Machine Vision and Applications最新文献

Salient object detection (SOD) is designed to mimic human visual mechanisms to identify and segment the most salient part of an image. Although related works have achieved great progress in SOD, they are limited when it comes to interferences of non-salient objects, finely shaped objects and co-salient objects. To improve the effectiveness and capability of SOD, we propose a supervised contrastive learning network with multi-scale interaction and integrity learning named SCLNet. It adopts contrastive learning (CL), multi-reception field confusion (MRFC) and context enhancement (CE) mechanisms. Using this method, the input image is first divided into two branches after two different data augmentations. Unlike existing models, which focus more on boundary guidance, we add a random position mask on one branch to break the continuous of objects. Through the CL module, we obtain more semantic information than appearance information by learning the invariance of different data augmentations. The MRFC module is then designed to learn the internal connections and common influences of various reception field features layer by layer. Next, the obtained features are learned through the CE module for the integrity and continuity of salient objects. Finally, comprehensive evaluations on five challenging benchmark datasets show that SCLNet achieves superior results. Code is available at https://github.com/YuPangpangpang/SCLNet.

Accurate medical image classification poses a significant challenge in designing expert computer-aided diagnosis systems. While deep learning approaches have shown remarkable advancements over traditional techniques, addressing inter-class similarity and intra-class dissimilarity across medical imaging modalities remains challenging. This work introduces the advanced gating transformer network (MedTransNet), a deep learning model tailored for precise medical image classification. MedTransNet utilizes channel and multi-gate attention mechanisms, coupled with residual interconnections, to learn category-specific attention representations from diverse medical imaging modalities. Additionally, the use of gradient centralization during training helps in preventing overfitting and improving generalization, which is especially important in medical imaging applications where the availability of labeled data is often limited. Evaluation on benchmark datasets, including APTOS-2019, Figshare, and SARS-CoV-2, demonstrates effectiveness of the proposed MedTransNet across tasks such as diabetic retinopathy severity grading, multi-class brain tumor classification, and COVID-19 detection. Experimental results showcase MedTransNet achieving 85.68% accuracy for retinopathy grading, 98.37% ((pm ,0.44)) for tumor classification, and 99.60% for COVID-19 detection, surpassing recent deep learning models. MedTransNet holds promise for significantly improving medical image classification accuracy.

Few-shot learning for image classification comes up as a hot topic in computer vision, which aims at fast learning from a limited number of labeled images and generalize over the new tasks. In this paper, motivated by the idea of Fisher Score, we propose a Discriminative Local Descriptors Attention model that uses the ratio of intra-class and inter-class similarity to adaptively highlight the representative local descriptors without introducing any additional parameters, while most of the existing local descriptors based methods utilize the neural networks that inevitably involve the tedious parameter tuning. Experiments on four benchmark datasets show that our method achieves higher accuracy compared with the state-of-art approaches for few-shot learning. Specifically, our method is optimal on the CUB-200 dataset, and outperforms the second best competitive algorithm by 4.12(%) and 0.49(%) under the 5-way 1-shot and 5-way 5-shot settings, respectively.

Human–object interaction (HOI) detection aims to localize and infer interactions between human and objects in an image. Recent work proposed transformer encoder–decoder architectures for HOI detection with exceptional performance, but possess certain drawbacks: they do not employ a complete disentanglement strategy to learn more discriminative features for different sub-tasks; they cannot achieve sufficient contextual exchange within each branch, which is crucial for accurate relational reasoning; their transformer models suffer from high computational costs and large memory usage due to complex attention calculations. In this work, we propose a disentangled transformer network that disentangles both the encoder and decoder into three branches for human detection, object detection, and interaction classification. Then we propose a novel feature unify decoder to associate the predictions of each disentangled decoder, and introduce a multiplex relation embedding module and an attentive fusion module to perform sufficient contextual information exchange among branches. Additionally, to reduce the model’s computational cost, a position-sensitive axial attention is incorporated into the encoder, allowing our model to achieve a better accuracy-complexity trade-off. Extensive experiments are conducted on two public HOI benchmarks to demonstrate the effectiveness of our approach. The results indicate that our model outperforms other methods, achieving state-of-the-art performance.

Point cloud analysis has a wide range of applications in many areas such as computer vision, robotic manipulation, and autonomous driving. While deep learning has achieved remarkable success on image-based tasks, there are many unique challenges faced by deep neural networks in processing massive, unordered, irregular and noisy 3D points. To stimulate future research, this paper analyzes recent progress in deep learning methods employed for point cloud processing and presents challenges and potential directions to advance this field. It serves as a comprehensive review on two major tasks in 3D point cloud processing—namely, 3D shape classification and semantic segmentation.

Deep neural network models for image segmentation can be a powerful tool for the automation of motor claims handling processes in the insurance industry. A crucial aspect is the reliability of the model outputs when facing adverse conditions, such as low quality photos taken by claimants to document damages. We explore the use of a meta-classification model to empirically assess the precision of segments predicted by a model trained for the semantic segmentation of car body parts. Different sets of features correlated with the quality of a segment are compared, and an AUROC score of 0.915 is achieved for distinguishing between high- and low-quality segments. By removing low-quality segments, the average (m{textit{IoU}} ) of the segmentation output is improved by 16 percentage points and the number of wrongly predicted segments is reduced by 77%.

The extensive deployment of camera-based IoT devices in our society is heightening the vulnerability of citizens’ sensitive information and individual data privacy. In this context, thermal imaging techniques become essential for data desensitization, entailing the elimination of sensitive data to safeguard individual privacy. Meanwhile, thermal imaging techniques can also play a important role in industry by considering the industrial environment with low resolution, high noise and unclear objects’ features. Moreover, existing works often process the entire video as a single entity, which results in suboptimal robustness by overlooking individual actions occurring at different times. In this paper, we propose a lightweight algorithm for action recognition in thermal infrared videos using human skeletons to address this. Our approach includes YOLOv7-tiny for target detection, Alphapose for pose estimation, dynamic skeleton modeling, and Graph Convolutional Networks (GCN) for spatial-temporal feature extraction in action prediction. To overcome detection and pose challenges, we created OQ35-human and OQ35-keypoint datasets for training. Besides, the proposed model enhances robustness by using visible spectrum data for GCN training. Furthermore, we introduce the two-stream shift Graph Convolutional Network to improve the action recognition accuracy. Our experimental results on the custom thermal infrared action dataset (InfAR-skeleton) demonstrate Top-1 accuracy of 88.06% and Top-5 accuracy of 98.28%. On the filtered kinetics-skeleton dataset, the algorithm achieves Top-1 accuracy of 55.26% and Top-5 accuracy of 83.98%. Thermal Infrared Action Recognition ensures the protection of individual privacy while meeting the requirements of action recognition.