A multi-modal dance corpus for research into interaction between humans in virtual environments

We present a new, freely available, multimodal corpus for research into, amongst other areas, real-time realistic interaction between humans in online virtual environments. The specific corpus scenario focuses on an online dance class application scenario where students, with avatars driven by whatever 3D capture technology is locally available to them, can learn choreographies with teacher guidance in an online virtual dance studio. As the dance corpus is focused on this scenario, it consists of student/teacher dance choreographies concurrently captured at two different sites using a variety of media modalities, including synchronised audio rigs, multiple cameras, wearable inertial measurement devices and depth sensors. In the corpus, each of the several dancers performs a number of fixed choreographies, which are graded according to a number of specific evaluation criteria. In addition, ground-truth dance choreography annotations are provided. Furthermore, for unsynchronised sensor modalities, the corpus also includes distinctive events for data stream synchronisation. The total duration of the recorded content is 1 h and 40 min for each single sensor, amounting to 55 h of recordings across all sensors. Although the dance corpus is tailored specifically for an online dance class application scenario, the data is free to download and use for any research and development purposes

Detection of Orientation-Modulation Embedded Data in Color Printed Natural Images

This article addresses methods for detection of orientation-modulation data embedded in color dispersed-dot-halftone images. Several state-of-the-art methods for detection of orientation-embedded data in printed halftone images have been proposed, however they have only been evaluated independently without comparing with each other. We propose an improved detection method, which is using Principal Component Analysis (PCA) components as oriented-feature extractors, and a probabilistic model for the print-and-scan channel for maximum likelihood detection. The proposed detector and four state-of-the-art detectors are compared with each other in terms of correct detection rate, using a comprehensive testing set of printed natural images captured with three different devices. The proposed detector achieves highest correct detection rate using fewer feature extractors than the other methods, and it is significantly more robust to non-calibrated devices used for capturing the printed images. This is mostly due to the improved PCA-based oriented-feature extractors that are responsive to the embedded orientations and robust and insensitive to the other visual content

Detection of Orientation-Modulation Embedded Data in Color Printed Natural Images

This article addresses methods for detection of orientation-modulation data embedded in color dispersed-dot-halftone images. Several state-of-the-art methods for detection of orientation-embedded data in printed halftone images have been proposed, however they have only been evaluated independently without comparing with each other. We propose an improved detection method, which is using Principal Component Analysis (PCA) components as oriented-feature extractors, and a probabilistic model for the print-and-scan channel for maximum likelihood detection. The proposed detector and four state-of-the-art detectors are compared with each other in terms of correct detection rate, using a comprehensive testing set of printed natural images captured with three different devices. The proposed detector achieves highest correct detection rate using fewer feature extractors than the other methods, and it is significantly more robust to non-calibrated devices used for capturing the printed images. This is mostly due to the improved PCA-based oriented-feature extractors that are responsive to the embedded orientations and robust and insensitive to the other visual content

Halftone Modulation for Embedding UV Watermarks in Color Printed Images

A method for embedding ultraviolet (UV) responsive watermarks in CMY printed halftone images, named white modulation (WM), was proposed recently, which is based on the iterative color direct binary search (CDBS) halftoning framework. The CDBS-WM method embeds a visual watermark by modulating the local white paper coverage in order to create a differential response under UV illumination through the substrate fluorescence. In this paper, we present two main extensions of CDBS-WM. First, we propose a printer model suitable for UV watermark embedding in multi-channel printer scenarios - using four or more inks. Second, we propose an improved cost function that is minimized during the CDBS-based iterative embedding that takes into account the UV response of all primaries, as opposed to only the white primary in CDBS-WM. The proposed extensions increase the perceptual uniformity of the embedded UV watermark, as well as the UV watermark strength especially in certain image areas where the CDBS-WM failed to embed watermark

Orientation Modulation for Data Hiding in Chrominance Channels of Direct Binary Search Halftone Prints

In this article, we propose a joint halftoning and data hiding technique for color images. To ensure high quality of the printed image, the color direct binary search (CDBS) iterative halftoning algorithm is used. The proposed approach uses the commonly available cyan, magenta and yellow colorants to hide data in the chrominance channels. Orientation modulation is used for data embedding during the iterative CDBS halftoning stage. The detector is using PCA-learned components to extract the embedded data from the scanned image. Experimental results show that this proposed CDBS-based data hiding method offers both higher data hiding capacity and higher robustness to the print-and-scan channel when compared to the state-of-the-art grayscale counterpart method. The relatively high correct detection rate make this approach suitable for applications which require exact extraction of embedded data in prints

Masking in chrominance channels of natural images — Data, analysis, and prediction

This paper addresses the visual masking that occurs in the chrominance channels of natural images. We present results from a psychophysical experiment designed to obtain local thresholds of just noticeable log-Gabor distortion in the Cr and Cb channels of natural images. We analyzed the data and investigated the correlation between several low-level image features and the collected thresholds. As expected, features like variance, entropy, or edge density were correlated relatively high with the thresholds. We evaluated the performance of linear and non-linear regression (using neural networks and support vector machines) for thresholds prediction from multiple global image features; we also fitted a modified Watson-Solomon's computational model (based on log-Gabor features) for thresholds prediction. The evaluation showed that neural networks and support vector machines are most suitable for thresholds prediction. The computational model performed reasonably well, with further prospects of its improvement

Objective evaluation of relighting models on translucent materials from multispectral RTI images

In this paper, we evaluate the quality of reconstruction i.e. relighting from images obtained by a newly developed multispectral reflectance transformation imaging (MS-RTI) system. The captured MS-RTI images are of objects with different translucency and color. We use the most common methods for relighting the objects: polynomial texture mapping (PTM) and hemispherical harmonics (HSH), as well as the recent discrete model decomposition (DMD). The results show that all three models can reconstruct the images of translucent materials, with the reconstruction error varying with translucency but still in the range of what has been reported for other non-translucent materials. DMD relighted images are marginally better for the most transparent objects, while HSH- and PTM- relighted images appear to be better for the opaquer objects. The estimation of the surface normals of highly translucent objects using photometric stereo is not very accurate. Utilizing the peak of the fitted angular reflectance field, the relighting models, especially PTM, can provide more accurate estimation of the surface normals

Quality Assessment of 2.5D Prints Using 2D Image Quality Metrics

Quality assessment is an important aspect in a variety of application areas. In this work, the objective quality assessment of 2.5D prints was performed. The work is done on camera captures under both diffuse (single-shot) and directional (multiple-shot) illumination. Current state-of-the-art 2D full-reference image quality metrics were used to predict the quality of 2.5D prints. The results showed that the selected metrics can detect differences between the prints as well as between a print and its 2D reference image. Moreover, the metrics better detected differences in the multiple-shot set-up captures than in the single-shot set-up ones. Although the results are based on a limited number of images, they show existing metrics’ ability to work with 2.5D prints under limited conditions

Web Based Tools for Signals and Systems Course

In this paper we present web-based tools that visualize standard concepts (convolution of analog and discrete signals, pole-zero diagram, Fourier series, sampling and reconstruction) in a classical Signal and Systems course. The tools can be used by a teacher as a teaching aid to explain basic concepts in Signal and Systems course. They can also be used by students as a learning aid. They are easy for use and can be used in different locations using different operating systems