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Journal articles on the topic 'Computer generated images'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Kerlow, Isaac Victor. "Computer-generated images and traditional printmaking." Visual Computer 4, no. 1 (1988): 8–18. http://dx.doi.org/10.1007/bf01901075.

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2

Ramek, Michael. "Colour vision and computer-generated images." Journal of Physics: Conference Series 237 (June 1, 2010): 012018. http://dx.doi.org/10.1088/1742-6596/237/1/012018.

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3

Bouhali, Othmane, and Ali Sheharyar. "Distributed rendering of computer-generated images on commodity compute clusters." Qatar Foundation Annual Research Forum Proceedings, no. 2012 (October 2012): CSP16. http://dx.doi.org/10.5339/qfarf.2012.csp16.

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4

Lucas, Gale M., Bennett Rainville, Priya Bhan, Jenna Rosenberg, Kari Proud, and Susan M. Koger. "Memory for Computer-Generated Graphics: Boundary Extension in Photographic vs. Computer-Generated Images." Psi Chi Journal of Psychological Research 10, no. 2 (2005): 43–48. http://dx.doi.org/10.24839/1089-4136.jn10.2.43.

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5

Sando, Yusuke, Masahide Itoh, and Toyohiko Yatagai. "Color computer-generated holograms from projection images." Optics Express 12, no. 11 (2004): 2487. http://dx.doi.org/10.1364/opex.12.002487.

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6

Wanger, L. R., J. A. Ferwerda, and D. P. Greenberg. "Perceiving spatial relationships in computer-generated images." IEEE Computer Graphics and Applications 12, no. 3 (1992): 44–58. http://dx.doi.org/10.1109/38.135913.

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7

Katoh, N., and M. Ito. "Gamut Mapping for Computer Generated Images (II)." Color and Imaging Conference 4, no. 1 (1996): 126–28. http://dx.doi.org/10.2352/cic.1996.4.1.art00034.

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8

Pini, Ezequiel. "Computer Generated Inspiration." Temes de Disseny, no. 36 (October 1, 2020): 192–207. http://dx.doi.org/10.46467/tdd36.2020.192-207.

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This pictorial addresses the new use of Computer Generated Imagery as a tool for contextualising and inspiring futures. Using research through design experimental methodology, these techniques allow us to create utopic spaces by embracing accidental outcomes, displaying an as yet unexplored path lacking the limitations of the real world. The resulting images prove how 3D digital imagery used in the design context can serve as a new medium for artistic self-expression, as a tool for future designs and as an instrument to raise awareness about environmental challenges. The term we have coined, C

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Subong, Ryan, and AlmaJean Subong. "Computer-Generated Sand Mixtures and Sand-based Images." International Journal of Computing Sciences Research 8 (January 1, 2024): 3119–30. https://doi.org/10.25147/ijcsr.2017.001.1.207.

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Purpose–This paper aims to verify the effectiveness of the software implementation of the proposed algorithm in creating computer-generated images of sand mixtures using a photograph of sand as an input and its effectiveness in converting digital pictures into sand-based images out of the mixtures it generated. Method–Visually compare the photographed image of the actual mixtures to its computer-generated counterpart to verify if the mixture generation produces results as expected and compare the computer-generated sand-based images with its source to verify image reproduction maintains same i

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10

Wang, Yan, and Xiao Wang. "Preprocessing and Edge Detection of Natural Images and Computer Generated Images." International Journal of Signal Processing, Image Processing and Pattern Recognition 9, no. 5 (2013): 281–90. http://dx.doi.org/10.14257/ijsip.2016.9.5.25.

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11

Durbhakula, K., and S. Dhali. "Computer-generated images of streamer propagation in nitrogen." IEEE Transactions on Plasma Science 27, no. 1 (1999): 24–25. http://dx.doi.org/10.1109/27.763008.

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12

BALUJA, SHUMEET, DEAN POMERLEAU, and TODD JOCHEM. "Towards Automated Artificial Evolution for Computer-generated Images." Connection Science 6, no. 2-3 (1994): 325–54. http://dx.doi.org/10.1080/09540099408915729.

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13

Goncharsky, Anton, and Svyatoslav Durlevich. "Cylindrical computer-generated hologram for displaying 3D images." Optics Express 26, no. 17 (2018): 22160. http://dx.doi.org/10.1364/oe.26.022160.

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14

Barrett, Tara M., Hans R. Zuuring, and Treg Christopher. "Interpretation of forest characteristics from computer-generated images." Landscape and Urban Planning 80, no. 4 (2007): 396–403. http://dx.doi.org/10.1016/j.landurbplan.2006.09.006.

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15

Shim, Hyunjung, and Seungkyu Lee. "Automatic color realism enhancement for computer generated images." Computers & Graphics 36, no. 8 (2012): 966–73. http://dx.doi.org/10.1016/j.cag.2012.09.001.

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16

Hu, Bingtao, and Jinwei Wang. "Deep Learning for Distinguishing Computer Generated Images and Natural Images: A Survey." Journal of Information Hiding and Privacy Protection 2, no. 2 (2020): 95–105. http://dx.doi.org/10.32604/jihpp.2020.010464.

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17

Zhang, Rui-Song, Wei-Ze Quan, Lu-Bin Fan, Li-Ming Hu, and Dong-Ming Yan. "Distinguishing Computer-Generated Images from Natural Images Using Channel and Pixel Correlation." Journal of Computer Science and Technology 35, no. 3 (2020): 592–602. http://dx.doi.org/10.1007/s11390-020-0216-9.

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18

Dang, L., Syed Hassan, Suhyeon Im, Jaecheol Lee, Sujin Lee, and Hyeonjoon Moon. "Deep Learning Based Computer Generated Face Identification Using Convolutional Neural Network." Applied Sciences 8, no. 12 (2018): 2610. http://dx.doi.org/10.3390/app8122610.

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Generative adversarial networks (GANs) describe an emerging generative model which has made impressive progress in the last few years in generating photorealistic facial images. As the result, it has become more and more difficult to differentiate between computer-generated and real face images, even with the human’s eyes. If the generated images are used with the intent to mislead and deceive readers, it would probably cause severe ethical, moral, and legal issues. Moreover, it is challenging to collect a dataset for computer-generated face identification that is large enough for research pur

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19

Purcell, Patrick, and Wendy Plesniak. "Computer generated spatial imaging." ITNOW 30, no. 1 (1988): 9–11. https://doi.org/10.1093/combul/30.1.9.

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Abstract The refined and iridescent imagery of holography has long had a dual role as a medium for visualisation in the laboratory and as an object of aesthetic interest in the art gallery. Its unique translucent and reflective visual character owes much to the ultra-precision of its fabrication techniques in both optics and chemistry. Now, a further element, computational process has begun to expand the types of images, the techniques of fabrication and most significantly the new applications in which holograms are finding a place. A new range of imaginative objects can be digitally modelled

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20

Elkins, James. "Art History and the Criticism of Computer-Generated Images." Leonardo 27, no. 4 (1994): 335. http://dx.doi.org/10.2307/1576009.

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21

Han Zhe, 韩哲, 亓岩 Qi Yan, 王延伟 Wang Yanwei, and 颜博霞 Yan Boxia. "Zoom Technology of Reconstructed Images of Computer Generated Holograms." Chinese Journal of Lasers 45, no. 5 (2018): 0509001. http://dx.doi.org/10.3788/cjl201845.0509001.

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22

Rosen, Joseph. "Computer-generated holograms of images reconstructed on curved surfaces." Applied Optics 38, no. 29 (1999): 6136. http://dx.doi.org/10.1364/ao.38.006136.

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23

Tan, D. Q., X. J. Shen, J. Qin, and H. P. Chen. "Detecting computer generated images based on local ternary count." Pattern Recognition and Image Analysis 26, no. 4 (2016): 720–25. http://dx.doi.org/10.1134/s1054661816040167.

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24

Upfold, J. B., M. S. R. Smith, and M. J. Edwards. "Three-dimensional reconstruction of tissue using computer-generated images." Journal of Neuroscience Methods 20, no. 2 (1987): 131–38. http://dx.doi.org/10.1016/0165-0270(87)90045-8.

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25

Tepper, Belinda Emmily, Benjamin Francis, Lijing Wang, and Bin Lee. "Acquisition and Application of Reflectance for Computer-Generated Images." International Journal of Computer Vision and Image Processing 13, no. 1 (2023): 1–26. http://dx.doi.org/10.4018/ijcvip.331386.

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In the field of computer graphics, accurate representation of material properties is crucial for rendering realistic imagery. This paper focuses on the bidirectional reflectance distribution function (BRDF) and its role in determining how materials interact with light. The authors review the state of the art in reflectance measurement systems, with a focus on BRDF and bidirectional texture function (BTF) measurement. They discuss practical limitations in measuring multi-dimensional functions and provide examples of how researchers have addressed these challenges. Additionally, they analyse var

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26

Akram, Misbah, Maya Bint Yousaf, Muhammad Hassan Nafees, Burhan Ahmed, Muhammad Abbas, and Tabeer Arif. "Automated Image Forensics Based on Deep Learning for Discriminating Photorealistic Computer Graphic and Photographic Images." Asian Bulletin of Big Data Management 4, no. 4 (2024): 69–84. https://doi.org/10.62019/abbdm.v4i4.248.

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The rapid advancement in the field of digital image forensics from the recent few years is becoming very crucial nowadays. As it become easier to generate computer graphic images and forge the whole of the image or just part of image to perform illegal activities. Distinguishing Computer generated stuff with the Natural images is quite difficult task with naked human eye. In this research thesis we proposed the CNN-based Neural Network model for the identification of images. For the classification of images Columbia image dataset is used which includes Photorealistic Computer Generated (PRCG)

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27

Jiao, Yuzhong, Kayton Wai Keung Cheung, Mark Ping Chan Mok, and Yiu Kei Li. "Spatial Distance-based Interpolation Algorithm for Computer Generated 2D+Z Images." Electronic Imaging 2020, no. 2 (2020): 140–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.2.sda-140.

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Computer generated 2D plus Depth (2D+Z) images are common input data for 3D display with depth image-based rendering (DIBR) technique. Due to their simplicity, linear interpolation methods are usually used to convert low-resolution images into high-resolution images for not only depth maps but also 2D RGB images. However linear methods suffer from zigzag artifacts in both depth map and RGB images, which severely affects the 3D visual experience. In this paper, spatial distance-based interpolation algorithm for computer generated 2D+Z images is proposed. The method interpolates RGB images with

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28

Meena, Kunj Bihari, and Vipin Tyagi. "Distinguishing computer-generated images from photographic images using two-stream convolutional neural network." Applied Soft Computing 100 (March 2021): 107025. http://dx.doi.org/10.1016/j.asoc.2020.107025.

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29

Dang, L. Minh, Kyungbok Min, Sujin Lee, Dongil Han, and Hyeonjoon Moon. "Tampered and Computer-Generated Face Images Identification Based on Deep Learning." Applied Sciences 10, no. 2 (2020): 505. http://dx.doi.org/10.3390/app10020505.

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Image forgery is an active topic in digital image tampering that is performed by moving a region from one image into another image, combining two images to form one image, or retouching an image. Moreover, recent developments of generative adversarial networks (GANs) that are used to generate human facial images have made it more challenging for even humans to detect the tampered one. The spread of those images on the internet can cause severe ethical, moral, and legal issues if the manipulated images are misused. As a result, much research has been conducted to detect facial image manipulatio

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30

Duminil, Alexandra, Sio-Song Ieng, and Dominique Gruyer. "A Comprehensive Exploration of Fidelity Quantification in Computer-Generated Images." Sensors 24, no. 8 (2024): 2463. http://dx.doi.org/10.3390/s24082463.

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Generating realistic road scenes is crucial for advanced driving systems, particularly for training deep learning methods and validation. Numerous efforts aim to create larger and more realistic synthetic datasets using graphics engines or synthetic-to-real domain adaptation algorithms. In the realm of computer-generated images (CGIs), assessing fidelity is challenging and involves both objective and subjective aspects. Our study adopts a comprehensive conceptual framework to quantify the fidelity of RGB images, unlike existing methods that are predominantly application-specific. This is proba

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31

Alfaqheri, Taha, Akuha Solomon Aondoakaa, Mohammad Rafiq Swash, and Abdul Hamid Sadka. "Low-delay single holoscopic 3D computer-generated image to multiview images." Journal of Real-Time Image Processing 17, no. 6 (2020): 2015–27. http://dx.doi.org/10.1007/s11554-020-00991-y.

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Abstract Due to the nature of holoscopic 3D (H3D) imaging technology, H3D cameras can capture more angular information than their conventional 2D counterparts. This is mainly attributed to the macrolens array which captures the 3D scene with slightly different viewing angles and generates holoscopic elemental images based on fly’s eyes imaging concept. However, this advantage comes at the cost of decreasing the spatial resolution in the reconstructed images. On the other hand, the consumer market is looking to find an efficient multiview capturing solution for the commercially available autost

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32

Hushlak, Gerald, and Jennifer Eiserman. "The Mistake: The Importance of Errors in Computer-generated Images." International Journal of the Image 1, no. 2 (2011): 93–102. http://dx.doi.org/10.18848/2154-8560/cgp/v01i02/44173.

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33

Maciejewski, Ross, Tobias Isenberg, William M. Andrews, David S. Ebert, Mario Costa Sousa, and Wei Chen. "Measuring Stipple Aesthetics in Hand-Drawn and Computer-Generated Images." IEEE Computer Graphics and Applications 28, no. 2 (2008): 62–74. http://dx.doi.org/10.1109/mcg.2008.35.

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34

Kim, Eun-Seok. "Holographic stereogram using a geometric method for computer-generated images." Optical Engineering 37, no. 9 (1998): 2449. http://dx.doi.org/10.1117/1.601767.

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35

Gangu Rama Naidu. "A CNN based Discrimination between Natural and Computer Generated Images." Panamerican Mathematical Journal 35, no. 2s (2024): 580–89. https://doi.org/10.52783/pmj.v35.i2s.2948.

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The objective of this research is to enhance classification accuracy by employing a systematic pre- and post-processing pipeline, which includes a Convolutional Neural Network (CNN) approach for distinguishing between computer-generated and natural images. In order to improve the consistency of CNN inputs, the methodology implements preprocessing procedures, including image scaling and normalization. The initial phase entails the extraction of a variety of patterns by utilizing feature selection within CNN layers. Transfer learning employs pre-trained CNN architectures, specifically ResNet, to

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36

Bergen, S. D., C. A. Ulbricht, J. L. Fridley, and M. A. Ganter. "The validity of computer-generated graphic images of forest landscape." Journal of Environmental Psychology 15, no. 2 (1995): 135–46. http://dx.doi.org/10.1016/0272-4944(95)90021-7.

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37

Barbour, Christopher G., and Gary W. Meyer. "Visual cues and pictorial limitations for computer generated photorealistic images." Visual Computer 9, no. 3 (1992): 151–65. http://dx.doi.org/10.1007/bf01902554.

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38

Kurihara, Takayuki, and Yasuhiro Takaki. "Speckle-free, shaded 3D images produced by computer-generated holography." Optics Express 21, no. 4 (2013): 4044. http://dx.doi.org/10.1364/oe.21.004044.

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39

Colonna, Carl M., Michael T. Zugelder, and John E. Anderson. "Computer-generated images and the deceased actor: Intellectual property rights." International Advances in Economic Research 2, no. 2 (1996): 196. http://dx.doi.org/10.1007/bf02295065.

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40

Chryssafis, A. "Anti-Aliasing of Computer-Generated Images: A Picture Independent Approach." Computer Graphics Forum 5, no. 2 (1986): 125–29. http://dx.doi.org/10.1111/j.1467-8659.1986.tb00281.x.

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41

de Rezende, Edmar R. S., Guilherme C. S. Ruppert, Antônio Theóphilo, Eric K. Tokuda, and Tiago Carvalho. "Exposing computer generated images by using deep convolutional neural networks." Signal Processing: Image Communication 66 (August 2018): 113–26. http://dx.doi.org/10.1016/j.image.2018.04.006.

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42

Surma, Mateusz, Izabela Ducin, Przemyslaw Zagrajek, and Agnieszka Siemion. "Sub-Terahertz Computer Generated Hologram with Two Image Planes." Applied Sciences 9, no. 4 (2019): 659. http://dx.doi.org/10.3390/app9040659.

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An advanced optical structure such as a synthetic hologram (also called a computer-generated hologram) is designed for sub-terahertz radiation. The detailed design process is carried out using the ping-pong method, which is based on the modified iterative Gerchberg–Saxton algorithm. The novelty lies in designing and manufacturing a single hologram structure creating two different images at two distances. The hologram area is small in relation to the wavelength used (the largest hologram dimension is equivalent to around 57 wavelengths). Thus, it consists of a small amount of coded information,

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43

Lui, Nicholas, Bryan Chia, William Berrios, Candace Ross, and Douwe Kiela. "Leveraging Diffusion Perturbations for Measuring Fairness in Computer Vision." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (2024): 14220–28. http://dx.doi.org/10.1609/aaai.v38i13.29333.

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Computer vision models have been known to encode harmful biases, leading to the potentially unfair treatment of historically marginalized groups, such as people of color. However, there remains a lack of datasets balanced along demographic traits that can be used to evaluate the downstream fairness of these models. In this work, we demonstrate that diffusion models can be leveraged to create such a dataset. We first use a diffusion model to generate a large set of images depicting various occupations. Subsequently, each image is edited using inpainting to generate multiple variants, where each

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44

Banda, Anish. "Image Captioning using CNN and LSTM." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 2666–69. http://dx.doi.org/10.22214/ijraset.2021.37846.

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Abstract: In the model we proposed, we examine the deep neural networks-based image caption generation technique. We give image as input to the model, the technique give output in three different forms i.e., sentence in three different languages describing the image, mp3 audio file and an image file is also generated. In this model, we use the techniques of both computer vision and natural language processing. We are aiming to develop a model using the techniques of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to build a model to generate a Caption. Target image is comp

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45

Bouhamidi, Yacine, and Kai Wang. "Simple Methods for Improving the Forensic Classification between Computer-Graphics Images and Natural Images." Forensic Sciences 4, no. 1 (2024): 164–83. http://dx.doi.org/10.3390/forensicsci4010010.

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From the information forensics point of view, it is important to correctly classify between natural images (outputs of digital cameras) and computer-graphics images (outputs of advanced graphics rendering engines), so as to know the source of the images and the authenticity of the scenes described in the images. It is challenging to achieve good classification performance when the forensic classifier is tested on computer-graphics images generated by unknown rendering engines and when we have a limited number of training samples. In this paper, we propose two simple yet effective methods to im

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46

Vitkine, Alexandre. "Photographic and Electronically Generated Images." Leonardo 19, no. 4 (1986): 305. http://dx.doi.org/10.2307/1578376.

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47

Peng, Fei, Juan Liu, and Min Long. "Identification of Natural Images and Computer Generated Graphics Based on Hybrid Features." International Journal of Digital Crime and Forensics 4, no. 1 (2012): 1–16. http://dx.doi.org/10.4018/jdcf.2012010101.

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Examining the identification of natural images (NI) and computer generated graphics (CG), a novel method is proposed based on hybrid features. Since the image acquisition pipelines are different, some differences exist in statistical, visual, and noise characteristics between natural images and computer generated graphics. Firstly, the mean, variance, kurtosis, skew-ness, and median of the histograms of grayscale image in the spatial and wavelet domain are selected as statistical features. Secondly, the fractal dimensions of grayscale image and wavelet sub-bands are extracted as visual feature

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48

Salgado, Tomas Garcia. "Comment on "Art History and the Criticism of Computer-Generated Images"." Leonardo 29, no. 1 (1996): 82. http://dx.doi.org/10.2307/1576292.

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49

Koreshev, S. N., S. O. Starovoitov, D. S. Smorodinov, and M. A. Frolova. "Quality assessment of binary object images reconstructed by computer-generated holograms." Scientific and Technical Journal of Information Technologies, Mechanics and Optics 20, no. 3 (2020): 327–34. http://dx.doi.org/10.17586/2226-1494-2020-20-3-327-334.

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50

Pei, Chuang, Xingpeng Yan, and Xiaoyu Jiang. "Computer-generated phase-modulated full parallax holographic stereograms without conjugate images." Optical Engineering 53, no. 10 (2014): 103105. http://dx.doi.org/10.1117/1.oe.53.10.103105.

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