Leonid V. Tsap

Does colorspace transformation make any difference on skin detection

Skin detection is an important process in many of computer vision algorithms. It usually is a process that starts at a pixel-level, and that involves a pre-process of colorspace transformation followed by a classification process. A colorspace transformation is assumed to increase separability between skin and non-skin classes, to increase similarity among different skin tones, and to bring a robust performance under varying illumination conditions, without any sound reasonings. In this work, we examine if the colorspace transformation does bring those benefits by measuring four separability measurements on a large dataset of 805 images with different skin tones and illumination. Surprising results indicate that most of the colorspace transformations do not bring the benefits which have been assumed.

Scar assessment : Current problems and future solutions

Current problems in the assessment of scars are discussed. The concept of subjective and objective aspects of scar assessment is introduced. The patients own view of the scar (the subjective component) can currently be assessed and may be very influential in determining the patients quality of life, irrespective of the actual physical characteristics of the scar. The objective aspects of the scar, including size, shape, texture, and pliability, are currently difficult to measure. Although the Vancouver Scar Scale has been used as the standard for objective measurements, there are problems with both the validity and reliability of this instrument. Various imaging techniques may permit more reliable and accurate methods for measuring the quantitative aspects of scars.

Nonrigid motion analysis based on dynamic refinement of finite element models

We propose new algorithms for accurate nonrigid motion tracking. Given an initial model representing general knowledge of the object, a set of sparse correspondences, and incomplete or missing information about geometry or material properties, we can recover dense motion vectors using finite element models. The method is based on the iterative analysis of the differences between the actual and predicted behaviors. Unknown parameters are recovered using an iterative descent search for the best nonlinear finite element model that approximates nonrigid motion of the given object. During this search process, we not only estimate material properties, but also infer dense point correspondences from our initial set of sparse correspondences. Thus, during tracking, the model is refined which, in turn, improves tracking quality. Experimental results demonstrate the success of the proposed algorithm. Our work demonstrates the possibility of accurate quantitative analysis of nonrigid motion in range image sequences with objects consisting of multiple materials and 3D volumes.

Effect of colorspace transformation, the illuminance component, and color modeling on skin detection

Skin detection is an important preliminary process in human motion analysis. It is commonly performed in three steps: transforming the pixel color to a non-RGB colorspace, dropping the illuminance component of skin color, and classifying by modeling the skin color distribution. In this paper, we evaluate the effect of these three steps on the skin detection performance. The importance of this study is a new comprehensive colorspace and color modeling testing methodology that would allow for making the best choices for skin detection. Combinations of nine colorspaces, the presence of the absence of the illuminance component, and the two color modeling approaches are compared. The performance is measured by using a receiver operating characteristic (ROC) curve on a large dataset of 805 images with manual ground truth. The results reveal that (1) colorspace transformations can improve performance in certain instances, (2) the absence of the illuminance component decreases performance, and (3) skin color modeling has a greater impact than colorspace transformation. We found that the best performance was obtained by transforming the pixel color to the SCT or HSI colorspaces, keeping the illuminance component, and modeling the color with the histogram approach.

Gesture recognition using Bezier curves for visualization navigation from registered 3-D data☆

Abstract This paper presents a gesture recognition system for visualization navigation. Scientists are interested in developing interactive settings for exploring large data sets in an intuitive environment. The input consists of registered 3-D data. A geometric method using Bezier curves is used for the trajectory analysis and classification of gestures. The hand gesture speed is incorporated into the algorithm to enable correct recognition from trajectories with variations in hand speed. The method is robust and reliable: correct hand identification rate is 99.9% (from 1641 frames), modes of hand movements are correct 95.6% of the time, recognition rate (given the right mode) is 97.9%. An application to gesture-controlled visualization of 3D bioinformatics data is also presented.

A vision-based technique for objective assessment of burn scars

In this paper a method for the objective assessment of burn scars is proposed. The quantitative measures developed in this research provide an objective way to calculate elastic properties of burn scars relative to the surrounding areas. The approach combines range data and the mechanics and motion dynamics of human tissues. Active contours are employed to locate regions of interest and to find displacements of feature points using automatically established correspondences. Changes in strain distribution over time are evaluated. Given images at two time instances and their corresponding features, the finite element method is used to synthesize strain distributions of the underlying tissues. This results in a physically based framework for motion and strain analysis. Relative elasticity of the burn scar is then recovered using iterative descent search for the best nonlinear finite element model that approximates stretching behavior of the region containing the burn scar. The results from the skin elasticity experiments illustrate the ability to objectively detect differences in elasticity between normal and abnormal tissue. These estimated differences in elasticity are correlated against the subjective judgments of physicians that are presently the practice.

Objective evaluation of approaches of skin detection using ROC analysis

Skin detection is an important indicator of human presence and actions in many domains, including interaction, interfaces and security. It is commonly performed in three steps: transforming the pixel color to a non-RGB colorspace, dropping the illuminance component of skin color, and classifying by modeling the skin color distribution. In this paper, we evaluate the effect of these three steps on the skin detection performance. The importance of this study is a new comprehensive colorspace and color modeling testing methodology that would allow for making the best choices for skin detection. Combinations of nine colorspaces, the presence or the absence of the illuminance component, and the two color modeling approaches are compared for different settings (indoor or outdoor) and modeling parameters (the histogram size). The performance is measured by using a receiver operating characteristic (ROC) curve on a large dataset of 845 images (consisting more than 18.6 million pixels) with manual ground truth. The results reveal that (1) colorspace transformations can improve performance in certain instances, (2) the absence of the illuminance component decreases performance, and (3) skin color modeling has a greater impact than colorspace transformation. We found that the best performance was obtained on indoor images by transforming the pixel color to the HSI or SCT colorspaces, keeping the illuminance component, and modeling the color with the histogram approach using a larger size distribution.

Efficient Nonlinear Finite Element Modeling of Nonrigid Objects via Optimization of Mesh Models

In this paper we propose a new general framework for the application ofthe nonlinear finite element method(FEM) to nonrigid motion analysis. We construct the models by integrating image data and prior knowledge, using well-established techniques from computer vision, structural mechanics, and computer-aided design (CAD). These techniques guide the process of optimization of mesh models.Linear FEM proved to be a successful physically based modeling tool in solving limited types of nonrigid motion problems. However, linear FEM cannot handle nonlinear materials or large deformations. Application of nonlinear FEM to nonrigid motion analysis has been restricted by difficulties with high computational complexity and noise sensitivity.We tackle the problems associated with nonlinear FEM by changing the parametric description of the object to allow easy automatic control of the model, using physically motivated analysis of the possible displacements to address the worst effects of the noise, applying mesh control strategies, and utilizing multiscale methods. The combination of these methods represents a new systematic approach to a class of nonrigid motion applications for which sufficiently precise and flexible FEM models can be built.The results from the skin elasticity experiments demonstrate the success of the proposed method. The model allows us to objectively detect the differences in elasticity between normal and abnormal skin. Our work demonstrates the possibility of accurate computation of point correspondences and force recovery from range image sequences containing nonrigid objects and large motion.

Gesture-Tracking in Real Time with Dynamic Regional Range Computation

This paper presents a new approach to the range data utilization in a gesture-tracking system. The use of three-dimensional data is essential for human motion analysis; however, the speed of complete range estimation prohibits from including it in most real-time systems. This work describes a gesture-tracking system using real-time local range on-demand. The system represents a gesture-controlled interface for interactive visual exploration of large data sets. The paper describes a method performing range processing only when necessary and where necessary. Range data is processed only for non-static regions of interest. This is accomplished by a set of filters on the color, motion, and range data. The speed-up achieved is between 1.70 and 2.15. The algorithm also includes a robust skin-color segmentation insensitive to illumination changes. Selective range processing results in dynamic regional range images that contain only information needed by the system.

A modeling approach for burn scar assessment using natural features and elastic property

A modeling approach is presented for quantitative burn scar assessment. Emphases are given to: 1) constructing a finite-element model from natural image features with an adaptive mesh and 2) quantifying the Youngs modulus of scars using the finite-element model and regularization method. A set of natural point features is extracted from the images of burn patients. A Delaunay triangle mesh is then generated that adapts to the point features. A three-dimensional finite-element model is built on top of the mesh with the aid of range images providing the depth information. The Youngs modulus of scars is quantified with a simplified regularization functional, assuming that the knowledge of the scars geometry is available. The consistency between the relative elasticity index and the physicians rating based on the Vancouver scale (a relative scale used to rate burn scars) indicates that the proposed modeling approach has high potential for image-based quantitative burn scar assessment.