An improved photometric stereo through distance estimation and light vector optimization from diffused maxima region

© 2013 Elsevier B.V. All rights reserved. Although photometric stereo offers an attractive technique for acquiring 3D data using low-cost equipment, inherent limitations in the methodology have served to limit its practical application, particularly in measurement or metrology tasks. Here we address this issue. Traditional photometric stereo assumes that lighting directions at every pixel are the same, which is not usually the case in real applications, and especially where the size of object being observed is comparable to the working distance. Such imperfections of the illumination may make the subsequent reconstruction procedures used to obtain the 3D shape of the scene prone to low frequency geometric distortion and systematic error (bias). Also, the 3D reconstruction of the object results in a geometric shape with an unknown scale. To overcome these problems a novel method of estimating the distance of the object from the camera is developed, which employs photometric stereo images without using other additional imaging modality. The method firstly identifies Lambertian diffused maxima region to calculate the object distance from the camera, from which the corrected per-pixel light vector is able to be derived and the absolute dimensions of the object can be subsequently estimated. We also propose a new calibration process to allow a dynamic(as an object moves in the field of view) calculation of light vectors for each pixel with little additional computation cost. Experiments performed on synthetic as well as real data demonstrates that the proposed approach offers improved performance, achieving a reduction in the estimated surface normal error of up to 45% as well as mean height error of reconstructed surface of up to 6 mm. In addition, when compared to traditional photometric stereo, the proposed method reduces the mean angular and height error so that it is low, constant and independent of the position of the object placement within a normal working range

Obtaining malignant melanoma indicators through statistical analysis of 3D skin surface disruptions

Background/purpose: It has been observed that disruptions in skin patterns are larger for malignant melanoma (MM) than benign lesions. In order to extend the classification results achieved for 2D skin patterns, this work intends to investigate the feasibility of lesion classification using 3D skin surface texture, in the form of surface normals acquired from a previously built six-light photometric stereo device. Material and methods: The proposed approach seeks to separate MM from benign lesions through analysis of the degree of surface disruptions in the tilt and slant direction of surface normals, so called skin tilt pattern and skin slant pattern. A 2D Gaussian function is used to simulate a normal region of skin for comparison with a lesion's observed tilt and slant patterns. The differences associated with the two patterns are estimated as the disruptions in the tilt and slant pattern respectively for lesion classification. Results: Preliminary studies on11 MMs and 28 benign lesions have given Receiver operating characteristic areas of 0.73 and 0.85 for tilt and slant pattern, respectively, which are better than 0.65 previously obtained for the skin line direction using the same samples. Conclusions: This paper has demonstrated an important application of 3D skin texture for computer-assisted diagnosis of MM in vivo. By taking advantage of the extra dimensional information, preliminary studies suggest that some improvements over the existing 2D skin line pattern approach for the differentiation between MM and benign lesions. © 2009 John Wiley & Sons A/S

Multidimensional imaging for skin tissue surface characterization

Human skin, the outer and largest organ covering our body, can be described in terms of both its 3D spatial topography and its 2D spectral reflectance. Such a characterization normally requires the application of separate procedures using different kinds of equipment, where spectral reflectance can only be obtained from a small patch of the skin surface. This paper investigates the integration of multiple imaging modalities to simultaneously capture both spectral and spatial information from the skin surface over a wide area. By extending the imaging spectrum from the visible to the near-infrared (NIR), we improve general recovery, obtain a more detailed skin profile, and are able to identify the distribution of various principal chromophores within the deeper dermal layers. Experiments show that new dimensions of skin characterization can be generated through the recovered geometrical and spectral information, so that an enhanced visibility of important skin physiological phenomena can be achieved. © 2013 Elsevier B.V. All rights reserved

Examining the uncertainty of the recovered surface normal in three light photometric stereo

Although the three light photometric stereo technique has been used in many applications, there is little published work concerned with characterizing the uncertainty of these systems due to the involvement of a number of complicating factors. This paper presents a methodology used to analyze the uncertainty of the recovered unit surface normal with respect to irradiance variance. Illumination configurations and the values of the composite albedo are found to directly affect the stability of the photometric stereo technique. An orthogonally distributed illumination arrangement is proven to be the theoretically optimal configuration. Further practical considerations are also identified. The derived general uncertainty expression can be easily employed to optimize the location of the light sources. Hence, the work is of significance for the development of practical industrial applications of photometric stereo, including metrology, reverse engineering and various surface inspection tasks. © 2006 Elsevier B.V. All rights reserved

Simulation of an optical-sensing technique for tracking surgical tools employed in computer-assisted interventions

Establishing the accuracy of optical-sensing systems for tools used in surgical procedures is an essential and nontrivial task. This paper shows that an optical-tracking system may be regarded as a cooperative system in that its accuracy is related not only to the optical-tracking system itself, but also to the parameters of the tracked objects. A numerical simulation method is used to obtain the accuracy distribution of imaginary markers, and, through statistical analysis, it is concluded that accuracy is inversely proportional to the root of the number of real markers and varies in proportion to increasing noise on the real marker positions. The results can be used to optimize the design of sensor-navigated surgical tools and improve accuracy when placing reference frames in radiology tasks. The work is also relevant to any position-sensing application that involves point-based rigid transformations. © 2005 IEEE

Object surface recovery using a multi-light photometric stereo technique for non-Lambertian surfaces subject to shadows and specularities

This paper presents a new multi-light source photometric stereo system for reconstructing images of various characteristics of non-Lambertian rough surfaces with widely varying texture and specularity. Compared to the traditional three-light photometric stereo method, extra lights are employed using a hierarchical selection strategy to eliminate the effects of shadows and specularities, and to make the system more robust. We also show that six lights is the minimum needed in order to apply photometric stereo to the entire visible surface of any convex object. Experiments on synthetic and real scenes demonstrate that the proposed method can extract surface reflectance and orientation effectively, even in the presence of strong shadows and highlights. Hence, the method offers advantages in the recovery of dichromatic surfaces possessing rough texture or deeply relieved topographic features, with applications in reverse engineering and industrial surface inspection. Experimental results are presented in the paper

A computer assisted diagnosis system for malignant melanoma using 3D skin surface texture features and artificial neural network

It has been observed that disruptions in skin patterns are larger for malignant melanoma than for benign lesions. In contrast to existing work on 2D skin line patterns, this work proposes a computer assisted diagnosis system for malignant melanoma based on acquiring, analysing and classifying 3D skin surface texture features. Specifically, the 3D skin surface texture, in the form of surface normal vectors are acquired from a six-light photometric stereo device, the 3D features from the surface normals are extracted as the residuals between the acquired data and those from a 2D Gaussian model, while a three-layer feedforward neural classifier is used to classify the residuals. Preliminary studies on a sample set including 12 malignant melanomas and 34 benign lesions have given 91.7% sensitivity and 76.4% specificity using the proposed 3D skin surface normal features, which are better than 91.7% sensitivity and 25.7% specificity using the existing 2D skin line pattern features over the same lesion samples. This demonstrates that the proposed computer assisted diagnosis system of malignant melanoma based on 3D features offers an improvement over that based on 2D skin line patterns. Copyright © 2010 Inderscience Enterprises Ltd

Obtaining malignant melanoma indicators through statistical analysis of 3D skin surface disruptions

Background/purpose: It has been observed that disruptions in skin patterns are larger for malignant melanoma (MM) than benign lesions. In order to extend the classification results achieved for 2D skin patterns, this work intends to investigate the feasibility of lesion classification using 3D skin surface texture, in the form of surface normals acquired from a previously built six-light photometric stereo device. Material and methods: The proposed approach seeks to separate MM from benign lesions through analysis of the degree of surface disruptions in the tilt and slant direction of surface normals, so called skin tilt pattern and skin slant pattern. A 2D Gaussian function is used to simulate a normal region of skin for comparison with a lesion's observed tilt and slant patterns. The differences associated with the two patterns are estimated as the disruptions in the tilt and slant pattern respectively for lesion classification. Results: Preliminary studies on11 MMs and 28 benign lesions have given Receiver operating characteristic areas of 0.73 and 0.85 for tilt and slant pattern, respectively, which are better than 0.65 previously obtained for the skin line direction using the same samples. Conclusions: This paper has demonstrated an important application of 3D skin texture for computer-assisted diagnosis of MM in vivo. By taking advantage of the extra dimensional information, preliminary studies suggest that some improvements over the existing 2D skin line pattern approach for the differentiation between MM and benign lesions. © 2009 John Wiley & Sons A/S