机器视觉和应用程序发布机器视觉研究和开发方面的高质量技术贡献。具体来说，编辑鼓励在图像相关计算的所有应用和工程方面提交。特别是涉及机器视觉的科学、商业、工业、军事和生物医学应用的原创性贡献都在该期刊的范围内。特别强调图像处理和计算机视觉的工程和技术方面。以下方面机器视觉应用的兴趣：算法、架构、VLSI 实现、人工智能技术和机器视觉专家系统、前端传感、多维和多传感器机器视觉、实时技术、图像数据库、虚拟现实和可视化。论文必须包含重要的实验验证部分。

The journal "Machine Vision and Applications" publishes high-quality technical contributions in the field of machine vision research and development. Specifically, we encourage submissions related to all applications and engineering aspects of image-related computing. Original contributions involving scientific, commercial, industrial, military, and biomedical applications of machine vision are particularly welcome. The journal emphasizes the engineering and technical aspects of image processing and computer vision. We are especially interested in the following areas of machine vision applications: algorithms, architectures, VLSI implementations, artificial intelligence technologies and machine vision expert systems, front-end sensing, multi-dimensional and multi-sensor machine vision, real-time technologies, image databases, virtual reality, and visualization. All submitted papers must include significant experimental validation.

《Machine Vision and Applications》期刊致力于发布机器视觉领域的高质量技术研究和开发成果。我们鼓励提交与图像相关计算的各类应用和工程研究，尤其是涉及科学、商业、工业、军事和生物医学等领域的原创性贡献。期刊特别强调图像处理和计算机视觉的工程及技术方面。我们对以下机器视觉应用领域特别感兴趣：算法、架构、VLSI实现、人工智能技术和机器视觉专家系统、前端传感器、多维和多传感器机器视觉、实时技术、图像数据库、虚拟现实和可视化。所有提交的论文必须包含重要的实验验证部分。

Machine Vision and Applications

Peng Guo

Shuguo Pan

Wang Gao

Kourosh Khoshelham

Self-supervised monocular depth estimation via joint attention and intelligent mask loss

通过联合注意力和智能掩码损失实现自监督单目深度估计

Abstract We introduce a novel deep learning-based group activity recognition approach called the Pose Only Group Activity Recognition System (POGARS), designed to use only tracked poses of people to predict the performed group activity. In contrast to existing approaches for group activity recognition, POGARS uses 1D CNNs to learn spatiotemporal dynamics of individuals involved in a group activity and forgo learning features from pixel data. The proposed model uses a spatial and temporal attention mechanism to infer person-wise importance and multi-task learning for simultaneously performing group and individual action classification. Experimental results confirm that POGARS achieves highly competitive results compared to state-of-the-art methods on a widely used public volleyball dataset despite only using tracked pose as input. Further, our experiments show by using pose only as input, POGARS has better generalization capabilities compared to methods that use RGB as input.

Haritha Thilakarathne

Aiden Nibali

Zhen He

Stuart Morgan

Pose is all you need: the pose only group activity recognition system (POGARS)

姿态即一切：仅基于姿态的群体活动识别系统（POGARS）

Abstract In unmanned aerial vehicle (UAV) flights, power lines are considered as one of the most threatening hazards and one of the most difficult obstacles to avoid. In recent years, many vision-based techniques have been proposed to detect power lines to facilitate self-driving UAVs and automatic obstacle avoidance. However, most of the proposed methods are typically based on a common three-step approach: (i) edge detection, (ii) the Hough transform, and (iii) spurious line elimination based on power line constrains. These approaches not only are slow and inaccurate but also require a huge amount of effort in post-processing to distinguish between power lines and spurious lines. In this paper, we introduce LS-Net, a fast single-shot line-segment detector, and apply it to power line detection. The LS-Net is by design fully convolutional, and it consists of three modules: (i) a fully convolutional feature extractor, (ii) a classifier, and (iii) a line segment regressor. Due to the unavailability of large datasets with annotations of power lines, we render synthetic images of power lines using the physically based rendering approach and propose a series of effective data augmentation techniques to generate more training data. With a customized version of the VGG-16 network as the backbone, the proposed approach outperforms existing state-of-the-art approaches. In addition, the LS-Net can detect power lines in near real time. This suggests that our proposed approach has a promising role in automatic obstacle avoidance and as a valuable component of self-driving UAVs, especially for automatic autonomous power line inspection.

Van Nhan Nguyen

Robert Jenssen

Davide Roverso

LS-Net: fast single-shot line-segment detector

LS-Net：快速单步线段检测器

Gobinda Saha

Kaushik Roy

Online continual learning with saliency-guided experience replay using tiny episodic memory

使用微型情景记忆的显著性引导经验回放在线持续学习

Alessio Plebe

Giorgio Grasso

Localization of spherical fruits for robotic harvesting

球形水果的机器人采摘定位

Tao Tan

Guanglu Sun

Graph-based relational reasoning network for video question answering

基于图的关系推理网络用于视频问答

Andrea Asperti

Salvatore Fiorilla

Simone Nardi

Lorenzo Orsini

A review of recent techniques for person re-identification

最近用于行人重识别技术的综述

G. Devasena

V. Vidhya

Twinned attention network for occlusion-aware facial expression recognition

面向遮挡感知的面部表情识别的双注意网络

Robert A. Becker

Minh Dung Do

Thomas Schweiss

Tobias Albert

Thomas Bleistein

Dirk Werth

Synergizing LiDAR and Augmented Reality for precise real-time interior distance measurements for mobile devices

融合LiDAR与增强现实技术实现移动设备的精确实时室内距离测量

Abbreviation	Machine Vis. Apps.
Journal Impact	2.60
Quartiles(Global)	ENGINEERING, ELECTRICAL & ELECTRONIC(Q2)

ISSN	0932-8092, 1432-1769
h-index	77

Publication Information	Publisher: Springer Verlag，Publishing cycle: Bimonthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1988，Proportion of original research papers: 100.00%，Self Citation Rate:8.30%， Gold OA Rate: 13.76%
Average review cycle	网友分享经验：平均6.5个月
Average recruitment ratio	网友分享经验：容易

Machine Vision and Applications

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