of multimedia images currently circulating to be very high. This study took the Color Moment method to carry out the feature extraction process. Before the feature extraction process, a segmentation process was carried out to separate the background image and the foreground image. Next, each background and front image is stored in the database. Method performance measurement is done by calculating the value of precision and recall. The test image used is the Wang dataset consisting of ten image classes. The test results show the level of recall or completeness of the images that were found to have increased significantly after using the K-Means segmentation process. But a high enough recall value decreases the value of precision or the comparison of true images with the image found overall. Precision values decrease when compared to the CBIR method without running the K-Means segmentation.

20

Lai, Yi Qiang. "Rotation Moment Invariant Feature Extraction Techniques for Image Matching." Applied Mechanics and Materials 721 (December 2014): 775–78. http://dx.doi.org/10.4028/www.scientific.net/amm.721.775.

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In recently years, extracting images invariance features are gaining more attention in image matching field. Various types of methods have been used to match image successfully in a number of applications. But in mostly literatures, the rotation moment invariant properties of these invariants have not been studied widely. In this paper, we present a novel method based on Polar Harmonic Transforms (PHTs) which is consisted of a set of orthogonal projection bases to extract rotation moment invariant features. The experimental results show that the kernel computation of PHTs is simple and image features is extracted accurately in image matching. Hence polar harmonic transforms have provided a powerful tool for image matching.

21

Lu, Wei. "Image Retrieval Based on Contour and Relevance Feedback." Applied Mechanics and Materials 182-183 (June 2012): 1771–75. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1771.

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In this paper an algorithm is proposed to retrieve images based on contour moment invariants of image and relevance feedback. Firstly, the contour of each query image is extracted and its contour moment invariant is computed. Then according to Euclid Distance between the query image and each image in the image database, the most similar images to the query image can be found. Finally, the relevance feedback algorithm based on support vector machine (SVM) is applied to improve retrieval precision. Experimental results show that the algorithm is more accurate and efficient to retrieve images with monotonous background and clear object and meet the invariance on shift, rotation and scale transform of objects.

22

Xu, Zhong Lin, Wen Jie Zhao, Hong Wei Wang, and Quan Liang Liu. "Research on Automatic Identification of Airplane Targets on Infrared Images." Applied Mechanics and Materials 241-244 (December 2012): 1733–36. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1733.

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No matter in civil remote sensing or in military investigation, infrared images have an extensive application value. According to the characteristics of the infrared images, in this paper, we build comprehensive feature vectors of airplane identification based on comprehensive consideration of the characteristics of infrared images, including boundary invariant moments , normalized moment of inertia and geometric features. Identifying an air plane by calculating the comparability of feature vector between template image and the image to be identified, the algorithm has been proved by experiments to have a better stability and robustness.

23

Poidevin, Robin Le. "Time and the Static Image." Philosophy 72, no. 280 (April 1997): 175–88. http://dx.doi.org/10.1017/s0031819100056837.

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Photographs, paintings, rigid sculptures: all these provide examples of static images. It is true that they change—photographs fade, paintings darken and sculptures crumble—but what change they undergo (unless very damaging) is irrelevant to their representational content. A static image is one that represents by virtue of properties which remain largely unchanged throughout its existence. Because of this defining feature, according to a long tradition in aesthetics, a static image can only represent an instantaneous moment, or to be more exact the state of affairs obtaining at that moment'. It cannot represent movement and the passage of time. This traditional view mirrors a much older one in metaphysics: that change is to be conceived of as a series of instantaneous states and hence that an interval of time is composed of extensionless moments. The metaphysical view has been involved in more controversy than its aesthetic counterpart. Aristotle identified it as one of the premises of Zeno's arrow paradox and Augustine employed it in his proof of the unreality of time.

24

SAKAUE, Ken-ichi, and Youji IIGUNI. "Moment Invariants of the Weighted Image." IEICE Transactions on Information and Systems E93-D, no. 3 (2010): 666–70. http://dx.doi.org/10.1587/transinf.e93.d.666.

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25

Chiang, Amy, and Simon Liao. "Image Analysis with Legendre Moment Descriptors." Journal of Computer Science 11, no. 1 (January 1, 2015): 127–36. http://dx.doi.org/10.3844/jcssp.2015.127.136.

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26

Biswas, Bijita, Amit Konar, and Achintya K. Mukherjee. "Image matching with fuzzy moment descriptors." Engineering Applications of Artificial Intelligence 14, no. 1 (February 2001): 43–49. http://dx.doi.org/10.1016/s0952-1976(00)00058-0.

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27

Hu, Hai-tao. "Image description by harmonic Fourier moment." Journal of Physics: Conference Series 1486 (April 2020): 072037. http://dx.doi.org/10.1088/1742-6596/1486/7/072037.

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28

Lin, Shinfeng D., Kuo-Hae Chen, and Xin-Lun Yang. "Image indexing by Color Plane Moment." International Journal of Imaging Systems and Technology 12, no. 4 (2002): 139–48. http://dx.doi.org/10.1002/ima.10022.

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29

Atabay, HabibollahAgh. "Moment Features Weighting for Image Retrieval." IOSR Journal of Computer Engineering 18, no. 05 (May 2016): 32–37. http://dx.doi.org/10.9790/0661-1805043237.

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30

Gao, Xinbo, Qian Wang, Xuelong Li, Dacheng Tao, and Kaibing Zhang. "Zernike-Moment-Based Image Super Resolution." IEEE Transactions on Image Processing 20, no. 10 (October 2011): 2738–47. http://dx.doi.org/10.1109/tip.2011.2134859.

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Wang, Zu Jin, Xiao Diao Huang, and Ping Ping Gu. "An Inspection Method of SMD Component Type Based on Moment Features and BP Neural Network." Applied Mechanics and Materials 610 (August 2014): 296–301. http://dx.doi.org/10.4028/www.scientific.net/amm.610.296.

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The type inspection of surface mounted devices (SMD) components is an important part of the numerical control (NC) placement machine’s vision system. In order to improve the detection speed, accuracy rate and versatility, a detection method based on moment features and neural network is proposed. Firstly, component images are preprocessed in order to eliminate the influence of non-uniform illumination and simplify the calculation, so component lead images can be obtained, and then seven invariant moments and a zero-order Zernike moment of the lead images are extracted. Next, the moment features are corrected and normalized. Finally, back propagation (BP) neural network based on the Levenberg-Marquardt algorithm is taken as a classifier for training and testing the 8-dimensional mixed moment feature vectors, 0 and 1 are used to represent the degree of belonging of each image. The experimental results show that this method doesn’t need complex lighting system and has good versatility, and the correct rate can be up to 100%.

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Guerreiro, Manuela, Júlio Mendes, Carlos Fortuna, and Patrícia Pinto. "The dynamic nature of the city image." Tourism 68, no. 1 (2020): 83–99. http://dx.doi.org/10.37741/t.68.1.7.

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In this study, we propose that the city image is a multidimensional construct influenced by its image components which, together, affect tourist behaviour in a dynamic process. The general objective of this research is to understand the dynamic nature of a major tourism destination image and the relationships among its components from the tourists’ perspective. This study is exploratory and descriptive. Data was collected from tourists’ surveys applied in two different moments at Istanbul: before the launching of the European Capital of Culture (ECoC) (Moment 1), and one year later, during the ECoC (Moment 2). The proposed model was estimated and tested using structural equation modelling (SEM). The comparison of data from the two moments indicates different patterns of relationships. Findings contribute to a better understanding of the dynamic nature of a city image by investigating the relationships among different image components in two different moments, before and after a major cultural event. Future studies should investigate further the unique image construct given the importance of local identity in brand and event communication. Additionally, research should investigate the impact of events on the formation of the affective component of image and behavioural intentions among tourists

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GOH, HOCK-ANN, CHEE-WAY CHONG, ROSLI BESAR, FAZLY SALLEH ABAS, and KOK-SWEE SIM. "TRANSLATION AND SCALE INVARIANTS OF HAHN MOMENTS." International Journal of Image and Graphics 09, no. 02 (April 2009): 271–85. http://dx.doi.org/10.1142/s0219467809003435.

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Hahn moments are a superset of Tchebichef and Krawtchouk moments. The formulation for Hahn moments is however comparably more complex than other moments. So far only research work on translation and scale invariants for Tchebichef moments has been presented but not on Hahn moments. In this paper, a moment normalization method to achieve translation and scale invariants of Hahn moments is proposed. This method applies the concept of mapping functions used in image normalization. The mapping functions, once determined, are plugged into the moment generating functions to generate moment invariants. The proposed method is simpler and flexible. Experimental results show that faster execution and more precise moment invariants can be achieved using the invariant generating functions.

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ASAITHAMBI, MYTHILI, JOSEPH JESU CHRISTOPHER, and SWAMINATHAN RAMAKRISHNAN. "QUALITATIVE ASSESSMENT OF TENSILE STRENGTH COMPONENTS OF HUMAN FEMUR TRABECULAR BONE USING RADIOGRAPHIC IMAGING AND SPECTRAL ANALYSIS." Journal of Mechanics in Medicine and Biology 09, no. 01 (March 2009): 21–29. http://dx.doi.org/10.1142/s0219519409002869.

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In this work, the primary and secondary tensile strength components of human femur trabecular bone are qualitatively assessed using planar radiographic images and spectral analysis. Normal and abnormal femur trabecular images (N = 40) were recorded using planar radiography following standard image acquisition protocol. From the images, the tensile strength components of the trabeculae are delineated using image processing procedures and are then subjected to Fourier transform. The zero (DC), First (FMOI), and Second Moments of Inertia (SMOI) are the parameters considered and are correlated with presence and absence of mineralization in the image. Results show that the values of moments correlate well with percentage mineralization in normal images when compared to abnormal images for both primary and secondary tensile strength components. Further, no or poor correlations were found for abnormals in all cases. Among all, the values of second moment showed highest correlation in the secondary tensile region. In this paper the objectives, methodology, significance results and the conclusions are presented.

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Irala-Hortal, Pilar. "La imagen como terapia = Image as therapy." REVISTA ESPAÑOLA DE COMUNICACIÓN EN SALUD 9, no. 2 (December 18, 2018): 237. http://dx.doi.org/10.20318/recs.2018.4502.

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Resumen: Cuando hablamos de fotografía solemos pensar en la captación de un momento o acontecimiento, probablemente caracterizado por breves impases de clímax y con una trascendencia cultural, social, artística o política. Bien se trate de fotografía artística o documental, tanto fotógrafos como historiadores o teóricos hemos abordado la imagen desde el enfoque de la preservación de un momento. En este caso la fotografía es un conservador de la memoria. Pero la fotografía puede cumplir otras funciones como la de ahondar, profundizar, extraer y exorcizar conocimientos o sentimientos íntimos con una finalidad terapéutica. No es baladí este poder de la imagen que ha sido contrastado en diferentes proyectos tanto médicos como sociales. El presente artículo tiene el objetivo de exponer qué es la fotografía terapéutica y cuáles son sus ámbitos de aplicación.Palabras clave: fotografía; enfermedad; terapia; cultura visual.Abstract: When we talk about photography, we are usually thinking of capturing one single moment. That event is probably characterized or classified by a cultural, social, artistic or political culture. Whether it is artistic or documentary photography, photographers, historians or theorists have thought the image as a visual conservator of our memory. However, photography can play other functions or roles such as deepening, extracting and exorcizing knowledge or profound feelings for therapeutic purposes. This is not a small power. In addition, this role of the image has been contrasted by different projects both medical and social. The purpose of this paper is to explain what the therapeutic photography is and what its scope of application is.Keywords: photography; illness; therapy; visual culture.

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Zhu, Zhengwei, Jianjiang Zhou, and Hongyu Chu. "Synthetic Aperture Radar Image Background Clutter Fitting Using SKS + MoM-BasedG0Distribution." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/864019.

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G0distribution can accurately model various background clutters in the single-look and multilook synthetic aperture radar (SAR) images and is one of the most important statistic models in the field of SAR image clutter modeling. However, the parameter estimation ofG0distribution is difficult, which greatly limits the application of the distribution. In order to solve the problem, a fast and accurateG0distribution parameter estimation method, which combines second-kind statistics (SKS) technique with Freitas’ method of moment (MoM), is proposed. First we deduce the first and second second-kind characteristic functions ofG0distribution based on Mellin transform, and then the logarithm moments and the logarithm cumulants corresponding to the above-mentioned characteristic functions are derived; finally combined with Freitas’ method of moment, a simple iterative equation which is used for estimating theG0distribution parameters is obtained. Experimental results show that the proposed method has fast estimation speed and high fitting precision for various measured SAR image clutters with different resolutions and different number of looks.

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CHEN, GUANGYI, SRIDHAR KRISHNAN, and TIEN D. BUI. "RAMANUJAN SUMS FOR IMAGE PATTERN ANALYSIS." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 01 (December 2013): 1450003. http://dx.doi.org/10.1142/s0219691314500039.

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Ramanujan Sums (RS) have been found to be very successful in signal processing recently. However, as far as we know, the RS have not been applied to image analysis. In this paper, we propose two novel algorithms for image analysis, including moment invariants and pattern recognition. Our algorithms are invariant to the translation, rotation and scaling of the 2D shapes. The RS are robust to Gaussian white noise and occlusion as well. Our algorithms compare favourably to the dual-tree complex wavelet (DTCWT) moments and the Zernike's moments in terms of correct classification rates for three well-known shape datasets.

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Wang, YuanBin, and XingWei Wang. "The Complete Set of Independent Affine Moment Invariants of Color Images." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 05 (April 8, 2019): 1954018. http://dx.doi.org/10.1142/s0218001419540181.

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Moment invariants of images are important features for pattern recognition and image processing. Many methods have been proposed to derive moment invariants of images under different group actions. However, the completeness and independence of a set of moment invariants are two open problems. In this paper, we use the moving frame method to derive affine moment invariants of color images. The moving frame for the normalized color moment space under the action of the affine group is presented. Using this moving frame, we obtain a complete and independent set of affine moment invariants of color images. This system of affine moment invariants is also invariant under diagonal photometric changes. Experimental results are provided to validate the correctness of the derivation.

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Hack, Lilian, and Édio Raniere Da Silva. "Escrever sob o fascínio da imagem – ressonâncias entre o pensamento de Maurice Blanchot e Georges Didi-Huberman." Visualidades 15, no. 2 (December 19, 2017): 69. http://dx.doi.org/10.5216/vis.v15i2.48066.

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Resumo O artigo problematiza o conceito de imagem a partir do pensamento de Maurice Blanchot e Georges Didi-Huberman. Explorando especialmente os conceitos de duplicidade da imagem em Blanchot e dupla distância do olhar em DidiHuberman, pretende-se verificar os pontos em que se faz convergir nesses autores um modo de operar o pensamento e a escrita sobre a arte em sua relação com a imagem. Nesse jogo, sujeito e objeto são lançados a uma instabilidade, movimento em que o sujeito se desfaz de si mesmo pela imagem, se desfaz de si no encontro com uma obra. Momento em que a imagem solicita a palavra e em que a língua torna-se o lugar desde onde podemos nos aproximar dela. AbstractThe article problematizes the concept of image from the thinking of Maurice Blanchot and Georges Didi-Huberman. Exploring especially the concepts of duplicity of the image in Blanchot and double distance of the look in Didi-Huberman, it is intended to compose a starting plan that will allow to verify the points in which one converges in these authors a way of operating the thought about art in its relation to the image. In this game subject and object are thrown into an instability, a movement in which the subject undoes himself by the image, he discards himself in the encounter with a work. Moment when the image asks for the word and where the language becomes the place from which we can approach it. ResumenEl artículo problematiza el concepto de imagen a partir del pensamiento de Maurice Blanchot y Georges DidiHuberman. Examinando especialmente los conceptos de duplicidad de la imagen en Blanchot y doble distancia de la mirada en Didi-Huberman, se pretende verificar los puntos en los que se hace converger en esos autores un modo de operar el pensamiento y la escritura sobre el arte en su relación con la imagen. En ese juego, sujeto y objeto son lanzados a una inestabilidad, movimiento en que el sujeto se deshace de sí mismo por la imagen, se deshace de sí en el encuentro con una obra. Momento en el que la imagen pide la palabra y en el que la lengua se convierte en el lugar desde donde podemos acercarnos a ella.

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Wu, Lan Lan, Jie Wu, You Xian Wen, Hui Peng, and Zhi Hui Zhu. "Detection for Corn/Weed Images Using Moment Invariants by BPNN Classifier." Advanced Materials Research 605-607 (December 2012): 2183–86. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.2183.

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This study was conducted to discriminate the weed from the corn in a field combined neural network classifier with image processing technology. The corn and weed images were scanned using a colour imaging system. In the first step, an approximate location of the object of interest was determined by minimum enclosing rectangle, in which image processing was done to obtain the binary image. In the second step, the seven invariant moments were extracted from binary images and used as input to the back propagation neural network (BPNN) classifier. The training set was used to construct shape model representing the objects. The detection accuracy was enhanced by adjusting the number of neurons in the network. Experimental results showed that the BPNN classifier achieved overall detection accuracy of 94.52% with 7-28-1.

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Abu. "AN IMAGE DITHERING VIA TCHEBICHEF MOMENT TRANSFORM." Journal of Computer Science 9, no. 7 (July 1, 2013): 811–20. http://dx.doi.org/10.3844/jcssp.2013.811.820.

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Rivera-López, J. S., and C. J. Camacho-Bello. "Color Image Reconstruction by Discrete Orthogonal Moment." Journal of Data Analysis and Information Processing 05, no. 04 (2017): 156–66. http://dx.doi.org/10.4236/jdaip.2017.54012.

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Xiao, Bin, Jian-Feng Ma, and Jiang-Tao Cui. "Radial Tchebichef moment invariants for image recognition." Journal of Visual Communication and Image Representation 23, no. 2 (February 2012): 381–86. http://dx.doi.org/10.1016/j.jvcir.2011.11.008.

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Raghuraman, G., J. P. Ananth, K. L. Shunmuganathan, and L. Sairamesh. "Krawtchouk Moment Based Interactive Image Retrieval Algorithm." Journal of Computational and Theoretical Nanoscience 12, no. 12 (December 1, 2015): 5562–65. http://dx.doi.org/10.1166/jctn.2015.4684.

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Park, Kil-Houm, and Song-Bai Park. "Image Reconstruction by Minimizing Second-Order Moment." Ultrasonic Imaging 12, no. 2 (April 1990): 71–83. http://dx.doi.org/10.1177/016173469001200201.

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Flusser, J. "Refined moment calculation using image block representation." IEEE Transactions on Image Processing 9, no. 11 (2000): 1977–78. http://dx.doi.org/10.1109/83.877219.

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Kumar, Ahlad, Raveendran Paramesran, and Barmak Honarvar Shakibaei. "Moment domain representation of nonblind image deblurring." Applied Optics 53, no. 10 (March 7, 2014): B167. http://dx.doi.org/10.1364/ao.53.00b167.

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Hui Zhang, Huazhong Shu, G. N. Han, G. Coatrieux, Limin Luo, and J. L. Coatrieux. "Blurred Image Recognition by Legendre Moment Invariants." IEEE Transactions on Image Processing 19, no. 3 (March 2010): 596–611. http://dx.doi.org/10.1109/tip.2009.2036702.

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Park, K. "Image reconstruction by minimizing second-order moment." Ultrasonic Imaging 12, no. 2 (April 1990): 71–83. http://dx.doi.org/10.1016/0161-7346(90)90151-m.

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Yang, Chen-Kuei, Tsu-Chin Wu, Ja-Chen Lin, and Wen-Hsiang Tsai. "Color image sharpening by moment-preserving technique." Signal Processing 45, no. 3 (September 1995): 397–403. http://dx.doi.org/10.1016/0165-1684(95)00066-m.

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