Edge Detection Principle Research Articles

Adaptive cone of influence-based blade-vortex interference signal detection

An adaptive cone of influence (A-COI)-based method in the form of a full convolutional neural network (FCNN) is proposed, which leverages the principle of edge detection to detect the blade-vortex interference (BVI) signal of a helicopter under low signal-to-noise ratio. First, the COI feature is defined, which captures the dominant information of a BVI signal based on the time-scale diagram obtained through the continuous wavelet transform. Then, the FCNN-based A-COI model is designed and trained, which can conduct edge detection for the time-scale diagram and then adaptively acquire the COI features. Finally, with the obtained COI feature, a model of BVI signal representation is constructed and then a generalized likelihood ratio-based detection algorithm in the wavelet domain is derived to detect the BVI signal under low signal-to-noise ratio. The simulated and measured data-based experimental results demonstrate that the proposed method can effectively improve the performance of detecting BVI signals under the low signal-to-noise ratio.

Fabric Defect Detection using Image Processing

Fabric defect is one of the most important and serious matters of quality control in textile industry in Bangladesh. This task takes a lot of time and money. For this reason we have introduced a simple process to find defects on fabric based on edge detection. This process is mainly focused on image processing which can be integrated with fabric defect detection automation system. In this paper we have tried a new approach using the filter method with edge detection and found good results. Our algorithm can detect defected fabric area successfully. It can be also used in real-time defect detection considering light intensity, zoom, fabric width, camera resolution etc. As our algorithm mainly works on the principle of edge detection, it cannot detect defect on multicoloured or patterned fabric. It works well on single coloured fabric without any fold or edge.

Developing Versatile Graphic Map Load Metrics

Graphic map load is a property of a map quantifying the amount of map content. It indicates the visual complexity of the map and helps cartographers to adapt maps and other geospatial visualizations to accomplish their purpose. Generally, map design needs to enable the user to quickly, comprehensively, and intuitively obtain the relevant spatial information from a map. Especially, this applies in cases like crisis management, immunology and military. However, there are no widely applicable metrics to assess the complexity of cartographic products. This paper evaluates seven simple metrics for graphic map load calculation based on image analytics using the set of 50 various maps on an easily understandable scale of 0–100%. The metrics are compared to values of user-perceived map load survey joined by 62 respondents. All the suggested metrics are designed for calculation with easy-accessible software and therefore suitable for use in any user environment. Metrics utilizing the principle of edge detection have been found suitable for a diversity of geospatial visualizations providing the best results among other metrics.

Machine vision based edge detection method for toilet seat

Trajectory planning plays a vital role in the stability and accuracy of the robot when working. Using the edge detection principle of machine vision for trajectory planning can provide high reliability for polishing the robot. Grinding the trajectory, which is more stable when grinding the edges of sanitary ceramics and can make the manipulator get rid of the harm caused by the strange posture and excessive working amplitude. According to the practical application characteristics of the toilet seat trimming process, this paper proposes a new method of toilet edge detection. When there is a reflective area for edge detection, the method can eliminate the false edge of the image caused by the reflection. There are high precision and strong robustness in this new edge detection algorithm, which has a high guiding significance to assist the toilet seat processing industry.

Machine vision based edge detection method for toilet seat

Trajectory planning plays a vital role in the stability and accuracy of the robot when working. Using the edge detection principle of machine vision for trajectory planning can provide high reliability for polishing the robot. Grinding the trajectory, which is more stable when grinding the edges of sanitary ceramics and can make the manipulator get rid of the harm caused by the strange posture and excessive working amplitude. According to the practical application characteristics of the toilet seat trimming process, this paper proposes a new method of toilet edge detection. When there is a reflective area for edge detection, the method can eliminate the false edge of the image caused by the reflection. There are high precision and strong robustness in this new edge detection algorithm, which has a high guiding significance to assist the toilet seat processing industry.

Depth edge detection using edge-preserving filter and morphological operations

Canny edge detection principle and morphological operations have been used for depth edge detection. Several image smoothing filters were proposed for the enhancement of the detection task. However, an image smoothing filter can blur the edges of an image. In this paper, we propose an enhancement of depth edge detection using an edge-preserving filter, bilateral filter. The filter smooths an image and reduces the edge blurring effects across the edge such as halos and phantom. We maintain a canny edge detection principle and incorporate it with morphological properties. The results show that, this method can detect edges of a depth image better than the method without the edge-preserving filter such as gaussian and median blur.

3D reconstruction method of forest landscape based on virtual reality

In the research and development of forest resource management information visualization, the virtual forest environment provides very important technical support. Therefore, this paper proposes the use of virtual reality technology to achieve three-dimensional reconstruction of forest landscape, so as to manage forest resources more effectively. Firstly, the internal parameters of the camera are calibrated, and the improved Canny operator is used to obtain the complete forest landscape image edge information. The SIFT algorithm is used to extract the corner points of green plant images on both sides of the highway, and some pseudo feature points are eliminated. To establish a three-dimensional forest terrain, the digital elevation model is used to superimpose the ground texture image theory, and the objective surface morphological feature points in a certain region are approximated by using known actual scene sample points. The DEM spatial interpolation method is used to determine the forest boundary edge according to the grid point elevation value of a certain requirement. At the same time, the texture feature method is used to map the surface feature information of the scene in the drawn model. Secondly, based on the three-dimensional forest terrain model established in the previous section, the Forstner operator image feature extraction principle and the edge detection principle are combined with the image information extraction of the actual forest landscape. The three-dimensional reconstruction of the forest landscape based on the actual scene is realized by matching the forest landscape image feature points, the image edges and the three-dimensional forest terrain model. Experiments show that the three-dimensional reconstruction of virtual forests provides reliable data support for forest resource management.

Depth edge detection by image-based smoothing and morphological operations

Abstract Since 3D measurement technologies have been widely used in manufacturing industries edge detection in a depth image plays an important role in computer vision applications. In this paper, we have proposed an edge detection process in a depth image based on the image based smoothing and morphological operations. In this method we have used the principle of Median filtering, which has a renowned feature for edge preservation properties. The edge detection was done based on Canny Edge detection principle and was improvised with morphological operations, which are represented as combinations of erosion and dilation. Later, we compared our results with some existing methods and exhibited that this method produced better results. However, this method works in multiframe applications with effective framerates. Thus this technique will aid to detect edges robustly from depth images and contribute to promote applications in depth images such as object detection, object segmentation, etc. Highlights A method is proposed that can detect edges from depth images more profoundly. We modified the Canny edge detection method using morphological operations. The proposed method works in multi-frames.

On High-precision Subpixel-based Drilling Technique for Suture Needles with Thread

In order to improve the automation of end-hole drilling process in the production of suture needles with thread, a high-precision subpixel-based drilling method is proposed. According to the edge detection principle in mathematical morphology, combined with the characteristics of the magnified images of the ends of suture needles to be drilled, the morphological edge detection operators with variable structural elements are constructed to achieve noise suppression and fully extract the detailed information of edges of images of needle end holes to be drilled. Then, the subdivision method of spatial moments is adopted to realize the subpixel positioning of pixel-level edges. Finally, least squares fitting method is used to achieve the high-precision positioning of center of needle end hole to be drilled. The experimental results of the 0.5 mm needle samples show that the drilling method proposed in this study has a concentricity error no more than ± 0.2 μm and an average drilling time of 0.65S. Moreover, the method also boasts good real-time performance and stability and meets the automated production needs of drilling process of suture needles with thread.

Image edge detection based on the grey prediction model and discrete wavelet transform

PurposeImage edge detection is an essential issue in image processing and computer vision. The purpose of this paper is to provide a novel and effective algorithm for image edge detection.Design/methodology/approachBecause GM (1,1) model is a typical model for tendency analysis, GM (1,1) model can be used for detecting edge. Prediction image data are close to the original image data by reason of the data being smooth in the non‐edge zone of image. The principle of edge detection by GM (1,1) model is that the predicted value at an edge point will be an overestimate or underestimate owing to the data changing drastically in the edge zone of the image. First, the edge image information is obtained by a preprocessed image subtracting from prediction image via GM (1,1). Second, median filter is used to eliminate isolated point noise in edge information images, and discrete wavelet transform is used to extract the image edge. Finally, this paper verifies the proposed algorithm by experiment.FindingsExperimental results show that the proposed algorithm has advantages such as precisely locating, abundant weak edge, and better anti‐noise performance.Practical implicationsThe algorithm proposed in the paper can precisely detect the information of edge image, and get a clear image detail.Originality/valueGrey system theory developed vigorously lays the foundation for image processing. Wavelet analysis in image processing has its characteristics. This paper combines grey prediction model with discrete wavelet transform (DWT) successfully and obtains a novel and effective algorithm for image edge detection.

Analysis and improvement of SUSAN algorithm

A modified Smallest Univalue Segment Assimilating Nucleus (SUSAN) algorithm based on the local gray value character of an image is presented here. Beginning with an explanation of the principle of edge detection and noise reduction, we find SUSAN algorithm is immune to all noise points but the isolated noise points. To improve this, an original edge response formulation is optimized by imposing constraint conditions. Then a set of anti-noise tests were run to compare our scheme with the original algorithm and other popular edge detectors. The results show that for Gaussian noise and salt-and-pepper noise, the improved SUSAN algorithm performs much better than the original one in view of sensitivity to noise and detection of edges, and especially for salt-and-pepper noise the improved SUSAN algorithm works best among the all detectors tested here.

エッジ検出の原理と画像計測への応用

エッジ検出の原理と画像計測への応用

Performance Amelioration of Edge Detection Algorithms Using Concurrent Programming

Edge detection is a terminology that aims at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities. The purpose of detecting sharp changes in image brightness is to capture changes such as discontinuities in depth, discontinuities in surface orientation, changes in material properties and variations in scene illumination. The basic principles of edge detection are applying projection processing, perform differentiation, and correct the differential waveform. The different edge detection techniques are first order derivative such as Sobel edge detector, second order derivative such as Laplacian of Gaussian and optimal edge detection called as canny edge detection. The classical edge detectors are simple, detects thick lines but they are sensitive to noise and inaccurate. The Laplacian of Gaussian finds the correct places of edges but malfunctions at corners, curves where the gray level intensity function varies. The canny edge detector improves signal to noise ratio, uses probability to find the error rate, better detection especially in noise conditions but its complex and time consuming. The algorithms are implemented sequentially using high level languages and tools such C, C++, and MATLAB, etc. These languages exploit the CPU alone, which makes the execution of the algorithm slower. In the proposed system, the algorithm will be implemented in OpenCL, a concurrent programming framework which exploits the parallelism that is inherently present in the algorithm. Using the OpenCL framework, the CPU and GPU is parallelized, thus making the algorithm run faster and reducing the running time of the algorithm. This minimizes the overhead and makes the sobel, laplace and canny to run faster thereby achieving 25x, 39x and 50x higher throughput.

Automatic demarcation of sequence stratigraphy using the method of well logging multiscale data fusion

Abstract Strata division with well logging utilizes the abrupt changing points or regions of signals, while the changing law of the wavelet modulus maxima under different scales can exactly describe the position and modality of the changing points. Based on the idea of reconstruction of wavelet modulus maxima, the well logging data fusion method built on the multiscale edge detection principle is put forward and is applied to the quantitative demarcation of sequence stratigraphy. For case study, single logging curve and multiscale fusion curve in well A in Shengtuo Oilfield are processed by quadric spline wavelet and thereby sequence boundaries on different scales can be detected. The result shows: by demarcating sequence stratigraphic units with the multiscale fusion curve analysis, better effect could be obtained. The energy accumulation and distribution on the wavelet time-frequency graph could clearly reflect the boundaries and cycle types of parasequences and parasequence sets. All these researches provide a completely new method for the quantitative demarcation of sequence and its inner depositional features.

10.1109/ICCMS.2010.51

The emulation of picture edge detection is a private issue. Here, we verify the analysis of evolutionary algorithm, which embodies the essential principles of edge detection. Here, we construct an application for the exploration of an improved evolutionary algorithm (TiredEos), which we use to prove that the foremost evolutionary algorithm for the understanding of picture edge detection is optimal.

A possible physiological correlate of frequency discrimination

The ability of an observer to detect a change in the frequency of a signal is called frequency discrimination (Δf). Frequency discrimination is dependent upon both the signal level and signal frequency. Here, the hypothesis is that frequency discrimination at 1.0 kHz and above is based upon a principle of edge detection of excitation patterns present in the auditory nerve. For lack of more complete data, the high-frequency slopes of tuning curves of primary fibers are used as the criterion measures of the edge detector. There is excellent qualitative agreement between psychophysical data, which describe Δf as a function of signal level and signal frequency, and physiological data, which describe the high-frequency slope of a tuning curve as a function of signal level and characteristic frequency. A quantitative test requires additional physiological and psychophysical data. [Supported in part by an NINCDS contract and a grant from NIEHS.]

Rapid, Precise, Computer-controlled Measurement of X-Y Coordinates

An experimental x-y measurement system is described that was designed for the high-speed, high-precision measurements required in integrated circuit manufacturing and for optical measurement applications in which a sufficiently large data base is required for statistical process analysis. The technology for this experimental system differs considerably from that of conventional optical measuring systems in current use and utilizes a computer for data acquisition, manipulation and evaluation. The system, utilizing the edge detection principle, presently operates at a measuring speed of 2.5 cm/s. An analysis gives both the short-term and the long-term precision of the system. The standard deviation for the short-term precision is 0.038 µm.