Recently, deep learning networks have demonstrated strong performance in hyperspectral image (HSI) classification tasks. 2D Convolutional Neural Networks (2D-CNNs) perform well in the spatial dimension, but tend to overlook spectral information. Meanwhile, 3D Convolutional Neural Networks (3D-CNNs) perform well in the spectral dimension, but are inadequate for the exploitation of spatial and spectral information. To address this issue, a cross-fusion network (CFN) is proposed for HSI classification. Rather than simply combining 2D and 3D convolutions, CFN introduces two dedicated modules: a cross-feature module (CFM) and an adaptive cross-fusion module (ACFM). The CFM performs bidirectional cross-dimensional feature exchange between the 2D and 3D branches to capture local spatial-spectral fusion representations. The ACFM further models long-range pixel dependencies by conducting cross-branch contextual interaction and cross-residual enhancement. Based on these two modules, CFN can jointly exploit local spatial-spectral representations and long-range contextual dependencies. Experiments on the Indian Pines, Houston2013, and Pavia University datasets demonstrate that CFN achieves competitive performance compared with state-of-the-art methods.
In a previous work, the theoretical existence of uncertainty relations governing chromatic opponency for general chromatic states within a quantum information-based model of color perception has been established. In the present study, we extend this result by deriving uncertainty relations for chromatic states perceived by a human observer, modeled as a quantum effect. Our analysis indicates that, once the presence of an observer is explicitly taken into account, only uncertainty relations associated with highly saturated stimuli are likely to be experimentally detectable.
With the development of deep learning, food image classification has become of great significance in the food industry and health management. However, high heterogeneity, diversity, complex background, and fine-grained features make recognition challenging. This paper proposes the SwinFood–MOE model based on Swin Transformer, two-branch convolution, and mixture of experts (MOE): data augmentation is used to improve generalization ability, Swin Transformer is used to extract features, and two-branch convolution is used to enhance representation. The Swin Transformer two-stage, four-stage, and two-branch convolution features are pyramid pooled, and four feedforward expert networks and routers are introduced. After automatic weighted aggregation, fully connected classification is used to achieve fine-grained recognition. Experiments on the Food-101 and UECFood-256 datasets show that the accuracy rates reach 99.61
The automatic verification of the compliance of Machine-Readable Travel Documents’ (MRTD) portraits is nowadays of big interest for issuing authorities or application providers. Despite the increase of performance and effectiveness of automatic services, there is still much room for the improvement of such systems. Specific particularities related to cultural or religious aspects may increase inequalities or social tension between different people. Additionally, regulations and standards allow portraits a few differences related to these aspects, yet current automatic systems to verify their compliance generally do not take into account these particularities. This paper then contributes with a dataset and an algorithm that automatically verifies the compliance with the ICAO requirements related to the use of head coverings on facial images used on MRTDs. All the methods found in the literature ignore that some coverings might be accepted because of religious or cultural reasons, and mainly only look for the presence of hats or caps. Our approach specifically includes the religious cases and distinguishes the head coverings that might be considered compliant, depending on the national rules. We built a dataset composed of 6759 facial images of 910 identities to accommodate these types of head coverings. That data was used to fine-tune and train a new classification model derived from the YOLOv8 framework and we achieved state-of-the-art results with an overall accuracy on the created data of 91.05
As core technical documents for the process industry, Piping and Instrumentation Diagrams (P IDs) and Process Flow Diagrams (PFDs) face severe challenges in automated object detection: small industrial symbols (e.g., arrows, stream numbers, valves) occupy minimal image areas, leading to insufficient feature extraction; limited labeled samples result in poor model generalization under few-shot conditions; and dense symbol distribution with complex background interference causes high false detection and missed detection rates. To address these issues, this study constructs a multi-industry P ID–PFD dataset based on 182 labeled original industrial flow diagrams. The dataset contains over 14,000 sliced image patches and approximately 160,000 annotated object instances across 308 core object categories, covering representative scenarios from the petrochemical and coal chemical industries. Furthermore, we propose a Hybrid Strategy Object Detection (HSOD) workflow that integrates parallel full-image and overlapping-patch detection and hybrid post-processing. Based on YOLOv12, the workflow employs SAHI with 640 × 640 patches and 50
