Invariant Color Research Articles

Geometrically Invariant Color Medical Image Null-Watermarking Based on Precise Quaternion Polar Harmonic Fourier Moments

The traditional embedded watermarking scheme achieves copyright protection of images by embedding watermarks into the original images. This process will deteriorate the original image, so it is not suitable for medical images. In order to overcome this limitation, this paper proposes a geometrically invariant color medical image null-watermarking scheme based on quaternion polar harmonic Fourier moments (QPHFM), which achieves the copyright protection without changing the original medical image. First, a high-precision QPHFM computation method based on wavelet numerical integration is designed, which can effectively improve the computation precision. Then based on the precise QPHFM (PQPHFM) and chaotic map, a robust null-watermarking scheme for color medical images is proposed to protect the copyright of color medical images. This scheme uses the PQPHFM to construct feature images and its good stability and geometric invariance make the proposed null-watermarking scheme has good robustness and resistance against geometric attacks. Extensive simulation experiments show that the proposed scheme is highly robust in resisting common attacks and geometric attacks such as scaling, rotation, change of length-width ratio, and flipping. The performance of the proposed scheme is superior to that of similar robust null-watermarking schemes and other color medical image watermarking schemes.

Image Segmentation Parameter Selection and Ant Colony Optimization for Date Palm Tree Detection and Mapping from Very-High-Spatial-Resolution Aerial Imagery

Accurate mapping of date palm trees is essential for their sustainable management, yield estimation, and environmental studies. In this study, we integrated geographic object-based image analysis, class-specific accuracy measures, fractional factorial design, metaheuristic feature-selection technique, and rule-based classification to detect and map date palm trees from very-high-spatial-resolution (VHSR) aerial images of two study areas. First, multiresolution segmentation was optimized through the synergy of the F1-score accuracy measure and the robust Taguchi design. Second, ant colony optimization (ACO) was adopted to select the most significant features. Out of 31 features, only 12 significant color invariants and textural features were selected. Third, based on the selected features, the rule-based classification with the aid of a decision tree algorithm was applied to extract date palm trees. The proposed methodology was developed on a subset of the first study area, and ultimately applied to the second study area to investigate its efficiency and transferability. To evaluate the proposed classification scheme, various supervised object-based algorithms, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (k-NN), were applied to the first study area. The result of image segmentation optimization demonstrated that segmentation optimization based on an integrated F1-score class-specific accuracy measure and Taguchi statistical design showed improvement compared with objective function, along with the Taguchi design. Moreover, the result of the feature selection by ACO outperformed, with almost 88% overall accuracy, several feature-selection techniques, such as chi-square, correlation-based feature selection, gain ratio, information gain, support vector machine, and principal component analysis. The integrated framework for palm tree detection outperformed RF, SVM, and k-NN classification algorithms with an overall accuracy of 91.88% and 87.03%, date palm class-specific accuracies of 0.91 and 0.89, and kappa coefficients of 0.90 and 0.85 for the first and second study areas, respectively. The proposed integrated methodology demonstrated a highly efficient and promising tool to detect and map date palm trees from VHSR aerial images.

Extended scale invariant local binary pattern for background subtraction

Background subtraction based on change detection is the first step in many video surveillance systems, an effective background subtraction algorithm should distinguish foreground from the background sensitively, and adapt to the variation of background scenes robustly. In this study, the authors propose a robust background subtraction algorithm which takes advantages of local texture features represented by an extended scale invariant local binary pattern and colour intensities to characterise pixel representations. Local texture features achieve good tolerance against illumination variations in rich texture regions but not so efficiently on uniform regions, so a photometric invariant colour measurement is proposed to overcome its limitation. Both quantitative and qualitative evaluations carried out on a well-known change detection dataset are provided to demonstrate the effectiveness of the proposed algorithm.

Invariant color images representation using accurate quaternion Legendre–Fourier moments

Color image representation using quaternion moments has gained more interest during the last few years. In this paper, invariant color image representation using quaternion Legendre–Fourier moments (QLFMs) is presented. A new method for exact, fast and stable computation of QLFMs in polar coordinates is proposed. Explicit rotation, scaling and translation (RST) invariants of quaternion Legendre–Fourier are derived. Numerical experiments are performed to compare the performance of QLFMs with the existing quaternion moments. The comparison clearly shows the superiority of the proposed method over all existing quaternion moments in terms of image reconstruction capability, RST invariances and numerical stability.

Copy-move forgery detection based on compact color content descriptor and Delaunay triangle matching

Copy-move (region duplication) is one of the most common types of image forgeries, in which at least one part of an image is copied and pasted onto another area of the same image. The main aims of the copy-move forgery are to overemphasize a concept or conceal objects by duplicating some regions. Keypoint based copy-move forgery detection (CMFD) method extracts image feature points and employs local image features to identify duplicated regions, which exhibits remarkable detection performance with respect to memory requirement, computational cost, and robustness. However, they usually do not work well when the objects are hidden in smooth background areas. Also, the detection and localization accuracy always be lowered because of poor local image feature computation. In this paper, we present a novel approach for the detection and localization of copy-move forgeries, which is based on color invariance SIFER (Scale-invariant feature detector with error resilience) and FQRHFMs (Fast quaternion radial harmonic Fourier moments). Firstly, the original forgery image is segmented into nonoverlapping and nearly uniform superpixel blocks, and the stable keypoints are extracted adaptively from each superpixel block by incorporating the superpixel contents and color invariance SIFER. Secondly, a set of connected Delaunay triangles is constructed using the extracted image keypoints, and suitable local image feature for each Delaunay triangle is computed by using FQRHFMs and gradient entropy. Thirdly, the local image features and coherency sensitive hashing (CSH) are utilized to match quickly the Delaunay triangles. Finally, the falsely matched Delaunay triangles are removed by employing dense linear fitting (DLF), and the duplicated regions are localized using optimized zero mean normalized cross-correlation (ZNCC) measure. We conduct extensive experiments to evaluate the performance of the proposed copy-move forgery detection scheme, in which encouraging results validate the effectiveness of the proposed technique.

Improved image retrieval and classification with combined invariant features and color descriptor

As the quantity of digital images grows in many applications in our daily life, users experience an increased difficulty in finding relevant images within their image collections and common image repositories. This paper proposes a novel image search scheme that extracts the features of an image using a combined invariant features and color description to retrieve specific images using query-by-example. The proposed method can be executed in real-time on an iPhone, and can be easily used to identify a natural color image with its invariant visual features. The proposed scheme is evaluated by assessing the performance of a simulation in terms of the average precision and F-score in image databases that are commonly used for image retrieval. The experimental results reveal that the proposed algorithm offers a significant improvement of more than 7.35 and 18.09% in retrieval effectiveness when compared to open source OpenSURF and MPEG-7 color and texture descriptor, respectively. The main contribution of this paper is that the proposed approach achieves a high accuracy and stability by using a combination of the improved SURF and color descriptor when searching for a natural image.

A new keypoint-based copy-move forgery detection for color image

Over the past decade, many efforts have been made in copy-move forgery detection (CMFD), and some promising methodologies have been proposed to detect copy-move forgeries. Keypoint based CMFD approaches extract image interest points and use local visual features to identify duplicated regions, which exhibit remarkable performance with respect to memory requirement and computational cost. But unfortunately, they usually use the pure gray-based detectors to extract interest points in which the significant color information is ignored. Also, local visual features are computed without considering the correlation between different color channels. All this lower inevitably the detection and localization accuracy for color tampered image. In this paper we propose a new technique for the detection and localization of copy-move forgeries, which is based on color invariance model and quaternion polar complex exponential transform (QPCET). First, stable color image interest points are extracted by using new interest point detector, in which the SURF (speeded up robust features) detector and color invariance model are incorporated. Then, a set of connected Delaunay triangles is built based on the extracted color image interest points, and suitable local visual features of the triangle mesh are computed using QPCET. Afterwards, local visual features are employed to match triangular meshes by a combination of reversed-generalized 2 nearest-neighbor (Rg2NN) and best bin first (BBF). Finally, the falsely matched triangular meshes are removed by customizing the random sample consensus, and the duplicated regions are localized using zero mean normalized cross-correlation measure. Compared with the state-of-the-art approaches, extensive experimental results prove that our proposed method can detect and localize color image copy-moves with good accuracy even in adverse conditions.

Robust color image watermarking using invariant quaternion Legendre-Fourier moments

In this paper, a geometrically invariant color image watermarking method using Quaternion Legendre-Fourier moments (QLFMs) is presented. A highly accurate, fast and numerically stable method is proposed to compute the QLFMs in polar coordinates. The proposed watermarking method consists of three main steps. First, the Arnold scrambling algorithm is applied to a binary watermark image. Second, the QLFMs of the original host color image are computed. Third, the binary digital watermark is embedding by performing the quantization of selected QLFMs. Two different groups of attacks are considered. The first group includes geometric attacks such as rotation, scaling and translation while the second group includes the common signal processing attacks such as image compression and noise. Experiments are performed where the performance of proposed method is compared with the existing moment-based watermarking methods. The proposed method is superior over all existing quaternion moment-based watermarking in terms of visual imperceptibility capability and robustness to different attacks.

融合彩色不变量和SURB检测的特征点匹配算法

融合彩色不变量和SURB检测的特征点匹配算法

Invariant color features–based foreground segmentation for human‐computer interaction

Foreground segmentation is a critical early step in most human‐computer interaction applications notably in action and gesture recognition domain. In this paper, an approach to model background which based on luminance‐invariant color with an adaptive Gaussian mixture is proposed to discriminate foreground object from their background in complex scene. Firstly, the background model is learned based on the spectral properties of shadows and scene activity. Secondly, the shadow with the hypotheses on color invariance is adaptively set up and updated. Finally, the log‐likelihood measurement is to conduct the adaptation. Our experiments are performed on a wide range of practical applications of gesture and action recognition videos. Additionally, the proposed approach is efficient and more robust than premature state‐of‐the‐art with no sacrificing real‐time performance.

Robust video copy detection based on ring decomposition based binarized statistical image features and invariant color descriptor (RBSIF-ICD)

Content based video copy detection (CBVCD) is considered as an active research field due to the requirement of copyright protection, business intelligence, video retrieval, etc. In this paper, we propose a CBVCD scheme using Ring decomposition based Binarized Statistical Image Features (RBSIF) and Invariant Color Descriptor (ICD), namely RBSIF-ICD. This hybrid descriptor is based on the combination of texture features extracted using RBSIF and a color characteristics derived using ICD. Firstly, a pre-processing is applied on each video sequence to remove borders. Secondly, the RBSIF descriptor is applied to each video frame to obtain the texture features. Then, ICD is applied to the video frames to construct an invariant color description which is very robust to geometrical attacks. Finally, RBSIF and ICD are fused to develop the RBSIF-ICD that will be very robust against several attacks such as rotation, flipping, strong compression, ... etc. For the evaluation purpose, the TRECVID 2009 database is used to test the robustness of the proposed system. Experiments reveal that the proposed RBSIF-ICD system outperforms the state-of-the-art algorithms under all the attacks and manipulations considered in this framework.

A Data Set for Camera-Independent Color Constancy.

In this paper, we provide a novel data set designed for Camera-independent color constancy research. Camera independence corresponds to the robustness of an algorithm's performance when it runs on images of the same scene taken by different cameras. Accordingly, the images in our database correspond to several laboratory and field scenes each of which is captured by three different cameras with minimal registration errors. The laboratory scenes are also captured under five different illuminations. The spectral responses of cameras and the spectral power distributions of the laboratory light sources are also provided, as they may prove beneficial for training future algorithms to achieve color constancy. For a fair evaluation of future methods, we provide guidelines for supervised methods with indicated training, validation, and testing partitions. Accordingly, we evaluate two recently proposed convolutional neural network-based color constancy algorithms as baselines for future research. As a side contribution, this data set also includes images taken by a mobile camera with color shading corrected and uncorrected results. This allows research on the effect of color shading as well.

Scene text detection via extremal region based double threshold convolutional network classification.

In this paper, we present a robust text detection approach in natural images which is based on region proposal mechanism. A powerful low-level detector named saliency enhanced-MSER extended from the widely-used MSER is proposed by incorporating saliency detection methods, which ensures a high recall rate. Given a natural image, character candidates are extracted from three channels in a perception-based illumination invariant color space by saliency-enhanced MSER algorithm. A discriminative convolutional neural network (CNN) is jointly trained with multi-level information including pixel-level and character-level information as character candidate classifier. Each image patch is classified as strong text, weak text and non-text by double threshold filtering instead of conventional one-step classification, leveraging confident scores obtained via CNN. To further prune non-text regions, we develop a recursive neighborhood search algorithm to track credible texts from weak text set. Finally, characters are grouped into text lines using heuristic features such as spatial location, size, color, and stroke width. We compare our approach with several state-of-the-art methods, and experiments show that our method achieves competitive performance on public datasets ICDAR 2011 and ICDAR 2013.

Learning-Based Shadow Recognition and Removal From Monochromatic Natural Images.

This paper addresses the problem of recognizing and removing shadows from monochromatic natural images from a learning-based perspective. Without chromatic information, shadow recognition and removal are extremely challenging in this paper, mainly due to the missing of invariant color cues. Natural scenes make this problem even harder due to the complex illumination condition and ambiguity from many near-black objects. In this paper, a learning-based shadow recognition and removal scheme is proposed to tackle the challenges above-mentioned. First, we propose to use both shadow-variant and invariant cues from illumination, texture, and odd order derivative characteristics to recognize shadows. Such features are used to train a classifier via boosting a decision tree and integrated into a conditional random field, which can enforce local consistency over pixel labels. Second, a Gaussian model is introduced to remove the recognized shadows from monochromatic natural scenes. The proposed scheme is evaluated using both qualitative and quantitative results based on a novel database of hand-labeled shadows, with comparisons to the existing state-of-the-art schemes. We show that the shadowed areas of a monochromatic image can be accurately identified using the proposed scheme, and high-quality shadow-free images can be precisely recovered after shadow removal.

A Modified Image Registration Algorithm Based on Color Invariance and RANSAC

Image category recognition and feature matching is an important access to visual information on the level of objects and scenes. So far, most works concerning local feature matching are limited in gray images. Designed for gray images, SIFT has been found to be a robust local invariant feature descriptor. However, much information is ignored since the color is discarded in traditional SIFT, and some mismatching problems happen due to this approach. To achieve a more accurate feature extraction and keypoints matching, more advanced method is desperately needed. In this paper, we present a group of color descriptors, to increase the performance traditional SIFT does in some occasions like outdoor scenes. Besides, a RANSAC algorithm is also analyzed and applied to our research, to leave out those obvious mismatching. RANSAC works well for images with many self-similar structures, which decreases mismatches significantly. Then we improved the method so that it converges in fewer iterations.

System-on-a-chip (SoC)-based hardware acceleration for foreground and background identification

The rapid growth of embedded vision applications and accessibility in recent years has instigated a philosophical shift in algorithm and implementation design for artificial intelligence. With the popularization of high-definition video, the amount of data available to be processed has also increased substantially, posing massive computational and communication demands. Hardware acceleration through specialization has received renewed interest in recent years; such acceleration has generally been implemented using two chips, with the image signal processing (ISP) part being performed by a DSP, a GPU or an FPGA and the video content analytics (VCA) part being executed by a processor. GPUs consume a substantial amount of power; thus, it is challenging to deploy them in embedded environments. However, the new generation of SoC-FPGAs that are fabricated with both the microprocessor and FPGA on a single chip consumes less power and can be built into small systems, thereby offering an attractive platform for embedded applications. This study presents the hardware acceleration of a real-time adaptive background and foreground identification algorithm in a SoC, including the capture, processing and display stages. The algorithm can be performed in either 2D or 3D space. The proposed platform uses photometric invariant color, depth data and local binary patterns (LBPs) to distinguish background from foreground. The system uses minimal cell resources, an elastically pipelined architecture is used to absorb variations in processing time, and each pipeline stage is optimized to use the available FPGA primitives. Additionally, the communication-centric architecture used in this work simplifies the implementation of embedded vision algorithms.

Naturally combined shape-color moment invariants under affine transformations

We proposed a kind of naturally combined shape-color affine moment invariants (SCAMI), which consider both shape and color affine transformations simultaneously in one single system. In the real scene, color and shape deformations always exist in images simultaneously. Simple shape invariants or color invariants cannot be qualified for this situation. The conventional method is just to make a simple linear combination of the two factors. Meanwhile, the manual selection of weights is a complex issue. Our construction method is based on the multiple integration framework. The integral kernel is assigned as the continued product of the shape and color invariant cores. It is the first time to directly derive an invariant to dual affine transformations of shape and color. The manual selection of weights is no longer necessary, and both the shape and color transformations are extended to affine transformation group. With the various of invariant cores, a set of lower-order invariants are constructed and the completeness and independence are discussed detailed. A set of SCAMIs, which called SCAMI24, are recommended, and the effectiveness and robustness have been evaluated on both synthetic and real datasets.

Recursive drivable road detection with shadows based on two-camera systems

This paper concerns the road reconstruction problem of on-road vehicles with shadows. To deal with the effects of shadows, images are transformed to the proposed illuminant invariant color space and are fused with raw images to obtain more details in both dark and bright regions. Then, the road region is reconstructed from a geometry point of view. Based on the two-view geometric model, the scene can be described by the projective parallax information with respect to a reference plane, which is the grounding plane of the vehicle in this paper. The road reconstruction is performed row-by-row recursively. For each row, a row-wise image registration method is used to estimate the parallax information, based on which height information with respect to the reference plane can be calculated. Based on the distributions of height information and image intensity, the road region is detected and is used to direct the image registration of the next row. This process stops when the road region is small enough and the entire road region is obtained. The proposed approach is general in the sense that it works with shadows for different road types and camera configurations. Experimental results are provided to show the effectiveness and robustness of the proposed method.

A shadow identification method using vegetation indices derived from hyperspectral data

ABSTRACTShadows in high-spatial-resolution remote-sensing images become more pronounced. The detection of shadows is an essential requirement for both detailed high-spatial land-cover classification and applications such as three-dimensional (3D) reconstruction of buildings as well as cloud removal. This article presents a method for integrating the photochemical reflectance index (PRI) and Red Edge normalized difference vegetation index (RENDVI) for shadow identification (IPRSI) using high-spatial-resolution airborne hyperspectral data. This method detects shadows by setting thresholds to the PRI and RENDVI to separate shadows from vegetated and non-vegetated areas. The proposed method outperformed the invariant colour spaces model and the object-based method in terms of shadow extraction accuracy. The overall shadow identification accuracy of the IPRSI was 88.97% with an F-score of 90.96 (81.32% with F-score 81.97 for the invariant colour spaces model and 78.02% with F-score 82.07 for the object-based method). The IPRSI is a potential method with the wide application of hyperspectral data in high spatial resolution that is increasingly easier to be obtained with the development of remote-sensing platforms (such as unmanned aerial vehicles (UAVs), small satellites, and airships).

Augment color discrimination of local descriptors

Scale invariance and color invariance are two critical characters of robust local descriptors. For scale invariance, most of local descriptors are constructed based on scale pyramid only, while lack of considering the resolution influence. For color invariance, most of local descriptors are constructed by joining the color features to gray features. This enhances color discrimination of local descriptors but doubles descriptors' length. In this paper, we analyze the resolution influence for descriptor robustness and give a way to construct the base layer of scale pyramid by resolution analysis. And then we propose a color transformation method to extract robust color discriminated local descriptors. At last, we validate the proposed method on different color and resolution image sets.