17 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…
Jimin Yu, Xiankun Yang, Shangbo Zhou, Shougang Wang + 4 more
'Zhaoyang Wang' 'Minh P. Vo' 'Hieu Nguyen'] Instance segmentation has been developing rapidly in recent years. Mask R-CNN, a two-stage instance segmentation approach, has demonstrated exceptional performance. However, the masks are still very coarse. The downsampling operation of the backbone network and the ROIAlign…
Qian Zhang, Lu Chen, Mingwen Shao, Hong Liang + 3 more
'Wenfa Li'] Instance segmentation is a challenging task in computer vision, as it requires distinguishing objects and predicting dense areas. Currently, segmentation models based on complex designs and large parameters have achieved remarkable accuracy. However, from a practical standpoint, achieving a balance between…
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.…
Peng Huang, Yan Yin, Kaifeng Hu, Weidong Yang + 1 more
Despite rapid progress in UAV-based infrared vehicle detection, achieving reliable target recognition remains challenging due to dynamic viewpoint variations and platform instability. The inherent limitations of infrared imaging, particularly low contrast ratios and thermal crossover effects, significantly compromise…
Áron Fóthi, Adrián Szlatincsán, Ellák Somfai, Yun Zhang
A novel approach for video instance segmentation is presented using semisupervised learning. Our Cluster2Former model leverages scribble-based annotations for training, significantly reducing the need for comprehensive pixel-level masks. We augment a video instance segmenter, for example, the Mask2Former architecture…
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…
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…
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…
Robert Kiewisz, Gunar Fabig, Will Conway, Jake Johnston + 10 more
'Victor A. Kostyuchenko' 'Cyril Bařinka' 'Oliver Clarke' 'Magdalena Magaj' 'Hossein Yazdkhasti' 'Francesca Vallese' 'Shee-Mei Lok' 'Stefanie Redemann' 'Thomas Müller-Reichert' 'Tristan Bepler'] It is now possible to generate large volumes of high-quality images of biomolecules at near-atomic resolution and in…
Felix Y. Zhou, Zach Marin, Clarence Yapp, Qiongjing Zou + 15 more
Cell segmentation is the foundation of a wide range of microscopy-based biological studies. Deep learning has revolutionized two-dimensional (2D) cell segmentation, enabling generalized solutions across cell types and imaging modalities. This has been driven by the ease of scaling up image acquisition, annotation and…
Sam Dillavou, Jesse M. Hanlan, Anthony T. Chieco, Hongyi Xiao + 3 more
'Sage Fulco' 'Kevin T. Turner' 'Douglas J. Durian'] The conversion of raw images into quantifiable data can be a major hurdle and time-sink in experimental research, and typically involves identifying region(s) of interest, a process known as segmentation. Machine learning tools for image segmentation are often…
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…
Issam Dagher, Elie Abboud
Background This paper presents an optimized clustering approach applied to image segmentation. Accurate image segmentation impacts many fields like medical, machine vision, object detection. Applications involve tumor detection, face detection and recognition, and video surveillance. Methods The developed approach is…
Binod Bhattarai, Ronast Subedi, Rebati Raman Gaire, Eduard Vazquez + 1 more
'Danail Stoyanov'] We present our novel deep multi-task learning method for medical image segmentation. Existing multi-task methods demand ground truth annotations for both the primary and auxiliary tasks. Contrary to it, we propose to generate the pseudo-labels of an auxiliary task in an unsupervised manner. To…
Yuexing Han, Ruiqi Li, Shen Yang, Qiaochuan Chen + 2 more
'Yi Liu'] Materials properties depend not only on their compositions but also their microstructures under various processing conditions. So far, the analyses of complex microstructure images rely mostly on human experience, lack of automatic quantitative characterization methods. Machine learning provides an emerging…