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Synchronous localization and map construction of indoor mobile robot based on the fusion of depth camera and 2D lidar 4

Synchronous localization and map construction of indoor mobi...

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4th international conference on Digital signal and Computer Communications, DSCC 2024

作者： Wei, Qianli Chen, Chao Huang, Mingxi Pang, Xueting Jiangsu University of Science and Technology College of Mechanical Engineering Zhenjiang212100 China Jiangsu University of Science and Technology Marine Equipment and Technology Institute Zhenjiang212001 China

ISBN: (纸本)9781510681538

In the research of synchronous location and map construction of indoor mobile robots, lidar is often used to build maps. However, three-dimensional lidar is expensive, two-dimensional lidar can only detect information above the plane, and the map constructed by depth camera is poor, so this paper proposes a low-cost map construction method based on the fusion of depth camera and lidar. First, the joint calibration method is used to make the two sensors in the same coordinate system, then the depth image is converted into laser data, and then the data is fused by selecting the minimum value method with error processing under the same Angle. Finally, the fusion data is released to the Gmapping mapping algorithm for the construction of two-dimensional maps. The effectiveness of the proposed method is verified by setting up gazebo simulation environment, and the experiment shows that the fusion method can build a complete map. © 2024 SPIE.

关键词： Optical radar

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Artificial Intelligence Algorithms for robotic Harvesting of Agricultural Produce 1

Artificial Intelligence Algorithms for Robotic Harvesting of...

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1st international conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Kolhalkar, Nilesh R. Pandit, Anupama A. Kedar, Shridhar Ashok Yedukondalu, G. MKSSS's Cummins College of Engg. For Women Department of Mechanical Engineering MH Pune India Department of Computer Science & Engg. Pune MH Pune India Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Mechanical Engineering A.P. Vijayawada India

ISBN: (纸本)9798350355611

robotic harvesting of fruits and vegetables is an advanced technology that leverages robotics, Artificial Intelligence, and Machine vision to harvest the fruits autonomously from plants or trees. This technology aims to address labor shortages, enhance efficiency, reduce costs, and minimize damage to the fruit during harvesting. AI algorithms for fruit detection and harvesting are increasingly used in agricultural automation to improve efficiency and accuracy. The accuracy of detection algorithms in fruit detection and harvesting can differ reliant on various factors, including the type of algorithm used, the quality and diversity of the training data, the complexity of the environment, and the specific fruits being targeted. Advanced control algorithms integrated with image processing ensure that the robotic arm moves smoothly and accurately, minimizing the risk of bruising or damaging the fruit. Soft robotics and adaptive gripping technologies are discussed in the paper which can handle delicate fruits like grapes, without applying excessive force. Machine vision integrated robot arm with novel gripper and cutter for harvesting cluster fruit like grapes is reported in the paper. Case studies of agricultural robots for Orchards, Greenhouses and Field Crops are discussed with detailed analysis along with challenges, future trends and innovations. © 2025 IEEE.

关键词： Machine vision

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Learning Binary-Antithetical Information Bottleneck for Generalizable Face Anti-Spoofing

Learning Binary-Antithetical Information Bottleneck for Gene...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Yu, Hao Chen, Haoyu Zhao, Guoying Center for Machine Vision and Signal Analysis University of Oulu Finland Department of Computer Science Aalto University Finland

ISBN: (纸本)9798350368741

We investigate generalizable face anti-spoofing (FAS) using information bottleneck theory. As generalizable FAS aims to detect spoofing in unseen scenarios, it has recently gained significant attention. Existing methods often use adversarial strategies or auxiliary modules to learn domain-invariant features by mining data relationships from distinct source domains. However, their learned feature space may still shift for unseen data due to the spurious correlations overfitted from training domains. Our rationale is that the problem of generalized pattern learning in FAS can be framed as a unified binary-antithetical information transition process, grounded in information bottleneck theory. Specifically, we leverage mutual-information optimization to preserve the instance-level spoof-aware information while compressing domain-related information modeled from the antithetical identity distribution. This enables the model to dynamically identify domain-agnostic, minimal sufficient representations that consistently describe the live/spoof distributions while mitigating spurious correlations through cross-identity compression. In light of this, we propose a novel learning framework for FAS, named Binary-Antithetical Information Bottleneck (BIB)-FAS, which is proven to be effectively generalized to unseen scenarios without using auxiliary information (e.g., domain labels) for training. Extensive cross-domain evaluations show that BIB-FAS significantly outperforms state-of-the-art methods. The code is available at: ***/CV-AC/BIB-FAS. © 2025 IEEE.

关键词： Binary-antithetical transition Domain generalization Face anti-spoofing Information bottleneck

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2D vision-Based with Monocular Depth Estimation for Pose Estimation 7

2D Vision-Based with Monocular Depth Estimation for Pose Est...

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7th international conference on Image processing and Machine vision, IPMV 2025

作者： Charoenwuttikajorn, Pitijit Ekkachai, Kittipong Horanont, Teerayut Hasegawa, Shoichi Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand National Electronics and Computer Technology Center Pathum Thani Thailand Institute of Science Tokyo Tokyo Japan

ISBN: (纸本)9781510691810

Pose estimation with a vision system is a major factor during bin picking on the ability of the system to grasp the object. It helps the robot visualize the position and orientation of each object, through the pose that has been identified correctly. 3D vision systems have accuracy but are expensive which restricts them in budget-constrained circumstances. To solve these limitations, this paper proposes a 2D vision-based approach that uses RGB systems for pose estimation in the bin-picking approach. The core of the approach consists of deep-learning techniques that use Mask R-CNN for object detection and segmentation. The Monocular depth estimation using a multi-scale network (MiDaS) model for effective depth estimation in the absence of 3D sensors. Through the estimation of the relative depth between the camera and the objects. Distance calculation of the object from the camera using the metric based on the object’s width and the focal length. The experiments show that the model is capable of correctly predicting pickup points, demonstrating its suitability for tasks where minor positional deviations are acceptable. © 2025 SPIE.

关键词： bin-picking problem deep-learning model Dense Prediction Transformers image segmentation instance segmentation. Mask R-CNN MiDaS RGB images

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Automatic classification of multi-attributes from person images using GPT-4 vision 24

Automatic classification of multi-attributes from person ima...

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6th international conference on Image, Video and signal processing, IVSP 2024

作者： Fujimoto, Yusei Bashar, Khayrul R and D Team Intelligence Design Inc.

ISBN: (纸本)9798400716829

Classifying multi-attributes is gaining interest in the research and business community, especially for person re-identification (ReID) and fashion trend analysis. However, manual annotation of multi-attributes is quite tedious for large data sets. The GPT-4 vision has opened doors to tackle this bottleneck towards multi-attribute classification and person re-identification. In this study, we propose a novel approach using GPT-4V that can perform zero-shot automatic classification and annotation of multi-attributes from human images. We design appropriate questions or queries and input them along with single-person images to the system, which successfully generates multiple attributes like top color, bottom color, hair length, age, gender, etc. An experiment with a well-known Market1501 dataset showed promising performance of the proposed approach in classifying multi-attributes when compared with the ResNet-50 model empowered by manual annotation with the same dataset. © 2024 ACM.

关键词： Classification (of information)

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Boosting signal Modulation Few-Shot Learning with Pre-Transformation 48

Boosting Signal Modulation Few-Shot Learning with Pre-Transf...

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Sun, Peng Su, Jie Wen, Zhenyu Zhou, Yejian Hong, Zhen Yu, Shanqing Zhou, Huaji Zhejiang University of Technology China Newcastle University United Kingdom Xidian University China

ISBN: (纸本)9781728163277

The recent flourish of deep learning on various tasks is largely accredited to the rich and high-quality labeled data. Nonetheless, collecting sufficient labeled samples is not very practical for many real applications. Few-shot Learning (FSL) provides a promising solution that allows a model to learn the concept of novel classes with a few labeled samples. However, many existing FSL methods are only designed for computer vision tasks and are not suitable for radio signal recognition. This paper calls for a radically different approach to FSL: in contrast to developing a new FSL model, we should focus on transforming the radio signal to be better processed by the state-of-the-art (SOTA) FSL model. We propose Modulated signal Pre-transformation (MSP), a parameterized radio signal transformation framework that encourages the signals having the same semantics to have similar representations. MSP currently adapts to various SOTA FSL models for signal modulation recognition and can support the mainstream deep learning backbone. Evaluation results show that MSP improves the performance gains for many SOTA FSL models while maintaining flexibility. © 2023 IEEE.

关键词： Few Shot Learning Machine Learning signal processing

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2024 international conference on Advances in Modern Age Technologies for Health and Engineering Science, AMATHE 2024

2024 International Conference on Advances in Modern Age Tech...

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2024 international conference on Advances in Modern Age Technologies for Health and Engineering Science, AMATHE 2024

ISBN: (纸本)9798350371567

The proceedings contain 206 papers. The topics discussed include: synergy of human language processing and artificial intelligence;utilizing ANN in an advanced machine learning framework;CNN and random forest fusion for enhanced steel defect classification;exploring the feasibility of forward algorithm in neural networks;big data: an essential route for creating new business prospects;automated detection and classification of cotton leaf diseases: a computer vision approach;advancements in digital signal processing: design and implementation of IIR bandpass optical filter using Optisystem;an analysis of artificial intelligence and bigdata cyber security its applications;advancing multiclass emotion recognition with CNN-RNN architecture and illuminating module for real-time precision using facial expressions;and Parkinson’s disease diagnosis using neutral matrix and machine learning.

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SFma-Unet: A Mamba-Based Spatial-Frequency Fusion Network for Medical Image Segmentation

SFma-Unet: A Mamba-Based Spatial-Frequency Fusion Network fo...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Liu, Zhanpeng Zhang, Yuqiang Wang, Bin Yang, Yang Cai, Lin Institute of Artificial Intelligence Xiamen University Xiamen China School of Artificial Intelligence Hubei University Wuhan China School of Life Sciences Hubei University Wuhan China

ISBN: (纸本)9798350368741

Recently, Mamba-based methods have gained popularity in medical image segmentation due to their ability to model long-range dependencies with linear computational complexity. However, current segmentation methods often face challenges such as low contrast, blurred boundaries, and unclear backgrounds in medical images. Considering that perceived objects and features exhibit greater discriminative power in the frequency domain, we propose a novel mamba-based spatial and frequency domain feature fusion network, SFMa-Unet, to address these challenges. Specifically, we designed the Spatial-Frequency Interaction (SFI) module, which leverages the powerful modeling capabilities of Mamba to fuse spatial and frequency domain features, enhancing feature representation. Additionally, we developed a Mamba-based multi-scale feature channel fusion (MFCF) bridge to capture local and global dependencies across different feature scales, further improving the model's representational capacity. We conduct comprehensive experiments on the ISIC17 and ISIC18 public datasets. Experimental results demonstrate the effectiveness and robustness of SFMa-Unet. Codes are available at https://***/RainCh-zyq/SFma-Unet. © 2025 IEEE.

关键词： Medical Image Segmentation spatial-frequency fusion vision State Space Models

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Dual-Triple Transformer Networks for Accurate CT Pleural Effusion Segmentation

Dual-Triple Transformer Networks for Accurate CT Pleural Eff...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Yang, Jianwei Fan, Wenkang Fang, Hao Zhu, Zirui Luo, Xiongbiao Department of Computer Science and Technology Xiamen University Xiamen361102 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361102 China Zhongshan Hospital Xiamen University Xiamen361004 China

ISBN: (纸本)9798350368741

Pleural effusion segmentation in computed tomography images is essential to its precise diagnosis and treatment but remains challenging due to blurred boundaries, heterogeneous morphology, and low contrast with adjacent anatomical structures. This work shows a first study on pleural effusion segmentation by introducing a new deep learning architecture of dual-triple transformer networks. Specifically, this architecture builds a dual encoder of swin transformer and 3D deformable convolution, leveraging the multiscale representation capability to capture global contextual information and model complex deformations. Moreover, a triple decoder with a fusion module, a boundary-awareness mechanism, and a transposed-residual convolution block is introduced to effectively propagate these global and local features and refine the segmentation by mitigating ambiguity at the interfaces with surrounding tissues. We validate our method on 143 chest computed tomography scans. The experimental results demonstrate that our proposed model significantly outperforms state-of-the-art segmentation approaches, greatly improving the dice similarity coefficient and mean intersection over union while critically reducing both Hausdorff distance 95 and average symmetric surface distance. © 2025 IEEE.

关键词： Boundary Awareness Dual Encoder Image Segmentation Pleural Effusion vision Transformer

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Google Tesseract: Optical Character Recognition (OCR) on HDD/SSD Labels Using Machine vision 14

Google Tesseract: Optical Character Recognition (OCR) on HDD...

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14th international conference on Computer and Automation Engineering (ICCAE)

作者： Estrada Bugayong, Vernon Flores Villaverde, Jocelyn Linsangan, Noel B. Mapua Univ Sch Grad Studies Manila Philippines Celest Elect Pte Ltd Woodlands Ave 5 Singapore Singapore Mapua Univ Sch Elect Elect & Comp Engn Manila Philippines

ISBN: (纸本)9781665483797

This paper is designed to have an optical character recognition system capable of interpreting captured images of hard disk drive and solid-state drive labels with high accuracy. Manual checking of the disk capacity size and part number found on the labels is time consuming, more prone to errors and utilizes more manpower. Automating the inspection through optical character recognition using image pre-processing and machine vision contributes to an easier inspection process, better management of records and faster cycle time. The images captured using a vision camera went through different stages of image pre-processing via OpenCV-Python and recognition through Google Tesseract. Different categorical variables including exposure time and location of texts in a captured image were used to determine and improve the overall recognition accuracy. By improving the lighting condition through the addition of light sources, the developed OCR system was able to achieve a character recognition accuracy of 99.375%.

关键词： Machine vision Tesseract Optical Character Recognition Image processing Python LabVIEW

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