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

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

Exemplar-free class incremental learning is a highly challenging setting where replay memory is unavailable. Methods relying on frozen feature extractors have drawn attention recently in this setting due to their impressive performances and lower computational costs. However, those methods are highly dependent on the data used to train the feature extractor and may struggle when an insufficient amount of classes are available during the first incremental step. To overcome this limitation, we propose to use a pre-trained text-to-image diffusion model in order to generate synthetic images of future classes and use them to train the feature extractor. Experiments on the standard benchmarks CIFAR100 and ImageNet-Subset demonstrate that our proposed method can be used to improve state-of-the-art methods for exemplar-free class incremental learning, especially in the most difficult settings where the first incremental step only contains few classes. Moreover, we show that using synthetic samples of future classes achieves higher performance than using real data from different classes, paving the way for better and less costly pre-training methods for incremental learning.

Quentin Jodelet

Xin Liu

Yin Jun Phua

Tsuyoshi Murata

Future-proofing class-incremental learning

面向未来的类增量学习

Joseph Meier

Ethan Welborn

Sotirios Diamantas

Pothole segmentation and area estimation with thermal imaging using deep neural networks and unmanned aerial vehicles

坑洞分割与面积估算：利用深度神经网络和无人机进行热成像

Ranjit Kumar Mishra

Arijit Mondal

Jimson Mathew

Swin transformer with part-level tokenization for occluded person re-identification

基于部分级标记化的遮挡行人重识别的Swin变换器

Abstract The success of visual-based localization requires the image features to be sufficiently captured in well-exposed images and reliably tracked among image sequences. In this paper, a learning-based exposure strategy algorithm is developed to improve the performance of visual odometry or simultaneous localization and mapping in scenes with sudden illumination changes and high dynamic range (HDR). The exposure value is predicted by optimizing a novel image information metric, which is calculated based on a multi-scale image histogram. Experimental observations show that the proposed metric is highly related to the localization performance, and achieves better localization accuracy when compared to other state-of-the-art metrics. Using this novel metric, the exposure control network is designed to predict the exposure value for the next image given a sequence of recently captured images as inputs. For different application scenarios, alternative network frameworks are proposed with the options to include an optimization module providing improved exposure values for feedback learning, but with additional computation complexity. The approach is light-weight for real-time video applications and the images captured using the predicted exposure values are well-exposed with better detected and matched features in a variety of challenging HDR scenes. Code will be available in https://github.com/leejieyu13/HDR-AE .

Jieyu Li

Ruiwen Zhen

Robert Stevenson

Automatic exposure strategy network for robust visual odometry in environments with high dynamic range

高动态范围环境下鲁棒视觉里程计的自动曝光策略网络

Abstract Semantic segmentation models need a large number of images to be effectively trained but manual annotation of such images has a high cost. Active domain adaptation addresses this problem by pretraining the model with a synthetically generated dataset and then fine-tuning it with a few selected label annotations (the “budget”) on real images to account for the domain shift. Previous works annotate a percentage of either individual pixels or whole target images. We argue that the first is infeasible in practice, and the second spends part of the budget on classes that the pretrained model may have already learned well. We propose a method based on the annotation of regions computed by Segment Anything, a recently introduced foundation model for class-agnostic image segmentation. The key idea is to assign a ground truth label to each of a tiny subset of regions, those for which the model is more uncertain. In order to increase the number of annotated regions we propagate the ground truth labels to most similar regions according to a hierarchical clustering algorithm that uses the features learned by the pretrained model. Our method outperforms the state-of-the-art on the GTA5 to Cityscapes benchmark by using fewer annotations, almost closing the gap between the synthetically pre-trained model and that obtained with full supervision of the real images. Furthermore, we present competitive results for budgets less than 1% of samples and also for a larger and more challenging target dataset, Mapillary Vistas.

Joan Serrat

Jose Luis Gómez

Antonio M. López

Closing the gap in domain adaptation for semantic segmentation: a time-aware method

领域自适应语义分割中的差距闭合：一种时间感知方法

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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