25 papers · ranked by Valyu relevance
Jun Shu, Juncheng He, Ling Li
Infrared image of power equipment is widely used in power equipment fault detection, and segmentation of infrared images is an important step in power equipment thermal fault detection. Nevertheless, since the overlap of the equipment, the complex background, and the low contrast of the infrared image, the current…
Wang Gaihua, Lin Jinheng, Cheng Lei, Dai Yingying + 2 more
'Yiming Tang'] Instance segmentation is more challenging and difficult than object detection and semantic segmentation. It paves the way for the realization of a complete scene understanding, and has been widely used in robotics, automatic driving, medical care, and other aspects. However, there are some problems in…
Tuomas Sormunen, Arttu Lämsä, Miguel Bordallo López
Instance segmentation is a computer vision task where separate objects in an image are detected and segmented. Stateof-the-art deep neural network models require large amounts of labeled data in order to perform well in this task. Making these annotations is time-consuming. We propose for the first time, an iterative…
Yunhao Ge, Jiashu Xu, Brian Nlong Zhao, Laurent Itti + 1 more
We propose EM-PASTE: an Expectation Maximization (EM) guided Cut-Paste compositional dataset augmentation approach for weakly-supervised instance segmentation using only image-level supervision. The proposed method consists of three main components. The first component generates high-quality foreground object masks. To…
Yizheng Wu, Zhiyu Pan, Kewei Wang, Xingyi Li + 4 more
'Liwen Xiao' 'Guosheng Lin' 'Zhiguo Cao'] Abstract—Large-scale datasets with point-wise semantic and instance labels are crucial to 3D instance segmentation but also expensive. To leverage unlabeled data, previous semi-supervised 3D instance segmentation approaches have explored self-training frameworks, which rely on…
Mingzhu Liu, Wei Zhang, Haoran Wei, Marco Leo
Video instance segmentation, a key technology for intelligent sensing in visual perception, plays a key role in automated surveillance, robotics, and smart cities. These scenarios rely on real-time and efficient target-tracking capabilities for accurate perception and intelligent analysis of dynamic environments.…
Yuqian Yuan, Wentong Li, Zhaocheng Li, Yutong Lin + 5 more
In this paper, we introduce InstructSAM, a unified and streamlined framework designed for multi-instance segmentation under arbitrary instructions. We formulates instruction-driven instance segmentation as a set-structured query prediction problem and propose an explicit reasoning-to-instance query interface that…
Rabi Sharma, Muhammad Saqib, C. T. Lin, Michael Blumenstein + 1 more
'Arslan Munir'] In the maritime environment, the instance segmentation of small ships is crucial. Small ships are characterized by their limited appearance, smaller size, and ships in distant locations in marine scenes. However, existing instance segmentation algorithms do not detect and segment them, resulting in…
Trung-Nghia Le, Tam Nguyen, Minh–Triet Tran
In this paper, we propose Contextual Guided Segmentation (CGS) framework for video instance segmentation in three passes. In the first pass, i.e. preview segmentation, we propose Instance Re-Identification Flow to estimate main properties of each instance (i.e., human/non-human, rigid/deformable, known/unknown…
Yuchen Shen, Dong Zhang, Yuhui Zheng, Zechao Li + 2 more
'Qiaolin Ye'] Abstract—In recent years, the development of instance segmentation has garnered significant attention in a wide range of applications. However, the training of a fully-supervised instance segmentation model requires costly both instance-level and pixel-level annotations. In contrast, weakly-supervised…
Zipeng Wang, Xuehui Yu, Xumeng Han, Wenwen Yu + 3 more
'Jianbin Jiao' 'Zhenjun Han'] Point-level Supervised Instance Segmentation (PSIS) aims to enhance the applicability and scalability of instance segmentation by utilizing low-cost yet instanceinformative annotations. Existing PSIS methods usually rely on positional information to distinguish objects, but predicting…
Kaiyue Du, Jin Meng, Xin Meng, Shifeng Wang + 2 more
'Arturo de la Escalera Hueso'] Fine-grained urban environment instance segmentation is a fundamental and important task in the field of environment perception for autonomous vehicles. To address this goal, a model was designed with LiDAR pointcloud data and camera image data as the subject of study, and the reliability…
Felix Y. Zhou, Clarence Yapp, Zhiguo Shang, Stephan Daetwyler + 14 more
Cell segmentation is the fundamental task. Only by segmenting, can we define the quantitative spatial unit for collecting measurements to draw biological conclusions. Deep learning has revolutionized 2D cell segmentation, enabling generalized solutions across cell types and imaging modalities. This has been driven by…
Tim Scherr, Johannes Seiffarth, Bastian Wollenhaupt, Oliver Neumann + 5 more
In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell…
Farnoosh Arefi, Amir M. Mansourian, Shohreh Kasaei, Yawen Lu
Recently, there has been growing interest in deep spectral methods for image localization and segmentation, influenced by traditional spectral segmentation approaches. These methods reframe the image decomposition process as a graph partitioning task by extracting features using self-supervised learning and utilizing…
Yuxing Wang, Junhan Zhao, Hongye Xu, Cheng Han + 5 more
Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation and instance segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform…
Christoph Reich, Tim Prangemeier, André O. Françani, Heinz Koeppl
— Extracting single-cell information from microscopy data requires accurate instance-wise segmentations. Obtaining pixel-wise segmentations from microscopy imagery remains a challenging task, especially with the added complexity of microstructured environments. This paper presents a novel dataset for segmenting yeast…
Rama El-khawaldeh, Mason Guy, Finn Bork, Nina Taherimakhsousi + 6 more
This work presents a generalizable computer vision (CV) and machine learning model that is used for automated real-time monitoring and control of a diverse array of workup processes. Our system simultaneously monitors multiple physical parameters (e.g., liquid level, homogeneity, turbidity, solid, residue, and color)…
Matthias Arzt, Joran Deschamps, Christopher Schmied, Tobias Pietzsch + 3 more
We present Labkit, a user-friendly Fiji plugin for the segmentation of microscopy image data. It offers easy to use manual and automated image segmentation routines that can be rapidly applied to single- and multi-channel images as well as to timelapse movies in 2D or 3D. Labkit is specifically designed to work…
Julian Hennies, José Miguel Serra Lleti, Constantin Pape, Sultan Bekbayev + 3 more
Segmentation of large-volume datasets obtained by volume SEM techniques is a challenging task that generally requires a considerable amount of human effort. Despite recent advances in deep learning leading to the successful segmentation of cellular organelles in a variety of datasets, it is still challenging and…
Alexander E. Siemenn, Eunice Aissi, Fang Sheng, Armi Tiihonen + 3 more
In materials research, the task of characterizing hundreds of different materials traditionally requires equally many human hours spent measuring samples one by one. We demonstrate that with the integration of computer vision into this material research workflow, many of these tasks can be automated, significantly…
Sandra Jardim, João António, Carlos Mora, Xiaohao Cai + 2 more
'Gaohang Yu'] With a wide range of applications, image segmentation is a complex and difficult preprocessing step that plays an important role in automatic visual systems, which accuracy impacts, not only on segmentation results, but directly affects the effectiveness of the follow-up tasks. Despite the many advances…
Yichen He, Marco Camaiti, Lucy E. Roberts, James M. Mulqueeney + 2 more
The increased availability of 3D image data requires improving the efficiency of digital segmentation, currently relying on manual labelling, especially when separating structures into multiple components. Automated and semi-automated methods to streamline segmentation have been developed, such as deep learning and…
Authors not listed
The exponential growth of chemical literature necessitates the development of automated tools for extracting and curating molecular information from unstructured scientific publications into open-access chemical databases. Current optical chemical structure recognition (OCSR) and named entity recognition solutions…
Kohulan Rajan, Henning Otto Brinkhaus, M. Isabel Agea, Achim Zielesny + 1 more
The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that…