In dermatology, image processing allows non-contact and non-invasive metrological measurements. Psoriasis is an incurable skin disease with an unknown origin. One of the most important tasks in the treatment of psoriasis is to evaluate the degree of the illness following a severity score. Dermatologists use visual and tactile senses to assess the lesions severity. In this article, we propose an automated methodology for assessing objectively the severity of psoriasis by measuring the physical parameters of the skin. Thus, from the colorimetry and geometry obtained by photometric-stereo, we determine the level of erythema and skin thickness. Our results show that for a low acquisition time, the scores obtained are highly correlated with those of dermatologists.
This paper deals with print quality control through a spectral color measurement. The aim is to estimate the spectral reflectance curve of each pixel of a printed sheet for a spectral matching with the reference image. The proposed method consists to perform a spectral characterization of the complete chain which includes the printing system and a digital trichromatic camera. First, the spectral printer model is presented and verified by experiments. Then, the camera spectral sensitivity curves are estimated through the capture of a color chart whose spectral reflectance curves have been previously measured. Finally, the spectral printer model is used to estimate the print spectral reflectance curves from camera responses.
Rough surface relief extraction is generally made by a mechanical method using a tactile sensor or by using
an auto-focus laser sensor. With these sensors we can estimate surface relief from the analysis of a series of
profiles. Since these measurements spend a lot of time, we hope that we can determine the relief by image
processing. Several methods in the field of image processing have been proposed for relief extraction, such
as shape from shading, optical flow, shape from focus and photometric stereovision. Our works are
based on the photometric stereovision. In 1980, Woodham indicated that the relief of a Lambertian surface can
be determined by the exploitation of a photometric model, which takes into account camera and light source
positions according to the plan of surface. The proposed model expresses the gray level on the image according
to the local relief variations. Three images of the same relief obtained under different angles of lighting are used
to reconstruct the surface relief. From the method of Woodham, several important ameliorations have been
proposed by other researchers. But a limit study in section 2.1.3 proves that the above methods worked with
Lambert's model is adapted to the diffuse reflection, but not to the specular reflection.
Thus, we propose another method to extract the relief of rough textured reflecting surface. In the proposed
method, we show that the diffuse and specular components of the acquired images can be decomposed in two
independent components. The diffuse component can be processed by Lambert's model, the specular component
can be processed according to the position knowledge of facets. Finally, section 3 presents the experimental
results obtained by this method, and compares measurement precision with the experimental results obtained
by Lambert's model.
The study of rough textured surfaces such as road coverings, is generally made on gray-level images. This supposes that the variations of gray levels are representative of the local variations of the relief. This assumption is justified, in the case of surfaces that are uniformly colored, but finds its limit in the case when these surfaces present variations of color or aspect. The corresponding image then presents variations of gray levels that can be related to the color variations or to the relief variations or both. It becomes difficult in this case to work out the criteria of roughness based on image analysis. It is then necessary to develop, before any study of roughness, an estimation of the luminance map linked to color variations. To do that, we have linked the gray-level value to the height variation of the road-covering surface. Thus, we express the luminance distribution according to these height variations, obtained through a laser sensor, and to the image gray level. This method enables us to compute the distribution of the luminance map. We characterize this distribution by considering statistical parameters of its histogram. Then we test the effectiveness of our approach by comparing the evolution of the criterion of roughness on road surfaces, first without considering the luminance distribution and then by taking it into account. The results obtained show that this approach leads to a good discrimination by the criterion of roughness in the case of colored surfaces.
In this study, we propose an original method for a 3D reconstruction of the relief of a textured rough surfaces. This 3D reconstruction is obtained through the elaboration of a photometric model, which takes into account camera and light source positions according to the plan of the rough surface. The proposed model expresses the gray level on the image according to the local relief variations. Three images of the same relief obtained under different angles of lighting are used to reconstruct the altitude map of the rough surface. The effectiveness of this method was checked by comparing the extracted relief to its corresponding relief obtained from a mechanical device method using autofocus laser sensor. This photometric model display good results in simulation experience and will be applied on real photographic images of road covering surface in order to study its wear level and its adherence.
The study of rough textured surface as road coverings, are generally made on grey level images. This suppose, that the variations of grey levels are representative of the local variations of the relief. This justified assumption, in the case of surfaces uniformly colored, finds its limit in the case when these surfaces present variations of color or aspect. The corresponding image will then present variations of grey levels which can be related to the color variations or to the relief variations or both. It then becomes difficult in this case to work out criteria of roughness based on image analysis. It is then necessary to work out, before any study of roughness, an estimation of the luminance map linked to color variations. In order to do that, we have linked the grey level value to the height variations, obtained through a laser sensor, and to the image grey level. The suggested method allows us to compute the distribution of the luminance map. We characterize this distribution by considering statistical parameters of its histogram. We have tested the effectiveness of our approach by comparing the evolutions of the criteria of roughness on road surfaces, without considering the luminance distribution and by taking it into account. The results obtained show that the developed approach leads to a good discrimination by the criteria of roughness in the case of colored surfaces.
The human visual system seems very powerful when it is a question to identifying an object or a portion of an object in movement, such as a textured surface moving in a 3D textured environment. In such a situation, the visual impression of an observer depends on many factors, including the nature of relative movement between the scene and the observer, the kind of lighting and the surface aspect of the plane being studied. In this paper, we propose a method of characterization of textured surfaces moving in a 3D scene. The various analyzed images are strongly textured, and do not necessarily include periodic elementary patterns. The autocorrelation function associated with an optical model with the system scene-camera under the hypothesis of a weak perspective projection is used. We use the fact that the autocorrelation function of an image and of its affine transformed version are related by this transformation. 3D-rotations of the textured plane are studied by means of Euler's angles. On a set of 1008 synthetic images, the accuracy obtained for the angles of rotation is characterized by a standard deviation of about 9 degrees. It attains 4.1 degrees on a small database of real images.
Car road holding is linked to many factors including adherence, which is strongly related to the roughness of the road coating and of its evolution under the effect of traffic. Traffic induces a progressive wear of the road surface which results in a modification of its local relief and of its roughness. We study road coverings in order to know the variations of their wear levels over time by analyzing the micro texture of these road images. The transcription of roughness criteria in image analysis requires, on one hand, the development of a photometric model for the coating surface, and, on the other hand, a modeling of the profile of the road coating. The method suggested in this article is based on a photometric model of the surface of the coating from which we study the statistical properties of : the distribution of the gray levels in the image, the distribution of the absolute value of its gradient, and the form of its autocorrelation function. Experiments have been done with images of road coverings at different wear levels. The obtained results are similar to those obtained by a direct method of contact measuring.