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Journal articles on the topic 'Natural images'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 September 2023

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1

Moorhead, Ian, and Tom Trościanko. "Natural Images." Perception 29, no. 9 (September 2000): 1013–15. http://dx.doi.org/10.1068/p2909ed.

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2

Jeurissen, D. J. J. D. M., and P. R. Roelfsema. "Image Parsing, From Curves to Natural Images." Journal of Vision 12, no. 9 (August 10, 2012): 269. http://dx.doi.org/10.1167/12.9.269.

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3

Tanaka, Go, Noriaki Suetake, and Eiji Uchino. "Simple multiscale image enhancement for natural images." Optical Review 17, no. 3 (May 2010): 130–38. http://dx.doi.org/10.1007/s10043-010-0023-6.

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4

George, J., G. Padmanabhan, and M. Brady. "Image features predict edge causation in natural images." Journal of Vision 9, no. 8 (March 22, 2010): 1044. http://dx.doi.org/10.1167/9.8.1044.

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5

Zhang, S., C. Abbey, and M. Eckstein. "Classification Images for Search in Natural Images." Journal of Vision 10, no. 7 (August 17, 2010): 1355. http://dx.doi.org/10.1167/10.7.1355.

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6

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

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7

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

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8

Ahmad, Khairul Adilah, Sharifah Lailee Syed Abdullah, and Mahmod Othman. "Natural Images Contour Segmentation." Journal of Computing Research and Innovation 2, no. 4 (January 30, 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, no. 3 (April 25, 2020): 213–16. http://dx.doi.org/10.1007/s11153-020-09757-9.

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10

SATO, TAKASHI, MAKOTO MATSUOKA, and HIDEKI TAKAYASU. "FRACTAL IMAGE ANALYSIS OF NATURAL SCENES AND MEDICAL IMAGES." Fractals 04, no. 04 (December 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, and Parveen Lehana. "Image Content Enhancement of Natural Images Using Genetic Algorithm." Physical Science International Journal 4, no. 9 (January 10, 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, no. 1 (February 3, 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, and Lindar. "Content Based Image Retrieval on Natural and Artificial Images." IOP Conference Series: Materials Science and Engineering 917 (September 22, 2020): 012061. http://dx.doi.org/10.1088/1757-899x/917/1/012061.

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14

Ramesh, Chiluka, Dr D. Venkat Rao, and Dr K. S. N. Murthy. "Restoration of Natural Images Using Multi Resoluted Pscs Analysis." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12-SPECIAL ISSUE (December 31, 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, and Yutaka Satoh. "Pre-Training Without Natural Images." International Journal of Computer Vision 130, no. 4 (February 24, 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, no. 1 (January 1, 2022): 17. http://dx.doi.org/10.1364/jot.89.000017.

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17

Hunter, D. W., and P. B. Hibbard. "Statistics of Natural Binocular Images." i-Perception 4, no. 7 (October 2013): 485. http://dx.doi.org/10.1068/ig9.

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18

Buffat, S., C. Roumes, and J. Lorenceau. "Repetition blindness with natural images." Journal of Vision 5, no. 8 (September 1, 2005): 857. http://dx.doi.org/10.1167/5.8.857.

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19

Martin, A., J. F. Barraza, and L. A. Issolio. "Velocity constancy in natural images." Journal of Vision 6, no. 6 (March 18, 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, no. 4 (January 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, no. 4 (January 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, no. 5198 (February 3, 1995): 620–24. http://dx.doi.org/10.1126/science.7839137.

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23

Dai, Xiao Yan, and Junji Maeda. "Unsupervised Segmentation of Natural Images." Optical Review 9, no. 5 (September 2002): 197–201. http://dx.doi.org/10.1007/s10043-002-0197-7.

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24

Tolhurst, D. J., Y. Tadmor, and Tang Chao. "Amplitude spectra of natural images." Ophthalmic and Physiological Optics 12, no. 2 (December 19, 2007): 229–32. http://dx.doi.org/10.1111/j.1475-1313.1992.tb00296.x.

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25

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

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26

Lee, Chien-Cheng, and Shang-Fei Shen. "Text Localization in Natural Images Using Discriminative Local Color Information." International Journal of Signal Processing Systems 5, no. 3 (September 2017): 89–93. http://dx.doi.org/10.18178/ijsps.5.3.89-93.

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27

Sebastian, Stephen, Johannes Burge, and Wilson S. Geisler. "Defocus blur discrimination in natural images with natural optics." Journal of Vision 15, no. 5 (April 24, 2015): 16. http://dx.doi.org/10.1167/15.5.16.

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28

Wu, Qiong, Shaojie Sun, Wei Zhu, and Guohui Li. "Identification of inpainted images and natural images for digital forensics." Journal of Electronics (China) 26, no. 3 (May 2009): 341–45. http://dx.doi.org/10.1007/s11767-007-0219-5.

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29

BO, Yihang, Siwei LUO, and Qi ZOU. "Salient Edge Detection in Natural Images." IEICE Transactions on Information and Systems E92-D, no. 5 (2009): 1209–12. http://dx.doi.org/10.1587/transinf.e92.d.1209.

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30

Denemark, Tomáš, Patrick Bas, and Jessica Fridrich. "Natural Steganography in JPEG Compressed Images." Electronic Imaging 2018, no. 7 (January 28, 2018): 316–1. http://dx.doi.org/10.2352/issn.2470-1173.2018.07.mwsf-316.

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31

Kumar, Manoj, and Guee-Sang Lee. "Text Extraction from Complex Natural Images." International Journal of Contents 6, no. 2 (June 28, 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, no. 1_suppl (August 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, and Junbo Liu. "Detecting dense text in natural images." IET Computer Vision 14, no. 8 (December 1, 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, no. 12 (December 1, 1987): 2395. http://dx.doi.org/10.1364/josaa.4.002395.

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35

Ranzato, Marc'Aurelio, Volodymyr Mnih, Joshua M. Susskind, and Geoffrey E. Hinton. "Modeling Natural Images Using Gated MRFs." IEEE Transactions on Pattern Analysis and Machine Intelligence 35, no. 9 (September 2013): 2206–22. http://dx.doi.org/10.1109/tpami.2013.29.

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36

Baker, D. H., and E. W. Graf. "Natural images dominate in binocular rivalry." Proceedings of the National Academy of Sciences 106, no. 13 (March 16, 2009): 5436–41. http://dx.doi.org/10.1073/pnas.0812860106.

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37

Takeuchi, T., and K. K. Valois. "Perceived sharpness of moving natural images." Journal of Vision 4, no. 8 (August 1, 2004): 490. http://dx.doi.org/10.1167/4.8.490.

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38

Bex, P. J., I. Mareschal, and S. C. Dakin. "Contrast gain control in natural images." Journal of Vision 5, no. 8 (March 17, 2010): 597. http://dx.doi.org/10.1167/5.8.597.

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39

Chang, D. L., R. W. Stone, and B. T. Backus. "Natural images and the McCullough effect." Journal of Vision 5, no. 8 (March 17, 2010): 599. http://dx.doi.org/10.1167/5.8.599.

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40

Manap, Redzuan Abdul, Ling Shao, and Alejandro F. Frangi. "Nonparametric Quality Assessment of Natural Images." IEEE MultiMedia 23, no. 4 (October 2016): 22–30. http://dx.doi.org/10.1109/mmul.2016.2.

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41

Hancock, Peter J. B., Roland J. Baddeley, and Leslie S. Smith. "The principal components of natural images." Network: Computation in Neural Systems 3, no. 1 (January 1992): 61–70. http://dx.doi.org/10.1088/0954-898x_3_1_008.

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42

Dong, Dawei W., and Joseph J. Atick. "Statistics of natural time-varying images." Network: Computation in Neural Systems 6, no. 3 (January 1995): 345–58. http://dx.doi.org/10.1088/0954-898x_6_3_003.

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43

Olman, C., P. Schrater, and D. Kersten. "BOLD fMRI response to natural images." Journal of Vision 2, no. 7 (March 15, 2010): 134. http://dx.doi.org/10.1167/2.7.134.

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44

Takeuchi, T., and K. K. D. Valois. "Motion sharpening in moving natural images." Journal of Vision 2, no. 7 (March 14, 2010): 377. http://dx.doi.org/10.1167/2.7.377.

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45

Denison, Rachel N., Jacob Sheynin, and Michael A. Silver. "Perceptual suppression of predicted natural images." Journal of Vision 16, no. 13 (October 31, 2016): 6. http://dx.doi.org/10.1167/16.13.6.

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46

Turiel, Antonio, and Néstor Parga. "Multifractal Wavelet Filter of Natural Images." Physical Review Letters 85, no. 15 (October 9, 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, no. 23 (December 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, no. 12 (June 2008): 1427–39. http://dx.doi.org/10.1016/j.visres.2008.03.013.

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49

Hibbard, P. B., and S. Bouzit. "Binocular energy responses to natural images." Journal of Vision 6, no. 6 (March 24, 2010): 833. http://dx.doi.org/10.1167/6.6.833.

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50

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

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