Adaptive Albedo Compensation for Accurate Phase-Shift Coding

Pistellato, Mara; Cosmo, Luca; Bergamasco, Filippo; Gasparetto, Andrea; Albarelli, Andrea

doi:10.1109/icpr.2018.8545465

Cited by 11 publications

(7 citation statements)

References 21 publications

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“…The hardware system and calibration results are shown in Figure 7 . In addition, a three-step phase-shifting algorithm in [ 29 ] was implemented and the reconstruction results and comparisons were also given. The phase-shifting pattern is set to have a period of 32 pixels and be shifted twice.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…N -step phase-shifting profilometry [ 11 , 24 , 25 , 26 , 27 , 28 ] is another commonly used technique for 3D measurements. In recent work, for textured surface, the method in [ 29 ] corrected the recovered phases by template convolution in 3×3 or 5×5 pixel windows. As the intensities of camera images reach the maximum intensity limitation of the camera sensor for shiny surface, a high dynamic range (HDR) 3D measurement technique were proposed in [ 25 , 30 ].…”

Section: Related Workmentioning

confidence: 99%

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Eliminating the Effect of Reflectance Properties on Reconstruction in Stripe Structured Light System

Song

2020

Sensors

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The acquisition of the geometry of general scenes is related to the interplay of surface geometry, material properties and illumination characteristics. Surface texture and non-Lambertian reflectance properties degrade the reconstruction results by structured light technique. Existing structured light techniques focus on different coding strategy and light sources to improve reconstruction accuracy. The hybrid system consisting of a structured light technique and photometric stereo combines the depth value with normal information to refine the reconstruction results. In this paper, we propose a novel hybrid system consisting of stripe-based structured light and photometric stereo. The effect of surface texture and non-Lambertian reflection on stripe detection is first concluded. Contrary to existing fusion strategy, we propose an improved method for stripe detection to reduce the above factor’s effects on accuracy. The reconstruction problem for general scene comes down to using reflectance properties to improve the accuracy of stripe detection. Several objects, including checkerboard, metal-flat plane and free-form objects with complex reflectance properties, were reconstructed to validate our proposed method, which illustrates the effectiveness on improving the reconstruction accuracy of complex objects. The three-step phase-shifting algorithm was implemented and the reconstruction results were given and also compared with ours. In addition, our proposed framework provides a new feasible scheme for solving the ongoing problem of the reconstruction of complex objects with variant reflectance. The problem can be solved by subtracting the non-Lambertian components from the original grey values of stripe to improve the accuracy of stripe detection. In the future, based on stripe structured light technique, more general reflection models can be used to model different types of reflection properties of complex objects.

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Section: Experiments and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Eliminating the Effect of Reflectance Properties on Reconstruction in Stripe Structured Light System

Song

2020

Sensors

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“…Numerous examples can be found in the literature, where researchers reconstructed HDR using multi-exposure images [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. One of the earliest efforts in creating HDR images using multiple exposures was made by [ 16 ]; the authors introduced a novel method to recover the CRF as well as an HDR radiance map using multi-exposure images.…”

Section: Related Workmentioning

confidence: 99%

Exploiting Light Polarization for Deep HDR Imaging from a Single Exposure

Pistellato

Fatima

Wimmer

2023

Sensors

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In computational photography, high dynamic range (HDR) imaging refers to the family of techniques used to recover a wider range of intensity values compared to the limited range provided by standard sensors. Classical techniques consist of acquiring a scene-varying exposure to compensate for saturated and underexposed regions, followed by a non-linear compression of intensity values called tone mapping. Recently, there has been a growing interest in estimating HDR images from a single exposure. Some methods exploit data-driven models trained to estimate values outside the camera’s visible intensity levels. Others make use of polarimetric cameras to reconstruct HDR information without exposure bracketing. In this paper, we present a novel HDR reconstruction method that employs a single PFA (polarimetric filter array) camera with an additional external polarizer to increase the scene’s dynamic range across the acquired channels and to mimic different exposures. Our contribution consists of a pipeline that effectively combines standard HDR algorithms based on bracketing and data-driven solutions designed to work with polarimetric images. In this regard, we present a novel CNN (convolutional neural network) model that exploits the underlying mosaiced pattern of the PFA in combination with the external polarizer to estimate the original scene properties, and a second model designed to further improve the final tone mapping step. The combination of such techniques enables us to take advantage of the light attenuation given by the filters while producing an accurate reconstruction. We present an extensive experimental section in which we validate the proposed method on both synthetic and real-world datasets specifically acquired for the task. Quantitative and qualitative results show the effectiveness of the approach when compared to state-of-the-art methods. In particular, our technique exhibits a PSNR (peak signal-to-noise ratio) on the whole test set equal to 23 dB, which is 18% better with respect to the second-best alternative.

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“…In general, the main upside associated with CNNs is their speed and how efficient their latent representations are. Conversely, other properties that could be exploited while working with images, such as location invariance and local compositionality [67,68], make little sense when analysing text. Many approaches have been proposed, one of the most popular being TextCNN [69], a comparatively simple CNN-based model with a one layer convolution structure that is placed on top of word embeddings.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A Survey on Text Classification Algorithms: From Text to Predictions

et al. 2022

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In recent years, the exponential growth of digital documents has been met by rapid progress in text classification techniques. Newly proposed machine learning algorithms leverage the latest advancements in deep learning methods, allowing for the automatic extraction of expressive features. The swift development of these methods has led to a plethora of strategies to encode natural language into machine-interpretable data. The latest language modelling algorithms are used in conjunction with ad hoc preprocessing procedures, of which the description is often omitted in favour of a more detailed explanation of the classification step. This paper offers a concise review of recent text classification models, with emphasis on the flow of data, from raw text to output labels. We highlight the differences between earlier methods and more recent, deep learning-based methods in both their functioning and in how they transform input data. To give a better perspective on the text classification landscape, we provide an overview of datasets for the English language, as well as supplying instructions for the synthesis of two new multilabel datasets, which we found to be particularly scarce in this setting. Finally, we provide an outline of new experimental results and discuss the open research challenges posed by deep learning-based language models.

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Adaptive Albedo Compensation for Accurate Phase-Shift Coding

Cited by 11 publications

References 21 publications

Eliminating the Effect of Reflectance Properties on Reconstruction in Stripe Structured Light System

Eliminating the Effect of Reflectance Properties on Reconstruction in Stripe Structured Light System

Exploiting Light Polarization for Deep HDR Imaging from a Single Exposure

A Survey on Text Classification Algorithms: From Text to Predictions

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