Local Orthogonal Moments for Local Features.

Image analysis with logarithmic Zernike moments

Changes in illumination condition, pose, facial expression and others is not an easy task in face recognition. Solving these problems requires not only a feature extraction method that can generate distinct features for each class of image but requires other additional technique that able to improve the overall classification accuracy. This paper presents the face recognition using combined features of original and pre-processed face images. This technique is experimented using orthogonal moments namely Zernike moments (ZMs) and Krawtchouk moments (KMs). The classification technique used in the recognition stage is Euclidean square distance or Nearest Neighbour (NN) classifier. Database face images from Olivetti research laboratory (ORL) consisting of 40 subjects of 10 images each where none of them are identical, is used in the experiments. The face images vary in position, rotation, scale and expression, with and without spectacles. From the experiments, the new technique is able to improve the classification accuracy significantly.

Decision letter: Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila

This paper proposes an approach combining the set of Krawtchouk moments and HMMs (Hidden Markov Models) for unconstrained offline handwritten Chinese character recognition. The Krawtchouk moments can be effectively used as pattern features in analysis of two-dimensional images. Since the basis set is orthogonal in the discrete domain of the image coordinate space, the implementation of Krawtchouk moments doesn't involve any numerical approximation or coordinate transformation. This property makes Krawtchouk moments superior to conventional, more continuous moments such as Legendre moments and Zernike moments. In the field of character recognition, HMMs architecture has been used widely because of its strong adaptability to written distortion and variability. In this paper, Krawtchouk moments and HMMs are combined to recognize unconstrained offline handwritten Chinese amount in words. Experiments show that the average recognition of Krawtchouk is higher than Zernike and Legendre moments within the HMM framework.

For the existing 3D point cloud model classification method PointNet only extracts global feature information, but for the problem of insufficient local feature information extraction, a network that can take into account the combination of global features and local features is proposed, so as to realize 3D point Classification of cloud models. This paper proposes a classification network based on local location information. First, the point cloud model is sampled in multiple groups to obtain the center point of each group of samples, and then the radius is set to obtain its spherical neighborhood, and the points in the neighborhood and their center points are used as local location features. In information fusion, the acquired local information features are used as high-dimensional features of the neighborhood space through feature mapping. By analogy, multiple sets of sampled local features are integrated to realize the classification of the point cloud model. The experimental results show that the classification network can achieve a classification accuracy of 92.5% on the ModelNet40 data set, which is higher than the current mainstream algorithm.

As an orthogonal moment, Zernike moment (ZM) is an attractive image feature in a number of application scenarios due to its distinguishing properties. However, we find that for digital images, the commonly used Cartesian method for ZM computation has compromised the advantages of ZMs because of their non-ideal accuracy stemming from two inherent sources of errors, i.e., the geometric error and the integral error. There exists considerable errors in image reconstruction using ZMs calculated with the Cartesian method. In this paper, we propose a polar coordinate based algorithm for the computation of ZMs, which avoids the two kinds of errors and greatly improves the accuracy of ZM computation. We present solutions to the key issues in ZM computation under polar coordinate system, including the derivation of computation formulas, the polar pixel arrangement scheme, and the interpolation-based image conversion etc. As a result, ZM-based image reconstruction can be performed much more accurately.

Abstract This paper presents a novel feature extraction algorithm based on the local binary features for automatic target recognition (ATR) in infrared imagery. Since the inception of the local binary pattern (LBP) and local ternary pattern (LTP) features, many extensions have been proposed to improve their robustness and performance in a variety of applications. However, most attentions were paid to improve local feature extraction with little consideration on the incorporation of global or regional information. In this work, we propose a new concave-convex partition (CCP) strategy to improve LBP and LTP by dividing local features into two distinct groups, i.e., concave and convex, according to the contrast between local and global intensities. Then two separate histograms built from the two categories are concatenated together to form a new LBP/LTP code that is expected to better reflect both global and local information. Experimental results on standard texture images demonstrate the improved discriminability of the proposed features and those on infrared imagery further show that the proposed features can achieve competitive ATR results compared with state-of-the-art methods.

Medical image classification is essential for contemporary clinical diagnosis and decision support systems. However, medical images generally have similar inter-class features and complex structure patterns, making it a challenging task. While both local and global features are critical for noise reduction and discriminative pattern extraction in medical images, conventional approaches exhibit limitations. Specifically, convolutional neural networks (CNNs) focus on local features extraction but lack a comprehensive understanding of global semantic. Conversely, vision transformers (ViTs) can model long-range feature dependencies but may cause disruption to local features. To address these limitations, we propose Hierarchical Enhanced Multi-attention Feature (HEMF), an adaptive hierarchical enhanced multi-attention feature fusion framework to synergistically extract and fuse multi-scale local and global features. It comprises two core components: (1) the enhanced local and global feature extraction modules to extract multi-scale local and global features in parallel; (2) the hierarchical enhanced feature fusion module integrating a novel attention mechanism named Mixed Attention (MA) and a novel inverted residual block named Squeezed Inverted Residual Multi-Layer Perceptron (SIRMLP) to effectively fuse multi-scale features. Experimental results demonstrate that with nearly minimal model parameters compared to other advanced models, HEMF achieves the accuracy and F1-score of 87.34% and 78.89% on the ISIC2018 dataset, 87.03% and 87.02% on the Kvasir dataset, and 82.26% and 82.20% on the COVID-19 CT dataset, which are the state-of-the-art performance. Our code is open source and available from https://github.com/Esgjgd/HEMF.

AEA-Net:Affinity-supervised entanglement attentive network for person re-identification

Convolutional neural networks (CNNs) have been widely used in the field of hyperspectral image change detection (HSI-CD) due to their excellent feature extraction capabilities. However, traditional convolution methods usually rely on a limited number of convolution kernels when extracting local features, and the local information only covers a small range of details, making it difficult to flexibly extract local changes at different levels in the image. In addition, CNNs have certain limitations in modeling global dependencies. In recent years, state-space model (SSM)-based methods (such as mamba) have effectively solved the quadratic complexity problem of transformer in capturing long-distance global dependencies. However, due to the processing method of expanding images into sequences, the spatial location structure of the image is destroyed, the spatial correlation between adjacent pixels is weakened, and spatial location information is easily lost, resulting in poor performance on complex hyperspectral datasets. To address these issues, we propose a wavelet-augmented dual-branch position-embedding mamba network (WDP-Mamba) for hyperspectral image change detection. This network combines the complementary advantages of mamba and wavelet convolution (WTconv) in global feature extraction and local edge feature extraction. Specifically, our method includes the following key innovations: 1) a novel state-space model, the adaptive position residual state space block (APRSSB) is designed, embedding learnable positional encodings within the mamba framework. Through a dual-temporal spatial-spectral scoring mechanism, the model dynamically preserves the topological relationships of pixel positions, enabling global dependency modeling with linear complexity; 2) a multi-level frequency-domain aware (MLFA) module is proposed to fully extract representative local features by decomposing information into frequency components of different scales and analyzing signals locally in multiple time and frequency windows. Extensive experiments on three hyperspectral datasets show that WDP-Mamba outperforms existing methods in both qualitative and quantitative assessments. Compared with other comparative models, WDP-Mamba achieves significant improvements in multiple performance metrics with a lower number of parameters, demonstrating its superior performance in the HSI-CD task.

This paper proposes a watermark insertion and detection scheme using Krawtchouk moments. These moments are selected for image watermarking because image reconstruction with these moments is better than other orthogonal moments like Legendre, Zernike and Tchebichef. The watermark signal is obtained by modifying some of the Krawtchouk moments of the original image. Further, the strength of the watermark is adjusted based on a threshold value (T) which is obtained by maximum root mean square error (rms value) between Krawtchouk moment invariants of the original image and Krawtchouk moment invariants of original image subjected to possible distortions like rotation, scale and translation. Krawtchouk moment invariants are used since they are invariant to image rotation, scale and translation. In order to detect the added watermark, we calculate the rms value between Krawtchouk moment invariants of test image and Krawtchouk moment invariants of the watermarked image. The watermark is detected if the above rms value is less than a threshold (T). It is noted from the simulation results that the proposed approach is robust with respect to geometric distortions like scale, translation and rotation

B-cell epitopes (BCEs) are a set of specific sites on the surface of an antigen that binds to an antibody produced by B-cell. The recognition of BCEs is a major challenge for drug design and vaccines development. Compared with experimental methods, computational approaches have strong potential for BCEs prediction at much lower cost. Moreover, most of the currently methods focus on using local information around target residue without taking the global information of the whole antigen sequence into consideration. We propose a novel deep leaning method through combing local features and global features for BCEs prediction. In our model, two parallel modules are built to extract local and global features from the antigen separately. For local features, we use Graph Convolutional Networks (GCNs) to capture information of spatial neighbors of a target residue. For global features, Attention-Based Bidirectional Long Short-Term Memory (Att-BLSTM) networks are applied to extract information from the whole antigen sequence. Then the local and global features are combined to predict BCEs. The experiments show that the proposed method achieves superior performance over the state-of-the-art BCEs prediction methods on benchmark datasets. Also, we compare the performance differences between data with or without global features. The experimental results show that global features play an important role in BCEs prediction. Our detailed case study on the BCEs prediction for SARS-Cov-2 receptor binding domain confirms that our method is effective for predicting and clustering true BCEs.

Analysis of noise sensitivity of Tchebichef and Zernike moments with application to image watermarking

Most of existing local-based person re-identification (re-ID) methods extract powerful feature representations from multiple body regions of a pedestrian. However, local-based person re-ID methods employ horizontal or grid slicing to divide pedestrian images, which can easily cause network redundancy and misalignment of pedestrian body parts. Attention mechanisms have been recently introduced to computer vision tasks to strengthen local salient features and suppress irrelevant features simultaneously. However, attention mechanisms overemphasize local salient features, which may lead to potential significant information being missed. To address the above issues, we proposed a local information interaction enhancement network (LIEN) for person re-ID. The network integrates attention mechanism with local-based strategy to achieve mutually facilitating and complementary effects. Specifically, LIEN was composed of three parts: a backbone network, a dual attention module (DAM), and a local information interaction module (LIM). DAM was used to guide the backbone network to extract the local salient information, and LIM was used to mine the potential significant information that DAM ignored by exploring the correlations between different channel-level local features. With the combination of DAM and LIM, LIEN mined the salient features while obtaining more potential effective features. In particular, the way that it extracts local features, LIEN effectively avoided pedestrian misalignment caused by uniformly dividing pedestrian images. We evaluated the performance of LIEN on public person datasets, and experimental results indicated that LIEN achieved a more advanced performance.

LocalFace: Learning significant local features for deep face recognition