Robust Specularity Removal from Hand-held Videos

Specular reflection exists when one tries to record a photo or video through a transparent glass medium or opaque surfaces such as plastics, ceramics, polyester and human skin, which can be well described as the superposition of a transmitted layer and a reflection layer. These specular reflections often confound the algorithms developed for image analysis, computer vision and pattern recognition. To obtain a pure diffuse reflection component, specularity (highlights) needs to be removed. To handle this problem, a novel and robust algorithm is formulated. The contributions of this work are three-fold.;First, the smoothness of the video along with the temporal coherence and illumination changes are preserved by reducing the flickering and jagged edges caused by hand-held video acquisition and homography transformation respectively.;Second, this algorithm is designed to improve upon the state-of-art algorithms by automatically selecting the region of interest (ROI) for all the frames, reducing the computational time and complexity by utilizing the luminance (Y) channel and exploiting the Augmented Lagrange Multiplier (ALM) with Alternating Direction Minimizing (ADM) to facilitate the derivation of solution algorithms.;Third, a quantity metrics is devised, which objectively quantifies the amount of specularity in each frame of a hand-held video. The proposed specularity removal algorithm is compared against existing state-of-art algorithms using the newly-developed quantity metrics. Experimental results validate that the developed algorithm has superior performance in terms of computation time, quality and accuracy

Shape from X: Psychophysics and Computation

This chapter contains sections titled: The Many Routes to Shape, The Need for Integration, Shape From Stereo and Shading (Local Measurements) 1 , Shape from Shading and Texture (Global Measurements), Shape from Disparate Shading (Intensity-Based Stereo), Shape from Highlights 2 , Integration of Depth Modules, A Bayesian Framework for Cue Integration 3 , Final Remarks, Acknowledgments, Appendices, Reference

Surface analysis and visualization from multi-light image collections

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

Photometric Stereo-Based Defect Detection System for Steel Components Manufacturing Using a Deep Segmentation Network

This paper presents an automatic system for the quality control of metallic components using a photometric stereo-based sensor and a customized semantic segmentation network. This system is designed based on interoperable modules, and allows capturing the knowledge of the operators to apply it later in automatic defect detection. A salient contribution is the compact representation of the surface information achieved by combining photometric stereo images into a RGB image that is fed to a convolutional segmentation network trained for surface defect detection. We demonstrate the advantage of this compact surface imaging representation over the use of each photometric imaging source of information in isolation. An empirical analysis of the performance of the segmentation network on imaging samples of materials with diverse surface reflectance properties is carried out, achieving Dice performance index values above 0.83 in all cases. The results support the potential of photometric stereo in conjunction with our semantic segmentation network

Recovery of surface orientation from diffuse polarization

When unpolarized light is reflected from a smooth dielectric surface, it becomes partially polarized. This is due to the orientation of dipoles induced in the reflecting medium and applies to both specular and diffuse reflection. This paper is concerned with exploiting polarization by surface reflection, using images of smooth dielectric objects, to recover surface normals and, hence, height. This paper presents the underlying physics of polarization by reflection, starting with the Fresnel equations. These equations are used to interpret images taken with a linear polarizer and digital camera, revealing the shape of the objects. Experimental results are presented that illustrate that the technique is accurate near object limbs, as the theory predicts, with less precise, but still useful, results elsewhere. A detailed analysis of the accuracy of the technique for a variety of materials is presented. A method for estimating refractive indices using a laser and linear polarizer is also given

Photometric single-view dense 3D reconstruction in endoscopy

Visual SLAM inside the human body will open the way to computer-assisted navigation in endoscopy. However, due to space limitations, medical endoscopes only provide monocular images, leading to systems lacking true scale. In this paper, we exploit the controlled lighting in colonoscopy to achieve the first in-vivo 3D reconstruction of the human colon using photometric stereo on a calibrated monocular endoscope. Our method works in a real medical environment, providing both a suitable in-place calibration procedure and a depth estimation technique adapted to the colon's tubular geometry. We validate our method on simulated colonoscopies, obtaining a mean error of 7% on depth estimation, which is below 3 mm on average. Our qualitative results on the EndoMapper dataset show that the method is able to correctly estimate the colon shape in real human colonoscopies, paving the ground for truescale monocular SLAM in endoscopy