![]() Detailed reviews on these topics can be found in. However, there are new design rules and unique constraints induced by AM, which introduce new challenges such as support structure design/elimination, minimum component size constraints, directional material properties, topology design interpretation, variable-density cellular structure design, and many others. Because the shape and topology are concurrently designed, topology optimization makes the greatest design freedom possible compared with shape-only or size-only optimization. Topology optimization has been treated as the main computational design method for AM. Several numerical examples were investigated to verify the effectiveness of the proposed method, while satisfactory optimization results have been derived. An asynchronous starting strategy is proposed to prevent the local minimum solutions caused by the concurrent optimization scheme. With this set-up, a concurrent optimization problem was formulated to simultaneously optimize the topological structure of the printing layer, the multipatch distribution, and the corresponding deposition directions. The level set method was employed to represent and track the layer shape evolution discrete material optimization (DMO) model was adopted to realize the material property interpolation among the patches. The ‘multipatch’ concept consists of each printing layer disintegrated into multiple patches with different zigzag-type filament deposition directions. We additionally provide a MATLAB file which illustrates how this dataste was created for image resolution of 1024x1024 and also describes how to add rotation to create JPEG+Roation+Resampling+JPEG manipulation dataste which is much harder case to deal with.This paper presents a hybrid topology optimization method for multipatch fused deposition modeling (FDM) 3D printing to address the process-induced material anisotropy. Our code does not uses the validation dataset during the training phase and so the validation dataset can also be used for evaluation. This is charachteristicly distinct from recent works that evaluate on images of fixed resolution - mostly of VGA resolution.For each set the images are equally divided into 5 resampling factors namely - 0.6,0.8,1,1.2 and 1.4.The complete dataset has 75,000 training images 15,000 validation images and another 15,000 images for testing. ![]() However if you wish to download the full JPEG+Resampling+JPEG dataset which is over a 100GB, use this link.To run the jupyter file you just need test data containing 15,000 images.It should run well for Pytorch>=1.00 and torchvision>=0.2. However for running it on your own system you need to download the test dataset containing 15000 images and model checkpoint.For reference the file is pre-computed with results. We provide the jupyter file containing the test code with all the dependencies in a single file.Sample Output Image Resolution after Resampling The project has been summarised in the following video. Compared to existing strategies and Max-pooling it gives up to 7-8% improvement on public datasets. This pooling strategy can be used with any of the existing deep models and for demonstration purposes, we show its utility on Resnet-18 for the case of resampling detection a fundamental operation for any image sought of image manipulation. ![]() This pooling strategy can dynamically adjust input tensors of different size and shapes without much loss of information as in ROI Max-pooling. To handle this issue, we propose a novel pooling strategy called Iterative Pooling. However, in our experiments, we observed that many state-of-the-art forensic algorithms are sensitive to image size and their performance quickly degenerates when operated on images of diverse dimensions despite re-training them using multiple image sizes. It is therefore imperative for forensic algorithms such as resampling detection to scale well for images of varying dimensions. Images captured nowadays are of varying dimensions with smartphones and DSLR’s allowing users to choose from a list of available image resolutions. The arXiv version of the paper can found at. The project is the official implementation of our IEEE ICASSP paper, “Multi-patch Aggregation Models for Resampling Detection” Multi-patch Aggregation Models for Resampling Detection "Multi-patch Aggregation Models for Resampling Detection", IEEE ICASSP 2020
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