A photometric model for specular highlights and lighting changes. Application to feature points tracking

International audienceThis article proposes a local photometric model that compensates for specular highlights and lighting variations due to position and intensity changes. We define clearly on which assumptions it is based, according to widely used reflection models. Moreover, its theoritical validity is studied according to few configurations of the scene geometry (lighting, camera and object relative locations). Next, this model is used to improve the robustness of points tracking in luminance images with respect to specular highlights and lighting changes

Feature points tracking using photometric model and colorimetric invariants

International audienceThis article proposes several colour points tracking methods which are robust to illumination changes. Firstly, the illumination correction is achieved by computing simultaneously a local photometric model and a motion model during the image sequence. Secondly, some colour invariants are used to compensate in each point for the illumination changes. Then, since most of these attributes are not sufficiently robust to model specular highlights occurrence, we propose a third method which jointly uses a local photometric model and a global correction, by using colour invariants. To finish, the three methods are compared through experimental results, on images sequences where specular highlights and lighting changes appear

Feature point tracking : Robustness to specular highlights and lighting variations

International audienceSince the precise modeling of reflection is a difficult task, most feature points trackers assume that objects are lambertian and that no lighting change occurs. To some extent, a few approaches answer these issues by computing an affine photometric model or by achieving a photometric normalization. Through a study based on specular reflection models, we explain explicitly the assumptions on which these techniques are based. Then we propose a tracker that compensates for specular highlights and lighting variations more efficiently when small windows of interest are considered. Experimental results on image sequences prove the robustness and the accuracy of this technique in comparison with the existing trackers. Moreover, the computation time of the tracking is not significantly increased

Preference estimation from point allocation experiments

Hervorming Sociale Regelgevin

A photometric model for specular highlights and lighting changes. Application to feature points tracking

International audienceThis article proposes a local photometric model that compensates for specular highlights and lighting variations due to position and intensity changes. We define clearly on which assumptions it is based, according to widely used reflection models. Moreover, its theoritical validity is studied according to few configurations of the scene geometry (lighting, camera and object relative locations). Next, this model is used to improve the robustness of points tracking in luminance images with respect to specular highlights and lighting changes

The impact of image dynamic range on texture classification of brain white matter

<p>Abstract</p> <p>Background</p> <p>The Greylevel Cooccurrence Matrix method (COM) is one of the most promising methods used in Texture Analysis of Magnetic Resonance Images. This method provides statistical information about the spatial distribution of greylevels in the image which can be used for classification of different tissue regions. Optimizing the size and complexity of the COM has the potential to enhance the reliability of Texture Analysis results. In this paper we investigate the effect of matrix size and calculation approach on the ability of COM to discriminate between peritumoral white matter and other white matter regions.</p> <p>Method</p> <p>MR images were obtained from patients with histologically confirmed brain glioblastoma using MRI at 3-T giving isotropic resolution of 1 mm³. Three Regions of Interest (ROI) were outlined in visually normal white matter on three image slices based on relative distance from the tumor: one peritumoral white matter region and two distant white matter regions on both hemispheres. Volumes of Interest (VOI) were composed from the three slices. Two different calculation approaches for COM were used: i) Classical approach (CCOM) on each individual ROI, and ii) Three Dimensional approach (3DCOM) calculated on VOIs. For, each calculation approach five dynamic ranges (number of greylevels N) were investigated (N = 16, 32, 64, 128, and 256).</p> <p>Results</p> <p>Classification showed that peritumoral white matter always represents a homogenous class, separate from other white matter, regardless of the value of N or the calculation approach used. The best test measures (sensitivity and specificity) for average CCOM were obtained for N = 128. These measures were also optimal for 3DCOM with N = 128, which additionally showed a balanced tradeoff between the measures.</p> <p>Conclusion</p> <p>We conclude that the dynamic range used for COM calculation significantly influences the classification results for identical samples. In order to obtain more reliable classification results with COM, the dynamic range must be optimized to avoid too small or sparse matrices. Larger dynamic ranges for COM calculations do not necessarily give better texture results; they might increase the computation costs and limit the method performance.</p

Lack of robustness of textural measures obtained from 3D brain tumor MRIs impose a need for standardization

Purpose Textural measures have been widely explored as imaging biomarkers in cancer. However, their robustness under dynamic range and spatial resolution changes in brain 3D magnetic resonance images (MRI) has not been assessed. The aim of this work was to study potential variations of textural measures due to changes in MRI protocols. Materials and methods Twenty patients harboring glioblastoma with pretreatment 3D T1-weighted MRIs were included in the study. Four different spatial resolution combinations and three dynamic ranges were studied for each patient. Sixteen three-dimensional textural heterogeneity measures were computed for each patient and configuration including co-occurrence matrices (CM) features and run-length matrices (RLM) features. The coefficient of variation was used to assess the robustness of the measures in two series of experiments corresponding to (i) changing the dynamic range and (ii) changing the matrix size. Results No textural measures were robust under dynamic range changes. Entropy was the only textural feature robust under spatial resolution changes (coefficient of variation under 10% in all cases). Conclusion Textural measures of three-dimensional brain tumor images are not robust neither under dynamic range nor under matrix size changes. Standards should be harmonized to use textural features as imaging biomarkers in radiomic-based studies. The implications of this work go beyond the specific tumor type studied here and pose the need for standardization in textural feature calculation of oncological images

Texture analysis of MR images of patients with Mild Traumatic Brain Injury

<p>Abstract</p> <p>Background</p> <p>Our objective was to study the effect of trauma on texture features in cerebral tissue in mild traumatic brain injury (MTBI). Our hypothesis was that a mild trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection but could be detected with texture analysis (TA).</p> <p>Methods</p> <p>We imaged 42 MTBI patients by using 1.5 T MRI within three weeks of onset of trauma. TA was performed on the area of mesencephalon, cerebral white matter at the levels of mesencephalon, corona radiata and centrum semiovale and in different segments of corpus callosum (CC) which have been found to be sensitive to damage. The same procedure was carried out on a control group of ten healthy volunteers. Patients' TA data was compared with the TA results of the control group comparing the amount of statistically significantly differing TA parameters between the left and right sides of the cerebral tissue and comparing the most discriminative parameters.</p> <p>Results</p> <p>There were statistically significant differences especially in several co-occurrence and run-length matrix based parameters between left and right side in the area of mesencephalon, in cerebral white matter at the level of corona radiata and in the segments of CC in patients. Considerably less difference was observed in the healthy controls.</p> <p>Conclusions</p> <p>TA revealed significant changes in texture parameters of cerebral tissue between hemispheres and CC segments in TBI patients. TA may serve as a novel additional tool for detecting the conventionally invisible changes in cerebral tissue in MTBI and help the clinicians to make an early diagnosis.</p

Color segmentation of inked characters: application to meat traceability control

In this article, we study the color appearance of the ink printed on a background, according to both its concentration and the background color. We find some attributes, the concentration quotients ratios, that are more invariant to the ink concentration than simple color attributes. Our work deals with traceability of porcine products. We have to detect the animal identifier, printed with ink on the pork rind. Using the concentration quotients ratios, our segmentation technique succeeds for any quantity of ink and any hue of pork rind. This technique could be applied to segment any set of pixels, that are colorimetrically and spatially close, but nor necessarly all connected