Iva Bogdanova

Scale Space Analysis and Active Contours for Omnidirectional Images

A new generation of optical devices that generate images covering a larger part of the field of view than conventional cameras, namely catadioptric cameras, is slowly emerging. These omnidirectional images will most probably deeply impact computer vision in the forthcoming years, provided that the necessary algorithmic background stands strong. In this paper, we propose a general framework that helps define various computer vision primitives. We show that geometry, which plays a central role in the formation of omnidirectional images, must be carefully taken into account while performing such simple tasks as smoothing or edge detection. Partial differential equations (PDEs) offer a very versatile tool that is well suited to cope with geometrical constraints. We derive new energy functionals and PDEs for segmenting images obtained from catadioptric cameras and show that they can be implemented robustly using classical finite difference schemes. Various experimental results illustrate the potential of these new methods on both synthetic and natural images.

Scale Invariant Feature Transform on the Sphere: Theory and Applications

A SIFT algorithm in spherical coordinates for omnidirectional images is proposed. This algorithm can generate two types of local descriptors, Local Spherical Descriptors and Local Planar Descriptors. With the first ones, point matching between two omnidirectional images can be performed, and with the second ones, the same matching process can be done but between omnidirectional and planar images. Furthermore, a planar to spherical mapping is introduced and an algorithm for its estimation is given. This mapping allows to extract objects from an omnidirectional image given their SIFT descriptors in a planar image. Several experiments, confirming the promising and accurate performance of the system, are conducted.

The continuous wavelet transform on conic sections

We review the coherent state ( or group-theoretical) construction of the continuous wavelet transform (CWT) on the two-sphere. Next, we describe the construction of a CWT on the upper sheet of a two-sheeted hyperboloid, emphasizing the similarities between the two cases. Finally, we give some indications on the CWT on a paraboloid and we introduce a unified approach to the CWT on conic sections.

A multi-lead ECG classification based on random projection features

This paper presents a novel method for classification of multi-lead electrocardiogram (ECG) signals. The feature extraction is based on the random projection (RP) concept for dimensionality reduction. Furthermore, the classification is performed by a neuro-fuzzy classifier. Such a model can be easily implemented on portable systems for practical applications in both health monitoring and diagnostic purposes. Moreover, the RP implementation on portable systems is very challenging featuring both energy efficiency and feasibility. The proposed method is tested on a 12-lead ECG database consisting of 20 beats during normal sinus rhythm, 20 beats with myocardial infarction and 20 beats showing cardiomyopathy for 60 different subjects. The experiments give a recognition rate of 100% for a small number of RP coefficients (only 25), i.e. after a considerable dimensionality reduction of the input ECG signal. The results are very promising, not only from the classification performance point of view, but also while targeting a low-complexity feature extraction in terms of computation requirements and memory usage for real-time operation on a wireless wearable sensor platform.

Dynamic visual attention on the sphere

In this paper we present a computational model of dynamic visual attention on the sphere which combines static (intensity,chromaticity, orientation) and motion features in order to detect salient locations in omnidirectional image sequences while working directly in spherical coordinates. We build the motion pyramid on the sphere by applying block matching and varying the block size. The spherical motion conspicuity map is obtained by fusing together the spherical motion magnitude and phase conspicuities. Furthermore, we combine this map with the static spherical saliency map in order to obtain the dynamic saliency map on the sphere. Detection of the spots of attention based on the dynamic saliency map on the sphere is applied on a sequence of real spherical images. The effect of using only the spherical motion magnitude or phase for defining the spots of attention on the sphere is examined as well. Finally, we test the spherical versus Euclidean spots detection on the omnidirectional image sequence.

Virtual classroom for multimedia teaching on WWW

The specific issue addressed in this paper is tailoring specific functionality to allow a group of teachers and students to carry out the learning process in digital signal and image processing in an electronic virtual environment that is meant to replace the physical class environment. In order for such an environment to be obtained, not only the course content itself but also different Web-based tools for interaction between the students and teachers are needed. A graphical user interface (GUI), designed and presented in the paper, brings together the students and teachers through virtual class and lab-sessions help and chat rooms. As digital signal and image processing are areas that heavily depend on mathematics, the lab-sessions are performed through MATLAB Web Server. Some specific applications are shown as well as complementary tools like Web-calendar and digital library. Finally, benefits and future work on this virtual classroom are discussed.

Dynamic attentive system for omnidirectional video

In this paper, we propose a dynamic attentive system for detecting the most salient regions of interest in omnidirectional video. The spot selection is based on computer modeling of dynamic visual attention. In order to operate on video sequences, the process encompasses the multiscale contrast detection of static and motion information, as well as fusion of the information in a scalar map called saliency map. The processing is performed in spherical geometry. While the static contribution collected in the static saliency map relies on our previous work, we propose a novel motion model based on block matching algorithm computed on the sphere. A spherical motion field pyramid is first estimated from two consecutive omnidirectional images by varying the block size. This latter constitutes the input of the motion model. Then, the motion saliency map is obtained by applying a multiscale motion contrast detection method in order to highlight the most salient motion regions. Finally, both static and motion saliency maps are integrated into a spherical dynamic saliency map. To illustrate the concept, the proposed attentive system is applied to real omnidirectional video sequences.