Presence Of Specular Research Articles

Normalization of face illumination using basic knowledge and information extracted from a single image

This paper presents a method for face image normalization that can be applied to the extraction of illumination invariant facial features or used to remove bad lighting effects and produce high-quality, photorealistic results. Most of the existing approaches concentrate on separating the constant albedo from the variable light intensity; that concept, however, is based on the Lambertian model, which fails in the presence of specularities and cast shadows. Therefore, to tackle this problem, various methods use bootstrap sets to learn the reflectance model of a given person or a face in general. Unfortunately, algorithms of this type are usually not practical for real applications. The proposed approach does not require any training procedure, as the normalization is performed only on the basis of information that is contained in the image that is being processed. Still, external knowledge that is represented by the deformable shape model is employed to execute the localization of the important points, such as eye centers. Additionally, an assumption regarding the symmetry of the face is utilized. The rest of the normalization algorithm relies mostly on simple image processing techniques. Experiments on the CMU-PIE and Extended Yale B databases show that the new method not only provides satisfactory recognition results but is also able to generate natural-looking images.

Non-Lambertian Reflectance Modeling and Shape Recovery of Faces Using Tensor Splines

Modeling illumination effects and pose variations of a face is of fundamental importance in the field of facial image analysis. Most of the conventional techniques that simultaneously address both of these problems work with the Lambertian assumption and thus fall short of accurately capturing the complex intensity variation that the facial images exhibit or recovering their 3D shape in the presence of specularities and cast shadows. In this paper, we present a novel Tensor-Spline-based framework for facial image analysis. We show that, using this framework, the facial apparent BRDF field can be accurately estimated while seamlessly accounting for cast shadows and specularities. Further, using local neighborhood information, the same framework can be exploited to recover the 3D shape of the face (to handle pose variation). We quantitatively validate the accuracy of the Tensor Spline model using a more general model based on the mixture of single-lobed spherical functions. We demonstrate the effectiveness of our technique by presenting extensive experimental results for face relighting, 3D shape recovery, and face recognition using the Extended Yale B and CMU PIE benchmark data sets.

Real-time segmentation of surgical instruments inside the abdominal cavity using a joint hue saturation color feature

In this paper, the real-time segmentation of surgical instruments with color images used in minimally invasive surgery is addressed. This work has been developed in the scope of the robotized laparoscopic surgery, specifically for the detection and tracking of gray regions and accounting for images of metallic instruments inside the abdominal cavity. With this environment, the moving background due to the breathing motion, the non-uniform and time-varying lighting conditions and the presence of specularities are the main difficulties to overcome. Then, to achieve an automatic color segmentation suitable for robot control, we developed a technique based on a discriminant color feature with robustness capabilities with respect to intensity variations and specularities. We also designed an adaptive region growing with automatic region seed detection and a model-based region classification, both dedicated to laparoscopy. The foreseen application is a good training ground to evaluate the proposed technique and the effectiveness of this work has been demonstrated through experimental results with endoscopic image sequences to efficiently locate boundaries of a landmark-free needle-holder at half the video-rate.

Model-driven active visual tracking

We have previously demonstrated that the performance of tracking algorithms can be improved by integrating information from multiple cues in a model-driven Bayesian reasoning framework. Here we extend our work to active vision tracking, with variable camera geometry. Many existent active tracking algorithms avoid the problem of variable camera geometry by tracking view independent features, such as corners and lines. However, the performance of algorithms based on those single features will greatly deteriorate in the presence of specularities and dense clutter. We show, by integrating multiple cues and updating the camera geometry on-line, that it is possible to track a complicated object moving arbitrarily in three-dimensional (3D) space. We use a four degree-of-freedom (4-DoF) binocular camera rig to track three focus features of an industrial object, whose complete model is known. The camera geometry is updated by using the rig control commands and kinematic model of the stereo head. The extrinsic parameters are further refined by interpolation from a previously sampled calibration of the head work space. The 2D target position estimates are obtained by a combination of blob detection, edge searching and gray-level matching, with the aid of model geometrical structure projection using current estimates of camera geometry. The information is represented in the form of a probability density distribution, and propagated in a Bayes Net. The Bayesian reasoning that is performed in the 2D image is coupled by the rigid model geometry constraint in 3D space. An αβ filter is used to smooth the tracking pursuit and to predict the position of the object in the next iteration of data acquisition. The solution of the inverse kinematic problem at the predicted position is used to control the position of the stereo head. Finally, experiments show that the target undertaking arbitrarily 3D motion can be successfully tracked in the presence of specularities and dense clutter.