Component Segmentation Annotated Images

<p>Images and annotations of HDDs and GPUs.</p&gt

Screw Detection Model

<p>Model for the Screw Detection module.</p&gt

DCNN-Based Screw Detection for Automated Disassembly Processes

utomation of disassembly processes in electronic waste recycling is progressing but hindered by the lack of automated procedures for screw detection and removal. Here we specifically address the detection problem and implement a universal, generalizable, and extendable screw detector which can be deployed in automated disassembly lines. We selected the best performing state-of-the-art classifiers and compared their performance to that of our architecture, which combines a Hough transform with a novel integrated model of two deep convolutional neural networks for screw detection. We show that our method outperforms currently existing methods, while maintaining the high speed of computation. Data set and code of this study are made public

Deep Learning Based 3d Reconstruction for Phenotyping of Wheat Seeds: a Dataset, Challenge, and Baseline Method

We present a new data set for 3d wheat seed reconstruction, propose a challenge, and provide baseline methods. Individual plant seed properties influence early development of plants and are thus of interest in plant phenotyping experiments. Seed shape can be measured reliably from images using volume carving, as done in robotic setups such as phenoSeeder. However, about 36 images are needed to obtain a suitably accurate 3d model, where image acquisition takes approximately 20 s. For large-scale experiments with thousands of seeds higher throughput is required limiting image acquisition time. We present a deep-learning model that reconstructs an approximate 3d point cloud from fewer images, even only a single view. It has a significantly lower error than linear regression, which has been actively used so far in similar tasks. Using three images reduces imaging time by a factor of 10, where relative errors of volume length, width, and height are all around 2%. Inference time from the neural network is negligibly short compared with imaging time which enables this method for real-time measurements and sorting

Consensus on women's health aspects of polycystic ovary syndrome (PCOS)

Polycystic ovary syndrome (PCOS) is the most common endocrine disorder in females with a high prevalence. The etiology of this heterogeneous condition remains obscure and its phenotype expression varies. Two, widely cited, previous ESHRE/ASRM-sponsored PCOS consensus workshops focused on diagnosis (published in 2004) and infertility management (published in 2008). The present third PCOS consensus paper summarizes current knowledge and identifies knowledge gaps regarding various women’s health aspects of PCOS. Relevant topics addressed—all dealt with in a systematic fashion—include adolescence, hirsutism and acne, contraception, menstrual cycle abnormalities, quality of life, ethnicity, pregnancy complications, long-term metabolic and cardiovascular health and finally cancer risk. Additional, comprehensive background information is provided separately in an extended online publication.B.C.J.M. Fauser, B.C. Tarlatzis, R.W. Rebar, R.S. Legro, A.H. Balen, R. Lobo, H. Carmina, R.J. Chang, B.O. Yildiz, J.S.E. Laven, J. Boivin, F. Petraglia, C.N. Wijeyeratne, R.J. Norman, A. Dunaif, S. Franks, R.A. Wild, D. Dumesic and K. Barnhar