20 papers · ranked by Valyu relevance
Silvia Cascianelli, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
Handwritten Text Recognition (HTR) in free-layout pages is a challenging image understanding task that can provide a relevant boost to the digitization of handwritten documents and reuse of their content. The task becomes even more challenging when dealing with historical documents due to the variability of the writing…
Jun Xiang, Ruru Pan, Weidong Gao, Kelvin K.L. Wong
The traditional manual defect detection method has low efficiency and is time-consuming and laborious. To address this issue, this paper proposed an automatic detection framework for fabric defect detection, which consists of a hardware system and detection algorithm. For the efficient and high-quality acquisition of…
Effat Sahragard, Hassan Farsi, Sajad Mohamadzadeh, Antonio Agudo
This paper presents a novel method for improving semantic segmentation performance in computer vision tasks. Our approach utilizes an enhanced UNet architecture that leverages an improved ResNet50 backbone. We replace the last layer of ResNet50 with deformable convolution to enhance feature representation.…
Chengming Rao, Zunhao Hu, QiMing Zhao, Min Shan + 2 more
One of the main challenges encountered in visual object detection is the multi-scale issue. Many approaches have been proposed to tackle this issue. In this article, we propose a novel neck that can perform effective fusion of multi-scale features for a single-stage object detector. This neck, named the deformable…
Jiarui Feng, Shenghui Zhang, Zhaoyu Zhai, Hongfeng Yu + 1 more
Asian soybean rust (ASR) is one of the major diseases that causes serious yield loss worldwide, even up to 80%. Early and accurate detection of ASR is critical to reduce economic losses. Hyperspectral imaging, combined with deep learning, has already been proved as a powerful tool to detect crop diseases. However…
Yu Tian, Yanwen Liu, Baohang Lin, Peng Li + 1 more
To address the challenge of suboptimal object detection outcomes stemming from the deformability of marine flexible biological entities, this study introduces an algorithm tailored for detecting marine flexible biological targets. Initially, we compiled a dataset comprising marine flexible biological subjects and…
Chengkun Sun, Jinqian Pan, Renjie Liang, Zhengkang Fan + 3 more
In medical image segmentation, particularly in UNet-like architectures, upsampling is primarily used to transform smaller feature maps into larger ones, enabling feature fusion between encoder and decoder features and supporting multi-scale prediction. Conventional upsampling methods, such as transposed convolution and…
Ho Hin Lee, Quan Liu, Qi Yang, Xin Yu + 3 more
'Bennett A. Landman'] The application of 3D ViTs to medical image segmentation has seen remarkable strides, somewhat overshadowing the budding advancements in Convolutional Neural Network (CNN)-based models. Large kernel depthwise convolution has emerged as a promising technique, showcasing capabilities akin to…
Zhuang Tian, Fan Yang, Lei Yang, Yunjie Wu + 3 more
'Peng Qian' 'Diogo Ribeiro'] Thoroughly and accurately identifying various defects on concrete surfaces is crucial to ensure structural safety and prolong service life. However, in actual engineering inspections, the varying shapes and complexities of concrete structural defects challenge the insufficient robustness…
Miao Zou, Xi Li, Quan Yuan, Tao Xiong + 5 more
'Zhenhua Xiao' 'Chaoran Cui' 'Xiaohui Han'] In this article, we propose an effective grasp detection network based on an improved deformable convolution and spatial feature center mechanism (DCSFC-Grasp) to precisely grasp unidentified objects. DCSFC-Grasp includes three key procedures as follows. First, improved…
Xin Zhang, Yingze Song, Tingting Song, Degang Yang + 3 more
'Jie Zhou' 'Liming Zhang'] Neural networks based on convolutional operations have achieved remarkable results in the field of deep learning, but there are two inherent flaws in standard convolutional operations. On the one hand, the convolution operation is confined to a local window, so it cannot capture information…
Xin Zhong, Zhaoyi Yan, Jing Qin, Wangmeng Zuo + 1 more
—In recent years, crowd counting has become an important issue in computer vision. In most methods, the density maps are generated by convolving with a Gaussian kernel from the ground-truth dot maps which are marked around the center of human heads. Due to the fixed geometric structures in CNNs and indistinct…
Xinglong Sun, Jean Ponce, Yu-Xiong Wang
— Depth completion, which aims to generate highquality dense depth maps from sparse depth maps, has attracted increasing attention in recent years. Previous work usually employs RGB images as guidance, and introduces iterative spatial propagation to refine estimated coarse depth maps. However, most of the propagation…
Yik San Cheng, Runkai Zhao, Heng Wang, Hanchuan Peng + 2 more
Accurate reconstruction of neuronal morphology from three-dimensional (3D) light microscopy is fundamental to neuroscience. Nevertheless, neuronal arbors intrinsically exhibit slender, tortuous geometries with high orientation variability, posing significant challenges for standard 3D convolutions whose static…
Solomon J. E. T. Warsop, Soraya Caixeiro, Marcus Bischoff, Jochen Kursawe + 2 more
The estimation of full-field displacement between biological image frames or in videos is important for quantitative analyses of motion, dynamics and biophysics. However, the often weak signals, poor biological contrast and many noise processes typical to microscopy make this a formidable challenge for many…
Gaurav Malhotra, Marin Dujmovic, John Hummel, Jeff Bowers
The success of Convolutional Neural Networks (CNNs) in classifying objects has led to a surge of interest in using these systems to understand human vision. Recent studies have argued that when CNNs are trained in the correct learning environment, they can emulate a key property of human vision – learning to classify…
Authors not listed
Obtaining quantitative information about residence time behavior (i.e., the residence time distribution function) in realistic experimental systems is oftentimes experimentally challenging and numerically complex. The conventional way is to conduct very simple pulse or step tracer experiments or construct elaborate…
Peter Kirchweger, Lev Melnikovsky, Shahar Seifer, Michael Elbaum
Cryo-electron tomography is an expanding technology for the study of macromolecules, viruses, and cells. It is often applied to specimens that are too large or heterogeneous for methods based on 2D image averaging such as single particle analysis, e.g., intracellular membranes or organelles. Current practice records a…
Ruiming Cao, Nikita Divekar, James Nuñez, Srigokul Upadhyayula + 1 more
Computational imaging reconstructions from multiple measurements that are captured sequentially often suffer from motion artifacts if the scene is dynamic. We propose a neural space-time model (NSTM) that jointly estimates the scene and its motion dynamics. Hence, we can both remove motion artifacts and resolve sample…
Robin Gutzen, Grace W Lindsay
Convolutional Neural Networks (CNNs) trained for image recognition have demonstrated remarkable conceptual similarities to the primate ventral visual pathway, but their standard feedforward architectures lack the recurrent connections that are ubiquitous in visual cortex. Such recurrence is thought to underlie…