@article{ge_richmond_zhong_marchitto_lobaton_2021, title={Enhancing the morphological segmentation of microscopic fossils through Localized Topology-Aware Edge Detection}, volume={45}, ISSN={["1573-7527"]}, url={https://doi.org/10.1007/s10514-020-09950-9}, DOI={10.1007/s10514-020-09950-9}, number={5}, journal={AUTONOMOUS ROBOTS}, publisher={Springer Science and Business Media LLC}, author={Ge, Qian and Richmond, Turner and Zhong, Boxuan and Marchitto, Thomas M. and Lobaton, Edgar J.}, year={2021}, month={Jun}, pages={709–723} }
 @article{zhao_ge_tian_cui_xie_hong_2021, title={Short-term load demand forecasting through rich features based on recurrent neural networks}, volume={15}, ISSN={["1751-8695"]}, DOI={10.1049/gtd2.12069}, abstractNote={With the emerging penetration of renewables and dynamic loads, the understanding of grid edge loading conditions becomes increasingly substantial. Load modelling researches commonly consist of explicitly expressed load models and non-explicitly expressed techniques, of which artificial intelligence approaches turn out to be the major path. This paper reveals the artificial intelligence-based load modelling technique to enhance the knowledge of current and future load information considering geographical and weather dependencies. This paper presents a recurrent neural network based sequence to sequence (Seq2Seq) model to forecast the short-term power loads. Also, a feature attention mechanism, which is along channel and time directions, is developed to improve the efficiency of feature learning. The experiments over three publicly available datasets demonstrate the accuracy and effectiveness of the proposed model.}, number={5}, journal={IET GENERATION TRANSMISSION & DISTRIBUTION}, author={Zhao, Dongbo and Ge, Qian and Tian, Yuting and Cui, Jia and Xie, Boqi and Hong, Tianqi}, year={2021}, month={Mar}, pages={927–937} }
 @article{mitra_marchitto_ge_zhong_kanakiya_cook_fehrenbacher_ortiz_tripati_lobaton_2019, title={Automated species-level identification of planktic foraminifera using convolutional neural networks, with comparison to human performance}, volume={147}, ISSN={["1872-6186"]}, url={http://dx.doi.org/10.1016/j.marmicro.2019.01.005}, DOI={10.1016/j.marmicro.2019.01.005}, abstractNote={Picking foraminifera from sediment samples is an essential, but repetitive and low-reward task that is well-suited for automation. The first step toward building a picking robot is the development of an automated identification system. We use machine learning techniques to train convolutional neural networks (CNNs) to identify six species of extant planktic foraminifera that are widely used by paleoceanographers, and to distinguish the six species from other taxa. We employ CNNs that were previously built and trained for image classification. Foraminiferal training and identification use reflected light microscope digital images taken at 16 different illumination angles using a light-emitting diode (LED) ring. Overall machine accuracy, as a combination of precision and recall, is better than 80% even with limited training. We compare machine performance to that of human pickers (six experts and five novices) by tasking each with the identification of 540 specimens based on images. Experts achieved comparable precision but poorer recall relative to the machine, with an average accuracy of 63%. Novices scored lower than experts on both precision and recall, for an overall accuracy of 53%. The machine achieved fairly uniform performance across the six species, while participants' scores were strongly species-dependent, commensurate with their past experience and expertise. The machine was also less sensitive to specimen orientation (umbilical versus spiral views) than the humans. These results demonstrate that our approach can provide a versatile ‘brain’ for an eventual automated robotic picking system.}, journal={MARINE MICROPALEONTOLOGY}, publisher={Elsevier BV}, author={Mitra, R. and Marchitto, T. M. and Ge, Q. and Zhong, B. and Kanakiya, B. and Cook, M. S. and Fehrenbacher, J. S. and Ortiz, J. D. and Tripati, A. and Lobaton, E.}, year={2019}, month={Mar}, pages={16–24} }
 @inproceedings{zhong_ge_kanakiya_mitra_marchitto_lobaton_2017, title={A comparative study of image classification algorithms for foraminifera identification}, url={http://dx.doi.org/10.1109/ssci.2017.8285164}, DOI={10.1109/ssci.2017.8285164}, abstractNote={Identifying Foraminifera (or forams for short) is essential for oceanographic and geoscience research as well as petroleum exploration. Currently, this is mostly accomplished using trained human pickers, routinely taking weeks or even months to accomplish the task. In this paper, a foram identification pipeline is proposed to automatic identify forams based on computer vision and machine learning techniques. A microscope based image capturing system is used to collect a labelled image data set. Various popular image classification algorithms are adapted to this specific task and evaluated under various conditions. Finally, the potential of a weighted cross-entropy loss function in adjusting the trade-off between precision and recall is tested. The classification algorithms provide competitive results when compared to human experts labeling of the data set.}, booktitle={2017 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher={IEEE}, author={Zhong, Boxuan and Ge, Q. and Kanakiya, B. and Mitra, R. and Marchitto, T. and Lobaton, E.}, year={2017}, pages={3199–3206} }
 @inproceedings{ge_zhong_kanakiya_mitra_marchitto_lobaton_2017, title={Coarse-to-fine Foraminifera image segmentation through 3d and deep features}, url={http://dx.doi.org/10.1109/ssci.2017.8280982}, DOI={10.1109/ssci.2017.8280982}, abstractNote={Foraminifera are single-celled marine organisms, which are usually less than 1 mm in diameter. One of the most common tasks associated with foraminifera is the species identification of thousands of foraminifera contained in rock or ocean sediment samples, which can be a tedious manual procedure. Thus an automatic visual identification system is desirable. Some of the primary criteria for foraminifera species identification come from the characteristics of the shell itself. As such, segmentation of chambers and apertures in foraminifera images would provide powerful features for species identification. Nevertheless, none of the existing image-based, automatic classification approaches make use of segmentation, partly due to the lack of accurate segmentation methods for foraminifera images. In this paper, we propose a learning-based edge detection pipeline, using a coarse-to-fine strategy, to extract the vague edges from foraminifera images for segmentation using a relatively small training set. The experiments demonstrate our approach is able to segment chambers and apertures of foraminifera correctly and has the potential to provide useful features for species identification and other applications such as morphological study of foraminifera shells and foraminifera dataset labeling.}, booktitle={2017 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher={IEEE}, author={Ge, Q. and Zhong, Boxuan and Kanakiya, B. and Mitra, R. and Marchitto, T. and Lobaton, E.}, year={2017} }
 @inproceedings{ge_lobaton_2017, title={Obstacle detection in outdoor scenes based on multi-valued stereo disparity maps}, url={http://dx.doi.org/10.1109/ssci.2017.8280990}, DOI={10.1109/ssci.2017.8280990}, abstractNote={In this paper, we propose a methodology for robust obstacle detection in outdoor scenes for autonomous driving applications using a multi-valued stereo disparity approach. Traditionally, disparity maps computed from stereo pairs only provide a single estimated disparity value for each pixel. However, disparity computation suffers heavily from reflections, lack of texture and repetitive patterns of objects. This may lead to wrong estimates, which can introduce some bias on obstacle detection approaches that make use of the disparity map. To overcome this problem, instead of a single-valued disparity estimation, we propose making use of multiple candidates per pixel. The candidates are selected from a statistical analysis that characterizes the performance of the underlying matching cost function based on two metrics: The number of candidates extracted, and the distance from these candidates to the true disparity value. Then, we construct an aggregate occupancy map in u-disparity space from which obstacle detection is obtained. Experiments show that our approach can recover the correct structure of obstacles on the scene when traditional estimation approaches fail.}, booktitle={2017 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher={IEEE}, author={Ge, Qian and Lobaton, Edgar}, year={2017}, month={Nov} }
 @article{ge_lobaton_2016, title={Consensus-Based Image Segmentation via Topological Persistence}, ISSN={["2160-7508"]}, url={http://dx.doi.org/10.1109/cvprw.2016.135}, DOI={10.1109/cvprw.2016.135}, abstractNote={Image segmentation is one of the most important lowlevel operation in image processing and computer vision. It is unlikely for a single algorithm with a fixed set of parameters to segment various images successfully due to variations between images. However, it can be observed that the desired segmentation boundaries are often detected more consistently than other boundaries in the output of state of-the-art segmentation results. In this paper, we propose a new approach to capture the consensus of information from a set of segmentations generated by varying parameters of different algorithms. The probability of a segmentation curve being present is estimated based on our probabilistic image segmentation model. A connectivity probability map is constructed and persistent segments are extracted by applying topological persistence to the probability map. Finally, a robust segmentation is obtained with the detection of certain segmentation curves guaranteed. The experiments demonstrate our algorithm is able to consistently capture the curves present within the segmentation set.}, journal={PROCEEDINGS OF 29TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, (CVPRW 2016)}, publisher={IEEE}, author={Ge, Qian and Lobaton, Edgar}, year={2016}, pages={1050–1057} }
 @article{ge_lokare_lobaton_2015, title={Non-Rigid Image Registration under Non-Deterministic Deformation Bounds}, volume={9287}, ISSN={["1996-756X"]}, url={http://dx.doi.org/10.1117/12.2072530}, DOI={10.1117/12.2072530}, abstractNote={Image registration aims to identify the mapping between corresponding locations in an anatomic structure. Most traditional approaches solve this problem by minimizing some error metric. However, they do not quantify the uncertainty behind their estimates and the feasibility of other solutions. In this work, it is assumed that two images of the same anatomic structure are related via a Lipschitz non-rigid deformation (the registration map). An approach for identifying point correspondences with zero false-negative rate and high precision is introduced under this assumption. This methodology is then extended to registration of regions in an image which is posed as a graph matching problem with geometric constraints. The outcome of this approach is a homeomorphism with uncertainty bounds characterizing its accuracy over the entire image domain. The method is tested by applying deformation maps to the LPBA40 dataset.}, journal={10TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION PROCESSING AND ANALYSIS}, publisher={SPIE}, author={Ge, Qian and Lokare, Namita and Lobaton, Edgar}, editor={Romero, Eduardo and Lepore, NatashaEditors}, year={2015} }
 @inproceedings{chattopadhyay_ge_wei_lobaton_2015, title={Robust multi-target tracking in outdoor traffic scenarios via persistence topology based robust motion segmentation}, url={http://dx.doi.org/10.1109/globalsip.2015.7418308}, DOI={10.1109/globalsip.2015.7418308}, abstractNote={In this paper, we present a motion segmentation based robust multi-target tracking technique for on-road obstacles. Our approach uses depth imaging information, and integrates persistence topology for segmentation and min-max network flow for tracking. To reduce time as well as computational complexity, the max flow problem is solved using a dynamic programming algorithm. We classify the sensor reading into regions of stationary and moving parts by aligning occupancy maps obtained from the disparity images and then, incorporate Kalman filter in the network flow algorithm to track the moving objects robustly. Our algorithm has been tested on several real-life stereo datasets and the results show that there is an improvement by a factor of three on robustness when comparing performance with and without the topological persistent detections. We also perform measurement accuracy of our algorithm using popular evaluation metrics for segmentation and tracking, and the results look promising.}, booktitle={2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)}, publisher={IEEE}, author={Chattopadhyay, Somrita and Ge, Qian and Wei, Chunpeng and Lobaton, Edgar}, year={2015}, month={Dec}, pages={805–809} }