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.

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 metrics with computer vision algorithms. Temporal metrics of a person’s dwell time, display in-view time and attention time are extracted. The system also determines demographic metrics of the gender and age group. The digital signage display was deployed in a real-world environment of a clothing boutique, where demographic and viewership data of 1294 store customers were recorded, manually verified and analysed. The analysis shows that 35% of customers specifically looked-at the display, having the average attention time of 0.7 s. Interestingly, the attention time was substantially higher for men (1.2 s) than for women (0.4 s). Age group comparison reveals that children (1–14 years) are the most responsive to the digital signage. Finally, the analysis shows that the average attention time is significantly higher when displaying the dynamic content (0.9 s) when compared with the static content (0.6 s).

Visualizing music in a meaningful and intuitive way is a challenge. Our aim is to visualize music by interconnecting similar aspects in music and in visual perception. We focus on visualizing harmonic relationships between tones and colours. Related existing visualizations map tones or keys into a discrete set of colours. As concurrent (simultaneous) tones are not perceived as entirely separate, but also as a whole, we present a novel method for visualizing a group of concurrent tones (limited to the pitches of the 12-tone chromatic scale) with one colour for the whole group. The basis for calculation of colour is the assignment of key spanning circle of thirds to the colour wheel. The resulting colour is not limited to discrete set of colours: similar tones, chords and keys have similar colour hue; dissonance and consonance are represented by low and high colour saturation respectively. The proposed method is demonstrated as part of our prototype music visualization system using extended 3-dimensional piano roll notation.

"15 seconds of fame" is an interactive installation which every 15 seconds generates a newpop-art portrait of a randomly selected person from the audience. The installation was inspired by Andy Warhol's ironical statement that "In the future everybody will be famous for 15 minutes". The installation detects human faces in digital images of people who are standing in front of the installation. Pop-art portraits are then generated from randomly chosen faces in the audience by applying randomlyselected filters. These portraits are shown in 15 second intervals on the flat-panel computer monitor which is framed as a painting. Electronic copies of each displayed portrait can be ordered by e-mail.

Cartesian moments are frequently used global geometrical features in computer vision for object pose estimation and recognition. We derive a closed form expression for 3-D Cartesian moment of order p+q+r of a superellipsoid in its canonical coordinate system. We also show how 3-D Cartesian moment of a globally deformed superellipsoid in general position and orientation can be computed as a linear combination of 3-D Cartesian moments of the corresponding nondeformed superellipsoid in canonical coordinate system. Additionally, moments of objects that are compositions of superellipsoids can be computed as simple sums of moments of individual parts. To demonstrate practical application of the derived results we register pairs of range images based on moments of recovered compositions of superellipsoids. We use a standard technique to find centers of gravity and principal axes in pairs of range images while third-order moments are used to resolve the four-way ambiguity. Experimental results show expected improvement of recovered rigid transformation based on moments of recovered superellipsoids as compared to the registration based on moments of raw range image data. Besides object pose estimation the presented results can be directly used for object recognition with moments and/or moment invariants as object features.

"15 seconds of fame" is an interactive art installation, which elevates the face of a randomly selected gallery visitor for 15 seconds into a "work of art". The installation was inspired by Andy Warhol's statement that "In the future everybody will be world famous for fifteen minutes" as well as by the pop-art style of his works. The installation consists of a computer with a flat-panel monitor, a digital camera and a proprietary software that can detect human faces in images and graphically transform them. In this paper we present the technical background of the installation, in particular, how computer vision techniques were applied in this art installation.

We present an approach to reliable and efficient recovery of part-descriptions in terms of superquadric models from range data. We show that superquadrics can directly be recovered from unsegmented data, thus avoiding any presegmentation steps (e.g. in terms of surfaces). The approach is based on the recover-and-select paradigm. We present several experiments on real and synthetic range images, where we demonstrate the stability of the results with respect to viewpoint and noise.

While there are simple and fast techniques for querying conventional databases and collections of texts, content-based retrieval of images still remains a problem to be solved. To eliminate the need for a trained user and refining queries, needed by some recent systems, we propose the use of machine learning to induct decision trees as a substitute for conventional queries.

Volumetric part models play an important part in robotic applications such as grasping, path planning, object avoidance, and modeling kinematic chains. The authors present a novel method for reliable and efficient recovery of part-descriptions in terms of superquadric models from range images. In contrast to usual approaches which perform the recovery of volumetric models in several steps (from curves, surfaces to volumes), the authors show that a direct recovery is possible. This is achieved by combining two existing methods: recover-and-select paradigm and recovery of superquadric models. A redundant set of superquadratics is initiated in the image and only the recovered models resulting in the simplest overall description are selected. The author show the results on several real range images.>

Proposes an efficient architecture for selective image modeling. The authors give an example in which models of different scale are reconstructed in parallel. It is shown that this redundant representation can effectively be pruned using the criterion of minimum description length. Models that are selected in the final description indicate the appropriate scale of observation.>

A method for recovery of compact volumetric models for shape representation of single-part objects in computer vision is introduced. The models are superquadrics with parametric deformations (bending, tapering, and cavity deformation). The input for the model recovery is three-dimensional range points. Model recovery is formulated as a least-squares minimization of a cost function for all range points belonging to a single part. During an iterative gradient descent minimization process, all model parameters are adjusted simultaneously, recovery position, orientation, size, and shape of the model, such that most of the given range points lie close to the model's surface. A specific solution among several acceptable solutions, where are all minima in the parameter space, can be reached by constraining the search to a part of the parameter space. The many shallow local minima in the parameter space are avoided as a solution by using a stochastic technique during minimization. Results using real range data show that the recovered models are stable and that the recovery procedure is fast.>