Color Image Edge Research Articles

QRFODD: Quaternion Riesz fractional order directional derivative for color image edge detection

Edge detection is the prominent method to determine the discontinuities present in an image. There exists an issue of loss of information and correlation during the extraction of edges in color images. To resolve it, a novel quaternion domain based Riesz fractional order directional derivative approach is presented in this paper to extract more edge details in color images. The Riesz fractional masks obtained by utilizing Aitken interpolation are applied on color images in the quaternion domain for yielding the edge map as there is no need for recalculation of basis polynomials in case of the addition of any data point. The performance of the proposed quaternion based technique is analyzed on the benchmark BSDS300 and BSDS500 datasets in the terms of Figure of Merit and F-Score. The results obtained exhibit the efficiency of proposed technique over the classical and state-of-the-art edge detection approaches. The robustness of this approach is further established by taking into account uncontrolled conditions such as noise, JPEG compression, and varying illumination as it provided more features than the classical integer-order detectors. Moreover, the superiority of the proposed quaternion based technique is validated for medical images as well as its application in image enhancement.

Color UAV Image Edge Detection Based on Improved Fireworks Algorithm

Image edge detection plays a crucial role in image analysis and recognition. However, when dealing with color images captured by unmanned aerial vehicles (UAVs), there are certain limitations, such as large operations, multiple noise sources, easy distortion, and missing information in edge detection. To address these shortcomings, this study proposes a UAV color image edge detection method based on an enhanced fireworks algorithm. In this method, the color image pixels of the UAV are represented using quaternions. The explosion amplitude formula of the fireworks is divided into two categories based on the mean value of the number of fireworks explosions. For each category, an explosion formula is proposed, and the explosion mutation operator of the fireworks algorithm is improved accordingly. By applying the proposed algorithm, the preliminary edges of a UAV color image are obtained. Additionally, a novel approach for color image edge refinement is introduced. This approach involves classifying the edge points based on their degree of attachment, which leads to the formation of the edges in a UAV color image. Experimental results demonstrate that the algorithm proposed in this study offers several advantages, including fast calculation, strong denoising capability, and high-quality edge detection.

Color Image Edge Detection Method Based on the Improved Whale Optimization Algorithm

A high level of computation is required for edge detection in color images captured by unmanned aerial vehicles (UAVs) to address issues, such as noise, distortion, and information loss. Thus, an edge detection method for UAV-captured color images based on the improved whale optimization algorithm (WOA) is proposed in this study. In this method, the color image pixels are represented by quaternions, and the global random position variables and information exchange mechanism are introduced into the random walk foraging formula of the WOA. Further, a random disturbance factor is also introduced into the predator-prey formula of the spiral bubble net. The proposed improved WOA is then used to obtain the preliminary edge of the UAV-captured color image. An edge-point classification method using the radius of the shortest distance between the whale and the current global optimum in each iteration is presented to enhance a preliminary edge. The experimental results show that the proposed edge detection method has the advantages of strong denoising, fast speed, and good quality.

Ultrasonic pseudo-color imaging in strongly attenuating materials with Filtered-Delay Multiply And Sum nonlinear beamforming

To detect flaws in strongly attenuating materials, we propose an ultrasonic phased array imaging method that combines Filtered-Delay Multiply And Sum (F-DMAS) nonlinear beamforming algorithm with pseudo-color technique. Based on the spatial coherence of signals, three filters with different frequency ranges are selected to reconstruct F-DMAS images from the full matrix capture (FMC) data. Then, three channels of F-DMAS images are combined with the RGB color model to obtain the pseudo-color image. Finally, an automatic flaw extraction algorithm is developed using the techniques of color image segmentation, data fusion, and edge detection. Experiments are performed on samples of polymer bonded explosives with side drilled holes. The results show that the proposed method has significant advantages over the linear beamformed Total Focusing Method (TFM) in terms of signal-to-noise ratio, detection ability, and visual presentation of flaws.

A Robust Color Edge Detection Algorithm Based on the Quaternion Hardy Filter

This paper presents a robust filter called the quaternion Hardy filter (QHF) for color image edge detection. The QHF can be capable of color edge feature enhancement and noise resistance. QHF can be used flexibly by selecting suitable parameters to handle different levels of noise. In particular, the quaternion analytic signal, which is an effective tool in color image processing, can also be produced by quaternion Hardy filtering with specific parameters. Based on the QHF and the improved Di Zenzo gradient operator, a novel color edge detection algorithm is proposed; importantly, it can be efficiently implemented by using the fast discrete quaternion Fourier transform technique. From the experimental results, we conclude that the minimum PSNR improvement rate is 2.3% and the minimum SSIM improvement rate is 30.2% on the CSEE database. The experiments demonstrate that the proposed algorithm outperforms several widely used algorithms.

Edge color difference detection of color image based on artificial intelligence technology

In order to solve the problems of the traditional methods in detecting color image edge chromatic aberration, such as the poor accuracy of detection and the poor detection effect, a color image edge chromatic aberration detection method based on artificial intelligence technology is proposed. The approximate principal component analysis method is used to segment the color image and smooth the image denoising; The linear gray-scale transformation is applied to the color image to enlarge the smaller gray-scale space to the larger gray-scale space according to the linear relationship and obtain the edge information of the color image; The artificial intelligence technology is used to locate the edge sub-pixel of the image to complete the edge color difference detection of the color image. The experimental results show that the detection accuracy of the proposed method is about 98%, and the detection effect is good, which is feasible.

Digital Color Image Processing Using Intuitionistic Fuzzy Hypergraphs

Hypergraphs are considered a useful mathematical tool for digital image processing and analysis since they can represent digital images as complex relationships between pixels or block of pixels. The notion of hypergraphs has been extended in fuzzy theory leading to the concept of fuzzy hypergraphs, then in intuitionistic fuzzy theory conducting to the concept of intuitionistic fuzzy hypergraphs or IFHG. The latter is very suitable to model digital images with uncertain or imprecise knowledge. This paper deals with color image denoising, segmentation, and edge detection in a color image initially represented in RGB space using intuitionistic fuzzy hypergraphs. First, the RGB image is transformed to HLS space resulting in three separated components. Then each component is intuitionistically fuzzified based on entropy measure from which an intuitionistic fuzzy hypergraph is generated automatically. The generated hypergraphs will be used for denoising, segmentation, and edge detection.

Color Image Processing Under Uncertainty

Most digital images have uncertainties associated with the intensity levels of pixels and/or edges. These uncertainties can be traced back to the acquisition chain, to uneven lighting conditions used during imaging or to the noisy environment. On the other hand, intuitionistic fuzzy hypergraphs are considered a useful mathematical tool for digital image processing since they can represent digital images as complex relationships between pixels and model uncertain or imprecise knowledge explicitly. This paper presents the approach for noisy color image segmentation and edge detection based on intuitionistic fuzzy hypergraphs. First, the RGB image is transformed to the HLS space resulting in three separated components. Then each component is intuitionistically fuzzified based on entropy measure from which an intuitionistic fuzzy hypergraph is generated automatically. The generated hypergraphs will be used for denoising, segmentation, and edges detection. The first experimentations showed that the proposed approach gave good results especially in the case of dynamic threshold.

Convolution Kernel Operations on a Two-Dimensional Spin Memristor Cross Array.

In recent years, convolution operations often consume a lot of time and energy in deep learning algorithms, and convolution is usually used to remove noise or extract the edges of an image. However, under data-intensive conditions, frequent operations of the above algorithms will cause a significant memory/communication burden to the computing system. This paper proposes a circuit based on spin memristor cross array to solve the problems mentioned above. First, a logic switch based on spin memristors is proposed, which realizes the control of the memristor cross array. Secondly, a new type of spin memristor cross array and peripheral circuits is proposed, which realizes the multiplication and addition operation in the convolution operation and significantly alleviates the computational memory bottleneck. At last, the color image filtering and edge extraction simulation are carried out. By calculating the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the image result, the processing effects of different operators are compared, and the correctness of the circuit is verified.

Content Based Image Retrieval for Community Retrieval from Given Nationality using an Efficient Combination Algorithm

There is tremendous requirement of such technique which can fulfill the entire requirement for retrieval of an image from available dataset which comes under computer vision. In this paper we discussed about the one of the application of CBIR using an efficient combination of two techniques. The application is retrieval of people images from database that comes under minority. In this paper we used an efficient combination of color image histogram technique and edge orientation histogram technique by dividing original image into small subblocks. The feature vector is formed by combination of two features obtained by above methodologies. The final features obtained by query image will be compared with the feature vector of database images using a new Canberra Distance classifier. Proposed method is designed for multiple self-prepared and some collected from internet databases. Our method includes the efficient integration of features such as color, texture, shape and orientation. The proposed method is compared with state of art techniques to prove the stable and highest accuracy of proposed work.

Noise-robust color edge detection using anisotropic morphological directional derivative matrix

In this paper, a color edge detector using the anisotropic morphological directional derivatives (AMDDs) is presented to detect edges in color images corrupted by Gaussian or impulsive noise. The AMDD matrix, robust to impulsive noise owing to the underlying morphological operators, is constructed to represent edge information at each pixel of a color image. The color edge strength map and color edge direction map of a color image are extracted by spatial and directional matched filtering and singular value decomposition of the AMDD matrices. Embedding them in the route of the Canny detector yields a noise-robust color edge detector. Moreover, a color image database with groundtruths (GTs) of edges are built. The GT of a color image is generated in three steps. First, the contours and results of multiple color edge detectors are fused into a candidate edge map (CEM). Next, the CEM, the original image, and a special software for edge modification are sent to twenty experienced observers to modify the CEM. Finally, their results are used to yield the edge pixels, non-edge pixels, and don't care regions in the GT by the voting rule. The proposed detector is compared with existing color edge detectors on the database.

Color Image Edge Detection Based on Fuzzy Rule Based Approach

Edge detection method is one of the important techniques in Image Segmentation, which is used to find out the objects in the input image in exact manner. An edge is the boundary between an object and background and it indicates the boundary between overlapping objects. One of the most commonly used operation analysis is edge detection, which is used for enhancing and detecting edges in the image. It removes useless data, noise and frequencies while preserving the important structural properties in an image. Fuzzy Logic techniques have been used in image understanding applications such as detection of edges, feature extraction, classification, and clustering. Fuzzy logic possess the ability to mimic the human mind to employ modes of reasoning that are approximate rather than exact form effectively. Edge detection in gray images has lot of methods to locate edges and it has several set of algorithms to represent it. But the images produce more information in scenes i.e., color images have few numbers of methods to segment it. So, this paper represent color image segmentation methods in the literature and getting to prepare novel segmentation method by extracting the color channels in the RGB image with combined form of masking, filtering and Thresholding methods. This paper discuss about RGB color model and fuzzy membership functions method and particularly explain about the usage of fuzzy membership functions which are used to create different combination of mask with some sort of rules based on RGB channel extraction to scan the separated channel image and include Threshold and filtering concepts for further to produce the output image in well enhanced way.

Restoration for Out-of-Focus Color Image Based on Gradient Profile Sharpness

In color images, out-of-focus problems often occur when different wavelengths of rays are focused at different positions in the focal plane. This occurs because of lenses that have different refractive indices for different wavelengths of light. These color images in turn become blurred, and noticeable colored edges appear around objects. These misaligned edges thus degrade the overall quality of the images. In this study, we propose a restoration algorithm for misaligned edges in color images. This algorithm is based on the assumption that the gradients of color channels are highly correlated such that all edges spatially overlap in the same manner as the desired gradient. The constraint term in least squares optimization is proposed to align edges to match the desired gradient based on the transformation theory of gradient profile sharpness. The proposed constraint term adaptively uses the gradients of the channels with different weights to estimate sharp edges. We also design a new measurement to compute the energy of aligned edges in a color image. The proposed algorithm can be applied to images captured by various sensors in different environments. Experimental results show that the proposed algorithm performs effectively when estimating high-quality color images.

Increasing Security for Cloud Computing By Steganography in Image Edges

The security of data storage in “cloud” is big challenge because the data keep within resources that may be accessed by particular machines. The managing of these data and services may not be high reliable. Therefore, the security of data is highly challenging. To increase the security of data in data center of cloud, we have introduced good method to ensure data security in “cloud computing” by methods of data hiding using color images which is called steganography. The fundamental objective of this paper is to prevent "Data Access” by unauthorized or opponent users. This scheme stores data at data centers within edges of color images and retrieves data from it when it is wanted.

Wheat leaf lesion color image segmentation with improved multichannel selection based on the Chan–Vese model

Because of the characteristics of intensity inhomogeneity, noise, and blurred edges in crop lesion color images, an improved Chan–Vese (C–V) model for wheat leaf lesion segmentation is proposed. First, to make full use of the color information, three color channels are selected from the R, G, B, H, S, and V channels using principal component analysis. In addition, an initial K-means segmentation is used to obtain the initial lesion curve. Second, because the channel weights are artificially determined in the C–V model, the ratio of the average object pixel value to average background pixel value is used as the adaptive weight for each of the three channels. Finally, to avoid a long iteration process for the level-set evolution, an efficient termination criterion is presented. The proposed algorithm has the advantages of an adaptive channel selection, adaptive channel weight computation, and very few iterations. Compared with the traditional C–V and gradient descent C–V (g-CV) models, the proposed segmentation approach has fewer iterations and higher segmentation accuracy.

Robust Color Guided Depth Map Restoration.

One of the most challenging issues in color guided depth map restoration is the inconsistency between color edges in guidance color images and depth discontinuities on depth maps. This makes the restored depth map suffer from texture copy artifacts and blurring depth discontinuities. To handle this problem, most state-of-the-art methods design complex guidance weight based on guidance color images and heuristically make use of the bicubic interpolation of the input depth map. In this paper, we show that using bicubic interpolated depth map can blur depth discontinuities when the upsampling factor is large and the input depth map contains large holes and heavy noise. In contrast, we propose a robust optimization framework for color guided depth map restoration. By adopting a robust penalty function to model the smoothness term of our model, we show that the proposed method is robust against the inconsistency between color edges and depth discontinuities even when we use simple guidance weight. To the best of our knowledge, we are the first to solve this problem with a principled mathematical formulation rather than previous heuristic weighting schemes. The proposed robust method performs well in suppressing texture copy artifacts. Moreover, it can better preserve sharp depth discontinuities than previous heuristic weighting schemes. Through comprehensive experiments on both simulated data and real data, we show promising performance of the proposed method.

A Color Image Edge Detection Algorithm Based on Color Difference

Although image edge detection algorithms have been widely applied in image processing, the existing algorithms still face two important problems. On one hand, to restrain the interference of noise, smoothing filters are generally exploited in the existing algorithms, resulting in loss of significant edges. On the other hand, since the existing algorithms are sensitive to noise, many noisy edges are usually detected, which will disturb the subsequent processing. Therefore, a color image edge detection algorithm based on color difference is proposed in this paper. Firstly, a new operation called color separation is defined in this paper, which can reflect the information of color difference. Then, for the neighborhood of each pixel, color separations are calculated in four different directions to detect the edges. Experimental results on natural and synthetic images show that the proposed algorithm can remove a large number of noisy edges and be robust to the smoothing filters. Furthermore, the proposed edge detection algorithm is applied in road foreground segmentation and shadow removal, which achieves good performances.

Robust image hashing based on color vector angle and Canny operator

Image hashing is a novel technology of multimedia processing, and finds many applications, such as image forensics, image retrieval and image indexing. Conventional image hashing algorithms have limitations in reaching desirable classification performances between rotation robustness and discrimination. Aiming at this issue, we propose a robust image hashing based on color vector angle and Canny operator. Specifically, our hashing firstly converts input image to a normalized image by interpolation and Gaussian low-pass filtering. And then, color vector angles and image edges are both extracted from the normalized image. Finally, statistical features incorporating color vector angles and image edges are calculated to form image hash. We conduct experiments with 2762 images to validate efficiency of our hashing. The experimental results show that our hashing is robust against normal digital processing, such as image rotation, brightness/contrast adjustment and JPEG compression, and reaches good discrimination. Receiver operating characteristics (ROC) curve comparisons with some state-of-the-art algorithms indicate that our hashing outperforms these compared algorithms in classification performances between robustness and discriminative capability.

基于FPGA的实时彩色图像边缘检测算法的实现

基于FPGA的实时彩色图像边缘检测算法的实现

Detection of Edges in Color Images

Edge detection considers the important technical details of digital image processing. Many edge detection operators already perform edge detection in digital color imaging. In this study, the edge of many real color images that represent the type of digital image was detected using a new operator in the least square approximation method, which is a type of numerical method. The Linear Fitting algorithm is computationally more expensive compared to the Canny, LoG, Sobel, Prewitt, HIS, Fuzzy, Parametric, Synthetic and Vector methods, and Robert’ operators. The results showed that the new method can detect an edge in a digital color image with high efficiency compared to standard methods used for edge detection. In addition, the suggested operator is very useful for detecting the edge in a digital color image.