Zernike moments features for shape-based gait recognition

Shape descriptors and mapping methods for full-field comparison of experimental to simulation data

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

Traffic obstacle identification has been widely studied as part of the broader obstacle detection area for Autonomous Vehicles (AV). Existing in-vehicle perceptual systems are concentrated on obstacle detection for pedestrian or vehicle, and limited work has been conducted on obstacle comprehensive identification. In general, the feature selection is particularly critical in the process of obstacle identification. As a set of features to describe a given shape or contour, shape descriptor have attracted much attention in recent years and play an important roles in target recognition. This paper proposed a shape descriptor based obstacle comprehensive identification method where the traffic obstacles be classified into four classes: vehicle, lateral moving pedestrian, longitudinal moving pedestrian, and unknown (such as trees, road lamp etc.). Here first a variety of shape descriptors be extracted from the contour curve, such as Hu moment, Zernike moments, Krawtchouk moments, Shape Context, and Axis of Least Inertia. Then, these shape descriptors be used to identification obstacles respectively. Finally, the identification results are contrastive analyzed. Though a single shape descriptor does not achieve ideal identification results for traffic obstacle, but this will provide a new idea for obstacle comprehensive identification using shape descriptor in video for AV.

A new gait recognition method based on Zernike moments and BP neural network is proposed. Zernike moments are calculated to extract gait features based on the introduced concept of normalized gait cycle. All gait Zernike moments compose the gait feature space. PCA algorithm is used to compress Zernike moments and a new lower dimension feature space containing gait spatio-temporal features is generated. Each normalized gait cycle's Zernike moments are mapped to this new feature space and compose an eigen-matrix, whose row square error vectors are used as the gait recognition eigenvectors. BP neural network is used to classify the gait features. To increase recognition accuracy, multiple training samples and multiple inputs are used for each to be recognized gait class. Experimental results show that the method can obtain accurate gait recognition in relatively simple scenes.

Selective Laser Melting (SLM) is a powder-bed additive manufacturing technique whose part quality depends critically on feedstock morphology. However, conventional powder characterization methods are low-throughput and qualitative, failing to capture the heterogeneity of industrial-scale batches. We present an automated, machine learning framework that couples high-throughput imaging with shape extraction and clustering to profile metallic powder morphology at scale. We evaluate three complementary approaches. The first uses an orientation-invariant variational autoencoder (VAE) to learn shape features that are insensitive to particle rotation, after which the resulting latent representations are grouped using clustering methods. The second applies classic shape descriptors – centroid distance functions, Fourier descriptors, and Zernike moments – followed by k-means or Gaussian mixture clustering to group particles with similar outlines. The third treats each particle’s radial profile as a continuous function and clusters these functional representations using the GPmix algorithm, allowing subtle differences in lobing and eccentricity to be captured. Across a dataset of approximately 126,000 powder images (0.5 – 102 μ m diameter), internal validity metrics identify the Fourier-descriptor + k-means pipeline as the most effective, achieving the lowest Davies–Bouldin index and highest Calinski–Harabasz score while maintaining sub-millisecond runtime per particle on a standard desktop workstation. The VAE provides orientation-invariant representations but at significantly higher computational cost, whereas the functional-data approach captures finer shape variations but requires a more complex configuration. Although the present work focuses on establishing the morphological-clustering framework, the resulting shape groups form a basis for future studies examining their relationship to flowability, packing density, and SLM part quality. Overall, this unsupervised learning framework enables rapid, automated assessment of powder morphology and supports tracking of shape evolution across reuse cycles, offering a path toward real-time feedstock monitoring in SLM workflows. • Automated machine learning framework for large-scale Selective Laser Melting (SLM) powder morphology profiling. • Evaluated three unsupervised pipelines: using variational autoencoder, shape descriptors, and functional clustering. • Fourier-descriptor + K-means achieved best accuracy, speed, and efficiency. • Framework distinguishes spherical, lobed, elongated, and defective particles. • Enables real-time monitoring of powder quality across reuse cycles in SLM.

Terahertz signal analysis and substance identification via Zernike moments

With the development of 3D printing technology and lots of available 3D model digital files in the form of stl, 3ds, obj, etc., on the internet, there are many illegally distributed 3D model digital files which are protected by copyright law, and some are already printed. In this paper, we propose a method that matching shape between printed 3D model and digital 3D model based on 2D views and Zernike Moments. The printed 3D model is first photographed into several pictures by a camera, the digital 3D model is also captured into several rendering images by a virtual camera using a computer program. Next, both sides of the images are converted into binary images with denoising image processing. Then, Zernike Moments features are extracted from the binary images. Finally, we can yield similarity between the printed one and digital one by comparing the Zernike Moments.

This paper proposes a method of facial expression recognition based on Zernike moments and the minimum classification error (MCE) based hidden Markov model (HMM). In the feature extraction of face, the method of Zernike moments feature extraction based on local feature regions is adopted. First, eyes and mouths are segmented from the facial expression image and Zernike moments feature vectors of eyes and mouth are extracted. In the classification of expression, using MCE criteria is adopted to improve the performance of HMM. The results show that the performance of expression recognition system can be improved to some extent by using Zernike moments and MCE criterion.

We present a way to construct a complete set of scaling rotation and translation invariants extract directly from Zernike moments. Zernike moment can be constructed by Radial moment. In our method in order to construct invariant Zernike moment is to achieve invariant Radial moment which is component of Zernike moment. We use matrix form to denote relationship between Radial and Zernike moment, which makes derivation more comprehensible. The translation invariant Radial moment is first introduced, for it is most complicated part of all the three invariant. Rotation and scaling invariant Radial moment is achieved by normalizing the factor caused by rotation and scaling. The form of invariant radial moment is to combine three parts of invariant. Some experiment has done to test the performance of invariance. In this experiment we take an image library containing 23,329 files which are built by translation rotation and zoom in out of one origin Latin character image. Most of the value of standard deviation ratio by mean of proposed moments is nearly 1%. In addition, retrieval experiment is to test the discrimination ability. MPEG-7 CE shape1 - Part A library is taken in this experiment. The recall rate in part A1 is 96.6% and is 100% in part A2.

Decomposing Strain Maps Using Fourier-Zernike Shape Descriptors

Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson’s coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99.89% for the Swedish Leaf dataset. In addition, the Relief feature selection method achieved the highest classification accuracy of 98.13% after 80 (or 60%) of the original features were reduced, from 133 to 53 descriptors in the myDAUN dataset with the reduction in computational time. Subsequently, the hybridisation of four descriptors gave the best results compared to others. It is proven that the combination MSD and HOG were good enough for tropical shrubs species classification. Hu and ZM descriptors also improved the accuracy in tropical shrubs species classification in terms of invariant to translation, rotation and scale. ANN outperformed the others for tropical shrub species classification in this study. Feature selection methods can be used in the classification of tropical shrub species, as the comparable results could be obtained with the reduced descriptors and reduced in computational time and cost.

Gait-based human identification is a new biometrics recognition technology arisen in recent years. Zernike moment applied in gait recognition and the analysis ability of wavelet moment in local made use of, a gait recognition method based on combined invariant moment is proposed by combining wavelet moment with Zernike moment as recognition features. In order to reduce the computation complexity because of increasing dimension of feature vector, an improved BP neural network is applied in recognition to ensure effectiveness of the classification and reduce computation complexity. The experimental results show that gait recognition based on combined invariant moment is superior to others based on single invariant moment in the recognition rate.

We present an on-line recognition method for hand-sketched symbols. The method is independent of stroke-order, -number, and -direction, as well as invariant to scaling, translation, rotation and reflection of symbols. Zernike moment descriptors are used to represent symbols and three different classification techniques are compared: support vector machines (SVM), minimum mean distance (MMD), and nearest neighbor (NN). We have obtained a 97% recognition accuracy rate on a dataset consisting of 7,410 sketched symbols using Zernike moment features and a SVM classifier.

Extraction of global face appearance and local interior differences is essential for any face recognition application. This paper presents a novel framework for face recognition by combining two effective descriptors namely, Zernike moments (ZM) and histogram of oriented gradients (HOG). ZMs are global descriptors that are invariant to image rotation, noise and scale. HOGs capture local details and are robust to illumination changes. Fusion of these two descriptors combines the merits of both local and global approaches and is effective against diverse variations present in face images. Further, as the processing time of HOG features is high owing to its large dimensionality, so, the study proposes to improve its performance by selecting only most discriminative HOG features (named discriminative HOG (DHOG)) for performing recognition. Efficacy of the proposed methods (DHOG, [Formula: see text] and [Formula: see text]) is tested on ORL, Yale and FERET databases. DHOG provides an improvement of 3% to 5% over the existing HOG approach. Recognition results achieved by [Formula: see text] and [Formula: see text] are up to 15% and 18% higher respectively than those obtained with these descriptors individually. Performance is also analyzed on LFW face database and compared with recent and state-of-the-art methods.

Plants are living organisms belonging to the vegetal kingdom that can live on land and in water. Plants form the critical base of food chains in nearly all ecosystems. Plants are vitally important for environmental protection and contribute to maintain biodiversity. Plant taxonomy has attracted many researchers to study the bio-diversities based on plants. Automated identification of plant species using leaf shape descriptor addresses the automatic classification of plants and simplifies taxonomic classification process. In this research work, we used Zernike moments (ZM) and Histogram of Oriented Gradient (HOG) method as a shape descriptor resulting 84.66 and 92.67 % accuracy for ZM and HOG, respectively, on ‘VISLeaf’ database.