Local Descriptor by Zernike Moments for Real-time Keypoint Matching

This paper presents a real-time keypoint matching algorithm using a local descriptor derived by Zernike moments. From an input image, we find a set of keypoints by using an existing corner detection algorithm. At each keypoint we extract a fixed size image patch and compute a local descriptor derived by Zernike moments. The proposed local descriptor is invariant to rotation and illumination changes. In order to speed up the computation of Zernike moments, we compute the Zernike basis functions in advance and store them in a set of lookup tables. The matching is performed with an Approximate Nearest Neighbor (ANN) method and refined by a RANSAC algorithm. In the experiments we confirmed that videos of frame size 320×240 with the scale, rotation, illumination and even 3D viewpoint changes are processed at 25~30Hz using the proposed method. Unlike existing keypoint matching algorithms, our approach also works in realtime for registering a reference image

An Evaluation of Popular Copy-Move Forgery Detection Approaches

A copy-move forgery is created by copying and pasting content within the same image, and potentially post-processing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.Comment: Main paper: 14 pages, supplemental material: 12 pages, main paper appeared in IEEE Transaction on Information Forensics and Securit

Maximum Energy Subsampling: A General Scheme For Multi-resolution Image Representation And Analysis

Image descriptors play an important role in image representation and analysis. Multi-resolution image descriptors can effectively characterize complex images and extract their hidden information. Wavelets descriptors have been widely used in multi-resolution image analysis. However, making the wavelets transform shift and rotation invariant produces redundancy and requires complex matching processes. As to other multi-resolution descriptors, they usually depend on other theories or information, such as filtering function, prior-domain knowledge, etc.; that not only increases the computation complexity, but also generates errors. We propose a novel multi-resolution scheme that is capable of transforming any kind of image descriptor into its multi-resolution structure with high computation accuracy and efficiency. Our multi-resolution scheme is based on sub-sampling an image into an odd-even image tree. Through applying image descriptors to the odd-even image tree, we get the relative multi-resolution image descriptors. Multi-resolution analysis is based on downsampling expansion with maximum energy extraction followed by upsampling reconstruction. Since the maximum energy usually retained in the lowest frequency coefficients; we do maximum energy extraction through keeping the lowest coefficients from each resolution level. Our multi-resolution scheme can analyze images recursively and effectively without introducing artifacts or changes to the original images, produce multi-resolution representations, obtain higher resolution images only using information from lower resolutions, compress data, filter noise, extract effective image features and be implemented in parallel processing

A novel algorithm for radar classification based on Doppler characteristics exploiting orthogonal pseudo-Zernike polynomials

Phase modulation induced by target micro-motions introduces side-bands in the radar spectral signature returns. Time-frequency distributions facilitate the representation of such modulations in a micro-Doppler signature that is useful in the characterization and classification of targets. Reliable micro-Doppler signature classification requires the use of robust features that is capable of uniquely describing the micro-motion. Moreover, future applications of micro-Doppler classification will require meaningful representation of the observed target by using a limited set of values. In this paper, the application of the pseudo-Zernike moments for micro-Doppler classification is introduced. Specifically, the proposed algorithm consists in the extraction of the pseudo-Zernike moments from the Cadence Velocity Diagram (CVD). The use of pseudo-Zernike moments allows invariant features to be obtained that are able to discriminate the content of two-dimensional matrices with a small number of coefficients. The analysis has been conducted both on simulated and on real radar data, demonstrating the effectiveness of the proposed approach for classification purposes

Application of a novel automatic method for determining the bilateral symmetry midline of the facial skeleton based on invariant moments

© 2020 by the authors. Assuming a symmetric pattern plays a fundamental role in the diagnosis and surgical treatment of facial asymmetry, for reconstructive craniofacial surgery, knowing the precise location of the facial midline is important since for most reconstructive procedures the intact side of the face serves as a template for the malformed side. However, the location of the midline is still a subjective procedure, despite its importance. This study aimed to automatically locate the bilateral symmetry midline of the facial skeleton based on an invariant moment technique using pseudo-Zernike moments. A total of 367 skull images were evaluated using the proposed technique. The technique was found to be reliable and provided good accuracy in the symmetry planes. This new technique will be utilized for subsequent studies to evaluate diverse craniofacial reconstruction techniques