Fuzzy Edge Detection Research Articles

Edge Detection Method Based on General Type-2 Fuzzy Logic Applied to Color Images

This paper presents a new general type-2 fuzzy logic method for edge detection applied to color format images. The proposed algorithm combines the methodology based on the image gradients and general type-2 fuzzy logic theory to provide a powerful edge detection method. General type-2 fuzzy inference systems are approximated using the α-planes approach. The edge detection method is tested on a database of color images with the idea of illustrating the advantage of applying the fuzzy edge detection approach on color images against grayscale format images, and also when the images are corrupted by noise. This paper compares the proposed method based on general type-2 fuzzy logic with other edge detection algorithms, such as ones based on type-1 and interval type-2 fuzzy systems. Simulation results show that edge detection based on a general type-2 fuzzy system outperforms the other methods because of its ability to handle the intrinsic uncertainty in this problem.

Development of the iterative edge detection method applied on blurred satellite images: state of the art

This paper aims to implement an iterative fuzzy edge detection (IFED) method on blurred satellite images. Some degradation effects such as atmospheric effects, clouds and their shadows, atmospheric aerosols, and fog remarkably decline the quality satellite images. Hence, some processes such as enhancement and edge detection in satellite images are challenging. One group of methods that can deal with these effects is fuzzy logic methods. Therefore, IFED method was applied in this work on the subimages of the Ikonos, Landsat 7, and SPOT 5 satellite images, contaminated by aforementioned effects. Such as most FED methods, IFED has two components: enhancement and edge detection. In this context, a six-step iterative method, using the if-then-else mechanism, was implemented on the images to perform fuzzy enhancement, and subsequently, edge detection was done. To evaluate the merit of the enhancement and select the best number of iterations, edge gray-value rate criterion was applied. The peak signal-to-noise ratio (PSNR) is applied for the quantitative evaluation of the IFED method. The results of IFED, in comparison with some prior edge detection methods, showed higher PSNR values and a high performance in the edge detection of the earth features in the blurred satellite images.

Image steganography based on interval-valued intuitionistic fuzzy edge detection and modified LSB

The Internet is becoming increasingly popular as a communication channel. However, message transmissions via the Internet have some problems, such as information security, copyright protection, and so on. Therefore, we need a secure communication method to transmit messages via the Internet. Steganography is a technique that hides secret data into cover media by altering its most insignificant components, such that an unauthorized user will not be aware of the existence of the secret data. In recent years the development of artificial intelligence and fuzzy logic has started a new period in the whole science. The proposed scheme has taken into consideration the property that more information can be hidden into the edge areas rather than in the smooth areas. This scheme makes the use of interval-valued intuitionistic fuzzy edge detecting method as well as the modified LSB substitution method that causes the image quality and capacity to increase. The use of intuitionistic fuzzy sets not only improves the image quality, but it is also more accurate in detecting the edges and smooth areas. The proposed scheme is tested on different standard images with secret messages of various lengths. The experimental results show the ability of the proposed scheme.

DESIGN OF A VEHICULAR TRACKING SYSTEM USING COMBINED CED ALGORITHM AND FUZZY LOGIC

Edge detection is the most popular and commonly used technique for an image analysis. Much evaluation, experiments and deployment is done on edge detection .The major problem in edge detection technique is discontinuity in the image brightness in the different regions of an image. In order to avoid this particular issue, combination of two edge detection algorithms have been proposed, canny edge detection algorithm and Fuzzy based edge detection algorithm. In proposed method, along with tracking, classification of vehicles is also determined by using the distance Euclidean square algorithm and Kalman filter. The position of each vehicle will be estimated and tracked. The Kalman filter classifies detected vehicles in different specified groups and count them separately to provide useful information for traffic flow analysis. On observing the obtained results it is concluded that fuzzy canny edge detection has better efficiency than canny algorithm alone. This approach has provided improved results over the traditional canny edge detection technique based on Gaussian filter for noisy images.

Combined fuzzy logic and random walker algorithm for PET image tumor delineation.

The random walk (RW) technique serves as a powerful tool for PET tumor delineation, which typically involves significant noise and/or blurring. One challenging step is hard decision-making in pixel labeling. Fuzzy logic techniques have achieved increasing application in edge detection. We aimed to combine the advantages of fuzzy edge detection with the RW technique to improve PET tumor delineation. A fuzzy inference system was designed for tumor edge detection from RW probabilities. Three clinical PET/computed tomography datasets containing 12 liver, 13 lung, and 18 abdomen tumors were analyzed, with manual expert tumor contouring as ground truth. The standard RW and proposed combined method were compared quantitatively using the dice similarity coefficient, the Hausdorff distance, and the mean standard uptake value. The dice similarity coefficient of the proposed method versus standard RW showed significant mean improvements of 21.0±7.2, 12.3±5.8, and 18.4%±6.1% for liver, lung, and abdominal tumors, respectively, whereas the mean improvements in the Hausdorff distance were 3.6±1.4, 1.3±0.4, 1.8±0.8 mm, and the mean improvements in SUVmean error were 15.5±6.3, 11.7±8.6, and 14.1±6.8% (all P's<0.001). For all tumor sizes, the proposed method outperformed the RW algorithm. Furthermore, tumor edge analysis demonstrated further enhancement of the performance of the algorithm, relative to the RW method, with decreasing edge gradients. The proposed technique improves PET lesion delineation at different tumor sites. It depicts greater effectiveness in tumors with smaller size and/or low edge gradients, wherein most PET segmentation algorithms encounter serious challenges. Favorable execution time and accurate performance of the algorithm make it a great tool for clinical applications.

Detecting Direction of Pepper Stem by Using CUDA-Based Accelerated Hybrid Intuitionistic Fuzzy Edge Detection and ANN

In recent years, computer vision systems have been used in almost every field of industry. In this study, image processing algorithm has been developed by using CUDA (GPU) which is 79 times faster than CPU. We had used this accelerated algorithm in destemming process of pepper. 65 percent of total national production of pepper is produced in our cities, Kahramanmaras and Gaziantep in Turkey. Firstly, hybrid intuitionistic fuzzy algorithm edge detection has been used for preprocessing of original image and Otsu method has been used for determining automatic threshold in this algorithm. Then the multilayer perceptron artificial neural network has been used for the classification of patterns in processed images. Result of ANN test for detection direction of pepper has shown high accuracy performance in CPU-based implementation and in GPU-based implementation.

Noise Reduction in Compressed Images Using Improved Fuzzy Transform Technique

In the area of digital image compression, computer algorithms are used to perform processing of images and compression. It deals with developing a digital system that perform operations on digital image. It has many advantages using in digital camera, film, Satellite, X-ray and many more applications. Image compression is a technique used to save the storage space normally used to compress images and videos. Number of compression algorithms are used like run length encoding, huffman coding, discrete cosine transform, vector quantization, fuzzy transform. This gives a brief idea on improved fuzzy technique to reduce noise in compressing image. There are so many techniques for compression but in this only present the techniques of improved fuzzy method to reduce noise and compressed the image by using edge detection. The main idea behind applying this is we have to preserve the well significant edges as Jpeg is the popular standard but at low bit rate Jpeg exhibits blocking artifacts means noisy effects that affect the visual image quality so to produce high visual quality image at low bit rate ,the algorithm is efficient and simple. The proposed algorithm consists of three steps. First, image is preprocessed using competitive fuzzy edge detection. Second, based on edge information image is compressed and decompressed using improved fuzzy transform. Third, reconstructed image is post processed using hybrid median filter for artifact reduction. Analysis proves the superiority of proposed algorithm. The results of different number of coefficients are compared with the value of PSNR, MSE of algorithm. After comparison of techniques it is found to be efficient for visualization.

Improved fuzzy transform based image compression and fuzzy median filter based its artifact reduction: pairFuzzy

During image compression, visually significant edges should be well preserved for human perception. Despite existence of many image compression standards, joint photographic experts group (JPEG) is the most popularly used standard for image compression. However at low bit rate, JPEG compressed images exhibit blocking artifacts that adversely affect the visual image quality. Thus, to produce a high visual quality image at low bit rate, pairFuzzy algorithm that is simple and more efficient as compared to JPEG alongwith the capability of reducing artifact is proposed. The proposed algorithm is carried out in three steps. First, an image is preprocessed using competitive fuzzy edge detection which efficiently detects the edge pixels contained in the image. Second, based on the edge information the image is compressed and decompressed using improved fuzzy transform. Third, the reconstructed image is postprocessed using fuzzy switched median filter for artifact reduction. The subjective as well as objective analysis alongwith the comparison to recent methods proves the superiority of proposed algorithm.

Towards a Heterogeneous Navigation Team of Aerial-Ground Robots Based on Fuzzy Image Processing

In this paper, aerial-ground robots system has been presented that composed by ground and blimp robots. The blimp robot equipped with single camera in order to scan the environment to detect the ground robot and any obstacles in the environment. The detection algorithm has been designed based on fuzzy edge detection and shape- color features techniques. The blimp will share the environment map to the ground control station. Then, the global optimal path with avoiding obstacles is generated by enhanced genetic algorithm by modified the search A*. Several experiments were carried out in a simulation as well as indoor environments to verify the system applicability. The proposed robotic system shows good results in simultaneous navigation and mapping applications. 

Optimization of interval type-2 fuzzy systems for image edge detection

This paper presents the optimization of a fuzzy edge detector based on the traditional Sobel technique combined with interval type-2 fuzzy logic. The goal of using interval type-2 fuzzy logic in edge detection methods is to provide them with the ability to handle uncertainty in processing real world images. However, the optimal design of fuzzy systems is a difficult task and for this reason the use of meta-heuristic optimization techniques is also considered in this paper. For the optimization of the fuzzy inference systems, the Cuckoo Search (CS) and Genetic Algorithms (GAs) are applied. Simulation results show that using an optimal interval type-2 fuzzy system in conjunction with the Sobel technique provides a powerful edge detection method that outperforms its type-1 counterparts and the pure original Sobel technique.

An improved sobel edge detection method based on generalized type-2 fuzzy logic

Edge detectors have traditionally been an essential part of many computer vision systems. There are different methods that have been proposed for improving edge detection in real images. This paper proposes an edge detection method based on the Sobel technique and generalized type-2 fuzzy logic systems. To limit the complexity of handling generalized type-2 fuzzy logic, the theory of $$\alpha $$?-planes is used. Simulation results are obtained with the Sobel operator (without fuzzy logic), then with a type-1 fuzzy logic system (T1FLS), an interval type-2 fuzzy logic system (IT2FLS) and with a generalized type-2 fuzzy logic system (GT2FLS). The proposed generalized type-2 fuzzy edge detection method is tested with synthetic images with promising results. To illustrate the advantages of using generalized type-2 fuzzy logic in combination with the Sobel operator, the figure of merit of Pratt measure is applied to measure the accuracy of the edge detection process.

High‐payload block‐based data hiding scheme using hybrid edge detector with minimal distortion

Data hiding is a technique that is embedding the secret message into the media. In 2010, Chen et al.’s proposed a scheme combined the ‘Canny’ edge detector and fuzzy edge detector to increase edge pixels, and embedded more secret data into the edge pixels than the non-edge pixels based on the least-significant-bit (LSB) substitution scheme. In this study, the authors use Kaur et al.’s fuzzy logic-based algorithm and extend the original design to block-based design. The experimental results show that the proposed method achieves higher payload with Kaur et al.'s fuzzy logic-based algorithm, and achieve minimal distortion by selecting the number of edge pixel's embedding length of each block which has minimum mean-squared error.

A Novel Extracting Algorithm of the Low Gray Fuzzy Edges for Infrared Images

The fuzzy edge detection algorithm proposed by Pal-King has some disadvantages for extracting the low gray level edge information for the infrared images, such as high computation complexity, low threshold segmentation inaccuracy and the leakage edge information. For overcoming the disadvantages, the improved image fuzzy edge detection algorithm is proposed in this paper. First, redefining membership function to simplify computation complexity, the new conversion function enable the function transform interval is [0, 1], thus the value of the low gray level edge is not to be set to 0. Second, Ostu's algorithm is used in the selection of segmentation threshold named as transit point. The traditional threshold value is improved in order to make the segmentation accurate. The experimental results show that the lower gray infrared image edge information is preserved via proposed algorithm in this paper. The detecting results are more accurate. The run time is decreased obviously than the traditional Pal - king algorithm.

방사선 의료영상 잡음제거를 위한 퍼지논리 활용 다해상도 기반 유도필터

방사선 의료영상의 잡음은 화질을 떨어트리며, 진단의 정확성을 낮추는 원인이 된다. 특별히 환자보호를 목적으로 방사선 피폭량을 줄인 저선량 의료영상은 잡음 수준이 높아 잡음 제거 기술이 더욱 중요하다. 본 논문에서는 다양한 해상도에서 경계선 특징을 보존하며 잡음을 효과적으로 제거하는 잡음제거 방법을 제안한다. 제안된 방법에서는 영상을 여러 개의 해상도의 피라미드 영상으로 분해하고, 각각의 분해된 영상으로 부터 퍼지 규칙기반 경계선 검출기를 통하여 안정된 경계 신뢰도 맵을 얻는다. 이들 다해상도 영상의 경계 신뢰도 맵은 유도 필터의 파라메터를 적응적으로 결정하며, 이를 이용하여 해당 영상의 경계를 보전하며 잡음을 제거한다. 이렇게 잡음이 제거된 다해상도 영상은 보간법을 통하여 확장되고 합성되어 결과영상을 얻는다. 제안된 방법은 미디언 필터, 양방향 필터, 유도 필터 등과 비교하여 경계선을 잘 보존하며, 잡음제거 능력이 우수함을 실험을 통해 확인하였다. Noise in biomedical X-ray image degrades the quality so that it might causes to decrease the accuracy of diagnosis. Especially the noise reduction techniques is quite essential for low-dose biomedical X-ray images obtained from low radiation power in order to protect patients, because their noise level is usually high to well discriminate objects. This paper proposes an efficient method to remove the noise in low-dose X-ray images while preserving the edges with diverse resolutions. In the proposed method, a noisy image is at first decomposed into several images with different resolutions in pyramidal representation, then the stable map of edge confidence is obtained from each of analyzed image using a fuzzy logic-based edge detector. This map is used to adaptively determine the parameter for guided filters, which eliminate the noise while preserving edges in the corresponding image. The filtered images in the pyramid are extended and synthesized into a resulted image using interpolation technique. The superiority of proposed method compared to the median, bilateral, and guided filters has been experimentally shown in terms of noise removal and edge preserving properties.

Performance improvement of fuzzy‐based algorithms for medical image retrieval

In this paper we present an algorithm for medical image retrieval system examines in particular image processing algorithms used for image retrieval based on transformation domain, segmentation and fuzzy logic. This is to implement image retrieval algorithm to achieve better classification accuracy for retrieving medical images. It is proposed to implement image segmentation using fuzzy logic with certain improvements for feature extraction with global optimisation using fast Hartley transform (FHT) to reduce the dimensionality of feature space. A new image segmentation algorithm, fuzzy edge detection and segmentation (FEDS) and a bell fuzzy multilayer perceptron (BF-MLP) for classifying images are used. Then BF-MLP is optimised using genetic algorithm (GA) for improved performance.

New Information Hiding Technique using Features of Image

Steganography is a word derived from two Greek words i.e. Stegno and Graphy. Stegno means cover and graphy means writing. It is an important branch of information security which protects the secret information from eavesdropper. Steganography hides the existence of secret information in order to protect it. In this paper we are dealing with image steganography, in which images are used to hide the information in them. Number of image steganography techniques has been proposed so far to achieve the goals of steganography i.e. high payload, less imperceptibility and more robustness. In this paper a new steganography technique for colored image (i.e. RGB images) is introduced. This new introduced technique makes the use of features to hide more information than in other areas of image. As features are considered to be the function of edges therefore in this technique we are using hybrid edge detector to extract the features. In this technique a combination of Canny and fuzzy edge detector is used to detect the optimal edges. After detecting edges 1 bit of red, 4 bits of green and 8 bits of blue channel are used for hiding the secret message bits. For smooth areas an adaptive least significant bit based scheme is used. The newly introduced scheme achieves all the three goals of steganography appropriately.

A Study on the Application of Fuzzy Information Seeded Region Growing in Brain MRI Tissue Segmentation

After long-term clinical trials, MRI has been proven to be used in humans harmlessly, and it is popularly used in medical diagnosis. Although MR is highly sensitive, it provides abundant organization information. Therefore, how to transform the multi-spectral images which is easier to be used for doctor’s clinical diagnosis. In this thesis, the fuzzy bidirectional edge detection method is used to solve conventional SRG problem of growing order in the initial seed stages. In order to overcome the problems of the different regions, although it is the same Euclidean distance for region growing and merging process stages, we present the peak detection method to improve them. The standard deviation target generation process (SDTGP) is applied to guarantee the regions merging process does not cause over- or undersegmentation. Experimental results reveal that FISRG segments a multispectral MR image much more effectively than FAST andK-means.

Intelligent Vision Methodology for Detection of the Cutting Tool Breakage

In this paper, a new Intelligent system based on neurofuzzy for detecting and diagnostics the wear and damage of the milling cutter is presented. The compatibility between the computer vision and neurofuzzy techniques is introduced. The proposed approaches consists of capturing the milling cutter image, Fuzzy edge detection, Chain code technique for feature extraction and finally, apply the neural network on the feature. The results of the study are three different diagnostics models, The first is diagnostic model for the original profile of the perfect cutter, the second is model for the wearied profile and the third is model for the damage profile. Experimental test results show that the proposed system is reliable, practical and can be used for the easy distinguish between the wear and damage automatically.

High Performance Color Image Processing in Multicore CPU using MFC Multithreading

Image processing is an engineering field where stored image data is readily available for parallel processing. Basically data processing algorithms developed in sequential approach are not capable of harnessing the computing power of individual cores present in a single-chip multicore processor. To utilize the multicore processor efficiently on windows platform for color image processing applications, a lock-free multithreading approach was developed using Visual C++ with Microsoft Foundation Class (MFC) support. This approach distributes the image data processing task on multicore Central Processing Unit (CPU) without using parallel programming framework like Open Multi-Processing (OpenMP) and reduces the algorithm execution time. In image processing, each pixel is processed using same set of high-level instruction which is time consuming. Therefore to increase the processing speed of the algorithm in a multicore CPU, the entire image data is partitioned into equal blocks and copy of the algorithm is applied on each block using separate worker thread. In this paper, multithreaded color image processing algorithms namely contrast enhancement using fuzzy technique and edge detection were implemented. Both the algorithms were tested on an Intel Core i5 Quad-core processor for ten different images of varying pixel size and their performance results are presented. A maximum of 71% computing performance improvement and speedup of about 3.4 times over sequential approach was obtained for large-size images using four thread model.

Morphological analysis of 3D SPECT images via nilpotent t-norms in diagnosis of Alzheimer's disease

We propose a fuzzy image processing approach serving as a potential diagnostic tool for Alzheimer's disease. We have set up a sequence of several image-processing methods based on morphological fuzzy edge detection followed by a region-growing segmentation. We applied these operations to two sets of three-dimensional SPECT images of human brains: one set of images was composed of patients with Alzheimer's disease and the second was represented by control brains from non-demented patients. Then we undertook an analysis of nilpotent t-norms forming the edge detectors and the parameter of Gaussian filter, and in the end we carried out an evaluation of segments that was computed after the final watershed segmentation. This article provides a detailed description of these methods as well as the analysis of evaluated data using Student's two-sample t-test. Traditional gradient magnitude edge detectors are included and compared with fuzzy detectors. Our goal was to demonstrate the usefulness of the Łukasiewicz BL-algebra in feature extraction of 3D biomedical images by using enhanced methods of image morphology.