Behavior description is conducive to the analysis of tiny objects, similar objects, objects with weak visual information and objects with similar visual information, playing a fundamental role in the identification and classification of dynamic objects in microscopic videos. To this end, we propose foldover features to describe the behavior of dynamic objects. First, we generate foldover for each object in microscopic videos in X, Y and Z directions, respectively. Then, we extract foldover features from the X, Y and Z directions with statistical methods, respectively. Finally, we use four different classifiers to test the effectiveness of the proposed foldover features. In the experiment, we use a sperm microscopic video dataset to evaluate the proposed foldover features, including three types of 1374 sperms, and obtain the highest classification accuracy of 96.5%.
Microscopic Vehicle Trajectory Datasets from UAV-collected Video for Heterogeneous, Area-Based Urban Traffic
Transportation Research & Injury Prevention Centre (TRIPC), Indian Institute of Technology Delhi（印度理工学院德里分校交通研究与伤害预防中心（TRIPC））；Indian Institute of Technology Delhi（印度理工学院德里分校）；Queensland University of Technology（昆士兰科技大学）
Westfälische Wilhelms-Universität（威斯特伐利亚·威廉姆斯大学（明斯特大学））；Technische Universität Dresden（德累斯顿工业大学）
This paper introduces a novel framework for the automated tracking of cells, with a particular focus on the challenging situation of phase contrast microscopic videos. Our framework is based on a topology preserving variational segmentation approach applied to normal velocity components obtained from optical flow computations, which appears to yield robust tracking and automated extraction of cell trajectories. In order to obtain improved trackings of local shape features we discuss an additional correction step based on active contours and the image Laplacian which we optimize for an example …
Northeastern University（东北大学）；China Medical University（中国医科大学）；Shengjing Hospital（盛京医院）；Jinghua Hospital（京华医院）；Stevens Institute of Technology（史蒂文斯理工学院）；University of Luebeck（吕贝克大学）
The detection of tiny objects in microscopic videos is a problematic point, especially in large-scale experiments. For tiny objects (such as sperms) in microscopic videos, current detection methods face challenges in fuzzy, irregular, and precise positioning of objects. In contrast, we present a convolutional neural network for tiny object detection (TOD-CNN) with an underlying data set of high-quality sperm microscopic videos (111 videos, $>$ 278,000 annotated objects), and a graphical user interface (GUI) is designed to employ and test the proposed model effectively. TOD-CNN is highly accura…
Northeastern University（东北大学）；National University of Defense Technology（国防科技大学）；Shengjing Hospital, China Medical University（中国医科大学附属盛京医院）；University of Luebeck（吕贝克大学）；The Chinese University of Hong Kong（香港中文大学）
The accurate detection of sperms and impurities is a very challenging task, facing problems such as the small size of targets, indefinite target morphologies, low contrast and resolution of the video, and similarity of sperms and impurities. So far, the detection of sperms and impurities still largely relies on the traditional image processing and detection techniques which only yield limited performance and often require manual intervention in the detection process, therefore unfavorably escalating the time cost and injecting the subjective bias into the analysis. Encouraged by the successes …
Northeastern University（东北大学）；University of Washington (Seattle Campus)（华盛顿大学（西雅图校区））；University of Luebeck（吕贝克大学）；Chengdu University of Information Technology（成都信息工程大学）
The Computer Assisted Sperm Analysis (CASA) plays a crucial role in male reproductive health diagnosis and Infertility treatment. With the development of the computer industry in recent years, a great of accurate algorithms are proposed. With the assistance of those novel algorithms, it is possible for CASA to achieve a faster and higher quality result. Since image processing is the technical basis of CASA, including pre-processing,feature extraction, target detection and tracking, these methods are important technical steps in dealing with CASA. The various works related to Computer Assisted …
One of the objectives of understanding pedestrian behavior is to predict the effect of proposed changes in the design or evaluation of pedestrian facilities. We want to know the impact to the user of the facilities, as the design of the facilities change. That impact was traditionally evaluated by level of service standards. Another design criterion to measure the impact of design change is measured by the pedestrian flow performance index. This paper describes the determination of pedestrian flow performance based video tracking or any microscopic pedestrian simulation models. Most of pedestr…
National Chiao Tung University（国立交通大学）；National Taiwan University of Science and Technology（国立台湾科技大学）
Computer vision based object tracking has been used to annotate and augment sports video. For sports learning and training, video replay is often used in post-match review and training review for tactical analysis and movement analysis. For automatically and systematically competition data collection and tactical analysis, a project called CoachAI has been supported by the Ministry of Science and Technology, Taiwan. The proposed project also includes research of data visualization, connected training auxiliary devices, and data warehouse. Deep learning techniques will be used to develop video-…
Phase-Specific Augmented Reality Guidance for Microscopic Cataract Surgery Using Long-Short Spatiotemporal Aggregation Transformer
Shanghai Jiao Tong University（上海交通大学）；Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属新华医院）；Rice University（莱斯大学）；University of Münster（明斯特大学）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Future Technology, University of Chinese Academy of Sciences（中国科学院大学未来技术学院）；Center for Excellence in Brain Science and Intelligence Technology（脑科学与智能技术卓越创新中心）
Recently, flow-based methods have achieved promising success in video frame interpolation. However, electron microscopic (EM) images suffer from unstable image quality, low PSNR, and disorderly deformation. Existing flow-based interpolation methods cannot precisely compute optical flow for EM images since only predicting each position's unique offset. To overcome these problems, we propose a novel interpolation framework for EM images that progressively synthesizes interpolated features in a coarse-to-fine manner. First, we extract missing intermediate features by the proposed temporal spatial…
SimulaMet（西穆拉梅特）；Oslo Metropolitan University（奥斯陆城市大学）；Kristiania University College（克里斯蒂安尼亚大学学院）
In this paper, we present a two-step deep learning method that is used to predict sperm motility and morphology-based on video recordings of human spermatozoa. First, we use an autoencoder to extract temporal features from a given semen video and plot these into image-space, which we call feature-images. Second, these feature-images are used to perform transfer learning to predict the motility and morphology values of human sperm. The presented method shows it's capability to extract temporal information into spatial domain feature-images which can be used with traditional convolutional neural…
School of Electrical Engineering and Informatics, Institut Teknologi Bandung（万隆理工学院电气工程与信息学院）；The University of Tokyo（东京大学）；School of Life Sciences and Technology, Institut Teknologi Bandung（万隆理工学院生命科学与技术学院）；Balai Inseminasi Buatan Lembang（伦邦人工授精中心）
Background and Objective: Object detection is a primary research interest in computer vision. Sperm-cell detection in a densely populated bull semen microscopic observation video presents challenges such as partial occlusion, vast number of objects in a single video frame, tiny size of the object, artifacts, low contrast, and blurry objects because of the rapid movement of the sperm cells. This study proposes an architecture, called DeepSperm, that solves the aforementioned challenges and is more accurate and faster than state-of-the-art architectures. Methods: In the proposed architecture, we…
