Log in

Relevant bibliographies by topics / Bounding boxes / Journal articles

To see the other types of publications on this topic, follow the link: Bounding boxes.

Journal articles on the topic 'Bounding boxes'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Bounding boxes.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Jung, Sejung, Ahram Song, Kirim Lee, and Won Hee Lee. "Advanced Building Detection with Faster R-CNN Using Elliptical Bounding Boxes for Displacement Handling." Remote Sensing 17, no. 7 (2025): 1247. https://doi.org/10.3390/rs17071247.

Full text

Abstract:

This study presents an enhanced Faster R-CNN framework that incorporates elliptical bounding boxes to significantly improve building detection in off-nadir imagery, effectively reducing severe geometric distortions caused by oblique sensor angles. Off-nadir imagery enhances architectural detail capture and reduces occlusions, but conventional bounding boxes, such as axis-aligned and rotated bounding boxes, often fail to localize buildings distorted by extreme perspectives. We propose a hybrid method integrating elliptical bounding boxes for curved structures and rotated bounding boxes for tilt

APA, Harvard, Vancouver, ISO, and other styles

2

Zhong, Bo, and Kai Ao. "Single-Stage Rotation-Decoupled Detector for Oriented Object." Remote Sensing 12, no. 19 (2020): 3262. http://dx.doi.org/10.3390/rs12193262.

Full text

Abstract:

Oriented object detection has received extensive attention in recent years, especially for the task of detecting targets in aerial imagery. Traditional detectors locate objects by horizontal bounding boxes (HBBs), which may cause inaccuracies when detecting objects with arbitrary oriented angles, dense distribution and a large aspect ratio. Oriented bounding boxes (OBBs), which add different rotation angles to the horizontal bounding boxes, can better deal with the above problems. New problems arise with the introduction of oriented bounding boxes for rotation detectors, such as an increase in

APA, Harvard, Vancouver, ISO, and other styles

3

Ravi, Niranjan, and Mohamed El-Sharkawy. "Addressing the Gaps of IoU Loss in 3D Object Detection with IIoU." Future Internet 15, no. 12 (2023): 399. http://dx.doi.org/10.3390/fi15120399.

Full text

Abstract:

Three-dimensional object detection involves estimating the dimensions, orientations, and locations of 3D bounding boxes. Intersection of Union (IoU) loss measures the overlap between predicted 3D box and ground truth 3D bounding boxes. The localization task uses smooth-L1 loss with IoU to estimate the object’s location, and the classification task identifies the object/class category inside each 3D bounding box. Localization suffers a performance gap in cases where the predicted and ground truth boxes overlap significantly less or do not overlap, indicating the boxes are far away, and in scena

APA, Harvard, Vancouver, ISO, and other styles

4

Oomori, Kotaro, Wataru Kawabe, Fabrice Matulic, Takeo Igarashi, and Keita Higuchi. "Interactive 3D Annotation of Objects in Moving Videos from Sparse Multi-view Frames." Proceedings of the ACM on Human-Computer Interaction 7, ISS (2023): 309–26. http://dx.doi.org/10.1145/3626476.

Full text

Abstract:

Segmenting and determining the 3D bounding boxes of objects of interest in RGB videos is an important task for a variety of applications such as augmented reality, navigation, and robotics. Supervised machine learning techniques are commonly used for this, but they need training datasets: sets of images with associated 3D bounding boxes manually defined by human annotators using a labelling tool. However, precisely placing 3D bounding boxes can be difficult using conventional 3D manipulation tools on a 2D interface. To alleviate that burden, we propose a novel technique with which 3D bounding

APA, Harvard, Vancouver, ISO, and other styles

5

Sang, E. T. K. "VORONOI DIAGRAMS WITHOUT BOUNDING BOXES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-2/W2 (October 19, 2015): 235–39. http://dx.doi.org/10.5194/isprsannals-ii-2-w2-235-2015.

Full text

Abstract:

We present a technique for presenting geographic data in Voronoi diagrams without having to specify a bounding box. The method restricts Voronoi cells to points within a user-defined distance of the data points. The mathematical foundation of the approach is presented as well. The cell clipping method is particularly useful for presenting geographic data that is spread in an irregular way over a map, as for example the Dutch dialect data displayed in Figure 2. The automatic generation of reasonable cell boundaries also makes redundant a frequently used solution to this problem that requires da

APA, Harvard, Vancouver, ISO, and other styles

6

Zhao, Lun, Yunlong Pan, Sen Wang, Liang Zhang, and Md Shafiqul Islam. "A Hybrid Crack Detection Approach for Scanning Electron Microscope Image Using Deep Learning Method." Scanning 2021 (August 9, 2021): 1–13. http://dx.doi.org/10.1155/2021/5558668.

Full text

Abstract:

The scanning electron microscope (SEM) is widely used in the analysis and research of materials, including fracture analysis, microstructure morphology, and nanomaterial analysis. With the rapid development of materials science and computer vision technology, the level of detection technology is constantly improving. In this paper, the deep learning method is used to intelligently identify microcracks in the microscopic morphology of SEM image. A deep learning model based on image level is selected to reduce the interference of other complex microscopic topography, and a detection method with

APA, Harvard, Vancouver, ISO, and other styles

7

Padmanabula, Sai Shilpa, Ramya Chowdary Puvvada, Venkatramaphanikumar Sistla, and Venkata Krishna Kishore Kolli. "Object Detection Using Stacked YOLOv3." Ingénierie des systèmes d information 25, no. 5 (2020): 691–97. http://dx.doi.org/10.18280/isi.250517.

Full text

Abstract:

Object detection is a stimulating task in the applications of computer vision. It is gaining a lot of attention in many real-time applications such as detection of number plates of suspect cars, identifying trespassers under surveillance areas, detecting unmasked faces in security gates during the COVID-19 period, etc. Region-based Convolution Neural Networks(R-CNN), You only Look once (YOLO) based CNNs, etc., comes under Deep Learning approaches. In this proposed work, an improved stacked Yolov3 model is designed for the detection of objects by bounding boxes. Hyperparameters are tuned to get

APA, Harvard, Vancouver, ISO, and other styles

8

Qi, Zhouming, Mian Zhou, Guoqiang Zhu, and Yanbing Xue. "Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking." Applied Sciences 13, no. 1 (2022): 440. http://dx.doi.org/10.3390/app13010440.

Full text

Abstract:

In order to reduce the negative impact of severe occlusion in dense scenes on the performance degradation of the tracker, considering that the head is the highest and least occluded part of the pedestrian’s entire body, we propose a new multiobject tracking method for pedestrians in dense crowds combined with head tracking. For each frame of the video, a head tracker is first used to generate the pedestrians’ head movement tracklets, and the pedestrians’ whole body bounding boxes are detected at the same time. Secondly, the degree of association between the head bounding boxes and the whole bo

APA, Harvard, Vancouver, ISO, and other styles

9

Jia, Qifei, Shikui Wei, Tao Ruan, Yufeng Zhao, and Yao Zhao. "GradingNet: Towards Providing Reliable Supervisions for Weakly Supervised Object Detection by Grading the Box Candidates." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1682–90. http://dx.doi.org/10.1609/aaai.v35i2.16261.

Full text

Abstract:

Weakly-Supervised Object Detection (WSOD) aims at training a model with limited and coarse annotations for precisely locating the regions of objects. Existing works solve the WSOD problem by using a two-stage framework, i.e., generating candidate bounding boxes with weak supervision information and then refining them by directly employing supervised object detection models. However, most of such works mainly focus on the performance boosting of the first stage, while ignoring the better usage of generated candidate bounding boxes. To address this issue, we propose a new two-stage framework for

APA, Harvard, Vancouver, ISO, and other styles

10

Liu, Yikun, Gongping Yang, Yuwen Huang, and Yilong Yin. "SE-Mask R-CNN: An improved Mask R-CNN for apple detection and segmentation." Journal of Intelligent & Fuzzy Systems 41, no. 6 (2021): 6715–25. http://dx.doi.org/10.3233/jifs-210597.

Full text

Abstract:

Fruit detection and segmentation is an essential operation of orchard yield estimation, the result of yield estimation directly depends on the speed and accuracy of detection and segmentation. In this work, we propose an effective method based on Mask R-CNN to detect and segment apples under complex environment of orchard. Firstly, the squeeze-and-excitation block is introduced into the ResNet-50 backbone, which can distribute the available computational resources to the most informative feature map in channel-wise. Secondly, the aspect ratio is introduced into the bounding box regression loss

APA, Harvard, Vancouver, ISO, and other styles

11

Suri, Subhash, Philip M. Hubbard, and John F. Hughes. "Analyzing bounding boxes for object intersection." ACM Transactions on Graphics 18, no. 3 (1999): 257–77. http://dx.doi.org/10.1145/336414.336423.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Saini, S. Bhupinder Singh, Shamsher Tiwari, Naman Gupta, and S. Jaishree. "A Literature Review of Object Detection using YOLOv4 Detector." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (2023): 1104–8. http://dx.doi.org/10.22214/ijraset.2023.49210.

Full text

Abstract:

Abstract: Object detection is an advanced form of image classification where a neural network predicts objectsin an image and points them out in the form of bounding boxes. Compared to the approach taken by object detection algorithms before YOLO, which repurpose classifiers to perform detection, YOLO proposes the use of an end-to-end neural network that makes predictions of bounding boxes and class probabilities all at once. Object detection not solely includes classifying and recognizing objects in an image but also includes localizing those objects and attracts bounding boxes around them. I

APA, Harvard, Vancouver, ISO, and other styles

13

Rahman, Mohammad Muntasir, Yanhao Tan, Jian Xue, Ling Shao, and Ke Lu. "3D object detection: Learning 3D bounding boxes from scaled down 2D bounding boxes in RGB-D images." Information Sciences 476 (February 2019): 147–58. http://dx.doi.org/10.1016/j.ins.2018.09.040.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Zhao, Wei, and Li Ming Ye. "A Collision Detection Algorithm Based on Spatial Partitioning and Bounding Volume." Applied Mechanics and Materials 433-435 (October 2013): 932–35. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.932.

Full text

Abstract:

In order to improve the real-time and accuracy in the collision detection technology, a collision detection algorithm based on spatial partitioning and bounding volume was proposed . This algorithm adopted different spatial division strategies for different locations of the spaces according to the details in the scenes to exclude objects which can not intersect.Thus defined the potential intersection areas. Then we used a dynamic S-AABB hierarchy bounding boxes to test whether the intersection happened between the objects in the same grids. We used the sphere boxes to rule out the disjoint obj

APA, Harvard, Vancouver, ISO, and other styles

15

Wan, Meng, Guannan Zhong, Qingshuang Wu, Xin Zhao, Yuqin Lin, and Yida Lu. "CR-Mask RCNN: An Improved Mask RCNN Method for Airport Runway Detection and Segmentation in Remote Sensing Images." Sensors 25, no. 3 (2025): 657. https://doi.org/10.3390/s25030657.

Full text

Abstract:

Airport runways, as the core part of airports, belong to vital national infrastructure, and the target detection and segmentation of airport runways in remote sensing images using deep learning methods have significant research value. Most of the existing airport target detection methods based on deep learning rely on horizontal bounding boxes for localization, which often contain irrelevant background information. Moreover, when detecting multiple intersecting airport runways in a single remote sensing image, issues such as false positives and false negatives are apt to occur. To address thes

APA, Harvard, Vancouver, ISO, and other styles

16

Song, Lingyun, Jun Liu, Buyue Qian, and Yihe Chen. "Connecting Language to Images: A Progressive Attention-Guided Network for Simultaneous Image Captioning and Language Grounding." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8885–92. http://dx.doi.org/10.1609/aaai.v33i01.33018885.

Full text

Abstract:

Image captioning and visual language grounding are two important tasks for image understanding, but are seldom considered together. In this paper, we propose a Progressive Attention-Guided Network (PAGNet), which simultaneously generates image captions and predicts bounding boxes for caption words. PAGNet mainly has two distinctive properties: i) It can progressively refine the predictive results of image captioning, by updating the attention map with the predicted bounding boxes. ii) It learns bounding boxes of the words using a weakly supervised strategy, which combines the frameworks of Mul

APA, Harvard, Vancouver, ISO, and other styles

17

Hu, H., L. Wang, M. Zhang, Y. Ding, and Q. Zhu. "FAST AND REGULARIZED RECONSTRUCTION OF BUILDING FAÇADES FROM STREET-VIEW IMAGES USING BINARY INTEGER PROGRAMMING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 365–71. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-365-2020.

Full text

Abstract:

Abstract. Regularized arrangement of primitives on building façades to aligned locations and consistent sizes is important towards structured reconstruction of urban environment. Mixed integer linear programing was used to solve the problem, however, it is extremely time consuming even for state-of-the-art commercial solvers. Aiming to alleviate this issue, we cast the problem into binary integer programming, which omits the requirements for real value parameters and is more efficient to be solved. Firstly, the bounding boxes of the primitives are detected using the YOLOv3 architecture in real

APA, Harvard, Vancouver, ISO, and other styles

18

El Amrani Abouelassad, S., and F. Rottensteiner. "VEHICLE INSTANCE SEGMENTATION WITH ROTATED BOUNDING BOXES IN UAV IMAGES USING CNN." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-1-2022 (May 17, 2022): 15–23. http://dx.doi.org/10.5194/isprs-annals-v-1-2022-15-2022.

Full text

Abstract:

Abstract. Vehicle instance segmentation is a major but challenging task in aerial remote sensing applications. More importantly, the current majority methods use horizontal bounding boxes which does not tell much about the orientation of vehicles and often leads to inaccurate mask proposals due to high background to foreground pixel-ratio. Given that the orientation of vehicles is important for numerous applications like vehicle tracking, we introduce in this paper a deep neural network to detect and segment vehicles using rotated bounding boxes in aerial images. Our method demonstrates that r

APA, Harvard, Vancouver, ISO, and other styles

19

Heryanto, Nur Adhianti, Mahmud Isnan, Matthew Martianus Henry, and Bens Pardamean. "Semi-automated meningioma segmentation with bounding boxes." Procedia Computer Science 245 (2024): 583–90. http://dx.doi.org/10.1016/j.procs.2024.10.285.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Guo, Qingwen, Chuntao Wang, Deqin Xiao, and Qiong Huang. "An Enhanced Insect Pest Counter Based on Saliency Map and Improved Non-Maximum Suppression." Insects 12, no. 8 (2021): 705. http://dx.doi.org/10.3390/insects12080705.

Full text

Abstract:

Accurately counting the number of insect pests from digital images captured on yellow sticky traps remains a challenge in the field of insect pest monitoring. In this study, we develop a new approach to counting the number of insect pests using a saliency map and improved non-maximum suppression. Specifically, as the background of a yellow sticky trap is simple and the insect pest object is small, we exploit a saliency map to construct a region proposal generator including saliency map building, activation region formation, background–foreground classifier, and tune-up boxes involved in region

APA, Harvard, Vancouver, ISO, and other styles

21

Lee, YJ, SH Lee, and DH Kim. "Mechanical parts picking through geometric properties determination using deep learning." International Journal of Advanced Robotic Systems 19, no. 1 (2022): 172988142210745. http://dx.doi.org/10.1177/17298814221074532.

Full text

Abstract:

In this study, a system for automatically picking mechanical parts required in the industrial automation field was proposed. In particular, using deep learning, bolts and nuts were recognized and geometric information of these parts was extracted. By applying YOLOv3 specialized in high recognition rate and fast processing speed, the recognition of target object, location, and postural information were obtained. The geometric information for the bolt can be obtained by creating two bounding boxes and calculating the orientation vector formed by these center values of two bounding boxes after su

APA, Harvard, Vancouver, ISO, and other styles

22

Lee, Yunju, and Jihie Kim. "ROM-Pose: restoring occluded mask image for 2D human pose estimation." PeerJ Computer Science 11 (May 2, 2025): e2843. https://doi.org/10.7717/peerj-cs.2843.

Full text

Abstract:

Human pose estimation (HPE) is a field focused on estimating human poses by detecting key points in images. HPE includes methods like top-down and bottom-up approaches. The top-down approach uses a two-stage process, first locating and then detecting key points on humans with bounding boxes, whereas the bottom-up approach directly detects individual key points and integrates them to estimate the overall pose. In this article, we address the problem of bounding box detection inaccuracies in certain situations using the top-down method. The detected bounding boxes, which serve as input for the m

APA, Harvard, Vancouver, ISO, and other styles

23

Martins, Jorge R., Vasco S. Costa, and João M. Pereira. "Efficient Hair Rendering with a GPU Cone Tracing Approach." International Journal of Creative Interfaces and Computer Graphics 8, no. 1 (2017): 1–19. http://dx.doi.org/10.4018/ijcicg.2017010101.

Full text

Abstract:

Rendering human hair can be a hard task because of the required high super-sampling rate to render thin hair fibers without noticeable aliasing. Additionally, the current state-of-the-art bounding volume hierarchies (BVHs) are not suitable to hair rendering. In fact, the axis-aligned bounding boxes (AABBs) do not tightly bind hair primitives which impacts negatively the intersection tests activity. Both limitations can degrade severely the rendering performance so described in this article, a cone tracing GPU approach coupled with a hybrid bounding volume hierarchy to tackle these problems. Th

APA, Harvard, Vancouver, ISO, and other styles

24

Ayle, Morgane, Jimmy Tekli, Julia El-Zini, Boulos El-Asmar, and Mariette Awad. "BAR — A Reinforcement Learning Agent for Bounding-Box Automated Refinement." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (2020): 2561–68. http://dx.doi.org/10.1609/aaai.v34i03.5639.

Full text

Abstract:

Research has shown that deep neural networks are able to help and assist human workers throughout the industrial sector via different computer vision applications. However, such data-driven learning approaches require a very large number of labeled training images in order to generalize well and achieve high accuracies that meet industry standards. Gathering and labeling large amounts of images is both expensive and time consuming, specifically for industrial use-cases. In this work, we introduce BAR (Bounding-box Automated Refinement), a reinforcement learning agent that learns to correct ina

APA, Harvard, Vancouver, ISO, and other styles

25

S, Mohanapriya, Mohana Saranya S, Kumaravel T, and Sumithra P. "Image Detection and Segmentation using YOLO v5 for surveillance." Applied and Computational Engineering 8, no. 1 (2023): 160–65. http://dx.doi.org/10.54254/2755-2721/8/20230109.

Full text

Abstract:

Segmentation an advancement of object detection where bounding boxes are placed around object in object detection whereas segmentation is used to classify every pixel in the given image. In Deep Learning, Yolov5 algorithm can be used to perform segmentation on the given data. Using YOLOv5 algorithm objects are detected and classified by surrounding the objects with the bounding boxes. Compared to the existing algorithms for segmentation, YOLOv5 algorithm has improved time complexity and accuracy. In this paper YOLOv5 algorithm is compared with the existing CNN algorithm.

APA, Harvard, Vancouver, ISO, and other styles

26

Su, Shuzhi, Runbin Chen, Xianjin Fang, Yanmin Zhu, Tian Zhang, and Zengbao Xu. "A Novel Lightweight Grape Detection Method." Agriculture 12, no. 9 (2022): 1364. http://dx.doi.org/10.3390/agriculture12091364.

Full text

Abstract:

This study proposes a novel lightweight grape detection method. First, the backbone network of our method is Uniformer, which captures long-range dependencies and further improves the feature extraction capability. Then, a Bi-directional Path Aggregation Network (BiPANet) is presented to fuse low-resolution feature maps with strong semantic information and high-resolution feature maps with detailed information. BiPANet is constructed by introducing a novel cross-layer feature enhancement strategy into the Path Aggregation Network, which fuses more feature information with a significant reducti

APA, Harvard, Vancouver, ISO, and other styles

27

Zhang, Li, Mengyang Song, Huaping Guo, Yange Sun, and Xinxia Wang. "Insulator Defect Detection via a Residual Denoising Diffusion Mechanism." Materials 18, no. 8 (2025): 1738. https://doi.org/10.3390/ma18081738.

Full text

Abstract:

Insulators are critical components of transmission lines, and defective insulators pose a serious threat to the safety of power supply systems. Timely detection of these defects is crucial to prevent catastrophic consequences for human lives and property. However, insulator defects are often small and easily affected by the noise of rain, fog, sunlight, dirt, and other pollutants, making detection challenging. We observe that diffusion models learn data distribution by progressively introducing noise and subsequently performing denoising. The progressive denoising mechanism can naturally simul

APA, Harvard, Vancouver, ISO, and other styles

28

Zhao, Liquan, and Shuaiyang Li. "Object Detection Algorithm Based on Improved YOLOv3." Electronics 9, no. 3 (2020): 537. http://dx.doi.org/10.3390/electronics9030537.

Full text

Abstract:

The ‘You Only Look Once’ v3 (YOLOv3) method is among the most widely used deep learning-based object detection methods. It uses the k-means cluster method to estimate the initial width and height of the predicted bounding boxes. With this method, the estimated width and height are sensitive to the initial cluster centers, and the processing of large-scale datasets is time-consuming. In order to address these problems, a new cluster method for estimating the initial width and height of the predicted bounding boxes has been developed. Firstly, it randomly selects a couple of width and height val

APA, Harvard, Vancouver, ISO, and other styles

29

Wang, Ming Quan, Wei Zhao, and Hui Yan Qu. "An Improved Collision Detection Algorithm Based on GPU." Applied Mechanics and Materials 687-691 (November 2014): 3893–96. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3893.

Full text

Abstract:

In order to improve the speed of collision detection between objects in the large-scale and complex scene, this paper proposed an improved collision detection algorithm based on GPU, In this method, we first divided the virtual space into several grids to rule out the impossible intersecting objects rapidly using the GPU acceleration technology; secondly, we adopted parallel technology to build K - DOP bounding boxes for the objects in the same grids and then detected whether the K - DOP bounding boxes intervene or collide to conform the potential colliding primitive pairs; Finally we traveled

APA, Harvard, Vancouver, ISO, and other styles

30

Lin, W., Y. Chen, C. Wang, and J. Li. "USING EDGECONV TO IMPROVE 3D OBJECT DETECTION FROM RGB-D DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 835–39. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-835-2019.

Full text

Abstract:

<p><strong>Abstract.</strong> In this paper, we proposed a novel 3D deep learning model for object localization and object bounding boxes estimation. To increase the detection efficiency of small objects in the large scale scenes, the local neighbourhood geometric structure information of objects has been taken into the Edgeconv model, which can operate the original point clouds. We evaluated the 3D bounding box with high resolution in the RGB-D dataset and acquired stable effectiveness even under the sparse points and the strong occlusion. The experimental results indicate t

APA, Harvard, Vancouver, ISO, and other styles

31

Gao, Zhu, X. M. Ji, and J. Zhang. "The Redevelopment of Prototype System of Industrial Product Forms Based on Rhino." Advanced Materials Research 764 (September 2013): 187–92. http://dx.doi.org/10.4028/www.scientific.net/amr.764.187.

Full text

Abstract:

This paper redevelops Rhino based on the Rhino script and extracts three kinds of bounding boxes (i.e. cuboid, cylinder and sphere) of product fission model, and calculates the optimal parameters of fission bounding boxes with the aid of the GUI interface built by MATLAB. The software of prototype reconstruction of product forms is developed by the use of VB.NET, which can reconstruct the product form prototype through the product CAD model. In the end, the system validity is tested and verified by the practical example of a chargeable electrical drill.

APA, Harvard, Vancouver, ISO, and other styles

32

Ke, Jingcheng, Waikeung Wong, Jia Wang, Mu Li, Lunke Fei, and Jie Wen. "DiffusionREC: Diffusion Model with Adaptive Condition for Referring Expression Comprehension." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 4 (2025): 4221–29. https://doi.org/10.1609/aaai.v39i4.32443.

Full text

Abstract:

The objective of referring expression comprehension (REC) is to accurately identify the object in an image described by a given expression. Existing REC methods, including transformer-based and graph-based approaches among others, have shown robust performance in REC tasks. In this study, we present a groundbreaking framework named DiffusionREC for REC task. This framework reimagines the REC task as a text guided bounding box denoising diffusion process, through which noisy bounding boxes are refined and distilled to pinpoint the target box. Throughout the training process, the bounding box of

APA, Harvard, Vancouver, ISO, and other styles

33

Prokopaki-Kostopoulou, Nefeli, and Stasinos Konstantopoulos. "Optimizing within-distance queries by approximating shapes with maximal bounded boxes." Open Research Europe 2 (May 9, 2022): 57. http://dx.doi.org/10.12688/openreseurope.14321.1.

Full text

Abstract:

Background: In geospatial query processing, spatial containment and intersection queries can be efficiently answered from the index. There is, however, a class of queries (such as within-distance) with a semantics that implies that every shape in the database is a potential match and should, in principle, be compared with the threshold. Naturally, this is impractical and optimizations have been developed that efficiently refine the set of candidate shapes before starting to actually compute distances and apply the threshold. In the case of the within-distance queries, many instances can be dis

APA, Harvard, Vancouver, ISO, and other styles

34

Li, Shuxin, Zhilong Zhang, Biao Li, and Chuwei Li. "Multiscale Rotated Bounding Box-Based Deep Learning Method for Detecting Ship Targets in Remote Sensing Images." Sensors 18, no. 8 (2018): 2702. http://dx.doi.org/10.3390/s18082702.

Full text

Abstract:

Since remote sensing images are captured from the top of the target, such as from a satellite or plane platform, ship targets can be presented at any orientation. When detecting ship targets using horizontal bounding boxes, there will be background clutter in the box. This clutter makes it harder to detect the ship and find its precise location, especially when the targets are in close proximity or staying close to the shore. To solve these problems, this paper proposes a deep learning algorithm using a multiscale rotated bounding box to detect the ship target in a complex background and obtai

APA, Harvard, Vancouver, ISO, and other styles

35

Assarsson, Ulf, and Tomas Moller. "Optimized View Frustum Culling Algorithms for Bounding Boxes." Journal of Graphics Tools 5, no. 1 (2000): 9–22. http://dx.doi.org/10.1080/10867651.2000.10487517.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Chu, Hsueh-Ting, and Chaur-Chin Chen. "On bounding boxes of iterated function system attractors." Computers & Graphics 27, no. 3 (2003): 407–14. http://dx.doi.org/10.1016/s0097-8493(03)00035-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Zhu, Dongyong, Zhong Li, Feng Xia, and Yong Xu. "Dynamic Garment Simulation based on Hybrid Bounding Volume Hierarchy." Autex Research Journal 16, no. 4 (2016): 241–49. http://dx.doi.org/10.1515/aut-2015-0054.

Full text

Abstract:

Abstract In order to solve the computing speed and efficiency problem of existing dynamic clothing simulation, this paper presents a dynamic garment simulation based on a hybrid bounding volume hierarchy. It firstly uses MCASG graph theory to do the primary segmentation for a given three-dimensional human body model. And then it applies K-means cluster to do the secondary segmentation to collect the human body’s upper arms, lower arms, upper legs, lower legs, trunk, hip and woman’s chest as the elementary units of dynamic clothing simulation. According to different shapes of these elementary u

APA, Harvard, Vancouver, ISO, and other styles

38

Yang, Xinpeng, Qiang Zhang, Qiulei Dong, Zhen Han, Xiliang Luo, and Dongdong Wei. "Ship Instance Segmentation Based on Rotated Bounding Boxes for SAR Images." Remote Sensing 15, no. 5 (2023): 1324. http://dx.doi.org/10.3390/rs15051324.

Full text

Abstract:

Ship instance segmentation in synthetic aperture radar (SAR) images is a hard and challenging task, which not only locates ships but also obtains their shapes with pixel-level masks. However, in ocean SAR images, because of the consistent reflective intensities of ships, the appearances of different ships are similar, thus making it far too difficult to distinguish ships when they are in densely packed groups. Especially when ships have incline directions and large aspect ratios, the horizontal bounding boxes (HB-Boxes) used by all the instance-segmentation networks that we know so far inevita

APA, Harvard, Vancouver, ISO, and other styles

39

Rajasekhar, Mr Jaisurya. "UNDERSTANDING YOLO: REAL-TIME OBJECT DETECTION EXPLAINED." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–9. http://dx.doi.org/10.55041/ijsrem36359.

Full text

Abstract:

YOLO (You Only Look Once) is a groundbreaking real-time object detection algorithm known for its speed and accuracy. This article provides an in-depth exploration of YOLO, from its innovative architecture to its practical applications. We discuss its grid-based approach, prediction of bounding boxes and class probabilities, and the use of Non-Max Suppression to refine detections. Additionally, we cover the training process, advantages, and future developments of YOLO, highlighting its significant impact on fields such as autonomous driving, surveillance, and healthcare. Keywords: YOLO, real-ti

APA, Harvard, Vancouver, ISO, and other styles

40

Prof. Vasudha Bahl and Prof. Nidhi Sengar, Akash Kumar, Dr Amita Goel. "Real-Time Object Detection Model." International Journal for Modern Trends in Science and Technology 6, no. 12 (2020): 360–64. http://dx.doi.org/10.46501/ijmtst061267.

Full text

Abstract:

Object Detection is a study in the field of computer vision. An object detection model recognizes objects of the real world present either in a captured image or in real-time video where the object can belong to any class of objects namely humans, animals, objects, etc. This project is an implementation of an algorithm based on object detection called You Only Look Once (YOLO v3). The architecture of yolo model is extremely fast compared to all previous methods. Yolov3 model executes a single neural network to the given image and then divides the image into predetermined bounding boxes. These

APA, Harvard, Vancouver, ISO, and other styles

41

Hamadneh, Tareq, Mohammed Ali, and Hassan AL-Zoubi. "Linear Optimization of Polynomial Rational Functions: Applications for Positivity Analysis." Mathematics 8, no. 2 (2020): 283. http://dx.doi.org/10.3390/math8020283.

Full text

Abstract:

In this paper, we provide tight linear lower bounding functions for multivariate polynomials given over boxes. These functions are obtained by the expansion of polynomials into Bernstein basis and using the linear least squares function. Convergence properties for the absolute difference between the given polynomials and their lower bounds are shown with respect to raising the degree and the width of boxes and subdivision. Subsequently, we provide a new method for constructing an affine lower bounding function for a multivariate continuous rational function based on the Bernstein control point

APA, Harvard, Vancouver, ISO, and other styles

42

Lin, Tzung Han. "Approximating the Minimum-Bounding Box of a 3D Model with Minimum Spans for Flush Edges." Materials Science Forum 505-507 (January 2006): 1099–104. http://dx.doi.org/10.4028/www.scientific.net/msf.505-507.1099.

Full text

Abstract:

In this paper, a method for determining the approximated minimum-bounding box is presented. This method can only be used in the model that has finite vertices. A concept of the minimum span is introduced to determine one length of the bounding box according to a flush edge. After calculating a minimum span, the minimum-area rectangle of the projection is required to be one candidate of minimum-bounding boxes. In many applications, the volume of the bounding box is requested as small as possible. This paper provides an additional property to keep both one length and one rectangle of the boundin

APA, Harvard, Vancouver, ISO, and other styles

43

Ding, Jiujie, Jiahuan Zhang, Zongqian Zhan, Xiaofang Tang, and Xin Wang. "A Precision Efficient Method for Collapsed Building Detection in Post-Earthquake UAV Images Based on the Improved NMS Algorithm and Faster R-CNN." Remote Sensing 14, no. 3 (2022): 663. http://dx.doi.org/10.3390/rs14030663.

Full text

Abstract:

The results of collapsed building detection act as an important reference for damage assessment after an earthquake, which is crucial for governments in order to efficiently determine the affected area and execute emergency rescue. For this task, unmanned aerial vehicle (UAV) images are often used as the data sources due to the advantages of high flexibility regarding data acquisition time and flying requirements and high resolution. However, collapsed buildings are typically distributed in both connected and independent pieces and with arbitrary shapes, and these are generally more obvious in

APA, Harvard, Vancouver, ISO, and other styles

44

Liu, Shuang-Shuang. "Self-adaptive scale pedestrian detection algorithm based on deep residual network." International Journal of Intelligent Computing and Cybernetics 12, no. 3 (2019): 318–32. http://dx.doi.org/10.1108/ijicc-12-2018-0167.

Full text

Abstract:

Purpose The conventional pedestrian detection algorithms lack in scale sensitivity. The purpose of this paper is to propose a novel algorithm of self-adaptive scale pedestrian detection, based on deep residual network (DRN), to address such lacks. Design/methodology/approach First, the “Edge boxes” algorithm is introduced to extract region of interests from pedestrian images. Then, the extracted bounding boxes are incorporated to different DRNs, one is a large-scale DRN and the other one is the small-scale DRN. The height of the bounding boxes is used to classify the results of pedestrians and

APA, Harvard, Vancouver, ISO, and other styles

45

Pirhonen, Jesse, Risto Ojala, Klaus Kivekäs, Jari Vepsäläinen, and Kari Tammi. "Brake Light Detection Algorithm for Predictive Braking." Applied Sciences 12, no. 6 (2022): 2804. http://dx.doi.org/10.3390/app12062804.

Full text

Abstract:

There has recently been a rapid increase in the number of partially automated systems in passenger vehicles. This has necessitated a greater focus on the effect the systems have on the comfort and trust of passengers. One significant issue is the delayed detection of stationary or harshly braking vehicles. This paper proposes a novel brake light detection algorithm in order to improve ride comfort. The system uses a camera and YOLOv3 object detector to detect the bounding boxes of the vehicles ahead of the ego vehicle. The bounding boxes are preprocessed with L*a*b colorspace thresholding. The

APA, Harvard, Vancouver, ISO, and other styles

46

Waldman, Emmy. "Art Spiegelman’s Bounding Boxes: Mishkan, Midrash, Maus." Twentieth Century Literature 70, no. 4 (2024): 317–66. https://doi.org/10.1215/0041462x-11534708.

Full text

Abstract:

As vexing questions of Jewish identity and Holocaust memory loom large, this essay asks what it would mean to read Maus as a Jewish text. Thinking, with Hillary Chute’s landmark 2006 essay, “The Shadow of a Past Time:, History and Graphic Representation in Maus,” it uses Spiegelman’s (slippery), metaphor of comics panels as built boxes to link the book to Jewish traditions, surrounding the material preservation and transmission of historical texts., Reconsidering Maus through and as “boxes,” whether humble cardboard or, wooden coffin, reveals Spiegelman’s survivor’s “tale” as a text, commentar

APA, Harvard, Vancouver, ISO, and other styles

47

Rukhovich, D. D. "Iterative Scheme for Object Detection in Crowded Environments." Programmnaya Ingeneria 12, no. 1 (2021): 31–39. http://dx.doi.org/10.17587/prin.12.31-39.

Full text

Abstract:

Deep learning-based detectors usually produce a redundant set of object bounding boxes including many duplicate detections of the same object. These boxes are then filtered using non-maximum suppression (NMS) in order to select exactly one bounding box per object of interest. This greedy scheme is simple and provides sufficient accuracy for isolated objects but often fails in crowded environments, since one needs to both preserve boxes for different objects and suppress duplicate detections. In this work we develop an alternative iterative scheme, where a new subset of objects is detected at e

APA, Harvard, Vancouver, ISO, and other styles

48

Ye, Zixun, Hongying Zhang, Jingliang Gu, and Xue Li. "YOLOv7-3D: A Monocular 3D Traffic Object Detection Method from a Roadside Perspective." Applied Sciences 13, no. 20 (2023): 11402. http://dx.doi.org/10.3390/app132011402.

Full text

Abstract:

Current autonomous driving systems predominantly focus on 3D object perception from the vehicle’s perspective. However, the single-camera 3D object detection algorithm in the roadside monitoring scenario provides stereo perception of traffic objects, offering more accurate collection and analysis of traffic information to ensure reliable support for urban traffic safety. In this paper, we propose the YOLOv7-3D algorithm specifically designed for single-camera 3D object detection from a roadside viewpoint. Our approach utilizes various information, including 2D bounding boxes, projected corner

APA, Harvard, Vancouver, ISO, and other styles

49

Zheng, Tu, Shuai Zhao, Yang Liu, Zili Liu, and Deng Cai. "SCALoss: Side and Corner Aligned Loss for Bounding Box Regression." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 3535–43. http://dx.doi.org/10.1609/aaai.v36i3.20265.

Full text

Abstract:

Bounding box regression is an important component in object detection. Recent work achieves promising performance by optimizing the Intersection over Union (IoU). However, IoU-based loss has the gradient vanish problem in the case of low overlapping bounding boxes, and the model could easily ignore these simple cases. In this paper, we propose Side Overlap (SO) loss by maximizing the side overlap of two bounding boxes, which puts more penalty for low overlapping bounding box cases. Besides, to speed up the convergence, the Corner Distance (CD) is added into the objective function. Combining th

APA, Harvard, Vancouver, ISO, and other styles

50

Yu, Licun, Shuanhai He, Xiaosong Liu, Shuqing Jiang, and Shuiying Xiang. "Intelligent Crack Detection and Quantification in the Concrete Bridge: A Deep Learning-Assisted Image Processing Approach." Advances in Civil Engineering 2022 (March 3, 2022): 1–15. http://dx.doi.org/10.1155/2022/1813821.

Full text

Abstract:

We proposed a modified concrete bridge crack detector based on a deep learning-assisted image processing approach. Data augmentation technology is introduced to extend the limited dataset. In our proposed method, the bounding box for the crack is detected by YOLOv5. Then, the image covered by the bounding box is processed by the image processing techniques. Compared with the conventional image processing-based crack detection method, the deep learning-assisted image processing approach leads to higher detection accuracy and lower computation cost. More precisely, the mask filter is employed to

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!