Automatic 1 monitoring for the assessment of pain can significantly improve the psychological comfort of patients. Recently introduced databases with expert annotation opened the way for pain intensity estimation from facial analysis. In this contribution, pivotal face elements are identified using the Histograms of Topographical features (HoT) which are a generalization of the topographical primal sketch. In order to improve the discrimination between different pain intensity values and respectively the generalization with respect to the monitored persons, we transfer data representation from the emotion oriented Cohn-Kanade database to the UNBC McMaster Shoulder Pain database.

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While most of state-of-the-art image processing techniques were built under the so-called classical linear image processing, an alternative that presents superior behavior for specific applications comes in the form of Logarithmic Type Image Processing (LTIP). This refers to mathematical models constructed for the representation and processing of gray tones images. In this paper we describe a general mathematical framework that allows extensions of these models by various means while preserving their mathematical properties. We propose a parametric extension of LTIP models and discuss its similarities with the human visual system. The usability of the proposed extension model is verified for an application of contrast based auto-focus in extreme lighting conditions. The closing property of the named models facilitates superior behavior when compared with state-of-the-art methods.

Many applications pointed to the informative potential of the human eyes. In this paper we investigate the possibility of estimating the cognitive process used by a person when addressing a mental challenge, according to the Eye Accessing Cue (EAC) model from the Neuro-Linguistic Programming (NLP) theory [3]. This model states that there is a subtle, yet firm, connection between the non-visual gaze direction and the mental representation system used. From the point of view of computer vision, this work deals with gaze estimation under passive illumination. Using a multistage fusion approach, we show that it is possible to achieve highly accurate results in both terms of eye gaze localization or EAC case recognition.