Florin Rotaru

Emotion recognition in audio records

A novel approach to find the combination of acoustic features producing a more robust automatic recognition of a speaker emotion is proposed. Four discrete emotional states are classified. The emotional speech corpora used for training and evaluation are described in detail. The emotion recognition model using acoustic features is presented. The results achieved are presented and discussed.

Optic Disc Localization in Retinal Images

The paper proposes an optic disc localisation method in color retinal images. It is a first step of a retinal image analysis project which will be completed later with other tasks as fovea detection and measurement of retinal vessels. The final goal is to detect in early stages signs of ophthalmic pathologies as diabetic retinopathy or glaucoma, by successive analysis of ophthalmoscopy images. The proposed method first detects in the green component of RGB image the optic disc area and then on the segmented area extracts the optic disc edges and obtains a circular optic disc boundary approximation by a Hough transform.

New optic disc localization method for retinal images

The paper proposes an improved optic disc localisation method in color retinal images. First, the optic disc area in retinal images of any dimensions is identified. Then the method iteratively extracts the optic disc edges and obtains a circular optic disc boundary approximation by a Hough transform. However this is a first step of a retinal image analysis project which will be completed later with other tasks. The final goal is to detect in early stages signs of ophthalmic pathologies by successive analysis of ophthalmoscopy images.

Parallel image registration using bio-inspired computing

In this paper it is proposed a parallel approach for the pixel intensity based image registration (IR) problem on multi-core processors. While IR is an optimization problem which computes the optimal parameters for a geometric transform, two classes of bio-inspired algorithms are studied: Bacterial Foraging Optimization Algorithm (BFOA) and Genetic Algorithm (GA). The optimal transform is applied to a source image in order to align it to a model image by maximizing a similarity measure. In the presented experiment, mutual information (MI) is used to evaluate the IR quality and most of the processing time is spent in this evaluation. The proposed parallel approach aims to reduce the processing time by using the full computing power of multi-core processors. A comparison of the sequential and parallel versions for different registration problems is presented.

3D Breast Shape Reconstruction for a Non-Invasive Early Cancer Diagnosis System

Early breast cancer diagnosis becomes a must in nowadays society, related to the fact that this osteophyl neoplasia is the most frequent cancer in the world. The environment, solar radiations, weather changes, the increasing number of endocrine modifications, iatrogen agression, stress, or even fashion, have undoubtful effects on womens breasts health, too. Long term negative influence may appear due to invasive or even minim invasive cancer investigation techniques, so, alternative methods are continuously researched. As a stage in the implementation of a non-invasive microwaves technique for breast cancer diagnosis under research, meant to early uncover the developing malignant processes, we designed a 3D reconstruction process that we are extensively presenting in this paper. This reconstruction is a compulsory step previous to the tumor volumetric shape reconstruction, in order to be well positioned for further breast surgery, where our results could find a field of application. I.

Automatic multi-threshold image segmentation using metaheuristic algorithms

In this paper is presented an automatic segmentation approach for gray level images based on usage of metaheuristic swarming algorithms for multiple thresholds computing. The multi-threshold segmentation is an optimization problem while the thresholds must be determined and applied to the source image by minimizing an error measure. Because the number of possible solution may be very large in case of multiple thresholds, we used four metaheuristic swarming algorithms to obtain faster the optimal solution of the segmentation problem: Bacterial Foraging, Particle Swarming, Multi Swarm and Firefly optimization. As optimization criteria, root mean square error, peak signal-to-noise ratio and structural similarity index are used. Each optimization algorithm allows obtaining the optimal solution in a reasonable number of iterations and the obtained results were compared.

Image processing by means of some bio-inspired optimization algorithms

Image processing problems often require optimization algorithms to be applied. In this paper some aspects concerning the behavior of Bat and Cuckoo Search optimization algorithms are presented. The obtained accuracy and the processing time depend on the input images characteristics, chosen optimization criteria, dimension of the search space and, last but not least, on the chosen optimization algorithm and its parameters. The two nature inspired optimization algorithms were studied first in case of some mathematical functions minimization and then in case of bio-medical image registration.

Adaptive recognition method for 2D polygonal objects

A 2D convex polygons recognition method is proposed. The algorithm is suitable for robot vision, quality control or photogrammetry applications when the image objects to be processed have convex shapes. First, an adaptive convex polygonal vertex detection is applied. Then a polygonal fitting algorithm using as input the detected vertices and the object contour provides a precise object identification and description.

Social behavior in bacterial foraging optimization algorithm for image registration

In this paper a strategy to enhance performances in Bacterial Foraging Optimization Algorithm (BFOA) based Image Registration (IR) is proposed. The goal of the IR optimization problem is to determine a geometric transform which maximizes the similarity to a model image when is applied to a source image. In each step of standard BFOA, the bacteria move in random directions. Using the social behavior of bacteria colony, the proposed method directs the movement towards bacteria with lower values of cost function. This method allows reducing the number of iterations required to obtain the optimal geometric transform and offers better approximations.

Acoustic analysis methodology on Romanian language vowels for different emotional states

A methodology and an application for acoustic analysis of Romanian language vowels, for different emotional states and different vowel location in words, are presented. The application can be useful for expressivity analysis, speech synthesis and the speech to text approach. We focus on the data structures inside the software for annotation analysis of speech files. The formant statistical characteristic analysis of vowels on file subclasses selected by user is performed. The application allows refining the emotional state analysis at the level of vowels.