Local Zernike Research Articles

Binocular vision measurement method incorporating one-dimensional probabilistic Hough transform and local Zernike moment

Binocular vision measurement method incorporating one-dimensional probabilistic Hough transform and local Zernike moment

A multi features based background modeling approach for moving object detection

Background subtraction always remains an important and challenging task for different applications. Our previous work established the effectiveness of hybrid model by exploiting the oriented patterns present in a video sequences over other statistical method. To extend this approach further, we have proposed a novel approach herein by eliminating GLCM based features with an improved local Zernike moment and color components of intensity. These features are clubbed with the orientation based features extracted from angle co-occurrence matrices (ACMs) to model the background. Furthermore the Mahalanobis distance measure is replaced by Canberra distance to categorized foreground and background pixels, which significantly reduces the computational complexity of the proposed method due to the absence of covariance matrix measure. Comparative results have shown that our proposed method is effective than other competing method on different set of video sequences.

An unsupervised orthogonal rotation invariant moment based fuzzy C-means approach for the segmentation of brain magnetic resonance images

Brain magnetic resonance images (MRI) suffer from many artifacts such as noise and intensity inhomogeneity. Moreover, they contain an abundant amount of fine image structures, edges, and corners in various areas of the image. These anomalies and structural complexities affect the segmentation process of the brain MRI which is required by physicians for the diagnosis purpose. Recently, we have proposed a local Zernike moment (LZM)-based unbiased nonlocal means fuzzy C-means (LZM-UNLM-FCM) approach that has dealt with the noise artifact in the moment domain using the LZM approach. The method provides high segmentation results for the MR images corrupted with Rician noise. However, the method does not deal with the intensity inhomogeneity artifact effectively. Moreover, the method uses a regularization parameter that needs to be adjusted to obtain effective segmentation results. This paper presents an unsupervised local Zernike moment and unbiased nonlocal means-based bias corrected fuzzy C-means (LZM-UNLM-BCFCM) approach that deals with both noise and intensity inhomogeneity artifacts. The main concept behind the proposed method is to use the attractive properties of the LZMs to effectively filter the image by determining a large number of similar regions in an MR image which is mostly corrupted by Rician noise and intensity inhomogeneity. The ability of the LZMs to determine such regions in MR images consisting of fine tissue structures in any orientation is well utilized for dealing with the high levels of noise. The intensity inhomogeneity is removed by estimating the bias field pixel-by-pixel during the segmentation process using the filtered image without the use of regularization parameter. The bias field is estimated as a linear combination of the orthogonal polynomials in which the weights are obtained by minimizing the fuzzy objective function. Experimental results on both simulated and real MR images show the superiority of the proposed method as compared to other unsupervised state-of-the-art approaches.

An efficient framework for visible–infrared cross modality person re-identification

Visible–infrared cross-modality person re-identification (VI-ReId) is an essential task for video surveillance in poorly illuminated or dark environments. Despite many recent studies on person re-identification in the visible domain (ReId), there are few studies dealing specifically with VI-ReId. Besides challenges that are common for both ReId and VI-ReId such as pose/illumination variations, background clutter and occlusion, VI-ReId has additional challenges as color information is not available in infrared images. As a result, the performance of VI-ReId systems is typically lower than that of ReId systems. In this work, we propose a four-stream framework to improve VI-ReId performance. We train a separate deep convolutional neural network in each stream using different representations of input images. We expect that different and complementary features can be learned from each stream. In our framework, grayscale and infrared input images are used to train the ResNet in the first stream. In the second stream, RGB and three-channel infrared images (created by repeating the infrared channel) are used. In the remaining two streams, we use local pattern maps as input images. These maps are generated utilizing local Zernike moments transformation. Local pattern maps are obtained from grayscale and infrared images in the third stream and from RGB and three-channel infrared images in the last stream. We improve the performance of the proposed framework by employing a re-ranking algorithm for post-processing. Our results indicate that the proposed framework outperforms current state-of-the-art with a large margin by improving Rank-1/mAP by 29.79%∕30.91% on SYSU-MM01 dataset, and by 9.73%∕16.36% on RegDB dataset.

Image alignment based perceptual image hash for content authentication

Perceptual image hash is an emerging technology that is closely related to many applications such as image content authentication, image forging detection, image similarity detection, and image retrieval. In this work, we propose an image alignment based perceptual image hash method, and a hash-based image forging detection and tampering localization method. In the proposed method, we introduce an image alignment process to provide a framework for image hash method to tolerate a wide range of geometric distortions. The image hash is generated by utilizing hybrid perceptual features that are extracted from global and local Zernike moments combining with DCT-based statistical features of the image. The proposed method can detect various image forging and compromised image regions. Furthermore, it has broad-spectrum robustness, including tolerating content-preserving manipulations and geometric distortion-resilient. Compared with state-of-the-art schemes, the proposed method provides satisfactory comprehensive performances in content-based image forging detection and tampering localization.

Alone versus In-a-group

Recognition and analysis of human affect has been researched extensively within the field of computer science in the past two decades. However, most of the past research in automatic analysis of human affect has focused on the recognition of affect displayed by people in individual settings and little attention has been paid to the analysis of the affect expressed in group settings. In this article, we first analyze the affect expressed by each individual in terms of arousal and valence dimensions in both individual and group videos and then propose methods to recognize the contextual information, i.e., whether a person is alone or in-a-group by analyzing their face and body behavioral cues. For affect analysis, we first devise affect recognition models separately in individual and group videos and then introduce a cross-condition affect recognition model that is trained by combining the two different types of data. We conduct a set of experiments on two datasets that contain both individual and group videos. Our experiments show that (1) the proposed Volume Quantized Local Zernike Moments Fisher Vector outperforms other unimodal features in affect analysis; (2) the temporal learning model, Long-Short Term Memory Networks, works better than the static learning model, Support Vector Machine; (3) decision fusion helps to improve affect recognition, indicating that body behaviors carry emotional information that is complementary rather than redundant to the emotion content in facial behaviors; and (4) it is possible to predict the context, i.e., whether a person is alone or in-a-group, using their non-verbal behavioral cues.

Human action recognition using bag of global and local Zernike moment features

Human action recognition is a fundamental and challenging building block for many computer vision applications. It has been included in many applications such as: video surveillance, human computer interaction and multimedia retrieval systems. Various approaches have been proposed to solve human action recognition problem. Among others, moment-based methods considered as one of the most simple and successful approach. However, moment-based methods take into consideration only global features while neglect the discriminative properties of local features. In this paper, we propose a new efficient method which combine both Global and Local Zernike Moment (GLZM) features based on Bag-of-Features (BoF) technique. Since using only global features are not sufficient to discriminate similar actions like running, walking and jogging, augmenting these features with localized features helps to improve the recognition accuracy. The proposed method first calculate local temporal Motion Energy Images (MEI) by accumulating frame differences of short time consecutive frames. Then, global and local features are calculated using Zernike moments with different polynomial orders to represent global and local motion patterns respectively. Global features are calculated from the whole region of the human performing action while local features focused on localized regions of the human in order to represent local motion information. Both local and global features are preprocessed using whitening transformation, then bag-of-features algorithm is employed to combine those pool of features and represent each action using new GLZM feature descriptor. Finally, we use multi-class Support Vector Machine (SVM) classifier to recognize human actions. In order to validate the proposed method, we perform a set of experiments using three publicly available datasets: Weizmann, KTH and UCF sports action. Experimental results using leave-one-out strategy show that proposed method achieves promising results compared with other state-of-the-art methods.

Local Binary Pattern and Zernike Moments Based Face Recognition

Local Binary Pattern and Zernike Moments Based Face Recognition

A local Zernike moment-based unbiased nonlocal means fuzzy C-Means algorithm for segmentation of brain magnetic resonance images

Clustering based unsupervised segmentation approaches have become important tools to model data and infer knowledge. Such approaches are often used in various biomedical applications to assist experts in analyzing the data. Segmentation of brain magnetic resonance (MR) images has become a very challenging task due to the presence of Rician noise and intensity inhomogeneity. The existing methods dealing with such issues work in the spatial domain. These methods are prone to losing image details while reducing the effect of noise. The proposed method works in the moment domain, and for this purpose, we have selected Zernike moments (ZMs). The ZMs are orthogonal, rotation invariant and robust to image noise. The proposed method is based on our previous work on Zernike moments-based unbiased nonlocal means (ZM-UNLM) approach for denoising MR images affected by the Rician noise. The ZM-UNLM-based approach uses both the local and nonlocal information while eliminating Rician noise from brain MR images which have fine image details along with the noise components. Keeping in view the superior performance of the ZM-UNLM, we extend it to segment brain MR images and propose a method called local ZMs (LZM)-based unbiased nonlocal means fuzzy C-means (LZM-UNLM-FCM) approach which provides much-improved segmentation accuracy as compared to the existing spatial domain-based state-of-the-art techniques. It is shown how the LZMs based technique reduces the effect of the Rician noise while retaining the fine structures, edges, and other image details in the moment domain. The complete framework consists of an intelligent knowledge-driven approach that segments tissues from brain MR images using both the local and nonlocal information in the moment domain. The proposed approach has significance in developing future expert and intelligent systems that do not require any human intervention to manually annotate data and saves time for diagnostic related tasks. Detailed experimental results are provided to demonstrate the superior performance of the proposed method over the existing state-of-the-art methods.

An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition

In this study, we propose a face recognition scheme using local Zernike moments (LZM), which can be used for both identification and verification. In this scheme, local patches around the landmarks are extracted from the complex components obtained by LZM transformation. Then, phase magnitude histograms are constructed within these patches to create descriptors for face images. An image pyramid is utilized to extract features at multiple scales, and the descriptors are constructed for each image in this pyramid. We used three different public datasets to examine the performance of the proposed method:Face Recognition Technology (FERET), Labeled Faces in the Wild (LFW), and Surveillance Cameras Face (SCface). The results revealed that the proposed method is robust against variations such as illumination, facial expression, and pose. Aside from this, it can be used for low-resolution face images acquired in uncontrolled environments or in the infrared spectrum. Experimental results show that our method outperforms state-of-the-art methods on FERET and SCface datasets.

Patterns of approximated localised moments for visual loop closure detection

In the context of autonomous mobile robot navigation, loop closing is defined as the correct identification of a previously visited location. Loop closing is essential for the accurate self‐localisation of a robot; however, it is also challenging due to perceptual aliasing, which occurs when the robot traverses in environments with visually similar places (e.g. forests, parks, office corridors). In this study, the authors apply the local Zernike moments (ZMs) for loop closure detection. When computed locally, ZMs provide a high discrimination ability, which enables the distinguishing of similar‐looking places. Particularly, they show that increasing the density over which the local ZMs are computed improves loop closing accuracy significantly. Furthermore, they present an approximation of ZMs that allows the usage of integral images, which enable real‐time operation. Experiments on real datasets with strong perceptual aliasing show that the proposed ZM‐based descriptor outperforms state‐of‐the‐art methods in terms of loop closure accuracy. They also release the source‐code of the implementation for research purposes.

A dynamic framework based on local Zernike moment and motion history image for facial expression recognition

A dynamic descriptor facilitates robust recognition of facial expressions in video sequences. The current two main approaches to the recognition are basic emotion recognition and recognition based on facial action coding system (FACS) action units. In this paper we focus on basic emotion recognition and propose a spatio-temporal feature based on local Zernike moment in the spatial domain using motion change frequency. We also design a dynamic feature comprising motion history image and entropy. To recognise a facial expression, a weighting strategy based on the latter feature and sub-division of the image frame is applied to the former to enhance the dynamic information of facial expression, and followed by the application of the classical support vector machine. Experiments on the CK+ and MMI datasets using leave-one-out cross validation scheme demonstrate that the integrated framework achieves a better performance than using individual descriptor separately. Compared with six state-of-arts methods, the proposed framework demonstrates a superior performance.

Automated diagnosis of Lungs Tumor Using Segmentation Techniques

The Objective is to detect the cancerous lung nodules from 3D CT chest image and classify the lung disease and its severity. Although so many researches has been done in this stream, the problem still remains a challenging one. To extract the lung region FCM segmentation is used. Here we used six feature extraction techniques such as bag of visual words based on the histogram oriented gradients, the wavelet transform based features, the local binary pattern, SIFT and Zernike moment . The Particle swarm optimization algorithm is used to select the best features.

Robust image watermarking technique using triangular regions and Zernike moments for quantization based embedding

Watermarking is a tool to embed information in the image to provide authentication, copyrights protection, copy control, etc. Some watermarking techniques are robust to intentional /unintentional attacks on the watermarked image. In this study, we propose a robust watermarking approach that can resist geometrical attacks. The proposed technique exploits both the robust image feature points and local Zernike moments for embedding the information. Delaunay tessellation is employed to divide image into distinct triangular segments based on robust features. These features are identified using Harris detector. Zernike moments are calculated for each selected triangular segment, and then the watermark is embedded in the magnitude of Zernike moments using dither modulation. It can be observed from the experimental results that by using proposed approach, the watermark can be detected even in the presence of geometrical distortion, i.e. rotation, cropping, and scaling, and JPEG compression attack.

Feature extraction and local Zernike moments based geometric invariant watermarking

A robust and geometric invariant digital image watermarking scheme based on robust feature detector and local Zernike transform is proposed in this paper. The robust feature extraction method is proposed based on the Scale Invariant Feature Transform (SIFT) algorithm, to extract circular regions/patches for watermarking use. Then a local Zernike moments-based watermarking scheme is raised, where the watermarked regions/patches can be obtained directly by inverse Zernike Transform. Each extracted circular patch is decomposed into a collection of binary patches and Zernike transform is applied to the appointed binary patches. Magnitudes of the local Zernike moments are calculated and modified to embed the watermarks. Experimental results show that the proposed watermarking scheme is very robust against geometric distortion such as rotation, scaling, cropping, and affine transformation; and common signal processing such as JPEG compression, median filtering, and low-pass Gaussian filtering.

Geometric invariant watermarking by local Zernike moments of binary image patches

A novel digital image watermarking scheme based on feature extraction and local Zernike transform is proposed in this paper. We proposed a local Zernike moments based watermarking scheme where the watermarked image/region can be obtained directly by inverse Zernike Transform. An edge-based feature detector is proposed for local region extraction, with which, the distinct circular patch of given size can be extracted for watermark embedding and extraction. The extracted circular patch is decomposed into a collection of binary patches and Zernike transform is applied to the selected binary patches. Magnitudes of the local Zernike moments are calculated and modified to embed the watermarks. Experimental results show that the proposed watermarking scheme is very robust against geometric distortion such as rotation, scaling, cropping, and affine transformation; and common signal processing such as JPEG compression, median filtering, and low-pass Gaussian filtering.

Robust image watermarking using local Zernike moments

In this work, we propose a robust image watermarking algorithm using local Zernike moments, which are computed over circular patches around feature points. The proposed algorithm locally computes Zernike moments and modifies them to embed watermarks, achieving robustness against cropping and local geometric attacks. Moreover, to deal with scaling attacks, the proposed algorithm extracts salient region parameters, which consist of an invariant centroid and a salient scale, and transmits them to the decoder. The parameters are used at the decoder to normalize a suspect image and detect watermarks. Extensive simulation results show that the proposed algorithm detects watermarks with low error rates, even if watermarked images are distorted by various geometric attacks as well as signal processing attacks.

Robust optical flow estimation using semi-invariant local features

This paper presents a robust algorithm for computation of 2-D optical flow using the principle of conservation of a set of invariant local features that are representatives of local gray-level properties in an image. Specifically, a set of rotation-invariant local orthogonal Zernike moments is used as invariant features. These are inherently integral-based features, and therefore are expected to be robust against possible variations of intensity values that may occur over a sequence of images due to sensor noise, varying illumination etc. The 2-D local optical flow field is obtained using singular value decomposition of an overdetermined set of linear equations of velocity field components, resulting from the principle of conservation of invariant features in a small neighborhood. The proposed moment-based approach is compared with two existing optical flow techniques. Experimental results with synthetic as well as real sequences are presented to demonstrate the overall robustness of the proposed approach.