An experimental procedure for the Iosipescu composite specimen tested in the modified Wyoming fixture

A detailed description is given of the experimental procedure for testing composite Iosipescu specimens in the modified Wyoming fixture. Specimen preparation and strain gage instrumentation are addressed. Interpretation of the experimental results is discussed. With the proper experimental procedure and setup, consistent and repeatable shear properties are obtained

A comparison of three popular test methods for determining the shear modulus of composite materials

Three popular shear tests (the 10 degree off-axis, the plus or minus 45 degree tensile, and the Iosipescu specimen tested in the modified Wyoming fixture) for shear modulus measurement are evaluated for a graphite-epoxy composite material system. A comparison of the shear stress-strain response for each test method is made using conventional strain gage instrumentation and moire interferometry. The uniformity and purity of the strain fields in the test sections of the specimens are discussed, and the shear responses obtained from each test technique are presented and compared. For accurate measurement of shear modulus, the 90 degree Iosipescu specimen is recommended

Weakly-supervised Caricature Face Parsing through Domain Adaptation

A caricature is an artistic form of a person's picture in which certain striking characteristics are abstracted or exaggerated in order to create a humor or sarcasm effect. For numerous caricature related applications such as attribute recognition and caricature editing, face parsing is an essential pre-processing step that provides a complete facial structure understanding. However, current state-of-the-art face parsing methods require large amounts of labeled data on the pixel-level and such process for caricature is tedious and labor-intensive. For real photos, there are numerous labeled datasets for face parsing. Thus, we formulate caricature face parsing as a domain adaptation problem, where real photos play the role of the source domain, adapting to the target caricatures. Specifically, we first leverage a spatial transformer based network to enable shape domain shifts. A feed-forward style transfer network is then utilized to capture texture-level domain gaps. With these two steps, we synthesize face caricatures from real photos, and thus we can use parsing ground truths of the original photos to learn the parsing model. Experimental results on the synthetic and real caricatures demonstrate the effectiveness of the proposed domain adaptation algorithm. Code is available at: https://github.com/ZJULearning/CariFaceParsing .Comment: Accepted in ICIP 2019, code and model are available at https://github.com/ZJULearning/CariFaceParsin

Deep Image Harmonization

Compositing is one of the most common operations in photo editing. To generate realistic composites, the appearances of foreground and background need to be adjusted to make them compatible. Previous approaches to harmonize composites have focused on learning statistical relationships between hand-crafted appearance features of the foreground and background, which is unreliable especially when the contents in the two layers are vastly different. In this work, we propose an end-to-end deep convolutional neural network for image harmonization, which can capture both the context and semantic information of the composite images during harmonization. We also introduce an efficient way to collect large-scale and high-quality training data that can facilitate the training process. Experiments on the synthesized dataset and real composite images show that the proposed network outperforms previous state-of-the-art methods