Color image segmentation: advances and prospects

This paper presents a new reflectance model for rendering computer synthesized images. The model accounts for the relative brightness of different materials and light sources in the same scene. It describes the directional distribution of the reflected light and a color shift that occurs as the reflectance changes with incidence angle. The paper presents a method for obtaining the spectral energy distribution of the light reflected from an object made of a specific real material and discusses a procedure for accurately reproducing the color associated with the spectral energy distribution. The model is applied to the simulation of a metal and a plastic.

A reflectance model for computer graphics

Remote photoplethysmography (rPPG) enables contactless monitoring of the blood volume pulse using a regular camera. Recent research focused on improved motion robustness, but the proposed blind source separation techniques (BSS) in RGB color space show limited success. We present an analysis of the motion problem, from which far superior chrominance-based methods emerge. For a population of 117 stationary subjects, we show our methods to perform in 92% good agreement (±1.96σ) with contact PPG, with RMSE and standard deviation both a factor of 2 better than BSS-based methods. In a fitness setting using a simple spectral peak detector, the obtained pulse-rate for modest motion (bike) improves from 79% to 98% correct, and for vigorous motion (stepping) from less than 11% to more than 48% correct. We expect the greatly improved robustness to considerably widen the application scope of the technology.

Robust Pulse Rate From Chrominance-Based rPPG

This paper is concerned with the derivation of a progression of shadow-free image representations. First, we show that adopting certain assumptions about lights and cameras leads to a 1D, gray-scale image representation which is illuminant invariant at each image pixel. We show that as a consequence, images represented in this form are shadow-free. We then extend this 1D representation to an equivalent 2D, chromaticity representation. We show that in this 2D representation, it is possible to relight all the image pixels in the same way, effectively deriving a 2D image representation which is additionally shadow-free. Finally, we show how to recover a 3D, full color shadow-free image representation by first (with the help of the 2D representation) identifying shadow edges. We then remove shadow edges from the edge-map of the original image by edge in-painting and we propose a method to reintegrate this thresholded edge map, thus deriving the sought-after 3D shadow-free image.

/pdf/on-the-removal-of-shadows-from-images-20rcnm8x1p.pdf

On the removal of shadows from images

VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method

This paper describes a method for precisely estimating the surface properties of art paintings, including surface normal, surface spectral reflectance, and reflection model parameters. We use a multi-band camera system with six spectral channels. The surface of a painting is observed under different illumination directions by the camera. We use the Torrance-Sparrow model as a reflection model. The surface normal is estimated on the different illumination observations by the photometric stereo. The spectral reflectance is estimated on the multi-band data by the Wiener method. The model parameters are estimated on the statistical distribution of the specular component Finally, all estimates are combined for rendering various appearances of the painting under different illumination and viewing conditions.

Estimation of surface properties of art paintings using a multi-band camera

This paper proposes a method for classifying object materials on a raw circuit board into element materials by means of surface-spectral reflectance. First, we develop a spectral imaging system for observing the minute details of the board and capturing their spectral data. Second, the surface-spectral reflectance functions of the board are estimated by a direct method using narrow band sensor outputs. We investigate the reflection properties of various objects on the board under different illumination directions. Third, we find key features of the body spectral reflectances for different materials, and present a rule for classifying the objects into six element materials. Finally, experiments are executed using a real circuit board. The observed spectral reflectance image is segmented into the element material areas. The performance and robustness of the proposed method are examined in detail in comparison with other methods.

Material classification method for printed circuit boards using a spectral imaging system

On the Sensor-Based Navigation for Overcoming Position and Orientation Errors in a Dense 2D Uncertain Environment

The present paper proposes a nonlinear approach using a neural network for color control of projection displays, including the LCD and DLP types This approach accepts variations in primary color coordinates and coupling among RGBW channels We regard a display system as an unknown nonlinear system with RGB signal inputs and XYZ tristimulus outputs We determine the RGB values so that the projector outputs the desired XYZ values The neural network is used for estimating adaptively an inverse mapping from the XYZ space to the RGB space Because of a direct mapping, we can eliminate the need to predict white channel separation Moreover, we present a method for correcting the emitted luminance, according to the spatial location and the surface color of a screen The spatial correction is needed because a color image from a computer is not displayed uniformly on the screen Also the screen color correction is needed because color reproduction is based on the light reflected from the screen The correction makes it possible to reproduce accurate color images on a colored wall at any location

A color control method for image output with projection displays

A method is proposed for estimating spectral radiances of the surrounding scene from a reflected image on the corneal surface. A color camera and the human eyeball are used to develop a simple imaging system.

Scene Radiance Estimation from Human Corneal Reflections

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