Jiuai Sun

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.

Reflectance of human skin using colour photometric stereo: with particular application to pigmented lesion analysis

Background/purpose: The optical appearance of human skin is highly dependent on the interaction between the illumination (type and position), observer position and the skin surface structure. Different currently available photographic techniques record different aspects of this appearance, each providing its own incomplete description. This limits their usefulness, especially for pigmented skin lesion diagnosis. In this paper a new, easy to use, low‐cost photographic method is described,which aims to generate an efficiently encoded yet reasonably complete representation of skin appearance.

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.

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.

Unsupervised sub‐segmentation for pigmented skin lesions

Background: Early identification of malignant melanoma with the surgical removal of thin lesions is the most effective treatment for skin cancers. A computer‐aided diagnostic system assists to improve the diagnostic accuracy, where segmenting lesion from normal skin is usually considered as the first step. One of the challenges in the automated segmentation of skin lesions arises from the fact that darker areas within the lesion should be considered separate from the more general suspicious lesion as a whole, because these pigmented areas can provide significant additional diagnostic information.

Machine vision 3D skin texture analysis for detection of melanoma

Purpose – The purpose of this paper is to describe innovative machine vision methods that have been employed for the capture and analysis of 3D skin textures; and the resulting potential for assisting with identification of suspicious lesions in the detection of skin cancer.Design/methodology/approach – A machine vision approach has been employed for analysis of 3D skin textures. This involves an innovative application of photometric stereo for the capture of the textures, and a range of methods for analysing and quantifying them, including statistical methods and neural networks.Findings – 3D skin texture has been identified as a useful indicator of skin cancer. It can be used to improve realism of virtual skin reconstructions in tele‐dermatology. 3D texture features can also be combined with 2D features to obtain a more robust classifier for improving diagnostic accuracy, thereby assisting with the long‐term goal of implementing computer‐aided diagnostics for skin cancer.Originality/value – The device d...

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.

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

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

Biological indexes based reflectional asymmetry for classifying cutaneous lesions

This paper proposes a novel reflectional asymmetry descriptor to quantize the asymmetry of the cutaneous lesions for the discrimination of malignant melanoma from benign nevi. A pigmentation elevation model of the biological indexes is first constructed, and then the asymmetry descriptor is computed by minimizing the histogram difference of the global point signatures of the pigmentation model. Melanin and Erythema Indexes are used instead of the original intensities in colour space to characterize the pigmentation distribution of the cutaneous lesions. 311 dermoscopy images are used to validate the algorithm performance, where 88.50% sensitivity and 81.92% specificity have been achieved when employing an SVM classifier.

Sampling Light Field for Photometric Stereo

—To implement the photometric stereo technique, the radiance distribution of the respective light sources from the different illumination directions must be accurately known. Most previous work has tended to assume distance point sources, so that a collimated and uniform illumination distribution can be approximated, thereby allowing the photometric stereo problem to be easily solved in a linear way. However, there can be significant practical difficulties in realizing such idealized light sources in real world applications. In addition, the strategy of using distant light sources produces a low signal/noise ratio for the system, and is also unsuitable for applications where setup space is limited. These problems potentially limit new opportunities for the wider applications of photometric stereo beyond the research laboratory in evolving areas such as industrial inspection, security and medical applications. This paper proposes a compensation method for illumination radiance to allow the possibility of employing normal low-cost commercial light sources. A flat diffuse surface with either homogeneous or heterogeneous albedo distribution is used to sample the radiance distribution before implementing photometric stereo. The unevenly distributed light radiance is eliminated by using the acquired reference information. The experimental results demonstrate the efficacy of the proposed method.