Colour Object recognition combining Motion Descriptors, Zernike Moments and Support Vector Machine

Indian sign language (ISL) is used by deaf people in India for their internal communication. Sign language recognition (SLR) system recognizes gestures of sign language and converts them into text and voice, thus enabling deaf people to communicate with others. In this paper, we have presented an automatic system for ISL hand gesture recognition. Modern features like Histogram of Oriented Gradient (HOG), Speeded Up Robust Features (SURF), Fourier Descriptors, Hu Moments, and Zernike Moments have been extracted from training images of our own created dataset. Using these features, most commonly used classifiers like Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) have been trained and evaluated. Comparative performance analysis of these classifiers along with experimental results is the main topic of discussion included in this paper. Moreover, a user-friendly GUI of the system allows user to select any combination of features and classifiers to perform gesture recognition from both stored file and live camera. During experiment, it is found that the system is able to recognize gestures with a recognition rate up to 98.70%.

Face detection is an important part of any face recognition system and it is a difficult task to detect faces of arbitrary size, orientation and location. Fusion is a technique which combines the results from two or more distinct methods in such a way that better results are obtained. We propose two different fusion techniques, data fusion and sensor fusion, that combine statistical and shape based methods. Four different features, eigenweights from Principal Component Analysis (PCA), Legendre Moments (LM), Zernike Moments (ZM) and Generic Fourier Descriptor (GFD), are used for our fusion methods. In data fusion technique, the calculated eigenweights, moments, and descriptors are concatenated together to form a feature vector. This combined feature is then classified as face or non-face pattern using Support Vector Machine (SVM). In the case of sensor fusion technique, each of the feature vectors obtained by individual methods are first classified using individual SVMs. Then the class label obtained from the individual SVMs are concatenated together to form a feature vector. This feature vector is then classified using another SVM. Both the techniques produced better results compared with the individual methods. For a database containing 300 face images and 200 non-face images, both of our methods achieved 0% false rejection rate. 99.75% accuracy was achieved for data fusion method, while sensor fusion method produced 99.9% accuracy.

This paper introduces an efficient method for face localization and recognition in color images. The proposed method uses the location of eyes for computation and extraction of a face's bounding ellipse. In this way, parameters of a face's ellipse (center, orientation, major and minor axis), is computed by the location of eyes in a face image. In the next step, we apply Pseudo Zernike Moments (PZM), Zernike Moments (ZM) and Principal Component Analysis (PCA) for feature extraction. For classification of these feature vectors a new structure of RBF neural networks with a novel distance function is introduced and a new method for determination of RBF unit parameters is proposed. Finally, we compare the efficiency of the proposed system for three types of feature vectors (PZM, ZM and PCA). Results emphasize the high accuracy and efficiency of the PZM features proportion to other features (ZM and PCA) for use in the proposed recognition system.

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.

In this paper, we propose an approach for recognizing ground traffic signs based on Zernike moments and support vector machine. Traditional traffic signs recognition was focus on the signs beside road, but there are relatively few researches on the ground traffic signs recognition. Ground traffic signs are important indication signs to indicate the carriageway. During the process of vehicle running, drivers need to judge which carriageway they are in, and select the right carriageway according to the itinerary planning. In order to recognize different traffic signs, we propose that using Zernike moments as the features of traffic signs, and SVM is used to classify the features. Real road scene images are used to evaluate the performance, and five classes of traffic signs are considered. Simulations show that our approach can recognize ground traffic signs effectively and practically.

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.

: Problem Statement: Pecan weevil is one of the most destructive pests of Oklahoma. The scope of this study is to develop a recognition system that can serve in a wireless imaging network for monitoring pecan weevils. Approach: The recognition methods used in this study are based on template matching. Five recognition methods were implemented: Normalized cross-correlation, Fourier descriptors, Zernike moments, String matching and Regional properties. The training set consisted of 205 pecan weevils and the testing set included 30 randomly selected pecan weevils and 74 other insects which typically exist in pecan habitat. Results: It was found that Region-based methods were better in representing and recognizing biological objects such as insects. Different recognition rates were obtained at different order of Zernike moments. The optimum result among the tested orders of Zernike moments was found to be at the order 3. The results also showed that using different number of Fourier descriptors may not significantly increase the recognition rate of this method. Conclusion: The most robust and reliable recognition rate was achieved when the Zernike moments and Region properties recognition methods were used in a combination. A positive match from either of these two independent tests would yield reliable results. Therefore, 100% recognition could be achieved by adopting the proposed algorithm. The processing time for such recognition is 0.44 sec.

Brain tumor diagnosis based on Zernike moments and support vector machine optimized by chaotic arithmetic optimization algorithm

Explicit invariance of Cartesian Zernike moments

The performance of a visual speech recognition technique is greatly influenced by the choice of visual speech features. Speech information in the visual domain can be generally categorized into static (mouth appearance) and motion (mouth movement) features. This chapter reviews a number of computer-based lip-reading approaches using motion features. The motion-based visual speech recognition techniques can be broadly categorized into two types of algorithms: optical-flow and image subtraction. Image subtraction techniques have been demonstrated to outperform optical-flow based methods in lip-reading. The problem with image subtraction-based method using difference of frames (DOF) is that these features capture the changes in the images over time, but do not indicate the direction of the mouth movement. New motion features to overcome the limitation of the conventional image subtraction-based techniques in visual speech recognition are presented in this chapter. The proposed approach extracts features by applying motion segmentation on image sequences. Video data are represented in a 2-D space using grayscale images named as motion history images (MHI). MHIs are spatio-temporal templates that implicitly encode the temporal component of mouth movement. Zernike moments are computed from MHIs as image descriptors and classified using support vector machines (SVMs). Experimental results demonstrate that the proposed technique yield a high accuracy in a phoneme classification task. The results suggest that dynamic information is important for visual speech recognition.Request access from your librarian to read this chapter's full text.

Decomposing Strain Maps Using Fourier-Zernike Shape Descriptors

Image analysis with logarithmic Zernike moments

A simple and cost effective new tactile sensor is presented, based on a camera capturing images of the shading of a deformable rubber membrane. In Computer Vision, the issue of information encoding and classification is well studied. In this paper we explore different ways of encoding tactile images, including: Hu moments, Zernike Moments, Principal Component Analysis (PCA), Zernike PCA, and vectorized scaling. These encodings are tested by performing tactile shape recognition using a number of supervised approaches (Nearest Neighbor, Artificial Neural Networks, Support Vector Machines, Naive Bayes). In conclusion: the most effective way of representing tactile information is achieved by combining Zernike Moments and PCA, and the most accurate classifier is Nearest Neighbor, with which the system achieves a high degree (96.4%) of accuracy at recognising seven basic shapes.KeywordsHaptic recognitiontactile featurestactile sensorssupervised learning

Terahertz signal analysis and substance identification via Zernike moments

Content Based Image Retrieval (CBIR) is the huge field for image retrieval from the wide databases. It is one of the popular techniques from computer vision domain. CBIR consist of feature extraction followed by similarity comparison. Shape feature extraction can be made on two descriptors one is region based and other is contour based. This paper implements shape feature extraction with region based descriptors. Zernike moments and Hu’s seven moments have been used as a feature extraction techniques and Support Vector Machine (SVM) is used as a classifier. Different distance metrics are then used for similarity comparison with these feature extraction methods for efficient results. For performance evaluation distance metrics used are Euclidean, Chebyshev, Cityblock, Canberra, Standardized Euclidean (Seuclidean). Medical database with 6 classes consist of 100 images each namely head, hip, shoulder, pelvis, knee, ankle is used. After obtaining all the experimental results in terms of precision and recall, a comparative study is made for selected database.