scholarly journals EDGE DETECTION IN ATOMIC MAGNETOMETER IMAGING

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
N. Papi ◽  
E. Cali ◽  
C. Marinelli ◽  
E. Mariotti ◽  
V. Millucci
Keyword(s):  
Edge Detection ◽  
Electromagnetic Induction ◽  
Room Temperature ◽  
Signal To Noise Ratio ◽  
Imaging Techniques ◽  
Low Frequency ◽  
Detection Algorithm ◽  
X Rays ◽  
Excellent Spatial Resolution ◽  
Blurred Images

We present the results of an edge detection algorithm applied on Electromagnetic Induction Imaging provided by an Atomic radio-frequency Magnetometer operating in an unshielded environment and at room temperature. Atomic Magnetometers have been already used for Imaging Techniques in the last few years, but the image reconstruction and the object pattern recognition lacks nowadays in terms of quality: the effect of scattering of e.m. signals at low-frequency provides blurred images, and does not allow for a clean ray – optics response, as in the case of X rays. Our algorithm, based on solved Gaussian Noise Recognition, demonstrates excellent spatial resolution achieved despite low Signal-to-Noise-Ratio.

scholarly journals Pothole Classification Model Using Edge Detection in Road Image

2020 ◽  
Vol 10 (19) ◽  
pp. 6662
Author(s):  
Ji-Won Baek ◽  
Kyungyong Chung
Keyword(s):  
Edge Detection ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Image Data ◽  
Structural Similarity ◽  
Detection Algorithm ◽  
Classification Model ◽  
Specific Object ◽  
Pixel Value ◽  
Distortion Rate

Since the image related to road damage includes objects such as potholes, cracks, shadows, and lanes, there is a problem that it is difficult to detect a specific object. In this paper, we propose a pothole classification model using edge detection in road image. The proposed method converts RGB (red green and blue) image data, including potholes and other objects, to gray-scale to reduce the amount of computation. It detects all objects except potholes using an object detection algorithm. The detected object is removed, and a pixel value of 255 is assigned to process it as a background. In addition, to extract the characteristics of a pothole, the contour of the pothole is extracted through edge detection. Finally, potholes are detected and classified based by the (you only look once) YOLO algorithm. The performance evaluation evaluates the distortion rate and restoration rate of the image, and the validity of the model and accuracy of the classification. The result of the evaluation shows that the mean square error (MSE) of the distortion rate and restoration rate of the proposed method has errors of 0.2–0.44. The peak signal to noise ratio (PSNR) is evaluated as 50 db or higher. The structural similarity index map (SSIM) is evaluated as 0.71–0.82. In addition, the result of the pothole classification shows that the area under curve (AUC) is evaluated as 0.9.

Immediate and 1-Year Outcomes with a Slim Modiolar Cochlear Implant Electrode Array

2020 ◽  
Vol 162 (5) ◽  
pp. 731-736
Author(s):  
Nedim Durakovic ◽  
Dorina Kallogjeri ◽  
Cameron C. Wick ◽  
Jonathan L. McJunkin ◽  
Craig A. Buchman ◽  
...  
Keyword(s):  
Pure Tone ◽  
Signal To Noise Ratio ◽  
Case Series ◽  
Low Frequency ◽  
Electrode Array ◽  
Hearing Preservation ◽  
X Rays ◽  
Case Volume ◽  
Pure Tone Average ◽  
Academic Center

Objective To explore the immediate and 1-year outcomes of patients who underwent implantation with the slim modiolar electrode (SME). Study Design Consecutive case series with chart review. Setting Tertiary referral academic center. Subject and Methods Between May 2016 and August 2018, a total of 326 cochlear implantations (CIs) were performed. Intraoperative x-rays were performed in all cases to identify tip rollovers. Scalar location was identified for 76 CIs that had postoperative computed tomography reconstructions. Speech outcomes were measured at 3, 6, and 12 months with consonant-nucleus-consonant word and AzBio sentences in quiet and noise (+10-dB signal-to-noise ratio). Preservation of hearing was defined as maintaining a low-frequency pure tone average ≤80 dB at 250 and 500 Hz. Results Among 326 CIs, 23 (7%) had tip rollovers. Postoperative reconstructions revealed 5 of 76 (6.6%) scalar translocations. A subset of 177 cases met criteria for evaluation of speech perception scores. The marginal mean differences between presurgery and 12 months for speech tests were as follows: consonant-nucleus-consonant, 43.7 (95% CI, 39.8-47.6); AzBio in quiet, 49.7 (95% CI, 44.9-54.4); and AzBio in noise, 29.9 (95% CI, 25.2-34.7). Sixty-one patients were identified with preservable hearing (low-frequency pure tone average ≤80 dB), and 12 of 61 (20%) preserved hearing at 1 year. Conclusion CI with SME provides reliable scala tympani insertion in a consistent perimodiolar position. An initially increased tip rollover rate improved with case volume and sheath design improvement. For long-term outcomes, speech performance was comparable to that of other cochlear implants. While hearing preservation for the SME may be better than prior perimodiolar electrodes, consistent outcomes are unlikely.

Signal-to-noise estimates of time-reverse images

Geophysics ◽  
2011 ◽  
Vol 76 (2) ◽  
pp. MA1-MA10 ◽  
Author(s):  
Ben Witten ◽  
Brad Artman
Keyword(s):  
Energy Content ◽  
Signal To Noise Ratio ◽  
Imaging Techniques ◽  
Low Frequency ◽  
Velocity Model ◽  
Noise Model ◽  
Signal To Noise ◽  
Image Domain ◽  
Velocity Models ◽  
Confidence Threshold

Locating subsurface sources from passive seismic recordings is difficult when attempted with data that have no observable arrivals and/or a low signal-to-noise ratio (S/N). Energy can be focused at its source using time-reversal techniques. However, when a focus cannot be matched to a particular event, it can be difficult to distinguish true focusing from artifacts. Artificial focusing can arise from numerous causes, including noise contamination, acquisition geometry, and velocity model effects. We present a method that reduces the ambiguity of the results by creating an estimate of the (S/N) in the image domain and defining a statistical confidence threshold for features in the images. To do so, time-reverse imaging techniques are implemented on both recorded data and a noise model. In the data domain, the noise model approximates the energy of local noise sources. After imaging, the result also captures the effects of acquisition geometry and the velocity model. The signal image is then divided by the noise image to produce an estimate of the (S/N). The distribution of image (S/N) values due to purely stochastic noise provides a means by which to calculate a confidence threshold. This threshold is used to set the minimum displayed value of images to a statistically significant limit. Two-dimensional synthetic examples show the effectiveness of this technique under varying amounts of noise and despite challenging velocity models. Using this method, we collocate anomalous low-frequency energy content, measured over oil reservoirs in Africa and Europe, with the subsurface location of the productive intervals through 2D and 3D implementations.

scholarly journals A novel image edge smoothing method based on convolutional neural network

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092167 ◽  
Author(s):  
Hui-hong Xu ◽  
Dong-yuan Ge
Keyword(s):  
Neural Network ◽  
Edge Detection ◽  
Convolutional Neural Network ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Image Smoothing ◽  
Step Effect ◽  
Frequency Components ◽  
Robustness To Noise ◽  
Image Edge

In the field of visual perception, the edges of images tend to be rich in effective visual stimuli, which contribute to the neural network’s understanding of various scenes. Image smoothing is an image processing method used to highlight the wide area, low-frequency components, main part of the image or to suppress image noise and high-frequency interference components, which could make the image’s brightness smooth and gradual, reduce the abrupt gradient, and improve the image quality. At present, there are still problems such as easy blurring of the edges of the image, poor overall smoothing effect, obvious step effect, and lack of robustness to noise on image smoothing. Based on the convolutional neural network, this article proposes a method for edge detection and deep learning for image smoothing. The results show that the research method proposed in this article solves the problem of edge detection and information capture better, significantly improves the edge effect, and protects the effectiveness of edge information. At the same time, it reduces the signal-to-noise ratio of the smoothed image and greatly improves the effect of image smoothing.

Image Edge Detection Based on Neutrosophic Set Approach Combined with Chan–Vese Algorithm

2019 ◽  
Vol 33 (03) ◽  
pp. 1954008 ◽  
Author(s):  
Eser Sert ◽  
Ahmet Alkan
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Signal To Noise Ratio ◽  
Optimization Methods ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Neutrosophic Set ◽  
Neutrosophic Logic ◽  
Neutrosophic Statistics ◽  
Cv Model

Since edge detection is a field of study used by various disciplines, it is of vital importance to calculate it accuretly. In addition, an edge detection algorithm may be involved in many image processing phases. A recent and contemporary approach, neutrosophy is based on neutrosophic logic, neutrosophic probability, neutrosophic set and neutrosophic statistics. This method yields better results compared to various other optimization methods. Neutrosophic Set (NS) is based on the origin, nature and scope of neutralities. In NS, problems are separated into true, false and indeterminacy subsets. It helps solve indeterminate situations effectively. It has recently been used in the field of image processing as indeterminate situations are also encountered in this field. Chan–Vese (CV) model is one of the successful region-based segmentation methods. The present study proposes a new NS-based edge detection method using CV algorithm. The proposed method combines the philosophical view of NS with successful segmentation characteristics of CV model. Obtained edge detection results are compared with different edge detection methods. The performances of each method are analyzed by using Figure of Merit (FOM) and Peak Signal-To-Noise Ratio (PSNR). The results suggest that the proposed method displays a better performance assessment compared to the used well-known methods.

A New Kind of Image Edge Detection Based on The Theory of The Adaptive Lifting Wavelet and Morphology

2018 ◽  
Vol 11 (3) ◽  
pp. 90-104
Author(s):  
Honge Ren ◽  
Xiyan Xu ◽  
Meng Zhu ◽  
Dongxu Huo
Keyword(s):  
Edge Detection ◽  
Mathematical Morphology ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Detection Algorithm ◽  
Multi Scale ◽  
Cell Image ◽  
Image Edge Detection ◽  
Image Edge ◽  
Lifting Wavelet

This article describes how in traditional edge detection it is prone to defects such as fuzzy positioning, and noise influence. This article proposes a type of edge detection algorithm which combines lifting wavelet transform and adaptive mathematical morphology, which makes a lifting wavelet to analyze the wood cell image. Then, the high-frequency part is detected by using the algorithm fusing the wavelet packet and the rapid-combining multi-scale wavelet, which controls noise effectively; while for the low frequency part is detected with modified adaptive mathematical morphology, to locate the exact details. The final result will processes the edge of the image using “algebra” algorithm fusion. The example for a wood cell image which illustrates the algorithm is to detect the cell boundary relatively clearly, and effectively suppress the noise.

scholarly journals Enhanced Wavelets Transform Edge Detection Technique for X-Ray Images.

2020 ◽  
Vol 9 (6) ◽  
pp. 596-599
Keyword(s):  
Edge Detection ◽  
Wavelet Transforms ◽  
Bone Fractures ◽  
Imaging Techniques ◽  
Research Area ◽  
Discrete Wavelet ◽  
X Rays ◽  
X Ray ◽  
Image Objects ◽  
Aided Diagnosis

Target edge detection is one of the crucial and indispensable process used to detect the size of the fracture by using multi resolution discrete wavelet transforms in image processing field. It is a foremost step of image enhancement and is prior to segmentation procedure.Computerised imaging techniques such as X-ray, CT, Ultrasound and MRI are used by the radiologist helps in diagnosing diseases. Digital x-rays are economically agile helps in detecting microscopic bone fracture which are not detectable by human eye. The paper involve the use of daubechies wavelet transform (db1) undergoes multi resolution three level wavelet decomposition that isolate into higher and lower frequencies readily, results in finding edges in horizontal and vertical function which is the necessary aspect of edge detection for x ray images.Matlab code have been implemented for testing the boundaries of the image objects in authentic digital x ray images as well as for the standard dataset images. Computer-aided diagnosis system (CADD) is becoming a popular research area in diagnosing x-ray bone fractures,bone cancerdiseases

scholarly journals Ship infrared image edge detection based on an improved adaptive Canny algorithm

2018 ◽  
Vol 14 (3) ◽  
pp. 155014771876463 ◽  
Author(s):  
Lisang Liu ◽  
Fenqiang Liang ◽  
Jishi Zheng ◽  
Dongwei He ◽  
Jing Huang
Keyword(s):  
Edge Detection ◽  
Signal To Noise Ratio ◽  
Infrared Image ◽  
Histogram Equalization ◽  
Detection Algorithm ◽  
Equalization Algorithm ◽  
Canny Edge ◽  
Image Edge ◽  
Canny Algorithm ◽  
Edge Detection Method

Influenced by light reflection and water fog interference, ship infrared images are mostly blurred and have low signal-to-noise ratio. In this paper, an improved adaptive Canny edge detection algorithm for infrared image of ship is proposed, which aims to solve the threshold of the traditional Canny cannot be adjusted automatically and the shortcomings of sensitivity to noise. The contrast limited adaptive histogram equalization algorithm is adopted to enhance the infrared image, the morphological filter replaces the Gauss filter to smooth the image, and the OTSU algorithm is utilized to adjust the high and low thresholds dynamically. The experimental results show that the improved Canny algorithm, which can not only improve the contrast of the image and automatically adjust the threshold but also reduce the background sea clutter and false edges, is an effective edge detection method.

Studies of metaphase chromosomes in the scanning transmission x-ray microscope: Image fidelity, radiation damage and specimen preparation

1992 ◽  
Vol 50 (2) ◽  
pp. 1038-1039
Author(s):  
Shawn Williams ◽  
Xiaodong Zhang ◽  
Susan Lamm ◽  
Jack Van’t Hof
Keyword(s):  
Visible Light ◽  
Radiation Damage ◽  
Cross Section ◽  
Imaging Techniques ◽  
Dose Range ◽  
Specimen Preparation ◽  
X Rays ◽  
Metaphase Chromosomes ◽  
X Ray ◽  
Scanning Transmission

The Scanning Transmission X-ray Microscope (STXM) is well suited for investigating metaphase chromosome structure. The absorption cross-section of soft x-rays having energies between the carbon and oxygen K edges (284 - 531 eV) is 6 - 9.5 times greater for organic specimens than for water, which permits one to examine unstained, wet biological specimens with resolution superior to that attainable using visible light. The attenuation length of the x-rays is suitable for imaging micron thick specimens without sectioning. This large difference in cross-section yields good specimen contrast, so that fewer soft x-rays than electrons are required to image wet biological specimens at a given resolution. But most imaging techniques delivering better resolution than visible light produce radiation damage. Soft x-rays are known to be very effective in damaging biological specimens. The STXM is constructed to minimize specimen dose, but it is important to measure the actual damage induced as a function of dose in order to determine the dose range within which radiation damage does not compromise image quality.

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