Local structure analyzers as determinants of preattentive pattern discrimination.

Molecular simulation is central to modern drug discovery but is often limited by high computational cost and the complexity of molecular interactions. Deep-learning drug–target interaction (DTI) prediction can accelerate screening; however, many models underuse the local functional structure features—binding motifs, reactive groups, and residue-level fragments—that drive recognition. We present LoF-DTI, a framework that explicitly represents and couples such local features. Drugs are converted from SMILES into molecular graphs and targets from sequences into feature representations. On the drug side, a Jumping Knowledge (JK) enhanced Graph Isomorphism Network (GIN) extracts atom- and neighborhood-level patterns; on the target side, residual CNN blocks with progressively enlarged receptive fields, augmented by N-mer substructural statistics, capture multi-scale local motifs. A Gated Cross-Attention (GCA) module then performs atom-to-residue interaction learning, highlighting decisive local pairs and providing token-level interpretability through attention scores. By prioritizing locality during both encoding and interaction, LoF-DTI delivers competitive results across multiple benchmarks and improves early retrieval relevant to virtual screening. Case analyses show that the model recovers known functional binding sites, suggesting strong potential to provide mechanism-aware guidance for molecular simulation and to streamline the drug design pipeline.

A new scene classification method is proposed based on the combination of local Gabor features with a spatial pyramid matching model. First, new local Gabor feature descriptors are extracted from dense sampling patches of scene images. These local feature descriptors are embedded into a bag-of-visual-words (BOVW) model, which is combined with a spatial pyramid matching framework. The new local Gabor feature descriptors have sufficient discrimination abilities for dense regions of scene images. Then the efficient feature vectors of scene images can be obtained byK-means clustering method and visual word statistics. Second, in order to decrease classification time and improve accuracy, an improved kernel principal component analysis (KPCA) method is applied to reduce the dimensionality of pyramid histogram of visual words (PHOW). The principal components with the bigger interclass separability are retained in feature vectors, which are used for scene classification by the linear support vector machine (SVM) method. The proposed method is evaluated on three commonly used scene datasets. Experimental results demonstrate the effectiveness of the method.

Using methods from algebraic graph theory and convex optimization, we study the relationship between local structural features of a network and the eigenvalues of its Laplacian matrix. In particular, we propose a series of semidefinite programs to find new bounds on the spectral radius and the spectral gap of the Laplacian matrix in terms of a collection of local structural features of the network. Our analysis shows that the Laplacian spectral radius is strongly constrained by local structural features. On the other hand, we illustrate how local structural features are usually insufficient to accurately estimate the Laplacian spectral gap. As a consequence, random graph models in which only local structural features are prescribed are, in general, inadequate to faithfully model Laplacian spectral properties of a network.

Robust object detection based on local similar structure statistical matching

In this article, we develop a novel method for the detection of vineyard parcels in agricultural landscapes based on very high resolution (VHR) optical remote sensing images. Our objective is to perform texture-based image retrieval and supervised classification algorithms. To do that, the local textural and structural features inside each image are taken into account to measure its similarity to other images. In fact, VHR images usually involve a variety of local textures and structures that may verify a weak stationarity hypothesis. Hence, an approach only based on characteristic points, not on all pixels of the image, is supposed to be relevant. This work proposes to construct the local extrema-based descriptor (LED) by using the local maximum and local minimum pixels extracted from the image. The LED descriptor is formed based on the radiometric, geometric and gradient features from these local extrema. We first exploit the proposed LED descriptor for the retrieval task to evaluate its performance on texture discrimination. Then, it is embedded into a supervised classification framework to detect vine parcels using VHR satellite images. Experiments performed on VHR panchromatic PLEIADES image data prove the effectiveness of the proposed strategy. Compared to state-of-the-art methods, an enhancement of about 7% in retrieval rate is achieved. For the detection task, about 90% of vineyards are correctly detected.

The ear’s relatively stable structure makes it suitable for recognition. In common identification applications, only one sample per person (OSPP) is registered in a gallery; consequently, effectively training deep-learning-based ear recognition approach is difficult. The state-of-the-art (SOA) 3D ear recognition using the OSPP approach bottlenecks when large occluding objects are close to the ear. Hence, we propose a system that combines PointNet++ and three layers of features that are capable of extracting rich identification information from a 3D ear. Our goal is to correctly recognize a 3D ear affected by a large nearby occlusion using one sample per person (OSPP) registered in a gallery. The system comprises four primary components: (1) segmentation; (2) local and local joint structural (LJS) feature extraction; (3) holistic feature extraction; and (4) fusion. We use PointNet++ for ear segmentation. For local and LJS feature extraction, we propose an LJS feature descriptor–pairwise surface patch cropped using a symmetrical hemisphere cut-structured histogram with an indexed shape (PSPHIS) descriptor. Furthermore, we propose a local and LJS matching engine based on the proposed LJS feature descriptor and SOA surface patch histogram indexed shape (SPHIS) local feature descriptor. For holistic feature extraction, we use a voxelization method for global matching. For the fusion component, we use a weighted fusion method to recognize the 3D ear. The experimental results demonstrate that the proposed system outperforms the SOA normalization-free 3D ear recognition methods using OSPP when the ear surface is influenced by a large nearby occlusion.

The diesel engine fault data is nonlinear and it’s difficult to extract the characteristic information. Kernel Principal Component Analysis (KPCA) is used to extract features of nonlinear data, only considering global structure. Kernel Locality Preserving Projection (KLPP) considers the local feature structure. So an improved algorithm for global and local structure preserving is proposed to extract the feature of data. The improved feature extraction algorithm combining KPCA and KLPP, avoids the loss of information considering the global and local feature structure and then uses the modified K-NN algorithm for fault classification. In this paper, the software AVL BOOST is used to simulate the faults of diesel engine. The simulation experiments indicate the proposed method can extract the feature vectors effectively, and shows good classification performance.

During the continual transitions from lab research to real-world applications of vision-based algorithms, there are significant challenges, e.g. the robustness to adapt to complex environments and the high demands of multi-task, multi-modal learning models. More specifically on visual simultaneous localization and mapping (visual SLAM) for mobile robots, two notable limitations are: (i) the drift issue during pose estimation, especially in dynamic environments, makes the positioning system unstable; (ii) the additional loop detection takes many computational resources for image retrieval and global feature geometry checking. This thesis explores the approaches of visual localization on unmanned ground vehicles by solving those limitations. The conventional feature extraction and matching pipeline for vision-based tasks involve three steps: (i) local feature detection and description, (ii) feature matching, and (iii) outlier rejection. The first contribution of the thesis is adding a feature selection and anticipation stage to reduce tracking drift. We explore the noise model of image features to select a subset of all the observed image features with the best ”contribution” during data association and pose estimation across multiple frames. Conventional SLAM algorithms take a strong assumption of scene rigidity, which limits the application under challenging environments. The second part of the thesis addresses the tough issue in dynamic environments with moving objects. We presented GMC, namely the motion clustering approach, a lightweight dynamic object filtering method. It can distinguish moving objects from static landmarks. Based on the theory of motion coherence within a particular image area, GMC could segment dynamic objects in 3D space. We can provide an efficient and robust correspondence algorithm that can extract dynamic objects from a static background with the method. In this way, we propose a dynamic SLAM system that is real-time and free from expensive GPU processors. In contrast to GMC, the thesis’s third part turns to an object-aware learning-based model for more general dynamic scenarios. We use object detection and tracking as points, lines, planes, etc. We utilize semantic information and extract sparse image features simultaneously to keep track of dynamic objects. The static background and different dynamic objects are jointly optimized in a newly developed bundle adjustment sliding window. The estimated 3D bounding boxes can provide more robust camera tracking and better scene understanding, and better map merging. The fourth part of the thesis leverages the emerging feature learning framework. It proposes a unified self-supervised model called LGDNet to generate both global and local image feature descriptors end-to-end. Global feature descriptors embed the whole image into a compact representation, leading to easier scene comparison. On the other hand, local features focus more on the local region similarities of some salient parts for structure from motion. Our proposed method can directly extract features together with descriptors that encode both local maximum responses and global context information, avoiding duplicate calculations based on different feature extraction criteria.

Operating performance assessment of industrial process based on MIC-graph convolutional networks with local slow feature analysis.

Recently, random-dot textures were found that in spite of their identical second-order statistics (hence power spectra) could still be effortlessly discriminated, based on conspicuous local features which include quasi-collinearity, corner, closure, and connectivity. Here, it will be shown, using textures composed of line segments, that these local features are not independent of each other, but can be described by two elementary units: line segments and their terminators. Furthermore, the preattentive texture perception system can count the number of terminators but ignores their positions. The line segments are a special case of bars, which were identically stimulated in these experiments. Besides color, these bars (of given orientation, width and length), their terminators (and crossed bars, as special case of terminators) are the elements of preattentive vision, to be called textons. In preattentive texture discrimination only the first-order statistics of these textons has perceptual significance, while the relative positions between the textons are unnoticed.

Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationships of neighbor points, and it cannot effectively represent the intrinsic structure of hyperspectral imagery (HSI) that possesses many homogenous areas. In this paper, we propose a new dimensionality reduction (DR) method, termed local geometric structure Fisher analysis (LGSFA), for HSI classification. Firstly, LGSFA uses the intraclass neighbor points of each point to compute its reconstruction point. Then, an intrinsic graph and a penalty graph are constructed to reveal the intraclass and interclass properties of hyperspectral data. Finally, the neighbor points and corresponding intraclass reconstruction points are used to enhance the intraclass-manifold compactness and the interclass-manifold separability. LGSFA can effectively reveal the intrinsic manifold structure and obtain the discriminating features of HSI data for classification. Experiments on the Salinas, Indian Pines, and Urban data sets show that the proposed LGSFA algorithm achieves the best classification results than other state-of-the-art methods.

Hyperspectral images (HSIs) possess a large number of spectral bands, which easily lead to the curse of dimensionality. To improve the classification performance, a huge challenge is how to reduce the number of spectral bands and preserve the valuable intrinsic information in the HSI. In this letter, we propose a novel unsupervised dimensionality reduction method called local neighborhood structure preserving embedding (LNSPE) for HSI classification. At first, LNSPE reconstructs each sample with its spectral neighbors and obtains the optimal weights for constructing the adjacency graph by modifying its loss function. Then, to discover the scatter information of the training samples, LNSPE minimizes the scatter between the pixels and the corresponding neighbors and maximizes the total scatter of the HSI data. Finally, it incorporates the scatter information and the dual graph structure to enhance the aggregation of the HSI. As a result, LNSPE can effectively reveal the intrinsic structure and improve the classification performance of the HSI data. The experimental results on two real hyperspectral data sets exhibit the efficiency and superiority of LNSPE to some state-of-the-art methods.

As one of the most pervasive methods of individual identification and document authentication, signatures present convincing evidence and provide an important form of indexing for effective document image processing and retrieval in a broad range of applications. However, detection and segmentation of free-form objects such as signatures from clustered background is currently an open document analysis problem. In this paper, we focus on two fundamental problems in signature-based document image retrieval. First, we propose a novel multiscale approach to jointly detecting and segmenting signatures from document images. Rather than focusing on local features that typically have large variations, our approach captures the structural saliency using a signature production model and computes the dynamic curvature of 2D contour fragments over multiple scales. This detection framework is general and computationally tractable. Second, we treat the problem of signature retrieval in the unconstrained setting of translation, scale, and rotation invariant nonrigid shape matching. We propose two novel measures of shape dissimilarity based on anisotropic scaling and registration residual error and present a supervised learning framework for combining complementary shape information from different dissimilarity metrics using LDA. We quantitatively study state-of-the-art shape representations, shape matching algorithms, measures of dissimilarity, and the use of multiple instances as query in document image retrieval. We further demonstrate our matching techniques in offline signature verification. Extensive experiments using large real-world collections of English and Arabic machine-printed and handwritten documents demonstrate the excellent performance of our approaches.

Using methods of the scanning electron microscopy, Raman scattering of light(RS), and electron paramagnetic resonance (EPR), consistent research of the local structure and magnetic features of different types of raw coal samples from Donetsk basin is carried out. It is established that the ratio of the main peak intensities of RS spectrum D and G is inversely related to the volatile substance amount Vdaf in the coal samples. The study of the kinetic behavior of the EPR line width in hydrogen, oxygen, and methane sorption-desorption processes in each coal sample helped determine that the diffusion coefficient value for hydrogen in coal at room temperature is equal to DН = (2 ÷ 7) × 10−5 cm2/s. It is demonstrated that the oxygen diffusion occurs with time according to two different exponential laws with diffusion coefficients DO,1 = 5 × 10−6 cm2/s and DO,2 = 5.5 × 10−7 cm2/s, respectively. The smaller coefficient corresponds to the diffusion caused by the hopping process. Finally, it is established that the anthracite is a unique type of coal which does not possess the ability “to conserve” the significant EPR line width after oxygen pumping out from the samples.

The effect of samarium doping on the local structure and morphological features of the surface of a Se95Te5 chalcogenide glassy semiconductor film is investigated by X-ray diffraction analysis and atomic force microscopy, and the effect of doping on the current flow through Al–Se95Te5〈Sm〉–Te structures is examined by measuring the I–V characteristics in the steady-state mode. The results obtained are interpreted using Lampert’s theory of injection currents, the Elliott void-cluster model, and the Mott and Street charged defects model proposed for chalcogenide glasses.