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Artículos de revistas sobre el tema "Natural images"

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Publicado: 4 de junio de 2021
Última modificación: 6 de septiembre de 2023

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1

Moorhead, Ian y Tom Trościanko. "Natural Images". Perception 29, n.º 9 (septiembre de 2000): 1013–15. http://dx.doi.org/10.1068/p2909ed.

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2

Jeurissen, D. J. J. D. M. y P. R. Roelfsema. "Image Parsing, From Curves to Natural Images". Journal of Vision 12, n.º 9 (10 de agosto de 2012): 269. http://dx.doi.org/10.1167/12.9.269.

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3

Tanaka, Go, Noriaki Suetake y Eiji Uchino. "Simple multiscale image enhancement for natural images". Optical Review 17, n.º 3 (mayo de 2010): 130–38. http://dx.doi.org/10.1007/s10043-010-0023-6.

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4

George, J., G. Padmanabhan y M. Brady. "Image features predict edge causation in natural images". Journal of Vision 9, n.º 8 (22 de marzo de 2010): 1044. http://dx.doi.org/10.1167/9.8.1044.

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5

Zhang, S., C. Abbey y M. Eckstein. "Classification Images for Search in Natural Images". Journal of Vision 10, n.º 7 (17 de agosto de 2010): 1355. http://dx.doi.org/10.1167/10.7.1355.

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6

Banafar, Lokendra Singh y Dr Lalita Gupta. "Text Detection from Natural Images using MSER Algorithm". International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (30 de abril de 2018): 73–81. http://dx.doi.org/10.31142/ijtsrd10806.

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7

V. Seeri, Shivananda, J. D. Pujari y P. S. Hiremath. "PNN Based Character Recognition in Natural Scene Images". Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (31 de octubre de 2016): 109–13. http://dx.doi.org/10.9756/bijsesc.8254.

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8

Ahmad, Khairul Adilah, Sharifah Lailee Syed Abdullah y Mahmod Othman. "Natural Images Contour Segmentation". Journal of Computing Research and Innovation 2, n.º 4 (30 de enero de 2018): 39–47. http://dx.doi.org/10.24191/jcrinn.v2i4.62.

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This paper, a combination of edge detection and contour based segmentation approach for object contour delineation is proposed. The proposed approach employs a new methodology for segmenting the fruit contour from the indoor and outdoo r natural images more effectively. The overall process is carried out in five steps. The first step is to pre - process the image in order to convert the colour image to grayscale image. Second step is the adoption of Laplacian of Gaussian edge detection and a new corner template detection algorithm for adjustment of the pixels along the edge map in the interpolation process. Third step is the reconstruction process by implementing two morphology operators with embedded of inversion condition and dynamic thr eshold to preserve and reconstruct object contour. Fifth step is ground mask process in which the outputs of the inference obtained for each pixel is combined to a final segmented output, which provides a segmented foreground against the black background. This proposed algorithm is tested over 150 indoor and 40 outdoor fruit images in order to analyse its efficiency. From the experimental results, it has been observed that the proposed segmentation approach provides better segmentation accuracy of 100 % in segmenting indoor and outdoor natural images. This algorithm also present a fully automatic model based system for segmenting fruit images of the natural environment.
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9

Hall, Ronald L. "Images of natural evil". International Journal for Philosophy of Religion 87, n.º 3 (25 de abril de 2020): 213–16. http://dx.doi.org/10.1007/s11153-020-09757-9.

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10

SATO, TAKASHI, MAKOTO MATSUOKA y HIDEKI TAKAYASU. "FRACTAL IMAGE ANALYSIS OF NATURAL SCENES AND MEDICAL IMAGES". Fractals 04, n.º 04 (diciembre de 1996): 463–68. http://dx.doi.org/10.1142/s0218348x96000571.

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We construct color map images of fractal dimension distribution from natural scenes and medical images by applying the box-counting method locally. The map images clearly show the difference between clouds and rocks, as well as between cancer parts and normal tissue in the colon. The method is simple and may be expected to be applicable to a real-time video-data processing.
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11

Kumari, Santoresh y Parveen Lehana. "Image Content Enhancement of Natural Images Using Genetic Algorithm". Physical Science International Journal 4, n.º 9 (10 de enero de 2014): 1244–59. http://dx.doi.org/10.9734/psij/2014/6891.

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12

Kulkarni, Nilima. "Color Thresholding Method for Image Segmentation of Natural Images". International Journal of Image, Graphics and Signal Processing 4, n.º 1 (3 de febrero de 2012): 28–34. http://dx.doi.org/10.5815/ijigsp.2012.01.04.

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13

Irianto, Suhendro Y., Muhammad Galih, Isnandar Agus, Abdi Darmawan y Lindar. "Content Based Image Retrieval on Natural and Artificial Images". IOP Conference Series: Materials Science and Engineering 917 (22 de septiembre de 2020): 012061. http://dx.doi.org/10.1088/1757-899x/917/1/012061.

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14

Ramesh, Chiluka, Dr D. Venkat Rao y Dr K. S. N. Murthy. "Restoration of Natural Images Using Multi Resoluted Pscs Analysis". Journal of Advanced Research in Dynamical and Control Systems 11, n.º 12-SPECIAL ISSUE (31 de diciembre de 2019): 1376–82. http://dx.doi.org/10.5373/jardcs/v11sp12/20193357.

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15

Kataoka, Hirokatsu, Kazushige Okayasu, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Akio Nakamura y Yutaka Satoh. "Pre-Training Without Natural Images". International Journal of Computer Vision 130, n.º 4 (24 de febrero de 2022): 990–1007. http://dx.doi.org/10.1007/s11263-021-01555-8.

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AbstractIs it possible to use convolutional neural networks pre-trained without any natural images to assist natural image understanding? The paper proposes a novel concept, Formula-driven Supervised Learning (FDSL). We automatically generate image patterns and their category labels by assigning fractals, which are based on a natural law. Theoretically, the use of automatically generated images instead of natural images in the pre-training phase allows us to generate an infinitely large dataset of labeled images. The proposed framework is similar yet different from Self-Supervised Learning because the FDSL framework enables the creation of image patterns based on any mathematical formulas in addition to self-generated labels. Further, unlike pre-training with a synthetic image dataset, a dataset under the framework of FDSL is not required to define object categories, surface texture, lighting conditions, and camera viewpoint. In the experimental section, we find a better dataset configuration through an exploratory study, e.g., increase of #category/#instance, patch rendering, image coloring, and training epoch. Although models pre-trained with the proposed Fractal DataBase (FractalDB), a database without natural images, do not necessarily outperform models pre-trained with human annotated datasets in all settings, we are able to partially surpass the accuracy of ImageNet/Places pre-trained models. The FractalDB pre-trained CNN also outperforms other pre-trained models on auto-generated datasets based on FDSL such as Bezier curves and Perlin noise. This is reasonable since natural objects and scenes existing around us are constructed according to fractal geometry. Image representation with the proposed FractalDB captures a unique feature in the visualization of convolutional layers and attentions.
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16

Pronin, S. V. "Symmetrical patterns in natural images". Journal of Optical Technology 89, n.º 1 (1 de enero de 2022): 17. http://dx.doi.org/10.1364/jot.89.000017.

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17

Hunter, D. W. y P. B. Hibbard. "Statistics of Natural Binocular Images". i-Perception 4, n.º 7 (octubre de 2013): 485. http://dx.doi.org/10.1068/ig9.

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18

Buffat, S., C. Roumes y J. Lorenceau. "Repetition blindness with natural images". Journal of Vision 5, n.º 8 (1 de septiembre de 2005): 857. http://dx.doi.org/10.1167/5.8.857.

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19

Martin, A., J. F. Barraza y L. A. Issolio. "Velocity constancy in natural images". Journal of Vision 6, n.º 6 (18 de marzo de 2010): 1047. http://dx.doi.org/10.1167/6.6.1047.

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20

Hashimoto, Wakako. "Quadratic forms in natural images". Network: Computation in Neural Systems 14, n.º 4 (enero de 2003): 765–88. http://dx.doi.org/10.1088/0954-898x_14_4_308.

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21

Ruderman, Daniel L. "The statistics of natural images". Network: Computation in Neural Systems 5, n.º 4 (enero de 1994): 517–48. http://dx.doi.org/10.1088/0954-898x_5_4_006.

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22

Hall, S. "Protein images update natural history". Science 267, n.º 5198 (3 de febrero de 1995): 620–24. http://dx.doi.org/10.1126/science.7839137.

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23

Dai, Xiao Yan y Junji Maeda. "Unsupervised Segmentation of Natural Images". Optical Review 9, n.º 5 (septiembre de 2002): 197–201. http://dx.doi.org/10.1007/s10043-002-0197-7.

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24

Tolhurst, D. J., Y. Tadmor y Tang Chao. "Amplitude spectra of natural images". Ophthalmic and Physiological Optics 12, n.º 2 (19 de diciembre de 2007): 229–32. http://dx.doi.org/10.1111/j.1475-1313.1992.tb00296.x.

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25

Tyler, Christopher W. y Joshua A. Solomon. "Color perception in natural images". Current Opinion in Behavioral Sciences 30 (diciembre de 2019): 8–14. http://dx.doi.org/10.1016/j.cobeha.2019.04.002.

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26

Lee, Chien-Cheng y Shang-Fei Shen. "Text Localization in Natural Images Using Discriminative Local Color Information". International Journal of Signal Processing Systems 5, n.º 3 (septiembre de 2017): 89–93. http://dx.doi.org/10.18178/ijsps.5.3.89-93.

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27

Sebastian, Stephen, Johannes Burge y Wilson S. Geisler. "Defocus blur discrimination in natural images with natural optics". Journal of Vision 15, n.º 5 (24 de abril de 2015): 16. http://dx.doi.org/10.1167/15.5.16.

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28

Wu, Qiong, Shaojie Sun, Wei Zhu y Guohui Li. "Identification of inpainted images and natural images for digital forensics". Journal of Electronics (China) 26, n.º 3 (mayo de 2009): 341–45. http://dx.doi.org/10.1007/s11767-007-0219-5.

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29

BO, Yihang, Siwei LUO y Qi ZOU. "Salient Edge Detection in Natural Images". IEICE Transactions on Information and Systems E92-D, n.º 5 (2009): 1209–12. http://dx.doi.org/10.1587/transinf.e92.d.1209.

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30

Denemark, Tomáš, Patrick Bas y Jessica Fridrich. "Natural Steganography in JPEG Compressed Images". Electronic Imaging 2018, n.º 7 (28 de enero de 2018): 316–1. http://dx.doi.org/10.2352/issn.2470-1173.2018.07.mwsf-316.

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31

Kumar, Manoj y Guee-Sang Lee. "Text Extraction from Complex Natural Images". International Journal of Contents 6, n.º 2 (28 de junio de 2010): 1–5. http://dx.doi.org/10.5392/ijoc.2010.6.2.001.

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32

Elder, J. H. "Brightness Filling-in of Natural Images". Perception 26, n.º 1_suppl (agosto de 1997): 136. http://dx.doi.org/10.1068/v970332.

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There is both psychophysical and physiological evidence that the perception of brightness variations in an image may be the result of a filling-in process in which the luminance signal is encoded only at image contours and is then neurally diffused to form representations of surface brightness. Despite this evidence, the filling-in hypothesis remains controversial. One problem is that in previous experiments highly simplified synthetic stimuli have been used; it is unclear whether brightness filling-in is feasible for complex natural images containing shading, shadows, and focal blur. To address this question, we present a computational model for brightness filling-in and results of experiments which test the model on a large and diverse set of natural images. The model is based on a scale-space method for edge detection which computes a contour code consisting of estimates of position, brightness, contrast, and blur at each edge point in an image (Elder and Zucker, 1996, paper presented at ECCV). This representation is then inverted by a diffusion-based filling-in algorithm which reconstructs an estimate of the original image. Psychophysical assessment of results shows that while filling-in of brightness alone leads to significant artifact, parallel filling-in of both brightness and blur produces perceptually accurate reconstructions. The temporal dynamics of blur reconstruction predicted by the model are consistent with psychophysical studies of blur perception (Westheimer, 1991 Journal of the Optical Society of America A8 681 – 685). These results suggest that a scale-adaptive contour representation can in principle capture the information needed for the perceptually accurate filling-in of complex natural images.
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33

Jiang, Dianzhuan, Shengsheng Zhang, Yaping Huang, Qi Zou, Xingyuan Zhang, Mengyang Pu y Junbo Liu. "Detecting dense text in natural images". IET Computer Vision 14, n.º 8 (1 de diciembre de 2020): 597–604. http://dx.doi.org/10.1049/iet-cvi.2019.0916.

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34

Kersten, Daniel. "Predictability and redundancy of natural images". Journal of the Optical Society of America A 4, n.º 12 (1 de diciembre de 1987): 2395. http://dx.doi.org/10.1364/josaa.4.002395.

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35

Ranzato, Marc'Aurelio, Volodymyr Mnih, Joshua M. Susskind y Geoffrey E. Hinton. "Modeling Natural Images Using Gated MRFs". IEEE Transactions on Pattern Analysis and Machine Intelligence 35, n.º 9 (septiembre de 2013): 2206–22. http://dx.doi.org/10.1109/tpami.2013.29.

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36

Baker, D. H. y E. W. Graf. "Natural images dominate in binocular rivalry". Proceedings of the National Academy of Sciences 106, n.º 13 (16 de marzo de 2009): 5436–41. http://dx.doi.org/10.1073/pnas.0812860106.

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37

Takeuchi, T. y K. K. Valois. "Perceived sharpness of moving natural images". Journal of Vision 4, n.º 8 (1 de agosto de 2004): 490. http://dx.doi.org/10.1167/4.8.490.

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38

Bex, P. J., I. Mareschal y S. C. Dakin. "Contrast gain control in natural images". Journal of Vision 5, n.º 8 (17 de marzo de 2010): 597. http://dx.doi.org/10.1167/5.8.597.

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39

Chang, D. L., R. W. Stone y B. T. Backus. "Natural images and the McCullough effect". Journal of Vision 5, n.º 8 (17 de marzo de 2010): 599. http://dx.doi.org/10.1167/5.8.599.

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40

Manap, Redzuan Abdul, Ling Shao y Alejandro F. Frangi. "Nonparametric Quality Assessment of Natural Images". IEEE MultiMedia 23, n.º 4 (octubre de 2016): 22–30. http://dx.doi.org/10.1109/mmul.2016.2.

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41

Hancock, Peter J. B., Roland J. Baddeley y Leslie S. Smith. "The principal components of natural images". Network: Computation in Neural Systems 3, n.º 1 (enero de 1992): 61–70. http://dx.doi.org/10.1088/0954-898x_3_1_008.

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42

Dong, Dawei W. y Joseph J. Atick. "Statistics of natural time-varying images". Network: Computation in Neural Systems 6, n.º 3 (enero de 1995): 345–58. http://dx.doi.org/10.1088/0954-898x_6_3_003.

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43

Olman, C., P. Schrater y D. Kersten. "BOLD fMRI response to natural images". Journal of Vision 2, n.º 7 (15 de marzo de 2010): 134. http://dx.doi.org/10.1167/2.7.134.

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44

Takeuchi, T. y K. K. D. Valois. "Motion sharpening in moving natural images". Journal of Vision 2, n.º 7 (14 de marzo de 2010): 377. http://dx.doi.org/10.1167/2.7.377.

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45

Denison, Rachel N., Jacob Sheynin y Michael A. Silver. "Perceptual suppression of predicted natural images". Journal of Vision 16, n.º 13 (31 de octubre de 2016): 6. http://dx.doi.org/10.1167/16.13.6.

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46

Turiel, Antonio y Néstor Parga. "Multifractal Wavelet Filter of Natural Images". Physical Review Letters 85, n.º 15 (9 de octubre de 2000): 3325–28. http://dx.doi.org/10.1103/physrevlett.85.3325.

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47

Ruderman, Daniel L. "Origins of scaling in natural images". Vision Research 37, n.º 23 (diciembre de 1997): 3385–98. http://dx.doi.org/10.1016/s0042-6989(97)00008-4.

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48

Hibbard, Paul B. "Binocular energy responses to natural images". Vision Research 48, n.º 12 (junio de 2008): 1427–39. http://dx.doi.org/10.1016/j.visres.2008.03.013.

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49

Hibbard, P. B. y S. Bouzit. "Binocular energy responses to natural images". Journal of Vision 6, n.º 6 (24 de marzo de 2010): 833. http://dx.doi.org/10.1167/6.6.833.

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50

Bartsch, Hauke y Klaus Obermayer. "Second-order statistics of natural images". Neurocomputing 52-54 (junio de 2003): 467–72. http://dx.doi.org/10.1016/s0925-2312(02)00734-8.

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