BGrowth: an efficient approach for the segmentation of vertebral compression fractures in magnetic resonance imaging

Segmentation of medical images is a critical issue: several process of analysis and classification rely on this segmentation. With the growing number of people presenting back pain and problems related to it, the automatic or semi-automatic segmentation of fractured vertebral bodies became a challenging task. In general, those fractures present several regions with non-homogeneous intensities and the dark regions are quite similar to the structures nearby. Aimed at overriding this challenge, in this paper we present a semi-automatic segmentation method, called Balanced Growth (BGrowth). The experimental results on a dataset with 102 crushed and 89 normal vertebrae show that our approach significantly outperforms well-known methods from the literature. We have achieved an accuracy up to 95% while keeping acceptable processing time performance, that is equivalent to the state-of-the-artmethods. Moreover, BGrowth presents the best results even with a rough (sloppy) manual annotation (seed points).Comment: This is a pre-print of an article published in Symposium on Applied Computing. The final authenticated version is available online at https://doi.org/10.1145/3297280.329972

StateLens: A Reverse Engineering Solution for Making Existing Dynamic Touchscreens Accessible

Blind people frequently encounter inaccessible dynamic touchscreens in their everyday lives that are difficult, frustrating, and often impossible to use independently. Touchscreens are often the only way to control everything from coffee machines and payment terminals, to subway ticket machines and in-flight entertainment systems. Interacting with dynamic touchscreens is difficult non-visually because the visual user interfaces change, interactions often occur over multiple different screens, and it is easy to accidentally trigger interface actions while exploring the screen. To solve these problems, we introduce StateLens - a three-part reverse engineering solution that makes existing dynamic touchscreens accessible. First, StateLens reverse engineers the underlying state diagrams of existing interfaces using point-of-view videos found online or taken by users using a hybrid crowd-computer vision pipeline. Second, using the state diagrams, StateLens automatically generates conversational agents to guide blind users through specifying the tasks that the interface can perform, allowing the StateLens iOS application to provide interactive guidance and feedback so that blind users can access the interface. Finally, a set of 3D-printed accessories enable blind people to explore capacitive touchscreens without the risk of triggering accidental touches on the interface. Our technical evaluation shows that StateLens can accurately reconstruct interfaces from stationary, hand-held, and web videos; and, a user study of the complete system demonstrates that StateLens successfully enables blind users to access otherwise inaccessible dynamic touchscreens.Comment: ACM UIST 201

Abstract Learnable Swendsen-Wang Cuts for Image Segmentation

We propose a framework for Bayesian unsupervised image segmentation with descriptive, learnable models. Our approach is based on learning descriptive models for segmentation and applying Monte Carlo Markov chain to traverse the solution space. Swendsen-Wang cuts are adapted to make meaningful jumps in solution space

Robust and accurate eye contour extraction

This paper describes a novel algorithm for exact eye contour detection in frontal face image. The exact eye shape is a useful piece of input information for applications like facial expression recognition, feature-based face recognition and face modelling. In contrast to well-known eye-segmentation methods, we do not rely on deformable models or image luminance gradient (edge) map. The eye windows (rough eye regions) are assumed to be known. The detection algorithm works in several steps. First, iris center and radius is estimated, then, exact upper eyelid contour is detected by searching for luminance valley points. Finally, lower eyelid is estimated from the eye corners coordinates and iris. The proposed technique has been tested on images of about fifty individuals taken under different lighting conditions with different cameras. It proved to be sufficiently robust and accurate for wide variety of images

Abstract A Survey on Pixel-Based Skin Color Detection Techniques

Skin color has proven to be a useful and robust cue for face detection, localization and tracking. Image content filtering, content-aware video compression and image color balancing applications can also benefit from automatic detection of skin in images. Numerous techniques for skin color modelling and recognition have been proposed during several past years. A few papers comparing different approaches have been published [Zarit et al. 1999], [Terrillon et al. 2000], [Brand and Mason 2000]. However, a comprehensive survey on the topic is still missing. We try to fill this vacuum by reviewing most widely used methods and techniques and collecting their numerical evaluation results