@inproceedings {Bordeaux, France, title = {A Data-Driven Approach for Automated Integrated Circuit Segmentation of Scan Electron Microscopy Images}, year = {2022}, month = {10/2022}, address = {Bordeaux, France}, abstract = {

This paper proposes an automated data-driven integrated
circuit segmentation approach of scan electron microscopy
(SEM) images inspired by state-of-the-art CNN-based image
perception methods. Based on the requirements derived from
real industry applications, we take wire segmentation and via
detection algorithms to generate integrated circuit segmentation
maps from SEMs in our approach. On SEM images
collected in the industrial applications, our method achieves
an average of 50.71 on Electrically Significant Difference
(ESD) in the wire segmentation task and 99.05\% F1 score
in the via detection task, which achieves about 85\% and 8\%
improvements over the reference method, respectively.

}, keywords = {psclab}, author = {Zifan Yu and Bruno Machado Trindade and Michael Green and Zhikang Zhang and Pullela Sneha and Bank-Tavakoli, Erfan and Christopher Pawlowicz and Ren, Fengbo} } @inproceedings {London, UK, title = {STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset}, year = {2022}, month = {11/2022}, address = {London, UK}, abstract = {

Although various 3D datasets with different functions and scales have been proposed recently, it remains challenging for individuals to complete the whole pipeline of large- scale data collection, sanitization, and annotation. Moreover, the created datasets usually face the challenge of extremely imbalanced class distribution or partial low-quality data samples. Motivated by this, we explore the procedurally synthetic 3D data generation paradigm to equip individuals with the full capability of creating large-scale annotated photogrammetry point clouds. Specifically, we introduce a synthetic aerial photogrammetry point clouds generation pipeline that takes full advantage of open geospatial data sources and off-the-shelf commercial packages. Unlike generating synthetic data in virtual games, where the simulated data usually have limited gaming environments created by artists, the proposed pipeline simulates the reconstruction process of the real environment by following the same UAV flight pattern on different synthetic terrain shapes and building densities, which ensure similar quality, noise pattern, and diversity with real data. In addition, the precise semantic and instance annotations can be generated fully automatically, avoiding the expensive and time-consuming manual annotation. Based on\ the proposed pipeline, we present a richly-annotated synthetic 3D aerial photogrammetry point cloud dataset, termed STPLS3D, with more than 16 km2 of landscapes and up to 18 fine-grained semantic categories. For verification purposes, we also provide datasets collected from four areas in the real environment. Extensive experiments conducted on our datasets demonstrate the effectiveness and quality of the proposed synthetic dataset.

}, keywords = {psclab}, url = {https://www.stpls3d.com/}, author = {Meida Chen and Qingyong Hu and Zifan Yu and Hugues Thomas and Andrew Feng and Yu Hou and Kyle McCullough and Fengbo Ren and Lucio Soibelman} } @inproceedings {Seattle, WA, title = {Learning in the Frequency Domain}, year = {2020}, month = {06/2020}, pages = {1740-1749}, address = {Seattle, WA}, abstract = {

Deep neural networks have achieved remarkable success in computer vision tasks. Existing neural networks mainly operate in the spatial domain with fixed input sizes. For practical applications, images are usually large and have to be downsampled to the predetermined input size of neural networks. Even though the downsampling operations reduce computation and the required communication bandwidth, it removes both redundant and salient information obliviously, which results in accuracy degradation. Inspired by digital signal processing theories, we analyze the spectral bias from the frequency perspective and propose a learning-based frequency selection method to identify the trivial frequency components which can be removed with- out accuracy loss. The proposed method of learning in the frequency domain leverages identical structures of the well- known neural networks, such as ResNet-50, MobileNetV2, and Mask R-CNN, while accepting the frequency-domain information as the input. Experiment results show that learning in the frequency domain with static channel selection can achieve higher accuracy than the conventional spatial downsampling approach and meanwhile further reduce the input data size. Specifically for ImageNet classification with the same input size, the proposed method achieves 1.41\% and 0.66\% top-1 accuracy improvements on ResNet-50 and MobileNetV2, respectively. Even with half input size, the proposed method still improves the top-1 accuracy on ResNet-50 by 1\%. In addition, we observe a 0.8\% average precision improvement on Mask R-CNN for instance segmentation on the COCO dataset.

}, keywords = {psclab}, author = {Kai Xu and Minghai Qin and Fei Sun and Yuhao Wang and Yen-Kuang Chen and Fengbo Ren} } @inproceedings {Virtual Event, title = {MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time}, year = {2020}, month = {07/2020}, address = {Virtual Event}, abstract = {

Monocular multi-object detection and localization in 3D space has been proven to be a challenging task. The MoNet3D algorithm is a novel and effective framework that can predict the 3D position of each object in a monocular image and draw a 3D bounding box for each object. The MoNet3D method incorporates prior knowledge of the spatial geometric correlation of neighboring objects into the deep neural network training process to improve the accuracy of 3D object localization. Experiments on the KITTI dataset show that the accuracy for predicting the depth and horizontal coordinates of objects in 3D space can reach 96.25\% and 94.74\%, respectively. Moreover, the method can realize the real-time image processing at 27.85 FPS, showing promising potential for embedded advanced driving assistance system applications. Our code is publicly available at

}, keywords = {psclab}, author = {Xichuan Zhou and Yicong Peng and Chunqiao Long and Fengbo Ren and Cong Shi} } @inproceedings {29, title = {mCOPD: Mobile Phone Based Lung Function Diagnosis and Exercise System for COPD}, year = {2013}, month = {05/2013}, publisher = {ACM}, abstract = {

COPD (Chronic Obstructive Pulmonary Disease) is a serious lung disease that causes difficulty in breathing. COPD patients require lung function examinations and perform breathing exercises on a regular basis in order to manage and be more aware of their health status. In this paper, we designed and developed a mobile-phone based system for lung function diagnosis, called mCOPD. Besides enabling accurate COPD examinations at home, the mCOPD system also offers a video-game based guidance system for breathing exercises. We evaluated mCOPD in controlled and uncontrolled environments with 40 subjects. The experimental results show that our system is a promising tool for remote medical treatment of COPD.

}, keywords = {psclab}, url = {http://dl.acm.org/citation.cfm?id=2504383}, author = {Xu, Wenyao and Huang, Ming-Chun and Liu, Jason J and Ren, Fengbo and Shen, Xinchen and Liu, Xiao and Sarrafzadeh, Majid} }