8 research outputs found
Low-cost single-pixel 3D imaging by using an LED array
We propose a method to perform color imaging with a single photodiode by using
light structured illumination generated with a low-cost color LED array. The LED array is
used to generate a sequence of color Hadamard patterns which are projected onto the object
by a simple optical system while the photodiode records the light intensity. A field
programmable gate array (FPGA) controls the LED panel allowing us to obtain high refresh
rates up to 10 kHz. The system is extended to 3D imaging by simply adding a low number of
photodiodes at different locations. The 3D shape of the object is obtained by using a noncalibrated
photometric stereo technique. Experimental results are provided for an LED array
with 32 × 32 elements
Analysis and approximation of some Shape-from-Shading models for non-Lambertian surfaces
The reconstruction of a 3D object or a scene is a classical inverse problem
in Computer Vision. In the case of a single image this is called the
Shape-from-Shading (SfS) problem and it is known to be ill-posed even in a
simplified version like the vertical light source case. A huge number of works
deals with the orthographic SfS problem based on the Lambertian reflectance
model, the most common and simplest model which leads to an eikonal type
equation when the light source is on the vertical axis. In this paper we want
to study non-Lambertian models since they are more realistic and suitable
whenever one has to deal with different kind of surfaces, rough or specular. We
will present a unified mathematical formulation of some popular orthographic
non-Lambertian models, considering vertical and oblique light directions as
well as different viewer positions. These models lead to more complex
stationary nonlinear partial differential equations of Hamilton-Jacobi type
which can be regarded as the generalization of the classical eikonal equation
corresponding to the Lambertian case. However, all the equations corresponding
to the models considered here (Oren-Nayar and Phong) have a similar structure
so we can look for weak solutions to this class in the viscosity solution
framework. Via this unified approach, we are able to develop a semi-Lagrangian
approximation scheme for the Oren-Nayar and the Phong model and to prove a
general convergence result. Numerical simulations on synthetic and real images
will illustrate the effectiveness of this approach and the main features of the
scheme, also comparing the results with previous results in the literature.Comment: Accepted version to Journal of Mathematical Imaging and Vision, 57
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3D freeform surface measurement on coordinate measuring machine using photometric stereo method
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonSurface metrology has been widely used in manufacturing for many years. There has been a wide range of techniques applied for measuring surface topography. A photometric stereo technique is one of the best ways for the analysis of three-dimensional (3D) surface textural patterns. Many published works are concerned the developed approach for recovering the 3D profiles from surface normal. This research not only presents a methodology used to retrieve the profiles of surface roughness standards but also investigates the uncertainty estimation of textural measurement determined by the photometric stereo method. Various input quantities have been studied such as pixel error from recovered 3D surface textural patterns, the power of light source which involved with surface roughness average (Ra) value and the effect of room temperature. The surface roughness standards were utilized as the reference value. In term of increasing accuracy of the reference value, a contact method (stylus instrument) was used to calibrate them. Illumination angles of light source had some influence on the measurement results. A coordinate measuring machine (CMM) was used for holding the light source in order to study the effects of tilt and slant angles. The effect of tilt and slant angles were investigated. The results of these experiments successfully indicated that the angle used in photometric stereo method played an important role to the accuracy level of the roughness measurement results. The surface roughness specimen manufactured by a Computer Numerical Control (CNC) was applied to validate the capability of the photometric stereo system.The royal Thai government, ministry of sciences and technology and national institute of metrology Thailand (NIMT
制約付き回帰に基づく照度差ステレオ
学位の種別: 課程博士審査委員会委員 : (主査)東京大学准教授 山﨑 俊彦, 東京大学教授, 相澤 清晴, 東京大学教授 池内 克史, 東京大学教授 佐藤 真一, 東京大学教授 佐藤 洋一, 東京大学教授 苗村 健University of Tokyo(東京大学
Robust Algorithms for Low-Rank and Sparse Matrix Models
Data in statistical signal processing problems is often inherently matrix-valued, and a natural first step in working with such data is to impose a model with structure that captures the distinctive features of the underlying data. Under the right model, one can design algorithms that can reliably tease weak signals out of highly corrupted data. In this thesis, we study two important classes of matrix structure: low-rankness and sparsity. In particular, we focus on robust principal component analysis (PCA) models that decompose data into the sum of low-rank and sparse (in an appropriate sense) components. Robust PCA models are popular because they are useful models for data in practice and because efficient algorithms exist for solving them.
This thesis focuses on developing new robust PCA algorithms that advance the state-of-the-art in several key respects. First, we develop a theoretical understanding of the effect of outliers on PCA and the extent to which one can reliably reject outliers from corrupted data using thresholding schemes. We apply these insights and other recent results from low-rank matrix estimation to design robust PCA algorithms with improved low-rank models that are well-suited for processing highly corrupted data. On the sparse modeling front, we use sparse signal models like spatial continuity and dictionary learning to develop new methods with important adaptive representational capabilities. We also propose efficient algorithms for implementing our methods, including an extension of our dictionary learning algorithms to the online or sequential data setting. The underlying theme of our work is to combine ideas from low-rank and sparse modeling in novel ways to design robust algorithms that produce accurate reconstructions from highly undersampled or corrupted data. We consider a variety of application domains for our methods, including foreground-background separation, photometric stereo, and inverse problems such as video inpainting and dynamic magnetic resonance imaging.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143925/1/brimoor_1.pd