Contour Detection in Cassini ISS images based on Hierarchical Extreme Learning Machine and Dense Conditional Random Field
Jinan University（暨南大学）；Zhejiang University（浙江大学）；Sino-French Joint Laboratory for Astrometry, Dynamics and Space Science, Jinan University（暨南大学中法天文测量、动力学与空间科学联合实验室）
In Cassini ISS (Imaging Science Subsystem) images, contour detection is often performed on disk-resolved object to accurately locate their center. Thus, the contour detection is a key problem. Traditional edge detection methods, such as Canny and Roberts, often extract the contour with too much interior details and noise. Although the deep convolutional neural network has been applied successfully in many image tasks, such as classification and object detection, it needs more time and computer resources. In the paper, a contour detection algorithm based on H-ELM (Hierarchical Extreme Learning Machine) and DenseCRF (Dense Conditional Random Field) is proposed for Cassini ISS images. The experimental results show that this algorithm's performance is better than both traditional machine learning methods such as SVM, ELM and even deep convolutional neural network. And the extracted contour is closer to the actual contour. Moreover, it can be trained and tested quickly on the general configuration of PC, so can be applied to contour detection for Cassini ISS images.
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）；Indian Statistical Institute, Kolkata（加尔各答印度统计研究所）
Image segmentation is the initial step for every image analysis task. A large variety of segmentation algorithm has been proposed in the literature during several decades with some mixed success. Among them, the fuzzy energy based active contour models get attention to the researchers during last decade which results in development of various methods. A good segmentation algorithm should perform well in a large number of images containing noise, blur, low contrast, region in-homogeneity, etc. However, the performances of the most of the existing fuzzy energy based active contour models have be…
Imaging Science Subsystem (ISS) mounted on the Cassini spacecraft has taken a lot of images, which provides an important source of high-precision astrometry of some planets and satellites. However, some of these images are degraded by trailed stars. Previously, these degraded images cannot be used for astrometry. In this paper, a new method is proposed to detect and compute the centers of these trailed stars automatically. The method is then performed on the astrometry of ISS images with trailed stars. Finally, we provided 658 astrometric positions between 2004 and 2017 of several satellites t…
IRT Saint Exupéry（圣埃克苏佩里研究所）；Université Côte d’Azur - Inria（蔚蓝海岸大学-法国国家信息与自动化研究所）
While object detection methods traditionally make use of pixel-level masks or bounding boxes, alternative representations such as polygons or active contours have recently emerged. Among them, methods based on the regression of Fourier or Chebyshev coefficients have shown high potential on freeform objects. By defining object shapes as polar functions, they are however limited to star-shaped domains. We address this issue with SCR: a method that captures resolution-free object contours as complex periodic functions. The method offers a good compromise between accuracy and compactness thanks to…
Modern object detectors are vulnerable to adversarial examples, which brings potential risks to numerous applications, e.g., self-driving car. Among attacks regularized by $\ell_p$ norm, $\ell_0$-attack aims to modify as few pixels as possible. Nevertheless, the problem is nontrivial since it generally requires to optimize the shape along with the texture simultaneously, which is an NP-hard problem. To address this issue, we propose a novel method of Adversarial Semantic Contour (ASC) guided by object contour as prior. With this prior, we reduce the searching space to accelerate the $\ell_0$ o…
UCAD: Uncertainty-guided Contour-aware Displacement for semi-supervised medical image segmentation
University of Science and Technology of China（科学技术大学）；Suzhou Institute for Advanced Research（苏州先进研究院）；State Key Laboratory of Precision and Intelligent Chemistry（精密与智能化学国家重点实验室）
The Hong Kong Polytechnic University（香港理工大学）；Research Institute for Smart Energy（智慧能源研究院）
In this paper, we propose an active contour model with a local variance force (LVF) term that can be applied to multi-phase image segmentation problems. With the LVF, the proposed model is very effective in the segmentation of images with noise. To solve this model efficiently, we represent the regularization term by characteristic functions and then design a minimization algorithm based on a modification of the iterative convolution-thresholding method (ICTM), namely ICTM-LVF. This minimization algorithm enjoys the energy-decaying property under some conditions and has highly efficient perfor…
University of Wageningen（瓦赫宁根大学）；University of Toronto（多伦多大学）
The world is covered with millions of buildings, and precisely knowing each instance's position and extents is vital to a multitude of applications. Recently, automated building footprint segmentation models have shown superior detection accuracy thanks to the usage of Convolutional Neural Networks (CNN). However, even the latest evolutions struggle to precisely delineating borders, which often leads to geometric distortions and inadvertent fusion of adjacent building instances. We propose to overcome this issue by exploiting the distinct geometric properties of buildings. To this end, we pres…
Taking a large amount of images, the Cassini Imaging Science Subsystem (ISS) has been routinely used in astrometry. In ISS images, disk-resolved objects often lead to false detection of stars that disturb the camera pointing correction. The aim of this study was to develop an automated processing method to remove the false image stars in disk-resolved objects in ISS images. The method included the following steps: extracting edges, segmenting boundary arcs, fitting circles and excluding false image stars. The proposed method was tested using 200 ISS images. Preliminary experimental results sho…
The Chinese University of Hong Kong（香港中文大学）；Chinese Academy of Sciences（中国科学院）；Imsight Medical Technology, Co., Ltd.（艾迈医疗科技有限公司）；Imperial College London（帝国理工学院）
Accurate segmenting nuclei instances is a crucial step in computer-aided image analysis to extract rich features for cellular estimation and following diagnosis as well as treatment. While it still remains challenging because the wide existence of nuclei clusters, along with the large morphological variances among different organs make nuclei instance segmentation susceptible to over-/under-segmentation. Additionally, the inevitably subjective annotating and mislabeling prevent the network learning from reliable samples and eventually reduce the generalization capability for robustly segmentin…
Partial Decoder Attention Network with Contour-weighted Loss Function for Data-Imbalance Medical Image Segmentation
Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China（北京大学医学部医学技术研究所）；National Institute of Health Data Science, Peking University, Beijing, China（北京大学国家健康数据科学研究院）；Department of Radiology, Peking University Third Hospital, Beijing, China（北京大学第三医院放射科）；RIKEN Center for Computational Science (R-CCS), Kobe, Japan（日本京都大学RIKEN计算科学中心）；National Institute of Health Data Science, Peking University, the Institute of Medical Technology, Peking University Health Science Center, and the Institute for Artificial Intelligence, Peking University, Beijing, China（北京大学国家健康数据科学研究院、北京大学医学部医学技术研究所以及北京大学人工智能研究所）
UNIVERSITY OF CALIFORNIA（加利福尼亚大学）；LOS ANGELES（洛杉矶）
Image segmentation is a fundamental and challenging problem in computer vision with applications spanning multiple areas, such as medical imaging, remote sensing, and autonomous vehicles. Recently, convolutional neural networks (CNNs) have gained traction in the design of automated segmentation pipelines. Although CNN-based models are adept at learning abstract features from raw image data, their performance is dependent on the availability and size of suitable training datasets. Additionally, these models are often unable to capture the details of object boundaries and generalize poorly to un…
Background Intensity Estimation for Cassini-ISS Image Using Deep Learning-Based Diffusion Model
Jinan University（暨南大学）；China Unicom (Zhejiang) Industrial Internet Co., Ltd.（中国联通（浙江）工业互联网有限公司）；Shanghai Astronomical Observatory, Chinese Academy of Sciences（中国科学院上海天文台）
Phoebe is the only major satellite of Saturn with a retrograde orbit. The Cassini Imaging Science Subsystem (ISS) took a lot of Phoebe images between 2004 and 2017, but only a selection of them has been reduced. In this paper, we reduced the remaining ISS images of Phoebe. In the reduction, the Gaia EDR3 catalogue was used to provide the reference stars' positions, and the modified moment was used to measure the centre of image stars and Phoebe. Finally, a total of 834 ISS images of Phoebe have been reduced successfully. Compared with the JPL ephemeris SAT375, Phoebe's positions are consistent…
School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；School of Electronic and Information Engineering, Anhui University（安徽大学电子与信息工程学院）；Beijing Institute of Technology（北京理工大学）；Peng Cheng Laboratory（鹏城实验室）；School of Computer Science, Peking University（北京大学计算机科学系）；School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University（北京大学深圳研究生院电子与计算机工程系）
Contour-based instance segmentation methods include one-stage and multi-stage schemes. These approaches achieve remarkable performance. However, they have to define plenty of points to segment precise masks, which leads to high complexity. We follow this issue and present a single-shot method, called \textbf{VeinMask}, for achieving competitive performance in low design complexity. Concretely, we observe that the leaf locates coarse margins via major veins and grows minor veins to refine twisty parts, which makes it possible to cover any objects accurately. Meanwhile, major and minor veins sha…
School of intelligent systems engineering, Sun Yat-sen University（中山大学智能系统工程学院）；Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine（浙江大学医学院附属杭州市第一人民医院）；School of Engineering, Case Western Reserve University（凯斯西储大学工程学院）
3D object detection is a key module for safety-critical robotics applications such as autonomous driving. For these applications, we care most about how the detections affect the ego-agent's behavior and safety (the egocentric perspective). Intuitively, we seek more accurate descriptions of object geometry when it's more likely to interfere with the ego-agent's motion trajectory. However, current detection metrics, based on box Intersection-over-Union (IoU), are object-centric and aren't designed to capture the spatio-temporal relationship between objects and the ego-agent. To address this iss…
Variational Level Set (LS) has been a widely used method in medical segmentation. However, it is limited when dealing with multi-instance objects in the real world. In addition, its segmentation results are quite sensitive to initial settings and highly depend on the number of iterations. To address these issues and boost the classic variational LS methods to a new level of the learnable deep learning approaches, we propose a novel definition of contour evolution named Recurrent Level Set (RLS)} to employ Gated Recurrent Unit under the energy minimization of a variational LS functional. The cu…
The University of Hong Kong（香港大学）；Nanjing University（南京大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳市大数据研究院）
Reducing the complexity of the pipeline of instance segmentation is crucial for real-world applications. This work addresses this issue by introducing an anchor-box free and single-shot instance segmentation framework, termed PolarMask, which reformulates the instance segmentation problem as predicting the contours of objects in the polar coordinate, with several appealing benefits. (1) The polar representation unifies instance segmentation (masks) and object detection (bounding boxes) into a single framework, reducing the design and computational complexity. (2) Two modules are carefully desi…
