Search Results (19)

Hyperspectral imaging (HSI) plays a pivotal role in modern agriculture by capturing fine-grained spectral signatures that support crop classification, health assessment, and land-use monitoring. However, the transition from raw spectral data to reliable semantic understanding remains challenging—particularly under fragmented planting patterns, spectral ambiguity, and spatial heterogeneity. To address these limitations, we propose UniHSFormer-X, a unified transformer-based framework that reconstructs agricultural semantics through prototype-guided token routing and hierarchical context modeling. Unlike conventional models that treat spectral–spatial features uniformly, UniHSFormer-X dynamically modulates information flow based on class-aware affinities, enabling precise delineation of field boundaries and robust recognition of spectrally entangled crop types. Evaluated on three UAV-based benchmarks—WHU-Hi-LongKou, HanChuan, and HongHu—the model achieves up to 99.80% overall accuracy and 99.28% average accuracy, outperforming state-of-the-art CNN, ViT, and hybrid architectures across both structured and heterogeneous agricultural scenarios. Ablation studies further reveal the critical role of semantic routing and prototype projection in stabilizing model behavior, while parameter surface analysis demonstrates consistent generalization across diverse configurations. Beyond high performance, UniHSFormer-X offers a semantically interpretable architecture that adapts to the spatial logic and compositional nuance of agricultural imagery, representing a forward step toward robust and scalable crop classification. Full article

In this paper, the sampling and reconstruction problems in function subspaces of $L^p\left({\mathbb{R}}^n\right)$ associated with the multi-dimensional special affine Fourier transform (SAFT) are discussed. First, we give the definition of the multi-dimensional SAFT and study its properties including the Parseval’s relation, the canonical convolution theorems and the chirp-modulation periodicity. Then, a kind of function spaces are defined by the canonical convolution in the multi-dimensional SAFT domain, the existence and the properties of the dual basis functions are demonstrated, and the $L^p$-stability of the basis functions is established. Finally, based on the nonuniform samples taken on a dense set, we propose an iterative reconstruction algorithm with exponential convergence to recover the signals in a $L^p$-subspace associated with the multi-dimensional SAFT, and the validity of the algorithm is demonstrated via simulations. Full article

Three-dimensional registration with the affine transform is one of the most important steps in 3D reconstruction. In this paper, the modified grey wolf optimizer with behavior considerations and dimensional learning (BCDL-GWO) algorithm as a registration method is introduced. To refine the 3D registration result, we incorporate the iterative closet point (ICP). The BCDL-GWO with ICP method is implemented on the scanned commercial orthodontic tooth and regular tooth models. Since this is a registration from multi-views of optical images, the hierarchical structure is implemented. According to the results for both models, the proposed algorithm produces high-quality 3D visualization images with the smallest mean squared error of about 7.2186 and 7.3999 μm², respectively. Our results are compared with the statistical randomization-based particle swarm optimization (SR-PSO). The results show that the BCDL-GWO with ICP is better than those from the SR-PSO. However, the computational complexities of both methods are similar. Full article

The assemblage of oceanic islands and seamounts, arising from the widespread presence of mature oceans, plays a crucial role in reconstructing the evolutionary history of the paleoocean. Oceanic islands or seamounts within the Longmuco-Shuanghu metamorphic complex, a remnant of the Paleo-Tethys Ocean in the central Tibetan Plateau, have seldom been reported due to their remoteness. This study has identified an oceanic island-seamount in the Maoershan area, situated to the west of the Longmuco-Shuanghu metamorphic complex, composed of basalt, diabase, limestone, and siliceous rocks. Based on field observations, petrology, zircon U-Pb dating, whole-rock geochemistry, and Sr-Nd isotopes analyses, we have identified a suite of mafic rocks with OIB affinity. The youngest zircon U-Pb age cluster was concentrated at ~243–241 Ma. The geochemical characteristics of the siliceous rocks indicate a mixture of terrigenous material, suggesting that they formed in a continental margin. In combination with regional geological data, we conclude that the Longmuco-Shuanghu Paleo-Tethys Ocean remained open during the Middle Triassic. Furthermore, a fraction of the oceanic island-seamounts underwent scraping and transformed into a metamorphic complex, while other segments experienced deep subduction, resulting in the formation of high-pressure metamorphic rocks. Collectively, these processes gave rise to the distinctive high-pressure metamorphic complex within the central Qiangtang terrane. Full article

β-1,3-glucanase plays an important role in the biodegradation, reconstruction, and development of β-1,3-glucan. An endo-β-1,3-glucanase which was encoded by PeBgl1 was expressed, purified and characterized from Penicillium expansum for the first time. The PeBgl1 gene was amplified and transformed into the competent cells of E. coli Rosetta strain with the help of the pET-30a cloning vector. The recombinant protein PeBgl1 was expressed successfully at the induction conditions of 0.8 mmol/L IPTG at 16 °C for 16 h and then was purified by nickel ion affinity chromatography. The optimum reaction temperature of PeBgl1 was 55 °C and it had maximal activity at pH 6.0 according to the enzymatic analysis. Na₂HPO₄-NaH₂PO₄ buffer (pH 6.0) and NaCl have inhibitory and enhancing effects on the enzyme activities, respectively. SDS, TritonX-100 and some metal ions (Mg²⁺, Ca²⁺, Ba²⁺, Cu²⁺, and Zn²⁺) have an inhibitory effect on the enzyme activity. The results showed that PeBgl1 protein has good enzyme activity at 50–60 °C and at pH 5.0–9.0, and it is not a metal dependent enzyme, which makes it robust for storage and transportation, ultimately holding great promise in green biotechnology and biorefining. Full article

With the capability of capturing a target’s two-dimensional information, Inverse Synthetic Aperture Radar (ISAR) imaging is widely used in Radar Automatic Target Recognition. However, changes in the ship target’s attitude can lead to the scatterers’ rotation, occlusion, and angle glint, reducing the accuracy of ISAR image recognition. To solve this problem, we proposed a Triangle Preserving level-set-assisted Triangle-Points Affine Transform Reconstruction (TP-TATR) for ISAR ship target recognition. Firstly, three geometric points as initial information were extracted from the preprocessed ISAR images based on the ship features. Combined with these points, the Triangle Preserving level-set (TP) method robustly extracted the fitting triangle of targets depending on the intrinsic structure of the ship target. Based on the extracted triangle, the TP-TATR adjusted all the ship targets from the training and test data to the same attitude, thereby alleviating the attitude sensitivity. Finally, we created templates by averaging the adjusted training data and matched the test data with the templates for recognition. Experiments based on the simulated and measured data indicate that the accuracies of the TP-TATR method are 87.70% and 90.03%, respectively, which are higher than those of the contrast algorithms and have a statistical difference. These demonstrate the effectiveness and robustness of our proposed TP-TATR method. Full article

Aimingat non-side-looking airborne radar, we propose a novel unsupervised affinity propagation (AP) clustering radar detection algorithm to suppress clutter and detect targets. The proposed method first uses selected power points as well as space-time adaptive processing (STAP) weight vector, and designs matrix-transformation-based weighted input data, with which the first unsupervised weighted AP clustering is proposed by means of their similarity matrix, responsibility values and availability values. Then, new reconstructed weighted power inputs are designed, and the second weighted AP clustering is proposed. Finally, with their cluster results, a detection-discriminant criterion is designed for the judgment of target detection, and simultaneously, the clutter is suppressed. Compared with the conventional and important STAP, ADC and JDL algorithms, and several SO-based, GO-based and OS-based CFAR algorithms, the proposed unsupervised algorithm achieves much higher probability of detection and provides distinctly superior target-detection performance. With reasonable computation time, it can better conquer the range dependence in characteristic of clutter and better process non-independent identically distributed (non-IID) samples of non-side-looking radar. Sufficient simulations are performed, and they demonstrate that the proposed unsupervised algorithm is preferable and advantageous. Full article

Medical thermography provides an overview of the human body with two-dimensional (2D) information that assists the identification of temperature changes, based on the analysis of surface distribution. However, this approach lacks spatial depth information, which can be enhanced by adding multiple images or three-dimensional (3D) systems. Therefore, the methodology applied for this paper generates a 3D point cloud (from thermal infrared images), a 3D geometry model (from CT images), and the segmented inner anatomical structures. Thus, the following computational processing was employed: Structure from Motion (SfM), image registration, and alignment (affine transformation) between the 3D models obtained to combine and unify them. This paper presents the 3D reconstruction and visualization of the respective geometry of the neck/bust and inner anatomical structures (thyroid, trachea, veins, and arteries). Additionally, it shows the whole 3D thermal geometry in different anatomical sections (i.e., coronal, sagittal, and axial), allowing it to be further examined by a medical team, improving pathological assessments. The generation of 3D thermal anatomy models allows for a combined visualization, i.e., functional and anatomical images of the neck region, achieving encouraging results. These 3D models bring correlation of the inner and outer regions, which could improve biomedical applications and future diagnosis with such a methodology. Full article

To promote the development of deep learning for feature matching, image registration, and three-dimensional reconstruction, we propose a method of constructing a deep learning benchmark dataset for affine-invariant feature matching. Existing images often have large viewpoint differences and areas with weak texture, which may cause difficulties for image matching, with respect to few matches, uneven distribution, and single matching texture. To solve this problem, we designed an algorithm for the automatic production of a benchmark dataset for affine-invariant feature matching. It combined two complementary algorithms, ASIFT (Affine-SIFT) and LoFTR (Local Feature Transformer), to significantly increase the types of matching patches and the number of matching features and generate quasi-dense matches. Optimized matches with uniform spatial distribution were obtained by the hybrid constraints of the neighborhood distance threshold and maximum information entropy. We applied this algorithm to the automatic construction of a dataset containing 20,000 images: 10,000 ground-based close-range images, 6000 satellite images, and 4000 aerial images. Each image had a resolution of 1024 × 1024 pixels and was composed of 128 pairs of corresponding patches, each with 64 × 64 pixels. Finally, we trained and tested the affine-invariant deep learning model, AffNet, separately on our dataset and the Brown dataset. The experimental results showed that the AffNet trained on our dataset had advantages, with respect to the number of matching points, match correct rate, and matching spatial distribution on stereo images with large viewpoint differences and weak texture. The results verified the effectiveness of the proposed algorithm and the superiority of our dataset. In the future, our dataset will continue to expand, and it is intended to become the most widely used benchmark dataset internationally for the deep learning of wide-baseline image matching. Full article

We present a fast 3D analytical affine transformation (F3DAAT) method to obtain polygon-based computer-generated holograms (CGHs). CGHs consisting of tens of thousands of triangles from 3D objects are obtained by this method. We have attempted a revised method based on previous 3D affine transformation methods. In order to improve computational efficiency, we have derived and analyzed our proposed affine transformation matrix. We show that we have further increased the computational efficiency compared with previous affine methods. We also have added flat shading to improve the reconstructed image quality. A 3D object from a 3D camera is reconstructed holographically by numerical and optical experiments. Full article

To address the lack of fast and high-precision in situ measurement of large-sized pipes in current industrial applications, a pipe-diameter-measurement method based on stereo vision is designed in this paper. By using multiple sets of binocular cameras to perform 3D reconstruction and integration of multiple laser markers projected on measured cross sections of pipes, the pipe diameter can be estimated. In this method, a measurement algorithm is adopted to enable automatic matching of feature points through affine distance transformation, and an optimized point-cloud-registration algorithm with normal-vector constraints is used to ensure measurement robustness. To verify the feasibility of the method, an experimental system was built under laboratory conditions, and three types of pipes with outer diameters from 285 mm to 325 mm were measured. The experimental results show that the relative error is within ±0.570% and the maximum repeatability standard deviation is 0.551 mm. The experimental results basically meet industrial standards, and the proposed method therefore has good application prospects. Full article

The registration of point clouds in a three-dimensional space is an important task in many areas of computer vision, including robotics and autonomous driving. The purpose of registration is to find a rigid geometric transformation to align two point clouds. The registration problem can be affected by noise and partiality (two point clouds only have a partial overlap). The Iterative Closed Point (ICP) algorithm is a common method for solving the registration problem. Recently, artificial neural networks have begun to be used in the registration of point clouds. The drawback of ICP and other registration algorithms is the possible convergence to a local minimum. Thus, an important characteristic of a registration algorithm is the ability to avoid local minima. In this paper, we propose an ICP-type registration algorithm ($\lambda$-ICP) that uses a multiparameter functional ($\lambda$-functional). The proposed $\lambda$-ICP algorithm generalizes the NICP algorithm (normal ICP). The application of the $\lambda$-functional requires a consistent choice of the eigenvectors of the covariance matrix of two point clouds. The paper also proposes an algorithm for choosing the directions of eigenvectors. The performance of the proposed $\lambda$-ICP algorithm is compared with that of a standard point-to-point ICP and neural network Deep Closest Points (DCP). Full article

A formal, high-level representation of programs is typically needed for static and dynamic analyses performed by compilers. However, the source code of target applications is not always available in an analyzable form, e.g., to protect intellectual property. To reason on such applications, it becomes necessary to build models from observations of its execution. This paper details an algebraic approach which, taking as input the trace of memory addresses accessed by a single memory reference, synthesizes an affine loop with a single perfectly nested reference that generates the original trace. This approach is extended to support the synthesis of unions of affine loops, useful for minimally modeling traces generated by automatic transformations of polyhedral programs, such as tiling. The resulting system is capable of processing hundreds of gigabytes of trace data in minutes, minimally reconstructing 100% of the static control parts in PolyBench/C applications and 99.99% in the Pluto-tiled versions of these benchmarks. As an application example of the trace modeling method, trace compression is explored. The affine representations built for the memory traces of PolyBench/C codes achieve compression factors of the order of ${10}^6$ and ${10}^3$ with respect to gzip for the original and tiled versions of the traces, respectively. Full article

The registration between images is a crucial part of the 3-D tooth reconstruction model. In this paper, we introduce a registration method using our proposed statistical randomization-based particle swarm optimization (SR-PSO) algorithm with the iterative closet point (ICP) method to find the optimal affine transform between images. The hierarchical registration is also utilized in this paper since there are several consecutive images involving in the registration. We implemented this algorithm in the scanned commercial regular-tooth and orthodontic-tooth models. The results demonstrated that the final 3-D images provided good visualization to human eyes with the mean-squared error of 7.37 micrometer² and 7.41 micrometer² for both models, respectively. From the results compared with the particle swarm optimization (PSO) algorithm with the ICP method, it can be seen that the results from the proposed algorithm are much better than those from the PSO algorithm with the ICP method. Full article

Spectroscopic methods combined with statistics have recently gathered substantial interest in pedological studies. Near-infrared (NIR) spectroscopy has been utilized, for example, for reconstructions of the history and transformations of Chernozems, although no similar research was conducted based on mid-infrared (MIR). In this paper, the relevance of MIR spectroscopy was tested in studies on the origin/affinity of organic matter from chernozemic soils. Samples collected from three vegetation classes (grasslands, forests and arable lands) were investigated using MIR spectroscopy in order to create a statistical model, which was applied on buried profiles of unknown origin. The results showed a clear disjunction of vegetation classes. Samples of buried soil were placed in the space between all classes, indicating the relation to variable vegetation. Therefore, arable lands should not be omitted in paleoecological reconstructions, because we cannot exclude the cultivation of fertile soils before their burial. It was concluded that MIR methods may have similar applicability to NIR spectroscopy. Additionally, MIR spectra may also be discriminated according to the recognized soil type, which allows for direct reconstructions of the transformation trends in buried profiles. Full article