Laura Florea

A Pseudo-logarithmic Image Processing Framework for Edge Detection

The paper presents a new [pseudo-] Logarithmic Model for Image Processing (LIP), which allows the computation of gray-level addition, substraction and multiplication with scalars within a fixed gray-level range [0; D ] without the use of clipping. The implementation of Laplacian edge detection techniques under the proposed model yields superior performance in biomedical applications as compared with the classical operations (performed either as real axis operations, either as classical LIP models).

Learning Pain from Emotion: Transferred HoT Data Representation for Pain Intensity Estimation

Automatic 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.

Can Your Eyes Tell Me How You Think? A Gaze Directed Estimation of the Mental Activity.

We investigate the possibility of estimating the cognitive process used by a person when addressing a mental challenge by following the Eye Accessing Cue (EAC) model from the Neuro-Linguistic Programming (NLP) theory [1]. This model, showed in figure 1, describes the eyemovements that are not used for visual tasks (non visual movements) and suggests that the direction of gaze, in such a case, can be an indicator for the internal representational system used by a person facing a given query. The actual EAC is thought to be identified by distinguishing between the relative position of the iris and the eye socket (lid edge). Our approach is to determine the four limits of the eye socket: the inner and outer corners, the upper and lower lids and the iris center and to subsequently analyze the identified region. The entire method flowchart is presented in figure 2. The schematics of the method used for independently looking for the position of each eye landmark is described in figure 3. Given the face square position by Viola-Jones algorithm [4] and the eye centers given by the method from [3], we fuse information related to position, normalized luminance, template matching and shape constraining. For position and luminance, we construct priors over the training database, while for template matching we describe a patch by concatenation of integral and edge projections on horizontal and vertical directions. The score of how likely is a patch to be centered on the true landmark position is given by a Multi Layer Perceptron. For the shape constrain, inspired by the CLM [2], we construct the probability density function in the eigenspace of the shapes in the training set. By ordering the landmarks according to a prior confidence (e.g. eye outer corners are more reliable than upper and lower eye boundaries) and by keeping all points fixed with the exception of the current least reliable, we build the likelihood of various current landmark positions. This information is fused with previous stages and we iteratively improve the landmark position. The final landmark position is taken as the weighted center of mass of the convex combination between initial stages and shape likelihood. To study the specific of the gaze direction we introduce Eye-Chimera database, which comprises 1172 frontal face images, grouped according to the 7 gaze directions, with a set of 5 points marked for each eye: the iris center and 4 points delimiting the bounding box. Recognizing individual EACs. The recognition of the EAC case (gaze direction) is done by identifying the position of the iris center inside the eye socket complemented by the information of the interior of the eye delimited shape. The interior of the eye quadrilateral shape is described by the integral projections normalized to 32 samples. For the actual recognition we have trained a random forrest to take as input the EAC feature (landmarks positions and integral features). We consider two types of recognition situations: three cases (looking

Logarithmic model-based dynamic range enhancement of hip X-ray images

Digital capture with consumer digital still camera of the radiographic film significantly decreases the dynamic range and, hence, the details visibility. We propose a method that boosts the dynamic range of the processed X-ray image based on the fusion of a set of digital images acquired under different exposure values. The fusion is controlled by a fuzzy-like confidence information and the luminance range is oversampled by using logarithmic image processing operators.

Parametric logarithmic type image processing for contrast based auto-focus in extreme lighting conditions

Abstract 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.

NLP EAC Recognition by Component Separation in the Eye Region

This paper investigates the recognition of the Eye Accessing Cues (EACs) used in Neuro-Linguistic Programming (NLP) and shows how computer vision techniques can be used for understanding the meaning of non-visual gaze directions. Any specific EAC is identified by the relative position of the iris within the eye bounding box, which is determined from modified versions of the classical integral projections. The eye cues are inferred via a logistic classifier from features extracted within the eye bounding box. The here proposed solution is shown to outperform in terms of detection rate other classical approaches.

Robust eye centers localization with zero-crossing encoded image projections

This paper proposes a new framework for the eye centers localization by the joint use of encoding of normalized image projections and a multi-layer perceptron (MLP) classifier. The encoding is novel and it consists in identifying the zero-crossings and extracting the relevant parameters from the resulting modes. The compressed normalized projections produce feature descriptors that are inputs to a properly trained MLP, for discriminating among various categories of image regions. The proposed framework forms a fast and reliable system for the eye centers localization, especially in the context of face expression analysis in unconstrained environments. We successfully test the proposed method on a wide variety of databases including BioID, Cohn–Kanade, Extended Yale B and Labeled faces in the wild databases.

Gaze direction estimation by component separation for recognition of Eye Accessing Cues

This paper investigates the recognition of the Eye Accessing Cues used in the Neuro-Linguistic Programming as a method for inferring one’s thinking mechanisms, since the meaning of non-visual gaze directions may be directly related to the internal mental processes. The direction of gaze is identified by separating the components of the eye (i.e., iris, sclera and surrounding skin) followed by retrieving the relative position of the iris within the eye bounding box that was previously extracted from an eye landmarks localizer. The eye cues are retrieved via a logistic classifier from features that describe the typical regions within the eye bounding box. The simultaneous investigation of both eyes, as well as the eye tracking over consecutive frames, are shown to increase the overall performance. The here proposed solution is tested on four databases proving to have superior performance when compared in terms of recognition rate with methods relying on state of the art algorithms.

Automatic Tools for Diagnosis Support of Total Hip Replacement Follow-up

Total hip replacement is a common procedure in today orthopedics, with high rate of long-term success. Failure prevention is based on a regular follow-up aimed at checking the prosthes ...

A parametric non-linear algorithm for contrast based auto-focus

Automatic estimation of the lens position that provides the sharpest image given a subject scene is known as auto-focus. In this paper a new passive contrast based auto-focus algorithm for digital still and video cameras is proposed. The solution originates in the parametrization of NIP (Non-linear Image Processing) models . The closing property of these models allows superior performance especially for bright areas if compared with state of the art methods.