Adrian Ciobanu

PDE-based image restoration using variational denoising and inpainting models

A PDE-based image restoration model is proposed in this paper. It aims to restore degraded images that are affected by both noise and missing zones. The considered restoration approach is based on two PDE variational techniques. The first variational method performs an efficient noise reduction, while the second variational model provides the image reconstruction. By using both variational models, one achieves a much better enhancement of the degraded image.

Image Categorization Based on Computationally Economic LAB Colour Features

An easy to compute and small colour feature vector is introduced in this paper, as a tool to be used in the process of retrieval or classification of similarly coloured digital images from very large databases. A particular set of “ab” planes from the LAB colour system is used, along with a specific configuration of colour regions within them. The colour feature vector is low dimensional (only 96 components), computationally economic and performs very well on a carefully selected database of rose images, publicly available.

Iris classification using WinICC and LAB color features

We present the WinICC software package, designed to help in tasks like clusterization or classification of images based on different feature vectors. The capabilities of this software are proven on a classification test involving 1.205 already segmented iris images belonging to 241 persons (five iris images per person - part of the UBIRISv1 Internet available database). We used the k-NN feature of the WinICC applied on LAB color feature vectors with 80 components extracted from iris images. The resulted rates of correctly classified irises are over 88% if 3 or 4 images are used to classify the remaining images of the same person. As the data set is not perfect, this is a result that may suggest a possible identification of human irises based on color distribution.

Iris features using Dual Tree Complex Wavelet Transform in texture evaluation for biometrical identification

Iris characteristics are accurate and reliable for person identification. We used a new method to extract combined features for color and texture characterization using Dual Tree Complex Wavelet Transform. We applied this feature vectors selection on the UPOL iris color image database. The identification of the subsequent images of the same iris was obtained with competitive results.

A novel iris clustering approach using LAB color features

Interesting results of color clustering for the iris images in the UBIRISv1 database are presented. The iris colors are characterized by feature vectors with 80 components corresponding to histogram bins computed in the CIELAB color space. The feature extraction is applied to the first session eye images after undergoing an iris segmentation process. An agglomerative hierarchical algorithm is used to organize 1.205 segmented iris images in 8 clusters based on their color content.

Automatic fury recognition in audio records

The paper is focused on automatic detection of fury emotion in audio records, using data extracted from the vocalic analysis of formants. We have studied speech prosody and voice inflexions and we recognised fury using classification algorithms applied to two databases, one with professional voices and another with normal voices, both of them recorded on the base of selected texts in Romanian language. We used relevant stories for generating fury emotion. We obtained interesting results that can be used in a large variety of possible applications.

Color feature vectors based on optimal LAB histogram bins

In this paper we propose an automatic method for computing the bin boundaries of complex 3D LAB histograms in order to extract optimal color feature vectors from digital images. The size of the feature vectors can be adapted to particular application needs. We tested our approach with very good results on an iris recognition problem solved empirically before.

A study on automatic recognition of positive and negative emotions in speech

The paper is focused on an experimental study on positive and negative emotion vocal recognition. After some considerations about the positive and negative emotions, the paper gives a short description of the three corpuses used in the work we have accomplished. The paper describes three sets of coefficients used, the statistic features used to generate the three sets of feature vectors and the two classification methods used in this study. The recognition results obtained for every corpus are shown and some conclusions and directions of development are presented.

Improving Noninvasive Monitoring in Medical Care

Early detection of non-uniformities and abnormalities in human body behavior could save lives and might diminish health care assurance expenses. The range of monitored parameters is wide, from respiratory and cardio parameters survey to early cancer detection. Due to invasive methods important drawbacks, researchers are looking for new non-invasive methods. The is presenting new approaches, combining different physical methods, compared to the existent monitoring techniques, usually used in medical care. Our approach concerns studies of using microwave Doppler effect for the contactless detection of the cardiac and respiratory activity, for the continuous respiratory and cardiac monitoring. We are also describing a microwave body emission radiometry method in abnormal tissue increased activity detection.

Retinal Vessel Classification Technique

A retinal vessel classification procedure is proposed. From the image of thinned vessel network, landmarks are extracted and classified as branching, crossover and end points. Then a vascular graph is generated. Using a stratified graph edge labeling procedure the artery/vein map is built. In a first step the graph branches near the optic disc are localized and classified. Each label is propagated along the most significant segments linked to initial vessels. The next labeling phase aims the not processed branches starting from already classified vessels. Only branches and edges at crossings are labeled. Finally, using the current labels set, the uncertain cases are solved.