In this work we present a new approach to interest point detection. Different types of features in images are detected by using a common computational concept. The proposed approach consider the total variability of local regions. New entities are introduced: circumferences and radii. The total sum of squares computed on the intensity values of a local region is divided into three components: between-circumferences sum of squares, between-radii sum of squares, and the remainder. These three components normalized by the total sum of squares represent three new saliency measures, namely radial, tangential, and residual. The saliency measures are computed for different radii in a local region and scale-spaces are build in this way. Local extrema in scale-space of each of the saliency measures represent features with complementary image properties: blob-like features, corner-like feature and highly textured points. Results obtained on a wide variety of image sets compare favourably with the results obtained by the leading interest point detectors from the literature. The proposed approach gives a rich set of highly distinctive local regions that can be used for object recognition and image matching.
COBISS.SI-ID: 41304418
We have proposed an improved model for visual tracking using an adaptive coupled model. The model's advantage lays within the capability to track articulated objects using simple local features that are connected to a weak geometrical constellation. The model can robustly add and remove local features depending on probability maps of high-level image properties such as motion and color. The model also enables inclusion of additional probability maps based on arbitrary high-level image properties. We have analyzed the proposed tracker on a large video database and compared it with the current state-of-the-art. The experiments have shown that the proposed tracker outperforms the reference trackers based on multiple criteria. We published this work in the prestigious journal IEEE Transactions on pattern analysis and machine intelligence, which was at the time of publication ranked as the first of the 111 journals within the category EP computer science, artificial intelligence. The journal had an impact factor of 4.908 and is one of the two most prestigious journals in the field of computer vision. With this work we strengthen our position in the computer vision community working on visual tracking. We further strengthened our position by organizing the first challenge of short-term object tracking VOT2013, which we co-organized in conjunction with the major conference in the field of computer vision ICCV2013.
COBISS.SI-ID: 9431124
We have proposed a novel approach to online estimation of generative models, which is based on probability density estimation. As the theoretical framework we have chosen the kernel density estimation (KDE). The method maintains and updates a nonparametric model of the observed data, from which the KDE can be calculated. We propose an online bandwidth estimation approach and a compression/revitalization scheme which maintains the KDE's complexity low. We compare the proposed online KDE to the state-of-the-art approaches on examples of estimating stationary and non-stationary distributions, and on examples of classification. The results show that the online KDE outperforms or achieves a comparable performance to the state-of-the-art and produces models with a significantly lower complexity while allowing online adaptation. This approach has been developed and used for modelling categorical knowledge in the framework of a complex interactive system for continuous learning of visual concepts in dialogue with a human.
COBISS.SI-ID: 8289876
A classical or static anamorphic image requires a specific, usually a highly oblique view direction, from which the observer can see the anamorphosis in its correct form. This paper explains dynamic anamorphosis which adapts itself to the changing position of the observer so that wherever the observer moves, he sees the same undeformed image. This dynamic changing of the anamorphic deformation in concert with the movement of the observer requires from the system to track the 3D position of the observer’s eyes and the re-computation of the anamorphic deformation in real time. This is achieved using computer vision methods which consist of face detection and tracking the 3D position of the selected observer. An application of this system of dynamic anamorphosis in the context of an interactive art installation is described. We show that anamorphic deformation is also useful for improving eye contact in videoconferencing. Other possible applications involve novel user interfaces where the user can freely move and observe perspectively undeformed images.
COBISS.SI-ID: 9799252
We present a quantitative study of digital signage audience measurement using computer vision. We developed a camera enhanced digital signage display that acquires audience measurement metrices with computer vision algorithms. Temporal metrices of person’s dwell time, display in-view time, and attention time are extracted. The system also determines demographic metrices of gender and age group.
COBISS.SI-ID: 9659732