One-step deposition of nano-to-micron-scalable, high-quality digital image correlation patterns for high-strain in-situ multi-microscopy testing

Digital Image Correlation (DIC) is of vital importance in the field of experimental mechanics, yet, producing suitable DIC patterns for demanding in-situ mechanical tests remains challenging, especially for ultra-fine patterns, despite the large number of patterning techniques in the literature. Therefore, we propose a simple, flexible, one-step technique (only requiring a conventional deposition machine) to obtain scalable, high-quality, robust DIC patterns, suitable for a range of microscopic techniques, by deposition of a low melting temperature solder alloy in so-called 'island growth' mode, without elevating the substrate temperature. Proof of principle is shown by (near-)room-temperature deposition of InSn patterns, yielding highly dense, homogeneous DIC patterns over large areas with a feature size that can be tuned from as small as 10nm to 2um and with control over the feature shape and density by changing the deposition parameters. Pattern optimization, in terms of feature size, density, and contrast, is demonstrated for imaging with atomic force microscopy, scanning electron microscopy (SEM), optical microscopy and profilometry. Moreover, the performance of the InSn DIC patterns and their robustness to large deformations is validated in two challenging case studies of in-situ micro-mechanical testing: (i) self-adaptive isogeometric digital height correlation of optical surface height profiles of a coarse, bimodal InSn pattern providing microscopic 3D deformation fields (illustrated for delamination of aluminum interconnects on a polyimide substrate) and (ii) DIC on SEM images of a much finer InSn pattern allowing quantification of high strains near fracture locations (illustrated for rupture of a Fe foil). As such, the high controllability, performance and scalability of the DIC patterns offers a promising step towards more routine DIC-based in-situ micro-mechanical testing.Comment: Accepted for publication in Strai

Enhancing retinal images by nonlinear registration

Being able to image the human retina in high resolution opens a new era in many important fields, such as pharmacological research for retinal diseases, researches in human cognition, nervous system, metabolism and blood stream, to name a few. In this paper, we propose to share the knowledge acquired in the fields of optics and imaging in solar astrophysics in order to improve the retinal imaging at very high spatial resolution in the perspective to perform a medical diagnosis. The main purpose would be to assist health care practitioners by enhancing retinal images and detect abnormal features. We apply a nonlinear registration method using local correlation tracking to increase the field of view and follow structure evolutions using correlation techniques borrowed from solar astronomy technique expertise. Another purpose is to define the tracer of movements after analyzing local correlations to follow the proper motions of an image from one moment to another, such as changes in optical flows that would be of high interest in a medical diagnosis.Comment: 21 pages, 7 figures, submitted to Optics Communication

High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks

Synthesizing face sketches from real photos and its inverse have many applications. However, photo/sketch synthesis remains a challenging problem due to the fact that photo and sketch have different characteristics. In this work, we consider this task as an image-to-image translation problem and explore the recently popular generative models (GANs) to generate high-quality realistic photos from sketches and sketches from photos. Recent GAN-based methods have shown promising results on image-to-image translation problems and photo-to-sketch synthesis in particular, however, they are known to have limited abilities in generating high-resolution realistic images. To this end, we propose a novel synthesis framework called Photo-Sketch Synthesis using Multi-Adversarial Networks, (PS2-MAN) that iteratively generates low resolution to high resolution images in an adversarial way. The hidden layers of the generator are supervised to first generate lower resolution images followed by implicit refinement in the network to generate higher resolution images. Furthermore, since photo-sketch synthesis is a coupled/paired translation problem, we leverage the pair information using CycleGAN framework. Both Image Quality Assessment (IQA) and Photo-Sketch Matching experiments are conducted to demonstrate the superior performance of our framework in comparison to existing state-of-the-art solutions. Code available at: https://github.com/lidan1/PhotoSketchMAN.Comment: Accepted by 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)(Oral

SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset

Visual complexity identifies the level of intricacy and details in an image or the level of difficulty to describe the image. It is an important concept in a variety of areas such as cognitive psychology, computer vision and visualization, and advertisement. Yet, efforts to create large, downloadable image datasets with diverse content and unbiased groundtruthing are lacking. In this work, we introduce Savoias, a visual complexity dataset that compromises of more than 1,400 images from seven image categories relevant to the above research areas, namely Scenes, Advertisements, Visualization and infographics, Objects, Interior design, Art, and Suprematism. The images in each category portray diverse characteristics including various low-level and high-level features, objects, backgrounds, textures and patterns, text, and graphics. The ground truth for Savoias is obtained by crowdsourcing more than 37,000 pairwise comparisons of images using the forced-choice methodology and with more than 1,600 contributors. The resulting relative scores are then converted to absolute visual complexity scores using the Bradley-Terry method and matrix completion. When applying five state-of-the-art algorithms to analyze the visual complexity of the images in the Savoias dataset, we found that the scores obtained from these baseline tools only correlate well with crowdsourced labels for abstract patterns in the Suprematism category (Pearson correlation r=0.84). For the other categories, in particular, the objects and advertisement categories, low correlation coefficients were revealed (r=0.3 and 0.56, respectively). These findings suggest that (1) state-of-the-art approaches are mostly insufficient and (2) Savoias enables category-specific method development, which is likely to improve the impact of visual complexity analysis on specific application areas, including computer vision.Comment: 10 pages, 4 figures, 4 table