Earticle

이미지에서 에지의 속성을 찾아내고 그 정확한 위치를 지정하기 위하여 밝기 분포에 대한 국소적 미분 대신에 “적응성 방향 미분”(Adaptive Directional Derivative, ADD)이라는 비국소적 연산자를 도입함으로써 에지의 램프 폭의 변화에 무관하게 적용할 수 있는 알고리듬을 개발하였다. 본 논문에서는 이미 제안한 적응성 방향 미분을 이용한 에지 검출 시스템을 현재 에지 검출을 위해 보편적으로 사용되고 있는 Cany 알고리듬과의 성능평가를 실험을 통해 보여준다

In order to detect and locate edge features precisely in real we have developed an algorithm by introducing a nonlocal differentiation of intensity profiles called adaptive directional derivative (ADD), which is evaluated independently of varying ramp widths. In this paper, we first develop the edge detector system employing the ADD and then, the perform-ance of the algorithm is illustrated by comparing the results to those from the Canny’s edge detector.

Aiming at the problem that the gradient-based edge detection operators are sensitive to the noise, causing the pseudo edges, a triqubit-state measurement-based edge detection algorithm is presented in this paper. Combing the image local and global structure information, the triqubit superposition states are used to represent the pixel features, so as to locate the image edge. Our algorithm consists of three steps. Firstly, the improved partial differential method is used to smooth the defect image. Secondly, the triqubit-state is characterized by three elements of the pixel saliency, edge statistical characteristics and gray scale contrast to achieve the defect image from the gray space to the quantum space mapping. Thirdly, the edge image is outputted according to the quantum measurement, local gradient maximization and neighborhood chain code searching. Compared with other methods, the simulation experiments indicate that our algorithm has less pseudo edges and higher edge detection accuracy.

Edges are a robust feature for object detection. In this paper, we present an edge-based background modeling method for the detection of moving objects. The edges in the image frames were mapped using robust Canny edge detector. Two edge maps were created and combined to calculate the ultimate moving-edge map. By selecting all the edge pixels of the current frame above the defined threshold of the ultimate moving edges, a temporary background-edge map was created. If the frequencies of the temporary background edge pixels for several frames were above the threshold, then those edge pixels were treated as background edge pixels. We conducted a performance comparison with previous works. The existing edge-based moving-object detection algorithms pose some difficulty due to the changes in background motion, object shape, illumination variation, and noises. The result of the performance evaluation shows that the proposed algorithm can detect moving objects efficiently in real-world scenarios.

그레이스케일 수학적 형태학에 기초한 에지 검출 알고리즘의 방법은 영상 노이즈를 제거와 병렬처리 가능 하고 연산속도가 빠르다는 장점을 갖고 있다. 그러나 단일 구조적 스케일 요소를 사용하여 영상의 에지 검출을 하는 방법은 영상정보에 따라서 영향을 받을 수 있다. 그레이스케일 형태학의 특성은 구조적원소를 반복하여 확장, 침식, 열림, 담힘 연산을 함으로써 연산 결과 에지정보 결과에 제한적일 수 있다. 본 논문에서 잡음에 강인한 방향 성을 갖는 구조적원소를 적용한 후 원소내의 각 픽셀 정보에 가중치 엔트로피를 적용하는 에지 검출 알고리즘을 제안한다. 영상에 적용하는 멀티 스케일 구조적 요소를 적용한 결과와 방향성 가중치 엔트로피를 적용한 연산결과 를 비교분석하였으며, 시뮬레이션 결과는 제안된 알고리즘이 에지 검출에서 우수함을 보였다.

The method of the edge detection algorithm based on grayscale mathematical morphology has the advantage that image noise can be removed and processed in parallel, and the operation speed is fast. However, the method of detecting the edge of an image using a single structural scale element may be affected by image information. The characteristics of grayscale morphology may be limited to the edge information result of the operation result by repeatedly performing expansion, erosion, opening, and containment operations by repeating structural elements. In this paper, we propose an edge detection algorithm that applies a structural element with strong directionality to noise and then applies weighted entropy to each pixel information in the element. The result of applying the multi-scale structural element applied to the image and the result of applying the directional weighted entropy were compared and analyzed, and the simulation result showed that the proposed algorithm is superior in edge detection.

In this paper, for wavelet modulus maxima edge detection affected by noise largely, we propose an improved image fusion algorithm. Firstly, wavelet denoising and smoothing filter are conducted on the original image. Secondly, making edge detection with wavelet transform modulus maxima edge detection and improved canny edge detection get each of edge detection images. Thirdly, let the wavelet fusion follow some certain fusion rules. Finally, making the inverse wavelet transform reconstruct the fused image. The experimental results show that the fused image combines the advantages of edge detection methods, which is an effective image edge detection method.

Industrial furnace flame detection is directly related to the safety and economic operation of boiler. The flame edge detection is one of the most key parts of the flame detection. At present, there are many edge detection algorithms for normal object but few for industrial flame edge detection which cannot provide fully support for furnace flame combustion stability, flame 3D reconstruction and flame temperature field reconstruction. One new industrial flame image edge detection algorithm based on industrial flame image characteristics is proposed in this paper. This algorithm can effectively remove noise level in flame image and well maintain flame image edge features, compute the gradients from multiple-directions for entirely detecting flame edge information. Moreover, this algorithm is verified by experiment in this paper to present high performance in resisting noise, and clearly see continuous flame edge curve, which reveals better effect in industrial flame edge detection than other edge detection algorithms.

Flame edge detection from color images is a challenging research area recently. In this paper, an extension of active contour model is proposed by adding the two information types to both internal and external energy terms. Therefore, the combination of these two forces allows for flexible initialization of the contours. This energy is then incorporated into a level set formulation with a level set regularization term that is necessary for accurate computation in the corresponding level set method. Edge extraction of different flame images using C-V model and the classical edge detection operators are compared and analyzed. Experimental results show that the existing methods do not emphasize the continuity and clarity of the flame and fire edges while the proposed method identifies the continuous and clear edges of the flame fire.

Aimed at the defects of the traditional Canny operator, this paper puts forward an improved algorithm in edge detection. First, this paper gives a new method on selfadaptive image block based on threshold value. Next, by proposing a new hybrid filterbank of self-adaptive median and morphology, we adopt this hybrid filter-bank to smooth the noise image. Then, we add the information of gradient in two bevel directions, so that the information of gradient is more complete. Last, by using the threshold value to process the image of gradient which is after non-maxima suppression, we obtain the image edge. For the noise image, this improved algorithm not only can filter out noise well, but also the image edge is continuous, smooth, clear. The experimental results show that the improved algorithm has a good effect in edge detection, strong capability of noise immunity. The objective evaluation and visual effect are good, too.

With the development of embedded technology, the embedded image processing algorithms are becoming more and more complex. Edge detection is the basic algorithm of image processing, which is used in the extraction of information from the image. Laplacian operator is useful for the edge detection. Especially, the operator can be determined by the zero crossing point of the two differential positive and negative peaks. In order to meet the fast hardware implementation of embedded image processing, this paper presents the hardware acceleration of Laplacian operator by Zynq-7000. Using HLS method, the complex image algorithm is automatically translated into hardware language, and the function of the algorithm can be quickly completed. The experimental results show that, the embedded hardware accelerated image based on Zynq-7000 can not only realize software programming and hardware programmable combination, but also improve the embedded system flexibility, scalability, and accelerate embedded image processing product design time.

To improve the accuracy of digital image edge detection, an ENO nonlinear fourth-order interpolation based subpixel edge detection algorithm was proposed in this paper. A stencil was constructed through classical Canny operator, followed by processing gray images to generate gradient images. ENO nonlinear fourth-order interpolation was applied in the gradient direction of target edges, and then subpixel subdivision computation was performed to obtain subpixel accurate locations of target edges, the performance of our edge detection algorithm was compared with that of centered cubic convolution and non-centered cubic convolution based edge detection methods. Simulation results demonstrated that the ENO based interpolation detection algorithm improved the abilities of detecting image edges.

Analysis and several common edge detection algorithm is improved, and put them in the original experiment results were compared with the results in this paper, we defined the new threshold, fully explained in this paper, we define the rationality and validity of the threshold value. Kirsch of traditional algorithm was improved, and improve the efficiency of the algorithm, and verify the rationality of the defined threshold and improve the ability to suppress noise.

MRTD (minimum resolvable temperature difference) is an important parameter to evaluate the infrared thermal imager. MRTD is not only related to the spatial resolution of the system, but also to many factors such as optical system, detector array and so on. The measurement technique and principle of the medical infrared thermal imager’s MRTD is deeply analyzed, and the MRTD measurement model is established in this paper. The MRTD measurement system of infrared thermal imager based on CCD imaging technology is designed. CCD is used to carry on the interpretation of temperature difference in this system instead of the human eye, which can avoid the effect of some factors on the subjective image quality evaluation, such as the contrast, the human interference flaw and so on. The sub-pixel edge detection algorithm is used to deal with the four-bar target image, which can effectively suppress the interface of the random noise on four-bar target image. The experimental results show that the MRTD testing accuracy of the system can reach 0.055℃.

The images of different areas require different test results on the edge. In this paper, the images of red blood cells in medical field need to be detected theirs cell size, roundness, number and other features. For the test requirements, an image edge detection algorithm is proposed based on improved Canny operator. This algorithm calculates the optimal high and low dual-threshold by using iteration arithmetic, and uses mathematical morphology to thin the detected images. The results proved that this algorithm can effectively reduce interference and noise edge and make more prominent detection characteristics, which is good ready for subsequent processing of the image.

This paper proposes a modified bacterial foraging algorithm with a probabilistic derivative approach to detect edges in chromosome images. Chromosomal Edge Detection is fundamental for automatic karyotyping for noise reduction and getting useful messages from the edges. Subjected to staining and other imaging constraints, chromosomal banding patterns lack in resolution, contrast and suffer from noise. For this reason, chromosomal edge detection is highly preferred prior to the segmentation and classification of chromosomes. When the chromosomes occlude or overlap, edge detection becomes extremely difficult. Edge detection is highly challenging and this paper presents a Modified Bacterial Foraging Algorithm (MBFA) based on a probabilistic derivative methodology based on Ant Colony Optimization (ACO) for the detection of edges in chromosomes. Bacterium searches for the nutrients in the direction decided by a probabilistic derivative approach derived from ACO and the edge pixels are identified and traversed. The study reveals that MBFA gives the most promising results in detecting chromosomal edges, greatly reducing the computation time and memory requirements. Acceptable values of parameters for performance evaluation like Kappa (K) and Entropy (E) are achieved with the proposed algorithm in comparison to the other conventional methods of edge detection.

Medical image edge detection is an important work for object recognition of the human organs, and it is an essential pre-processing step in medical image segmentation and 3D reconstruction. Although many edge-detection evaluation methods have been developed in the past years, however this is still a challenging and unsolved problem. Conventionally, edge is detected according to some early brought forward algorithms like Canny, LOG, Sobel, Prewitt, Roberts algorithms but in theory they belong to the high pass filtering, which are not fit for noise medical image edge detection because noise and edge belong to the scope of high frequency. In real world applications, medical images contain object boundaries and object shadows and noise. Therefore, they may be difficult to distinguish the exact edge from noise or trivial geometric features. After studying all traditional methods of edge detection, it has been analyzed that for these situations, a new algorithm is needed which is optimal. In this paper, we propose a new algorithm for edge detection of noisy medical images based on both Tsallis and Shannon entropy together. The performance of our method is compared against other methods by using blood cells image corrupted with various levels of "salt and pepper". It is observed that the proposed algorithm displayed superior noise resilience and decrease the computation time.

Effectively extracting the contour edge of the defect in the infrared image can realize the recognition of the geometrical features of the defect. In view of the traditional Canny edge detection algorithm in the Gauss filter variance and high and low threshold selection need artificial intervention, does not have the adaptive ability, and its defects in the gradient calculation, proposed a method based on the improved Canny operator and image morphological fusion edge detection method. Using the improved Canny operator and image morphology to edge detection, the simulation results show that the algorithm has good anti noise ability, effectively improve the accuracy and integrity of the edge detection in the infrared image, and achieve a better extraction of geometric features of the edge of the defect.

Aiming at the existing edge detection algorithm of edge vague, the pseudo-edge cannot be removed and algorithm results do not achieve optimal results by virtue. In order to improve the reliability and effectiveness of edge detection, the proposed optimization tool template coefficient method, to design the coding, Sobel filter and fitness function of genetic fuzzy clustering algorithm. Through interpolating, smooth handling and filtering with the updated active contour model. Based on the traditional edge detection algorithm is analyzed, combined with fuzzy membership functions and genetic operators for edge detection algorithm was improved by genetic fuzzy clustering. Through the simulation results showed that this new algorithm was feasible. Theoretical analysis and experimental results demonstrate that, the new algorithm in this paper is highly antinoise and able to get better image edges.

Edge extraction is an important part in the detection of infrared thermal images. Ant colony algorithm has the characteristics of high efficiency, high noise suppression, and comprehensive information of edge information. The basic principle of ant colony algorithm is analyzed. An ant colony optimization algorithm for image edge detection is established. And to have defective parts for analysis of infrared thermography The ant colony algorithm and the classical Canny operator are compared and analyzed. The experimental results show that the algorithm has high efficiency, comprehensive information and high computational efficiency.

The knowledge involved in digital image processing is very wide, and there are many kinds of methods. Traditional image processing technology is mainly focused on the acquisition, transformation, enhancement, restoration, compression encoding, segmentation, edge extraction and so on. With the emergence of new tools and new methods, the image processing technology has been updated and developed. In this paper, an effective method for edge detection and image de-noising is proposed. In this article, the impulse noise detector is composed of a BP neural network (BPNN) and a decision switch. BPNN requires four input values, which are the current pixel value, grey median value, energy value, and contrast. To take these four values as the input values, the impulse noise detector can show good performance. The output of the BPNN is transferred to the decision switch, and the output value is converted to 0 or 1, which is used to distinguish whether the pixels are polluted. At this point, we introduce an additional impulse term and establish the improved BPNN model. The additional impulse term can effectively speed up the convergence of the network, avoid the emergence of the local minimum problem, and ensure the stability of the training process. In this way, the IBPNN filter of this paper only uses the information of the non polluted pixels to filter the noise pixels, which avoids the secondary pollution, and obtains a better performance. This algorithm has high PSNR value and strong detail information and edge preserving ability. Finally, the improved BPNN algorithm is applied to the image edge detection, and we use the improved neural network model to detect the edge of the image. Because the method can be used to include the prior knowledge, the IBPNN method is better than the traditional method in image edge detection.