检索结果-内蒙古大学图书馆

object segmentation dataset generation framework for robotic bin-picking: Multi-metric analysis between results trained with real and synthetic data

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2025年 205卷

作者： Cordeiro, Artur Rocha, Luis Freitas Boaventura-Cunha, Jose Pires, Eduardo J. Solteiro Souza, Joao Pedro Inst Syst & Comp Engn Technol & Sci INESC TEC Rua Dr Roberto Frias Campus FEUPRua Dr Roberto Frias P-4200465 Porto Portugal Univ Porto FEUP Fac Engn Rua Dr Roberto Frias P-4200465 Porto Portugal Univ Tras Os Montes & Alto Douro UTAD P-5000801 Vila Real Portugal

The implementation of deep learning approaches based on instance segmentation data remains a challenge for customized scenarios, owing to the time-consuming nature of acquiring and annotating real-world instance segmentation data, which requires a significant investment of semi-professional user labour. Obtaining high-quality labelled data demands expertise and meticulous attention to detail. This requirement can significantly impact the overall implementation process, adding to the complexity and resource requirements of customized scenarios with diverse objects. The proposed work addresses the challenge of generating labelled data for large-scale robotic bin-picking datasets by proposing an easy-to-use automated framework designed to create customized data with accurate labels from CAD models. The framework leverages a photorealistic rendering engine integrated with physics simulation, minimizing the gap between synthetic and real-world data. Models trained using the synthetic data generated by this framework achieved an Average Precision of 86.95%, comparable to the performance of models trained on real-world datasets. Furthermore, this paper provides a comprehensive multi-metric analysis across diverse objects representing distinct industrial applications, including naval, logistics, and aerospace domains. The evaluation also includes the use of three distinct instance segmentation networks, alongside a comparative analysis of the proposed approach against two generative model techniques.

关键词： Robotic bin-picking object segmentation Deep learning dataset Data synthesis Automation

来源：评论

学校读者我要写书评

暂无评论

Efficient Video object segmentation via Modulated Cross-Attention Memory

Efficient Video Object Segmentation via Modulated Cross-Atte...

引用

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Abdelrahman Shaker Syed Talal Martin Danelljan Salman Khan Ming-Hsuan Yang Fahad Shahbaz Khan Mohamed Bin Zayed University of Artificial Intelligence University of Bonn ETH Zürich University of California Merced Yonsei University Google Research Linköping University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Recently, transformer-based approaches have shown promising results for semi-supervised video object segmentation. However, these approaches typically struggle on long videos due to increased GPU memory demands, as they frequently expand the memory bank every few frames. We propose a transformer-based approach, named MAVOS, that introduces an optimized and dynamic long-term modulated cross-attention (MCA) memory to model temporal smoothness without requiring frequent memory expansion. The proposed MCA effectively encodes both local and global features at various levels of granularity while efficiently maintaining consistent speed regardless of the video length. Extensive experiments on multiple benchmarks, LVOS, Long-Time Video, and DAVIS 2017, demonstrate the effectiveness of our proposed contributions leading to real-time inference and markedly reduced memory demands without any degradation in segmentation accuracy on long videos. Compared to the best existing transformer-based approach, our MAVOS increases the speed by 7.6x, while significantly reducing the GPU memory by 87% with comparable segmentation performance on short and long video datasets. Notably, on the LVOS dataset, our MAVOS achieves a J&F score of 63.3% while operating at 37 frames per second (FPS) on a single V100 GPU. Our code and models will be publicly released at https://***/Amshaker/MAVOS.

关键词： Degradation Visualization Memory management Graphics processing units object segmentation Streaming media Benchmark testing Transformers Real-time systems Encoding

来源：评论

学校读者我要写书评

暂无评论

Automatic cassava disease recognition using object segmentation and progressive learning

引用

PEERJ COMPUTER SCIENCE 2025年 11卷 e2721-e2721页

作者： Che, Chang Xue, Nian Li, Zhen Zhao, Yilin Huang, Xin Harbin Univ Sch Civil Engn Elect & Informat Engn Harbin Heilongjiang Peoples R China Heilongjiang Urban Water Qual Monitoring Co Ltd Harbin Heilongjiang Peoples R China Shandong Univ Technol Sch Comp Sci & Technol Zibo Shandong Peoples R China NYU Tandon Sch Engn Dept Comp Sci & Engn Brooklyn NY USA Taiyuan Univ Technol Taiyuan Shanxi Peoples R China

Cassava is a vital crop for millions of farmers worldwide, but its cultivation is threatened by various destructive diseases. Current detection methods for cassava diseases are costly, time-consuming, and often limited to controlled environments, making them unsuitable for large-scale agricultural use. This study aims to develop a deep learning framework that enables early, accurate, and efficient detection of cassava diseases in real-world conditions. We propose a self-supervised object segmentation technique, combined with a progressive learning algorithm (PLA) that incorporates both triplet loss and classification loss to learn robust feature embeddings. Our approach achieves superior performance on the Cassava Leaf Disease Classification (CLDC) dataset from the Kaggle competition, with an accuracy of 91.43%, outperforming all other participants. The proposed method offers a practical and efficient solution for cassava disease detection, demonstrating the potential for large-scale, real-world application in agriculture.

关键词： Cassava disease recognition Progressive learning object segmentation Deep learning Computation efficiency

来源：评论

学校读者我要写书评

暂无评论

Real-Time Video object segmentation to Improve the Safety of Autonomous Systems

Real-Time Video Object Segmentation to Improve the Safety of...

引用

IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Brook W. Abegaz Jacob Bragg Lila Areephanthu Naxi Shah Engineering Department Loyola University Chicago Chicago IL USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Although the use of autonomous operation of cyber-physical systems is very interesting, it is exposed to various safety and security vulnerabilities. This is evident during the presence of obstacles in the operating environment of such systems. Identifying objects using video segmentation is crucial to the overall safety of the autonomous systems to determine whether an accident can be detected and avoided timely. This paper presents the implementation of a predictive, real-time video object segmentation to identify objects from videos taken from autonomous self-driving vehicles. The results of the object identification are then used in a model predicting control (MPC) designed particularly for this approach. The results of the implementation are tested and verified using repeated experiments. The proposed approach could be used to increase the safety of the operation of various cyber-physical systems including industrial robots and autonomous vehicles.

关键词： object segmentation Streaming media Cyber-physical systems Real-time systems Safety object recognition Reliability Collision avoidance Autonomous vehicles Predictive control

来源：评论

学校读者我要写书评

暂无评论

Multi-Context Temporal Consistent Modeling for Referring Video object segmentation

Multi-Context Temporal Consistent Modeling for Referring Vid...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sun-Hyuk Choi Hayoung Jo Seong-Whan Lee Department of Artificial Intelligence Korea University Seoul Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Referring video object segmentation aims to segment objects within a video corresponding to a given text description. Existing transformer-based temporal modeling approaches face challenges related to query inconsistency and the limited consideration of context. Query in-consistency produces unstable masks of different objects in the middle of the video. The limited consideration of context leads to the segmentation of incorrect objects by failing to adequately account for the relationship between the given text and instances. To address these issues, we propose the Multi-context Temporal Consistency Module (MTCM), which consists of an Aligner and a Multi-Context Enhancer (MCE). The Aligner removes noise from queries and aligns them to achieve query consistency. The MCE predicts text-relevant queries by considering multi-context. We applied MTCM to four different models, increasing performance across all of them, particularly achieving 47.6 $\mathcal{J}\& \mathcal{F}$ on the MeViS. Code is available at https://***/Choi58/MTCM.

关键词： Training Codes Noise object segmentation Transformers Acoustics Speech processing Faces Context modeling

来源：评论

学校读者我要写书评

暂无评论

Open-Vocabulary Saliency-Guided Progressive Refinement Network for Unsupervised Video object segmentation

Open-Vocabulary Saliency-Guided Progressive Refinement Netwo...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhidong Han Shenglong Hu Huihui Song Kaihua Zhang Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Existing leading unsupervised video object segmentation (UVOS) paradigm often leverages a dual-stream architecture with motion and appearance branches, where only the motion cues from optical flow are used as a guide to locating the primary foreground objects. When suffering from challenging factors such as static scenes, fast camera shaking, severe motion blur, etc., the estimated optical flow is noisy with low quality, leading to erroneous primary foreground objects estimation. To address this issue, we propose an open-vocabulary saliency-guided progressive refinement network for UVOS, dubbed as OVSNet. It is observed that most of the primary foreground objects also demonstrate saliency characteristics in the appearance branch. Based on this, our OVSNet complements motion cues with saliency cues predicted by a series of foundation models equipped with favorable zero-shot generalization capabilities. Specifically, we first leverage the off-the-shelf contrastive vision-language pre-training (CLIP) and CLIPSeg to generate an OVS attention map as saliency cues. Then, the saliency cues together with motion cues prompt the segment anything model (SAM) to generate a location map. In the location process, we design two lightweight adapters to fine-tune SAM, which makes SAM well adapt to the downstream UVOS task. Finally, the location map generated by SAM is used to progressively guide object representation refinement in the appearance branch, ultimately achieving an accurate segmentation mask prediction. Extensive evaluations on DAVIS-16, FBMS, and YouTube-objects demonstrate the favorable performance of our OVSNet over the state-of-the-art methods.

关键词： Accuracy Foundation models Motion segmentation Estimation object segmentation Transforms Signal processing Noise measurement Speech processing Optical flow

来源：评论

学校读者我要写书评

暂无评论

object segmentation of Database Images by Dual Multiscale Morphological Reconstructions and Retrieval Applications

引用

IEEE TRANSACTIONS ON IMAGE PROCESSING 2012年第2期21卷 828-843页

作者： Chen, Jiann-Jone Su, Chun-Rong Grimson, W. Eric L. Liu, Jun-Lin Shiue, De-Hui Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei 10673 Taiwan MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA Ind Technol Res Inst Informat & Commun Res Labs Hsinchu 10673 Taiwan

Processing images for specific targets on a large scale has to handle various kinds of contents with regular processing steps. To segment objects in one image, we utilized dual multiScalE Graylevel mOrphological open and close recoNstructions (SEGON) to build a background (BG) gray-level variation mesh, which can help to identify BG and object regions. It was developed from a macroscopic perspective on image BG gray levels and implemented using standard procedures, thus robustly dealing with large-scale database images. The image segmentation capability of existing methods can be exploited by the BG mesh to improve object segmentation accuracy. To evaluate the segmentation accuracy, the probability of coherent segmentation labeling, i.e., the normalized probability random index (PRI), between a computer-segmented image and the hand-labeled one is computed for comparisons. Content-based image retrieval (CBIR) was carried out to evaluate the object segmentation capability in dealing with large-scale database images. Retrieval precision-recall (PR) and rank performances, with and without SEGON, were compared. For multi-instance retrieval with shape feature, AdaBoost was used to select salient common feature elements. For color features, the histogram intersection between two scalable HSV descriptors was calculated, and the mean feature vector was used for multi-instance retrieval. The distance measure for color feature can be adapted when both positive and negative queries are provided. The normalized correlation coefficient of features among query samples was computed to integrate the similarity ranks of different features in order to perform multi-instance with multifeature query. Experiments showed that the proposed object segmentation method outperforms others by 21% in the PRI. Performing SEGON-enabled CBIR on large-scale databases also improves on the PR performance reported elsewhere by up to 42% at a recall rate of 0.5. The proposed object segmentation method c

关键词： Content-based image retrieval (CBIR) dual multiscale gray-level morphological reconstructions image background (BG) gray-level variation mesh object segmentation

来源：评论

学校读者我要写书评

暂无评论

object segmentation algorithm using snakes in stereo images

引用

OPTICAL ENGINEERING 2006年第3期45卷 037005-1-037005页

作者： Kim, Shin-Hyoung Jang, Jong-Whan Choi, Jae-Ho PaiChai Univ Dept Informat & Commun Engn Taejon 302435 South Korea Chonbuk Natl Univ Dept Comp Engn Chonju 561756 Chonbuk South Korea

A snake-based algorithm for segmenting an object from a pair of stereo images is presented. Unlike previously developed snake-based algorithms, this one performs well even when the objects in the picture are occluded and the background behind them is cluttered. Also, the algorithm is not sensitive to the placement of the initial snake points. The algorithm uses a new energy function defined over the disparity space between the pair of the stereo images to successfully locate the boundary of an object in a complex image. Experimental results have shown that the developed algorithm produces more accurate segmentation results than those of the well-known conventional snake algorithm reported by Kass et al. (c) 2006 Society of Photo-Optical Instrumentation Engineers.

关键词： object segmentation snake algorithm stereo images disparity space disparity information energy function

来源：评论

学校读者我要写书评

暂无评论

object segmentation-Assisted Inter Prediction for Versatile Video Coding

引用

IEEE TRANSACTIONS ON BROADCASTING 2024年第4期70卷 1236-1253页

作者： Li, Zhuoyuan Yuan, Zikun Li, Li Liu, Dong Tang, Xiaohu Wu, Feng Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & App Hefei 230027 Peoples R China Southwest Jiaotong Univ Informat Secur & Natl Comp Grid Lab Chengdu 610031 Peoples R China

In modern video coding standards, block-based inter prediction is widely adopted, which brings high compression efficiency. However, in natural videos, there are usually multiple moving objects of arbitrary shapes, resulting in complex motion fields that are difficult to represent compactly. This problem has been tackled by more flexible block partitioning methods in the Versatile Video Coding (VVC) standard, but the more flexible partitions require more overhead bits to signal and still cannot be made arbitrarily shaped. To address this limitation, we propose an object segmentation-assisted inter prediction method (SAIP), where objects in the reference frames are segmented by some advanced technologies. With a proper indication, the object segmentation mask is translated from the reference frame to the current frame as the arbitrary-shaped partition of different regions without any extra signal. Using the segmentation mask, motion compensation is separately performed for different regions, achieving higher prediction accuracy. The segmentation mask is further used to code the motion vectors of different regions more efficiently. Moreover, the segmentation mask is considered in the joint rate-distortion optimization for motion estimation and partition estimation to derive the motion vector of different regions and partition more accurately. The proposed method is implemented into the VVC reference software, VTM version 12.0. Experimental results show that the proposed method achieves up to 1.98%, 1.14%, 0.79%, and on average 0.82%, 0.49%, 0.37% BD-rate reduction for common test sequences, under the Low-delay P, Low-delay B, and Random Access configurations, respectively.

关键词： Motion segmentation Encoding Video coding Accuracy Standards Shape Vectors Inter prediction motion compensation motion estimation motion vector coding object segmentation partition estimation video coding VVC

来源：评论

学校读者我要写书评

暂无评论

object segmentation in cluttered environment based on gaze tracing and gaze blinking

引用

ROBOMECH JOURNAL 2021年第1期8卷 1-11页

作者： Ratsamee, Photchara Mae, Yasushi Kamiyama, Kazuto Horade, Mitsuhiro Kojima, Masaru Arai, Tatsuo Osaka Univ Grad Sch Informat Sci & Technol Osaka Japan Kansai Univ Grad Sch Engn Osaka Japan Takenaka Corp Takenaka Res & Dev Inst Tokyo Japan Natl Def Acad Sch Syst Engn Dept Mech Syst Engn Tokyo Japan Osaka Univ Grad Sch Engn Sci Osaka Japan Univ Elect Commun Tokyo Japan

People with disabilities, such as patients with motor paralysis conditions, lack independence and cannot move most parts of their bodies except for their eyes. Supportive robot technology is highly beneficial in supporting these types of patients. We propose a gaze-informed location-based (or gaze-based) object segmentation, which is a core module of successful patient-robot interaction in an object-search task (i.e., a situation when a robot has to search for and deliver a target object to the patient). We have introduced the concepts of gaze tracing (GT) and gaze blinking (GB), which are integrated into our proposed object segmentation technique, to yield the benefit of an accurate visual segmentation of unknown objects in a complex scene. Gaze tracing information can be used as a clue as to where the target object is located in a scene. Then, gaze blinking can be used to confirm the position of the target object. The effectiveness of our proposed method has been demonstrated using a humanoid robot in experiments with different types of highly cluttered scenes. Based on the limited gaze guidance from the user, we achieved an 85% F-score of unknown object segmentation in an unknown environment.

关键词： Gaze interface Human-robot interaction object segmentation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：