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Moments play an important role in image analysis and invariant pattern recognition. There are two types: orthogonal moments and non-orthogonal moments. Orthogonal moments perform better than non-orthogonal moments, they have properties such as robustness to image noise and geometrical invariant properties such as scale, rotation and translation. In this paper, an improvement in fingerprint recognition is done by using the Non-subsampled contourlet transform (NSCT) and the Zernike moments (ZMs). NSCT decomposes the fingerprint images into NSCT subbands. Thereafter, ZMs are used to evaluate the features of fingerprint images. Thereafter, feature selection technique is applied to select potential features from the obtained features using coefficient of determination. Thereafter, a well-known weighted-support vector machine is also used to train and test the evaluated features. Extensive experiments reveal that the proposed technique achieves significant improvement over the existing techniques in terms of accuracy, sensitivity, specificity, $f$-measure, kappa statistics and execution time.

In the sign language fingerspelling scheme, letters in the alphabet are presented by a distinctive finger shape or movement. The presented work is conducted for autokinetic translating fingerspelling signs to text. A recognition framework by using intensity and depth information is proposed and compared with some distinguished works. Histogram of Oriented Gradients (HOG) and Zernike moments are used as discriminative features due to their simplicity and good performance. A Deep Belief Network (DBN) composed of three Restricted Boltzmann Machines (RBMs) is used as a classifier. Experiments are executed on a challenging database, which consists of 120,000 pictures representing 24 alphabet letters over five different users. The proposed approach obtained higher average accuracy, outperforming all other methods. This indicates the effectiveness and the abilities of the proposed framework.

This paper presents an approach to the registration of individual images to one another to produce a larger composite mosaic. The approach is based on the use of the moments of Zernike orthogonal polynomials to compute the relative scale, rotation and translation between the images. A preliminary stage involves the use of an attention-like operation to estimate potential approximate correspondence points between the images based on extrema of local edge element density. Experimental results illustrate that the technique is effective in a range of environments and over a broad range of image registration parameters. In particular, our method makes few assumptions regarding the image content and yet, unlike several alternative approaches, can perform registration for images with only a limited amount of overlap.

This paper presents a vision based technique to identify the unspoken phones using a small camera that is located on the headset of the speaker. The system is based on temporal integration of the video data to generate motion history image (MHI). The paper proposes the use of global features to classify the MHI and compares the use of image moments with Discrete Cosine Transform (DCT). A comparison between Zernike moments (ZM) with DCT indicates that while the accuracy of classification for both techniques is very comparable (96% for ZM and 94% for DCT) when there is no relative motion between the camera and the mouth, ZM is resilient to rotation of the camera and continues to gives good results despite rotation but DCT is sensitive to rotation.

Based on the accuracy of the system and its resilience to movement artefacts such as rotation, the authors propose the use of such a system for human computer interface. Such a system could be invaluable when it is important to communicate without making a sound, such as giving passwords when in an open office or in public spaces.

Texture based Geometric invariance, which comprises of rotation scale and translation (RST) invariant is finding application in various areas including industrial inspection, estimation of object range and orientation, shape analysis, satellite imaging, and medical diagnosis. Moments based techniques, apart from being computationally simple as compared to other RST invariant texture operators, are also robust in presence of noise. Zernike moments (ZM) based techniques are one of the well-established methods used for texture identification. As ZM are continuous moments, when discretization is done for implementation, errors are introduced. Error, calculated as difference between theoretically computed values and simulated values is proved to be prominent for fine textures. Therefore, a novel approach to detect RST invariant textures present in image is presented in this paper. This approach calculates discrete Chebyshev moments (CM) of log-polar transformed images to achieve rotation and scale invariance. The image is made translation invariant by shifting it to its centroid. The data is collected as samples from Brodatz and Vistex data sets. Zernike moments and its modifications, along with proposed scheme are applied to the same and Performance evaluation apart from RST invariance is noise sensitivity and redundancy. The performance is also compared with circular Mellin Feature extractors.

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, $\mathrm{\text{ZM}}+\mathrm{\text{HOG}}$ and $\mathrm{\text{ZM}}+\mathrm{\text{DHOG}}$) 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 $\mathrm{\text{ZM}}+\mathrm{\text{HOG}}$ and $\mathrm{\text{ZM}}+\mathrm{\text{DHOG}}$ 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.

Investigation of drug-induced structural changes in cell lines at different concentrations using microscopic images is essential to understand their cytotoxic effects. In this study, geometric shape descriptors to evaluate the toxicity effects of a particular drug in cell images are formulated. For this, fluorescence microscopic images of drug-untreated and drug-treated mouse cardiac muscle HL1 cells are considered. Ratiometric index of cellular to non-cellular area and, Zernike moment measures are calculated for three different thresholds at different drug concentrations namely 0.6, 1.2, 2.5, 5, and 10$\mu$M. Statistical analysis is performed to find the significant features. Classification is performed using Support Vector Machine (SVM) to differentiate drug untreated with treated cells at different concentrations. Results demonstrate that the proposed features are able to characterize the shape variations in cell images at different concentrations, and validates the efficacy of segmentation. Mean cellular area ratio is found to decrease from drug-untreated to drug-treated at various concentrations. Significant shape alterations in cellular structures are also obtained using Zernike moment measures for these cases. The machine learning approach using SVM provides better performance in classifying the drug untreated with progressively increasing drug concentrations. Hence, the proposed pipeline of methods could be clinically used to determine the maximum permissible drug tolerance levels during the development of new drugs.

Motion-blurred object tracking method integrating Histogram of Oriented Gradients and Hue Saturation (HOGHS) with Zernike moments features and based on ECO_HC tracker was proposed in this study to deal with object blur caused by the motion of the camera or the object itself. HOGHS was constructed by integrating fHOG with color features, and the properties of Zernike moments were introduced. The object was represented by combining HOGHS and Zernike moments. Furthermore, a novel quality evaluation method of response map considering both positioning accuracy and robustness was proposed and based on the method an adaptive fusion strategy utilizing the complementary properties of HOGHS and Zernike moments was implemented. Experiments were performed on the motion blur sequences from OTB-100 datasheet. Our method was compared with four other state-of-the-art methods. The precision as well as success rate were 0.849 and 0.827, respectively. Speed was 38.4 FPS. It is superior to VOT-2016’s excellent tracker ECO_HC, with relative gains of 2.3% for Pre-20 and 2.4% for AUC. The results show that the proposed method can effectively achieve the objective of motion-blurred object tracking.

This paper presents an approach to the registration of individual images to one another to produce a larger composite mosaic. The approach is based on the use of the moments of Zernike orthogonal polynomials to compute the relative scale, rotation and translation between the images. A preliminary stage involves the use of an attention-like operation to estimate potential approximate correspondence points between the images based on extrema of local edge element density. Experimental results illustrate that the technique is effective in a range of environments and over a broad range of image registration parameters. In particular, our method makes few assumptions regarding the image content and yet, unlike several alternative approaches, can perform registration for images with only a limited amount of overlap.