In this work, one new approach for RSTB-invariant object representation is presented based on the modified Mellin–Fourier Transform (MFT). For this, in the well-known steps of MFT, the logarithm operation in the log-polar transform is replaced...
Typical representations for arbitrary-oriented object detection tasks include the oriented bounding box (OBB), the quadrilateral bounding box (QBB), and the point set (PointSet). Each representation encounters problems that correspond to its characte...
Object-level saliency detection is an attractive research field which is useful for many content-based computer vision and remote-sensing tasks. This paper introduces an efficient unsupervised approach to salient object detection from the perspective...
Object detection is a basic and important task in the field of aerial image processing and has gained much attention in computer vision. However, previous aerial image object-detection approaches have insufficient use of scene semantic information be...
Object Detection (OD) in Remote Sensing Imagery (RSI) encounters significant challenges such as multi-scale variation, high aspect ratios, and densely distributed objects. These challenges often result in misalignments among Bounding Box (BBox) repre...
This study investigated the structure of sensorimotor representations during goal-directed grasping actions and explored their relationship with object features. Sixteen 3D-printed spheres that varied in size (i.e., a diameter of 20 mm, 40 mm, 60 mm,...
Transmission line inspection plays an important role in maintaining power security. In the object detection of the transmission line, the large-scale gap of the fittings is still a main and negative factor in affecting the detection accuracy. In this...
Data augmentation methods offer a cost-effective and efficient alternative to the acquisition of additional data, significantly enhancing data diversity and model generalization, making them particularly favored in object detection tasks. However, ex...
Arbitrary-oriented object detection (AOOD) is a crucial task in aerial image analysis but is also faced with significant challenges. In current AOOD detectors, commonly used multi-scale feature fusion modules fall short in spatial and semantic inform...
Oriented object detection in remote-sensing imagery benefits from a geometric representation that remains well-defined across localization, label assignment, and post-processing. We present Ellipse-ABC, a YOLOv8-based detector that directly predicts...
RGB-thermal object detection harnesses complementary information from visible and thermal modalities to enhance detection robustness in challenging environments, particularly under low-light conditions. However, existing approaches suffer from limita...
Deep learning approaches to estimating full 3D orientations of objects, in addition to object classes, are limited in their accuracies, due to the difficulty in learning the continuous nature of three-axis orientation variations by regression or clas...
This paper presents the RaDICAL sensing framework, a monostatic passive radar concept that combines a Sparse Uniform Circular Array (SUCA), deterministic multifrequency dither, and dictionary-based waveform recognition for target detection and classi...
With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detec...
Point clouds are a fundamental sensing modality for object perception, scene understanding, and robotic interaction in modern three-dimensional sensing systems. Effective point-cloud understanding requires representations that preserve intrinsic geom...
Remote sensing object detection is a critical task in Earth observation. Despite the remarkable progress made in general object detection, existing detectors struggle with remote sensing scenarios due to the prevalence of numerous small objects with...
A principal component analysis network (PCANet), as one of the representative deep subspace learning networks, utilizes principal component analysis (PCA) to learn filters that represent the dominant structural features of objects. However, the filte...
Multi-purpose service robots must execute their tasks reliably in different situations, as well as learn from humans and explain their plans to them. We address these issues by introducing a knowledge representation scheme to facilitate skill general...
Siamese network-based trackers consider tracking as features cross-correlation between the target template and the search region. Therefore, feature representation plays an important role for constructing a high-performance tracker. However, all exis...
Constructing a surface representation from the sparse point cloud of a satellite is an important task for satellite on-orbit services such as satellite docking and maintenance. In related studies on surface reconstruction from point clouds, implicit...
Recent progress in prosthetic manipulation highlights the need for perception-driven control strategies that can adapt to diverse objects and user intent. This work presents a modular vision-guided grasping pipeline that integrates VLM-based object i...
In mobile robotics, LASER scanners have a wide spectrum of indoor and outdoor applications, both in structured and unstructured environments, due to their accuracy and precision. Most works that use this sensor have their own data representation and...
The amount of freely available satellite data is growing rapidly as a result of Earth observation programmes, such as Copernicus, an initiative of the European Space Agency. Analysing these huge amounts of geospatial data and extracting useful inform...
Contextual features play a critical role in geospatial object detection by characterizing the surrounding environment of objects. In existing deep learning-based studies of 3D point cloud classification and segmentation, these features have been repr...
Spatio-temporal association analysis has attracted attention in various fields, such as urban computing and crime analysis. The proliferation of positioning technology and location-based services has facilitated the expansion of association analysis...
Visual object tracking is a fundamental research area in the field of computer vision and pattern recognition because it can be utilized by various intelligent systems. However, visual object tracking faces various challenging issues because tracking...
Incremental object detection (IOD) is critical for autonomous underwater perception, where detectors deployed on long-duration underwater platforms must continuously adapt to evolving marine environments while retaining previously learned recognition...
Recent advancements in prosthetic technology have increasingly focused on enhancing dexterity and autonomy through intelligent control systems. Vision-based approaches offer promising results for enabling prosthetic hands to interact more naturally w...
Multi-modal 3D object detection is an important task in autonomous driving systems, where cameras and LiDAR provide complementary semantic and geometric information. Most existing BEV fusion methods are designed based on the Cartesian representation...
Embedded systems are widely used in automotive electronics, smart home, smart medical, aerospace and other fields. Aiming at the problem of formal modeling and verification analysis of embedded systems, a solution is proposed using extended Petri net...
RGB-D (Red, Green and Blue-Depth) cameras are novel sensing systems that can improve image recognition by providing high quality color and depth information in computer vision. In this paper we propose a model to study feature representation of combi...
The raw data from ground-based telescopic optical observations serve as a key foundation for the analysis and identification of optical scattering properties of space objects, providing an essential guarantee for object identification and state predi...
Infrared small object detection (ISOD) plays a crucial role in military reconnaissance, security surveillance, and remote sensing monitoring, where weak thermal responses and complex backgrounds impose significant challenges. The recent self-supervis...
This paper focuses on embedded system modeling, proposing a solution to obtain a refined net via the refinement operation of an extended Petri net. Object-oriented technology and Petri net with inhibitor-arcs-based representation for embedded systems...
In recent decades, few-shot object detection in SAR imagery has gained prominence as a major research focus. The unique imaging mechanism of SAR causes the model to suffer from foreground–background imbalance and inaccurate extraction of class...
In the synthetic aperture radar (SAR) ship image, the target size is small and dense, the background is complex and changeable, the ship target is difficult to distinguish from the surrounding background, and there are many ship-like targets in the i...
Panoramic object detection is constrained at three levels of the detection pipeline: equirectangular projection deforms the input representation, crowded scenes weaken feature discrimination and complicate overlapping predictions, and high-resolution...
Object detection in aerial images is a fundamental yet challenging task in remote sensing field. As most objects in aerial images are in arbitrary orientations, oriented bounding boxes (OBBs) have a great superiority compared with traditional horizon...
Recognizing highly occluded objects is believed to arise from the interaction between the brain’s vision and cognition-controlling areas, although supporting neuroimaging data are currently limited. To explore the neural mechanism during this a...
Compared with general object detection with horizontal bounding boxes in natural images, oriented object detection in remote sensing images is an active and challenging research topic as objects are usually displayed in arbitrary orientations. To mod...
Multiple object tracking (MOT) is a fundamental issue and has attracted considerable attention in the autonomous driving community. This paper presents a novel MOT framework for autonomous driving. The framework consists of two stages of object repre...
Recently, there has been great interest in AI-based approaches for de novo design of novel drug candidates. However, the generation of useful lead drug candidate compounds requires more than predicting engagement with the desired protein target. Cand...
Coded text representations often contain a large amount of personal sensitive information, which can easily lead to problems such as privacy leakage and model prediction bias. Most of the existing methods focus on optimizing a single objective, makin...
Model-based reinforcement learning (MBRL) is a promising approach for achieving high sample efficiency in learning control policies. The existing world models in MBRL typically represent the environment’s state as a single global latent vector....
The ideas of the theory social representations proposed by Moscovici and developed in the structural approach by Abric were used in the research in order to reveal the structure and content of students’ social representations of higher educatio...
Background: Customer orders are key in developing logistics processes and in strategic developments of customer orientation. This paper investigates the notion of customer orientation. In the literature, the concept of customer is underdeveloped in t...
With the advancement of deep neural networks, several methods leveraging convolution neural networks (CNNs) have gained prominence in the field of remote sensing object detection. Acquiring accurate feature representations from feature maps is a crit...
Current two-stage object detectors extract the local visual features of Regions of Interest (RoIs) for object recognition and bounding-box regression. However, only using local visual features will lose global contextual dependencies, which are helpf...
Object detection in remotely sensed (RS) satellite imagery has gained significance in smart city concepts, which include urban planning, disaster management, and environmental monitoring. Deep learning techniques have shown promising outcomes in obje...
Continuous implicit representations can flexibly describe complex 3D geometry and offer excellent potential for 3D point cloud analysis. However, it remains challenging for existing point-based deep learning architectures to leverage the implicit rep...
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