Nadia Brancati

Touchless Target Selection Techniques for Wearable Augmented Reality Systems

The paper deals with target selection techniques for wearable augmented reality systems. In particular, we focus on the three techniques most commonly used in distant freehand pointing and clicking on large displays: wait to click, air tap and thumb trigger. The paper details the design of the techniques for a touchless augmented reality interface and provides the results of a preliminary usability evaluation carried out in out-of-lab settings.

Experiencing touchless interaction with augmented content on wearable head-mounted displays in cultural heritage applications

The cultural heritage could benefit significantly from the integration of wearable augmented reality (AR). This technology has the potential to guide the user and provide her with both in-depth information, without distracting her from the context, and a natural interaction, which can further allow her to explore and navigate her way through a huge amount of cultural information. The integration of touchless interaction and augmented reality is particularly challenging. On the technical side, the human–machine interface has to be reliable so as to guide users across the real world, which is composed of cluttered backgrounds and severe changes in illumination conditions. On the user experience side, the interface has to provide precise interaction tools while minimizing the perceived task difficulty. In this study, an interactive wearable AR system to augment the environment with cultural information is described. To confer robustness to the interface, a strategy that takes advantage of both depth and color data to find the most reliable information on each single frame is introduced. Moreover, the results of an ISO 9241-9 user study performed in both indoor and outdoor conditions are presented and discussed. The experimental results show that, by using both depth and color data, the interface can behave consistently in different indoor and outdoor scenarios. Furthermore, the results show that the presence of a virtual pointer in the augmented visualization significantly reduces the users error rate in selection tasks.

A fully automatic one-scan adaptive zooming algorithm for color images

We present an interpolation algorithm for adaptive color image zooming. The algorithm produces the magnified image in one scan of the input image, and is fully automatic since does not involve any a priori fixed threshold. Given any integer zooming factor n, each pixel of the input image generates an nxn block of pixels in the zoomed image. For the currently visited pixel of the input image, the pixels of its associated block are first assigned tentative values, which are then adaptively updated before building the next block. The method is suggested for RGB images, but can equally be employed in other color spaces. Peak signal to noise ratio (PSNR) and Structural SIMilarity (SSIM) are used to evaluate the performance of the algorithm.

Subcellular chemical and morphological analysis by stimulated Raman scattering microscopy and image analysis techniques

The visualization of heterogeneous morphology, segmentation and quantification of image features is a crucial point for nonlinear optics microscopy applications, spanning from imaging of living cells or tissues to biomedical diagnostic. In this paper, a methodology combining stimulated Raman scattering microscopy and image analysis technique is presented. The basic idea is to join the potential of vibrational contrast of stimulated Raman scattering and the strength of imaging analysis technique in order to delineate subcellular morphology with chemical specificity. Validation tests on label free imaging of polystyrene-beads and of adipocyte cells are reported and discussed.

Usability Evaluation of a Wearable Augmented Reality System for the Enjoyment of the Cultural Heritage

The recent availability of low cost wearable augmented reality (WAR) technologies is leveraging the design of applications in the cultural heritage domain in order to support users in their emotional journey among the cultural artefacts and monuments of a city. In this paper, we describe a user study evaluating the usability of a wearable augmented reality touchless interface for the enjoyment of the cultural heritage in outdoor environments. The usability evaluation has been carried out in out-of-lab settings with inexperienced users, during a three day exhibition in the city of Naples. The presented results are related to the ease of use and learning of the system, and to the users satisfaction in the enjoyment of the system.

Robust fingertip detection in egocentric vision under varying illumination conditions

Wearable augmented reality (AR) systems have the potential to significantly lower the barriers to accessing information, while leaving the focus of the users attention on the real world. To reveal their true potential, the human-machine interface is crucial. A touchless point-and-click interface for wearable AR systems may be suitable for use in many real-world applications, but it demands fingertip detection techniques robust enough to cope with cluttered backgrounds and varying illumination conditions. In this paper we propose an approach that, by automatically choosing between color and depth features, allows to detect the hand and then the users fingertip both in indoor and outdoor scenarios, with or without adequate illumination.

Human skin detection through correlation rules between the YCb and YCr subspaces based on dynamic color clustering

Abstract This paper presents a novel rule-based skin detection method that works in the YCbCr color space. The method is based on correlation rules that evaluate the combinations of chrominance values to identify the skin pixels in the YCb and YCr subspaces. The correlation rules depend on the shape and size of dynamically generated skin color clusters, which are computed on a statistical basis in the YCb and YCr subspaces for each single image, and represent the areas that include most of the candidate skin pixels. Comparisons with six well-known rule-based methods in literature carried out on four publicly available databases show that the proposed method outperforms the others in terms of quantitative performance evaluation parameters. Moreover, the qualitative analysis shows that the method achieves satisfactory results also in critical scenarios, including severe variations in illumination conditions.

Image Segmentation Via Iterative Histogram Thresholding and Morphological Features Analysis

In this paper, we present a new segmentation algorithm, based on iterated thresholding and on morphological features. A first thresholding, based on the histogram of the image, is done to partition the image into three sets including respectively pixels belonging to foreground, pixels belonging to background, and unassigned pixels. Thresholding of components of unassigned pixels is then iteratively done, based on the histogram of the components. Components of unassigned pixels, possibly still present at the end of iterated thresholding, are assigned to foreground or background by taking into account area, minimum grey-level and spatial relationship with the adjacent sets.

Reconnecting Broken Ridges in Fingerprint Images

In this paper, we present a new method for reconnecting broken ridges in fingerprint images. The method is based on the use of a discrete directional mask and on the standard deviation of the gray-levels to determine ridge direction. The obtained direction map is smoothed by counting the occurrences of the directions in a sufficiently large window. The fingerprint image is, then, binarized and thinned. Linking paths to connect broken ridges are generated by using a morphological transformation to guide the process.

Multi-classification of Breast Cancer Histology Images by Using a Fine-Tuning Strategy.

The adoption of automatic systems to support the diagnosis of breast cancer from histology images analysis is rapidly becoming more widespread. Most of the works in literature focus principally on a two-class problem, namely benign and malignant tumors. However, the development of multi-classification approaches would also be greatly appreciated in order to support the determination of an ideal therapeutic schedule for the treatment of breast cancer. The multi-classification of histology images is particularly challenging due to the broad variability of appearance of the image, the great differences in the spatial arrangement of the histological structures, and the heterogeneity in the color distribution. In this work, a fine-tuning strategy of ResNet, a residual convolutional neural network, is presented to address the problem of multi-classification for breast cancer histology images in normal tissue, benign lesions, in situ carcinomas and invasive carcinomas. We have combined three configurations of ResNet, differing from each other in terms of the number of layers, by using a maximum probability rule to balance out their individual weaknesses during the testing. The proposed approach achieved a remarkable performance on the images provided for the Grand Challenge on Breast Cancer Histology Images (BACH), within the context of the International Conference ICIAR 2018.