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Literatura académica sobre el tema "Multi-Light Image Collections"

Autor: Grafiati

Publicado: 11 de marzo de 2023

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Artículos de revistas sobre el tema "Multi-Light Image Collections"

Fattal, Raanan, Maneesh Agrawala, and Szymon Rusinkiewicz. "Multiscale shape and detail enhancement from multi-light image collections." ACM Transactions on Graphics 26, no. 3 (2007): 51. http://dx.doi.org/10.1145/1276377.1276441.

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Pintus, Ruggero, Alberto Jaspe Villanueva, Antonio Zorcolo, Markus Hadwiger, and Enrico Gobbetti. "A practical and efficient model for intensity calibration of multi-light image collections." Visual Computer 37, no. 9-11 (2021): 2755–67. http://dx.doi.org/10.1007/s00371-021-02172-9.

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Pintus, R., T. G. Dulecha, I. Ciortan, E. Gobbetti, and A. Giachetti. "State‐of‐the‐art in Multi‐Light Image Collections for Surface Visualization and Analysis." Computer Graphics Forum 38, no. 3 (2019): 909–34. http://dx.doi.org/10.1111/cgf.13732.

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Ströbel, Bernhard, Sebastian Schmelzle, Nico Blüthgen, and Michael Heethoff. "An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging." ZooKeys 759 (May 17, 2018): 1–27. http://dx.doi.org/10.3897/zookeys.759.24584.

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Digitization of natural history collections is a major challenge in archiving biodiversity. In recent years, several approaches have emerged, allowing either automated digitization, extended depth of field (EDOF) or multi-view imaging of insects. Here, we present DISC3D: a new digitization device for pinned insects and other small objects that combines all these aspects. A PC and a microcontroller board control the device. It features a sample holder on a motorized two-axis gimbal, allowing the specimens to be imaged from virtually any view. Ambient, mostly reflection-free illumination is ascertained by two LED-stripes circularly installed in two hemispherical white-coated domes (front-light and back-light). The device is equipped with an industrial camera and a compact macro lens, mounted on a motorized macro rail. EDOF images are calculated from an image stack using a novel calibrated scaling algorithm that meets the requirements of the pinhole camera model (a unique central perspective). The images can be used to generate a calibrated and real color texturized 3Dmodel by ‘structure from motion’ with a visibility consistent mesh generation. Such models are ideal for obtaining morphometric measurement data in 1D, 2D and 3D, thereby opening new opportunities for trait-based research in taxonomy, phylogeny, eco-physiology, and functional ecology.

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Hussein, Zinah R. "Improvement of noisy images filtered by bilateral process using a multi-scale context aggregation network." Eastern-European Journal of Enterprise Technologies 2, no. 9 (116) (2022): 14–20. http://dx.doi.org/10.15587/1729-4061.2022.255789.

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Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for de-noising noisy CCTV images. Data-store is used tomanage our dataset, which is an object or collection of data that are huge to enter in memory, it allows to read, manage, and process data located in multiple files as a single entity. The CAN architecture provides integral deep learning layers such as input, convolution, back normalization, and Leaky ReLu layers to construct multi-scale. It is also possible to add custom layers like adaptor normalization (µ) and adaptive normalization (Lambda) to the network. The performance of the developed CAN approximation operator on the bilateral filtering noisy image is proven when improving both the noisy reference image and a CCTV foggy image. The three image evaluation metrics (SSIM, NIQE, and PSNR) evaluate the developed CAN approximation visually and quantitatively when comparing the created de-noised image over the reference image.Compared with the input noisy image, these evaluation metrics for the developed CAN de-noised image were (0.92673/0.76253, 6.18105/12.1865, and 26.786/20.3254) respectively

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Hashim, Ashwaq T., and Zina A. Saleh. "Fast Iris Localization Based on Image Algebra and Morphological Operations." JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 27, no. 2 (2019): 143–54. http://dx.doi.org/10.29196/jubpas.v27i2.2073.

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The localization of the iris is the most significant factor in biometrics of the iris, which traditionally assumes strictly controlled environments. The proposed method includes the pupillary and limbic iris boundaries localization. A primary advantage of image arithmetic operations is that the process is straightforward and therefore fast, these characteristics are employed and combined with the morphological operators in the designing of the proposed algorithm. The proposed algorithm takes into account the noise area which is found in various parts of the eye image such as light reflections, focus, and small visible iris. The experimental results are conducted on a collection of iris images consist of 756 images belong to Chinese Academy of Sciences Institute of Automation (CASIA V-1) and 450 images belong to Multi Media University (MMU V-1) databases. The results indicate a high level of accuracy using the proposed technique. Moreover, the comparison results with the state-of-the-art iris localization algorithms expose considerable improvement in segmentation accuracy while being computationally more efficient.

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Wang, Jing Yi, Hang Gao, and Chun Lin Shen. "Research on Applied-Information Technology with Data Distribution for Light Field Camera Array." Advanced Materials Research 1046 (October 2014): 436–39. http://dx.doi.org/10.4028/www.scientific.net/amr.1046.436.

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The collection of light field is a key part of 3D image rendering and multi-view display system construction. Data exchange of light field frequently used in light field camera array requires high real-time performance and high bandwidth. This paper presents a new method of light field data exchange by applying data distribution service (DDS). Experiment results show it is feasible to apply data distribution service to the light field camera array.

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Guo, Wenping, Yuan Huang, Chunhua Liu, et al. "Upgrade of Thomson Scattering Diagnostic on HL-2A." Instruments 7, no. 1 (2023): 12. http://dx.doi.org/10.3390/instruments7010012.

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The Thomson scattering diagnostic of the HL-2A tokamak device was upgraded to improve its multi-point diagnostic capability, including new collection optics, fibers bundles, and data analysis code. The small old collection lens was replaced by a six-piece lens with a Cooke optical design. The aperture of its first standard sphere face is 310.125 mm, which successfully increases the amount of collected scattering light by about three times. The new collection optic module allows for up to twenty-six spatial points. A kind of Y-type fiber bundle has also been used to ensure that the fiber end-face matches the image of the laser beam exactly. Additionally, the new data analysis code can provide preview results in seconds. Finally, the multi-point Te diagnostic ability has been significantly improved.

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Lai, Wei-Sheng, Yichang Shih, Lun-Cheng Chu, et al. "Face deblurring using dual camera fusion on mobile phones." ACM Transactions on Graphics 41, no. 4 (2022): 1–16. http://dx.doi.org/10.1145/3528223.3530131.

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Motion blur of fast-moving subjects is a longstanding problem in photography and very common on mobile phones due to limited light collection efficiency, particularly in low-light conditions. While we have witnessed great progress in image deblurring in recent years, most methods require significant computational power and have limitations in processing high-resolution photos with severe local motions. To this end, we develop a novel face deblurring system based on the dual camera fusion technique for mobile phones. The system detects subject motion to dynamically enable a reference camera, e.g., ultrawide angle camera commonly available on recent premium phones, and captures an auxiliary photo with faster shutter settings. While the main shot is low noise but blurry (Figure 1(a)), the reference shot is sharp but noisy (Figure 1(b)). We learn ML models to align and fuse these two shots and output a clear photo without motion blur (Figure 1(c)). Our algorithm runs efficiently on Google Pixel 6, which takes 463 ms overhead per shot. Our experiments demonstrate the advantage and robustness of our system against alternative single-image, multi-frame, face-specific, and video deblurring algorithms as well as commercial products. To the best of our knowledge, our work is the first mobile solution for face motion deblurring that works reliably and robustly over thousands of images in diverse motion and lighting conditions.

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Choe, Seungho, and Sheela Ramanna. "Cubical Homology-Based Machine Learning: An Application in Image Classification." Axioms 11, no. 3 (2022): 112. http://dx.doi.org/10.3390/axioms11030112.

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Persistent homology is a powerful tool in topological data analysis (TDA) to compute, study, and encode efficiently multi-scale topological features and is being increasingly used in digital image classification. The topological features represent a number of connected components, cycles, and voids that describe the shape of data. Persistent homology extracts the birth and death of these topological features through a filtration process. The lifespan of these features can be represented using persistent diagrams (topological signatures). Cubical homology is a more efficient method for extracting topological features from a 2D image and uses a collection of cubes to compute the homology, which fits the digital image structure of grids. In this research, we propose a cubical homology-based algorithm for extracting topological features from 2D images to generate their topological signatures. Additionally, we propose a novel score measure, which measures the significance of each of the sub-simplices in terms of persistence. In addition, gray-level co-occurrence matrix (GLCM) and contrast limited adapting histogram equalization (CLAHE) are used as supplementary methods for extracting features. Supervised machine learning models are trained on selected image datasets to study the efficacy of the extracted topological features. Among the eight tested models with six published image datasets of varying pixel sizes, classes, and distributions, our experiments demonstrate that cubical homology-based machine learning with the deep residual network (ResNet 1D) and Light Gradient Boosting Machine (lightGBM) shows promise with the extracted topological features.

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Tesis sobre el tema "Multi-Light Image Collections"

Dulecha, Tinsae Gebrechristos. "Surface analysis and visualization from multi-light image collections." Doctoral thesis, 2021. http://hdl.handle.net/11562/1043402.

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Multi-Light Image Collections (MLICs) are stacks of photos of a scene acquired with a fixed viewpoint and a varying surface illumination that provides large amounts of visual and geometric information. Over the last decades, a wide variety of methods have been devised to extract information from MLICs and have shown its use in different application domains to support daily activities. In this thesis, we present methods that leverage a MLICs for surface analysis and visualization. First, we provide background information: acquisition setup, light calibration and application areas where MLICs have been successfully used for the research of daily analysis work. Following, we discuss the use of MLIC for surface visualization and analysis and available tools used to support the analysis. Here, we discuss methods that strive to support the direct exploration of the captured MLIC, methods that generate relightable models from MLIC, non-photorealistic visualization methods that rely on MLIC, methods that estimate normal map from MLIC and we point out visualization tools used to do MLIC analysis. In chapter 3 we propose novel benchmark datasets (RealRTI, SynthRTI and SynthPS) that can be used to evaluate algorithms that rely on MLIC and discusses available benchmark for validation of photometric algorithms that can be also used to validate other MLIC-based algorithms. In chapter 4, we evaluate the performance of different photometric stereo algorithms using SynthPS for cultural heritage applications. RealRTI and SynthRTI have been used to evaluate the performance of (Neural)RTI method. Then, in chapter 5, we present a neural network-based RTI method, aka NeuralRTI, a framework for pixel-based encoding and relighting of RTI data. In this method using a simple autoencoder architecture, we show that it is possible to obtain a highly compressed representation that better preserves the original information and provides increased quality of virtual images relighted from novel directions, particularly in the case of challenging glossy materials. Finally, in chapter 6, we present a method for the detection of crack on the surface of paintings from multi-light image acquisitions and that can be used as well on single images and conclude our presentation.

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Actas de conferencias sobre el tema "Multi-Light Image Collections"

Fattal, Raanan, Maneesh Agrawala, and Szymon Rusinkiewicz. "Multiscale shape and detail enhancement from multi-light image collections." In ACM SIGGRAPH 2007 papers. ACM Press, 2007. http://dx.doi.org/10.1145/1275808.1276441.

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Maxey, L. C., and B. J. Hilson. "A Deterministic Method for Aligning Multiple Optical Waveguides to a Paraboloidal Collector." In ASME 2003 International Solar Energy Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/isec2003-44017.

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For solar lighting systems employing fiber optic waveguides1,2, to conduct the collected light, paraboloidal mirrors are the preferred reflector choice. To achieve optimum performance in systems with relatively small collection apertures, both the quality of the mirror and the quality of optical system alignment must be well controlled. In systems employing multiple waveguides with a single paraboloid, the focus of the paraboloid must be segmented into several separate focal points directed into individual fibers. Each waveguide entrance aperture must be accurately co-located with its designated focal point so that the image that is formed on the fiber will have the fewest possible aberrations and thus, the smallest possible focused spot size. Two methods for aligning individual optical waveguides in a multi-aperture paraboloidal collection system are described. The first method employs a commercially available collimation tester to incrementally improve the alignment. The second, a deterministic method, employs a cube corner retro-reflector and an easily constructed imaging system to reliably align the fibers to their respective segments of the parent paraboloid. The image of the focused spot formed by the light that is returned from the retro-reflector reveals alignment information that is easily interpreted to enable pitch, yaw and focus errors to be systematically removed. This ensures that the alignment of the system is optimized to reduce aberrations prior to final adjustment of the system “on-sun”.

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