Julien Dehos

Automatic Correction of Ma and Sonka's Thinning Algorithm Using P-Simple Points

Ma and Sonka proposed a fully parallel 3D thinning algorithm which does not always preserve topology. We propose an algorithm based on P-simple points which automatically corrects Ma and Sonkas algorithm. As far as we know, our algorithm is the only fully parallel curve thinning algorithm which preserves topology.

Radiometric compensation for a low-cost immersive projection system

Catopsys is a low-cost projection system aiming at making mixed reality (virtual, augmented or diminished reality) affordable. It combines a videoprojector, a camera and a convex mirror and works in a non-specific room. This system displays an immersive environment by projecting an image onto the different parts of the room. However, the presence of an uncalibrated projector, heterogeneous materials and light inter-reflections influence the colors of the environment displayed in the room. Radiometric compensation of the projection process enables the system to reduce this problem. In this paper, we present our low-cost immersive projection system and propose a radiometric model and a compensation method which handle the projector response, surface materials and inter-reflections between surfaces. Our method works in two stages. First, the radiometric response of the projection process is evaluated. Then, this radiometric response is used to compensate the projection process in the desired environments.

Visual Impact of Rendering on Image Quality

To generate photo-realistic images, modern renderers generally use stochastic algorithms such as the path-tracing algorithm. These algorithms can produce high-quality images but may require a long computation time. Therefore, rendering is generally stopped after a given amount of time and the output image may not be fully converged. In this case, the resulting variance can be seen as noise.

No-Reference Methods and Fuzzy Sets

This model can then be used in any progressive stochastic global illumination method in order to estimate the noise level of different parts of any image. A comparative study of this model with a simple test image demonstrates the good consistency between an added noise value and the results from the noise estimator.

Full-Reference Methods and Machine Learning

This chapter introduces the application of machine learning to Image Quality Assessment (IQA) in the case of computer-generated images. The classical learning machines, like SVMs, are quickly remained and RVMs are presented to deal with this particular IQA case (noise features learning). A recently performed psycho-visual experiment provides psycho-visual scores on some synthetic images (learning database), and comprehensive testing demonstrates the good consistency between these scores and the quality measures we obtain. The proposed measure has also been compared with close methods like RBFs, MLPs, and SVMs and gives satisfactory performance.

Reduced-Reference Methods

Reduced-reference image quality assessment needs no prior knowledge of reference image but only a minimal knowledge about processed images. A new reduced-reference image quality measure, based on SVMs and RVMs, using a supervised learning framework and synthetic images is proposed in this chapter. This new metric is compared with experimental psycho-visual data with success and shows that inductive learning is a good solution to deal with small sizes of the databases of computer-generated images. As reduced-reference techniques need only small size of labeled samples, thus the rapidity of the learning process is increased.

Monte Carlo Methods for Image Synthesis

Image synthesis (also called rendering) consists in generating an image from a virtual 3D scene (composed of light sources, objects, materials, and a camera). Numerous rendering algorithms have been proposed since the 1970s: z-buffer (Catmull, A subdivision algorithm for computer display of curved surfaces. Ph.D. thesis, 1974), ray tracing (Whitted, Commun ACM 23(6):343–349, 1980), radiosity (Goral et al., Modeling the interaction of light between diffuse surfaces, 1984), path tracing (Kajiya, ACM Comput Graph 20(4):143–150, 1986), and Reyes (Cook et al., The Reyes image rendering architecture, 1987)...

General Conclusion and Perspectives

High-quality computer-generated image generation techniques and the link between these techniques and perceptual noise are presented in the first two chapters (Chaps. 2 and 3). These chapters also present the high-quality images we can obtain with photo-realistic techniques on one hand and the difficulty to obtain a good visual quality assessment (particularly due to the complexity of the algorithms based on Monte Carlo methods) on the other hand.

Immersive front-projection analysis using a radiosity-based simulation method

Video projectors are designed to project onto flat white diffuse screens. Over the last few years, projector-based systems have been used, in virtual reality applications, to light non-specific environments such as the walls of a room. However, in these situations, the images seen by the user are affected by several radiometric disturbances, such as interreflection. Radiometric compensation methods have been proposed to reduce the disturbance caused by interreflection, but nothing has been proposed for evaluating the phenomenon itself and the effectiveness of compensation methods. In this paper, we propose a radiosity-based method to simulate light transfer in immersive environments, from a projector to a camera (the camera gives the image a user would see in a real room). This enables us to evaluate the disturbances resulting from interreflection. We also consider the effectiveness of interreflection compensation and study the influence of several parameters (projected image, projection onto a small or large part of the room, reflectivity of the walls). Our results show that radiometric compensation can reduce the influence of interreflection but is severely limited if we project onto a large part of the walls around the user, or if all the walls are bright.