More and more scholars are committed to light detection and ranging (LiDAR) as a roadside sensor to obtain traffic flow data. Filtering and clustering are common methods to extract pedestrians and vehicles from point clouds. This kind of method ignor...
The stage and duration of hypertension are connected to the occurrence of Hypertensive Retinopathy (HR) of eye disease. Currently, a few computerized systems have been developed to recognize HR by using only two stages. It is difficult to define spec...
Semantic information usually contains a description of the environment content, which enables mobile robot to understand the environment and improves its ability to interact with the environment. In high-level human–computer interaction application,...
Crater recognition on Mars is of paramount importance for many space science applications, such as accurate planetary surface age dating and geological mapping. Such recognition is achieved by means of various image-processing techniques employing tr...
Image segmentation is a fundamental step in computer vision and a cornerstone of robotic perception, serving as the foundation for interpreting data acquired from vision sensors, enabling robots to analyze complex visual environments, identify and lo...
Over the last decade, robotic perception algorithms have significantly benefited from the rapid advances in deep learning (DL). Indeed, a significant amount of the autonomy stack of different commercial and research platforms relies on DL for situati...
Object detection and recognition in aerial and remote sensing images has become a hot topic in the field of computer vision in recent years. As these images are usually taken from a bird’s-eye view, the targets often have different shapes and a...
Background: Burn photography is a stringent testbed for visual artificial intelligence (AI) in medical imaging, since it permits quantitative and qualitative clinically relevant parameters to be elicited. It is therefore a useful proxy for current vi...
Instance segmentation of plant point clouds is challenging due to intricate structures, non-uniform density, and large intra-class variation. Conventional methods often suffer from blurred boundaries, instance adhesion, and insufficient coupling of s...
Augmented reality applications involving human interaction with virtual objects often rely on segmentation-based hand detection techniques. Semantic segmentation can then be enhanced with instance-specific information to model complex interactions be...
3D Gaussian Splatting (3DGS) enables real-time photorealistic scene reconstruction, yet its segmentation tasks suffer from two critical flaws: poor 3D consistency (e.g., blurred instance boundaries and unstable cross-view semantic association) and in...
As one of the most important components of urban space, an outdated inventory of road-side trees may misguide managers in the assessment and upgrade of urban environments, potentially affecting urban road quality. Therefore, automatic and accurate in...
Fast and accurate semantic scene understanding is essential for mobile robots to operate in complex environments. An emerging research topic, panoptic segmentation, serves such a purpose by performing the tasks of semantic segmentation and instance s...
To enhance the intelligence of robots, constructing accurate object-level instance maps is essential. However, the diversity and clutter of objects in indoor scenes present significant challenges for instance map construction. To tackle this issue, w...
As a fundamental computer vision task, instance segmentation is widely used in the field of autonomous driving because it can perform both instance-level distinction and pixel-level segmentation. We propose CompleteInst based on QueryInst as a soluti...
Deep-learning-based segmentation methods have achieved excellent results. As two main tasks in computer vision, instance segmentation and semantic segmentation are closely related and mutually beneficial. Spatial context information from the semantic...
Panoptic segmentation provides a rich 2D environment representation by unifying semantic and instance segmentation. Most current state-of-the-art panoptic segmentation methods are built upon two-stage detectors and are not suitable for real-time appl...
In this paper, we present a novel framework for detecting individual trees in densely sampled 3D point cloud data acquired in urban areas. Given a 3D point cloud, the objective is to assign point-wise labels that are both class-aware and instance-awa...
Detecting the objects surrounding a moving vehicle is essential for autonomous driving and for any kind of advanced driving assistance system; such a system can also be used for analyzing the surrounding traffic as the vehicle moves. The most popular...
With the continuous improvement in oral health awareness, people’s demand for oral health diagnosis has also increased. Dental object detection is a key step in automated dental diagnosis; however, because of the particularity of medical data,...
Accurate organ-level segmentation is essential for achieving high-throughput, non-destructive, and automated plant phenotyping. To address the challenge of intelligent acquisition of phenotypic parameters in tomato plants, we propose TSINet, an end-t...
Recently, multi-level feature networks have been extensively used in instance segmentation. However, because not all features are beneficial to instance segmentation tasks, the performance of networks cannot be adequately improved by synthesizing mul...
This paper takes account of the fact that there is a lack of consideration for imaging methods and target characteristics of synthetic aperture radar (SAR) images among existing instance segmentation methods designed for optical images. Thus, we prop...
In this paper, we introduce a novel panoptic segmentation method called the Mask-Pyramid Network. Existing Mask RCNN-based methods first generate a large number of box proposals and then filter them at each feature level, which requires a lot of comp...
Instance segmentation in high-resolution (HR) remote sensing imagery is one of the most challenging tasks and is more difficult than object detection and semantic segmentation tasks. It aims to predict class labels and pixel-wise instance masks to lo...
Cell nuclei interpretation is crucial in pathological diagnostics, especially in tumor specimens. A critical step in computational pathology is to detect and analyze individual nuclear properties using segmentation algorithms. Conventionally, a seman...
The phenotyping of plant growth enriches our understanding of intricate genetic characteristics, paving the way for advancements in modern breeding and precision agriculture. Within the domain of phenotyping, segmenting 3D point clouds of plant organ...
Contrastive image representation learning has been essential for pre-training vision foundation models to deliver excellent transfer learning performance. It was originally developed based on instance discrimination, which focuses on instance-level r...
Inthis study, we introduce Point2Tree, a modular and versatile framework that employs a three-tiered methodology, inclusive of semantic segmentation, instance segmentation, and hyperparameter optimization analysis, designed to process laser point clo...
Object detection and segmentation can improve the accuracy of image recognition, but traditional methods can only extract the shallow information of the target, so the performance of algorithms is subject to many limitations. With the development of...
Building extraction from high-resolution remote sensing images has various applications, such as urban planning and population estimation. However, buildings have intraclass heterogeneity and interclass homogeneity in high-resolution remote sensing i...
Lineament is a unique geological structure. The study of Lunar lineament structure has great significance on understanding its history and evolution of Lunar surface. However, the existing geographic feature extraction methods are not suitable for th...
At present, the Beetle Antennae Search (BAS) algorithm has achieved remarkable success in image segmentation. However, when dealing with some complex image segmentation problems, particularly in the context of instance segmentation, which aims to ide...
Among the existing insulator defect detection methods, the automatic detection of inspection robots based on the instance segmentation algorithm is relatively more efficient, but the problem of the limited accuracy of the segmentation algorithm is st...
Panoramic radiographs, also known as orthopantomograms, are routinely used in most dental clinics. However, it has been difficult to develop an automated method that detects the various structures present in these radiographs. One of the main reasons...
It is very significant for rural planning to accurately count the number and area of rural homesteads by means of automation. The development of deep learning makes it possible to achieve this goal. At present, many effective works have been conducte...
Supervised semantic segmentation algorithms have been a hot area of exploration recently, but now the attention is being drawn towards completely unsupervised semantic segmentation. In an unsupervised framework, neither the targets nor the ground tru...
Law Enforcement Agencies (LEAs) typically analyse vast collections of media files, extracting visual information that helps them to advance investigations. While recent advancements in deep learning-based computer vision algorithms have revolutionise...
The highway is a crucial component of modern transportation systems, and its efficient management is essential for ensuring safety and facilitating communication. The automatic understanding and reconstruction of highway environments are therefore pi...
Instance segmentation of 3D point clouds is a fundamental task for scene understanding in applications such as autonomous driving, robotics, and augmented reality. The inherent irregularity and sparsity of point clouds, compounded by scale variations...
Semantic augmentation of 3D point clouds is a challenging problem with numerous real-world applications. While deep learning has revolutionised image segmentation and classification, its impact on point cloud is an active research field. In this pape...
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,...
With the rapid development of flexible vision sensors and visual sensor networks, computer vision tasks, such as object detection and tracking, are entering a new phase. Accordingly, the more challenging comprehensive task, including instance segment...
Accurate segmentation of surgical instruments in endoscopic videos is crucial for robot-assisted surgery and intraoperative analysis. This paper presents a Segment-then-Classify framework that decouples mask generation from semantic classification to...
Windows, as key components of building facades, have received increasing attention in facade parsing. Convolutional neural networks have shown promising results in window extraction. Most existing methods segment a facade into semantic categories and...
Although numerous effective Simultaneous Localization and Mapping (SLAM) systems have been developed, complex dynamic environments continue to present challenges, such as managing moving objects and enabling robots to comprehend environments. This pa...
Deep convolutional neural networks (CNNs) are effective in image classification, and are widely used in image segmentation tasks. Several neural netowrks have achieved high accuracy in segementation on existing semantic datasets, for instance PASCAL...
Simultaneous localization and mapping (SLAM) in rubber plantations is a challenging task for rubber-tapping robots. Due to the long-term stability of tree trunks in rubber plantations, a SLAM system based on semantic segmentation, called Se-LOAM, is...
In dynamic and low texture environments, traditional point-feature-based visual SLAM (vSLAM) often faces the challenges of poor robustness and low localization accuracy. To this end, this paper proposes a semantic vSLAM approach that fuses point-line...
The application of deep-learning technology for marine fishery resource investigation is still in its infancy stage. In this study, we applied YOLOv5 and YOLOv8 methods to identify and segment fish in the seabed. Our results show that both methods co...
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