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Accurate illuminant estimation from digital image data is a fundamental step of practically every image colour correction. Combinational illuminant estimation schemes have been shown to improve estimation accuracy significantly compared to other colour constancy algorithms. These schemes combine individual estimates of simpler colour constancy algorithms in some 'intelligent' manner into a joint and, usually, more efficient illuminant estimation. Among them, a combinational method based on Support Vector Regression (SVR) was proposed recently, demonstrating the more accurate illuminant estimation (Li et al. IEEE Trans. Image Process. 23(3), 1194-1209, 2014). We extended this method by our previously introduced convolutional framework, in which the illuminant was estimated by a set of image-specific filters generated using a linear analysis. In this work, the convolutional framework was reformulated, so that each image-specific filter obtained by principal component analysis (PCA) produced one illuminant estimate. All these individual estimates were then combined into a joint illuminant estimation by using SVR. Each illuminant estimation by using a single image-specific PCA filter within the convolutional framework actually represented one base algorithm for the combinational method based on SVR. The proposed method was validated on the well-known Gehler image dataset, reprocessed and prepared by author Shi, and, as well, on the NUS multi-camera dataset. It was shown that the median and trimean angular errors were (non-significantly) lower for our proposed method compared to the original combinational method based on SVR for which our method utilized just 6 image-specific PCA filters, while the original combinational method required 12 base algorithms for similar results. Nevertheless, a proposed method unified grey edge framework, PCA analysis, linear filtering theory, and SVR regression formally for the combinational illuminant estimation. [ABSTRACT FROM AUTHOR]

This article presents a reformulation of the Grey Edge framework for colour constancy. Colour constancy is the ability of a visual system to perceive objects' colours independently of their scenes' illuminants. Colour constancy algorithms try to estimate the colour of an illuminant from image values. This estimation can later be used to correct the image as though it were taken under a white illuminant. The modification presented allows the framework to incorporate image-specific filters instead of the commonly used edge detectors. A colour constancy algorithm is proposed using PCA and FastICA linear component analyses methods for the construction of such filters. The results show that the proposed method improves the accuracies of the Grey Edge framework algorithms whilst on the other hand, achieving comparable accuracies with the state-of-the-art methods, but improving their time efficiencies. [ABSTRACT FROM AUTHOR]

Modelling of patient knee joint from the MRI data and simulating its kinematics is presented. A flexion of the femur with respect to the tibia from 0° to around 40° is simulated. The finite element knee model is driven by compressive load measured 'in vivo' during MRI process by using specially developed optical force measurement system. Predicted kinematics is evaluated against the high-quality model obtained by registration from experimentally gathered low-quality MRI at fixed flexions. Validation pointed out that the mean square error (MSE) for the Euler rotation angles are bellow 1.73°, while the MSE for Euler translation is smaller than 5.93 mm. [ABSTRACT FROM AUTHOR]

A framework for construction of virtual models of the medical phenomena is proposed. Major construction steps are discussed in detail. The construction of virtual medical model is guided from acquisition of patient imaging material, to 3D reconstruction based on the image processing, to the basic modelling and simulation approaches. This framework is demonstrated on human knee joint virtual model construction. Statistical assessment of the built knee joint model points out sufficient quality and accuracy. Model assessment from clinical point of view confirmed this evaluation, and, simultaneously, verified the proposed construction chain as very prospective. [ABSTRACT FROM AUTHOR]