scholarly journals Image segmentation and robust edge detection for collision avoidance in machine tools

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
David Barton ◽  
Felix Hess ◽  
Patrick Männle ◽  
Sven Odendahl ◽  
Marc Stautner ◽  
...  
Keyword(s):  
Edge Detection ◽  
Machine Tools ◽  
Region Of Interest ◽  
Small Series ◽  
Additional Processing ◽  
Future Publication ◽  
Geometric Simulation ◽  
Optimal Illumination ◽  
Harsh Conditions ◽  
Processing Steps

Abstract Collisions are a major cause of unplanned downtime in small series manufacturing with machine tools. Existing solutions based on geometric simulation do not cover collisions due to setup errors. Therefore a solution is developed to compare camera images of the setup with the simulation, thus detecting discrepancies. The comparison focuses on the product being manufactured (workpiece) and the fixture holding the workpiece, thus the first step consists in segmenting the corresponding region of interest in the image. Subsequently edge detection is applied to the image to extract the relevant contours. Additional processing steps in the spatial and frequency domain are used to alleviate effects of the harsh conditions in the machine, including swarf, fluids and sub-optimal illumination. The comparison of the processed images with the simulation will be presented in a future publication.

scholarly journals Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 590
Author(s):  
Zhenqian Zhang ◽  
Ruyue Cao ◽  
Cheng Peng ◽  
Renjie Liu ◽  
Yifan Sun ◽  
...  
Keyword(s):  
Machine Vision ◽  
Edge Detection ◽  
Detection Method ◽  
Region Of Interest ◽  
Average Error ◽  
Vertical Projection ◽  
Cut Edge ◽  
Fitting Method ◽  
Combine Harvester ◽  
Edge Detection Method

A cut-edge detection method based on machine vision was developed for obtaining the navigation path of a combine harvester. First, the Cr component in the YCbCr color model was selected as the grayscale feature factor. Then, by detecting the end of the crop row, judging the target demarcation and getting the feature points, the region of interest (ROI) was automatically gained. Subsequently, the vertical projection was applied to reduce the noise. All the points in the ROI were calculated, and a dividing point was found in each row. The hierarchical clustering method was used to extract the outliers. At last, the polynomial fitting method was used to acquire the straight or curved cut-edge. The results gained from the samples showed that the average error for locating the cut-edge was 2.84 cm. The method was capable of providing support for the automatic navigation of a combine harvester.

scholarly journals Generating a Cancellable Fingerprint using Matrices Operations and Its Fingerprint Processing Requirements

2018 ◽  
Vol 14 (6) ◽  
pp. 1 ◽  
Author(s):  
Riki Mukhaiyar
Keyword(s):  
Region Of Interest ◽  
Original Form ◽  
Biometric Data ◽  
Matrix Operations ◽  
The Matrix ◽  
Robust Image ◽  
Reciprocal Matrix ◽  
Processing Steps ◽  
Fingerprint Feature

Cancellable fingerprint uses transformed or intentionally distorted biometric data instead of the original biometric data for identifying person. When a set of biometric data is found to be compromised, they can be discarded, and a new set of biometric data can be regenerated. This initial principal is identical with a non-invertible concept in matrices operations. In matrix domain, a matrix cannot be transformed into its original form if it meets several requirements such as non-square form matrix, consist of one zero row/column, and no row as multiple of another row. These conditions can be acquired by implementing three matrix operations using Kronecker Product (KP) operation, Elementary Row Operation (ERO), and Inverse Matrix (INV) operation. KP is useful to produce a non-square form matrix, to enlarge the size of matrix, to distinguish and disguise the element of matrix by multiplying each of elements of the matrix with a particular matrix. ERO can be defined as multiplication and addition force to matrix rows. INV is utilized to transform one matrix to another one with a different element or form as a reciprocal matrix of the original. These three matrix operations should be implemented together in generating the cancellable feature to robust image. So, if once three conditions are met by imposter, it is impossible to find the original image of the fingerprint. The initial aim of these operations is to camouflage the original look of the fingerprint feature into an abstract-look to deceive an un-authorized personal using the fingerprint irresponsibly. In this research, several fingerprint processing steps such as fingerprint pre-processing, core-point identification, region of interest, minutiae extration, etc; are determined to improve the quality of the cancellable feature. Three different databases i.e. FVC2002, FVC2004, and BRC are utilized in this work.

scholarly journals Fast horizon detection in maritime images using region-of-interest

2018 ◽  
Vol 14 (7) ◽  
pp. 155014771879075 ◽  
Author(s):  
Chi Yoon Jeong ◽  
Hyun S Yang ◽  
KyeongDeok Moon
Keyword(s):  
Edge Detection ◽  
State Of The Art ◽  
Region Of Interest ◽  
Least Square ◽  
Fast Method ◽  
Multi Scale ◽  
Art Methods ◽  
Horizon Line ◽  
Horizon Detection ◽  
Interest Detection

In this article, we propose a fast method for detecting the horizon line in maritime scenarios by combining a multi-scale approach and region-of-interest detection. Recently, several methods that adopt a multi-scale approach have been proposed, because edge detection at a single is insufficient to detect all edges of various sizes. However, these methods suffer from high processing times, requiring tens of seconds to complete horizon detection. Moreover, the resolution of images captured from cameras mounted on vessels is increasing, which reduces processing speed. Using the region-of-interest is an efficient way of reducing the amount of processing information required. Thus, we explore a way to efficiently use the region-of-interest for horizon detection. The proposed method first detects the region-of-interest using a property of maritime scenes and then multi-scale edge detection is performed for edge extraction at each scale. The results are then combined to produce a single edge map. Then, Hough transform and a least-square method are sequentially used to estimate the horizon line accurately. We compared the performance of the proposed method with state-of-the-art methods using two publicly available databases, namely, Singapore Marine Dataset and buoy dataset. Experimental results show that the proposed method for region-of-interest detection reduces the processing time of horizon detection, and the accuracy with which the proposed method can identify the horizon is superior to that of state-of-the-art methods.

Study of Various Image Segmentation Methodologies

2017 ◽  
pp. 1-27
Author(s):  
Abahan Sarkar ◽  
Ram Kumar
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
New Technologies ◽  
Region Of Interest ◽  
Literature Survey ◽  
Segmentation Algorithms ◽  
Segmentation Image

In day-to-day life, new technologies are emerging in the field of Image processing, especially in the domain of segmentation. Image segmentation is the most important part in digital image processing. Segmentation is nothing but a portion of any image and object. In image segmentation, the digital image is divided into multiple set of pixels. Image segmentation is generally required to cut out region of interest (ROI) from an image. Currently there are many different algorithms available for image segmentation. This chapter presents a brief outline of some of the most common segmentation techniques (e.g. Segmentation based on thresholding, Model based segmentation, Segmentation based on edge detection, Segmentation based on clustering, etc.,) mentioning its advantages as well as the drawbacks. The Matlab simulated results of different available image segmentation techniques are also given for better understanding of image segmentation. Simply, different image segmentation algorithms with their prospects are reviewed in this chapter to reduce the time of literature survey of the future researchers.

scholarly journals A retinal image sharpness metric based on histogram of edge width

2017 ◽  
Vol 11 (3) ◽  
pp. 292-300 ◽  
Author(s):  
Jia-Wen Lin ◽  
Qian Weng ◽  
Lan-Yan Xue ◽  
Xin-Rong Cao ◽  
Lun Yu
Keyword(s):  
Diabetic Retinopathy ◽  
Computational Complexity ◽  
Edge Detection ◽  
Retinal Image ◽  
Quality Evaluation ◽  
Region Of Interest ◽  
Fundus Image ◽  
Image Sharpness ◽  
Low Computational Complexity ◽  
Sharpness Metric

Retinal image sharpness assessment is one of the critical requirements of automatic quality evaluation in telemedicine screening for diabetic retinopathy. In this paper, a new sharpness metric measuring the spread of edges is presented to quantify fundus image clarity. After edge detection on the region of interest of retinal image, the width of each edge is calculated and the histogram of region of interest generated. Based on the histogram, a distance-based factor is introduced to gain the weighted edge width, which is defined as the sharpness metric for the fundus image. The method was tested on Messidor dataset and a proprietary dataset. The results show that the proposed metric performs well over different image distortion levels and resolutions and is of low computational complexity. The weighted edge width value of gradable retinal image, which is irrelevant to resolution, is always within the range of 3–7 pixels.

scholarly journals Design and Preliminary Testing of the Volcanic Ash Plume Receiver Network

2019 ◽  
Vol 36 (3) ◽  
pp. 353-367
Author(s):  
Nicholas Rainville ◽  
Scott Palo ◽  
Kristine M. Larson ◽  
Mario Mattia
Keyword(s):  
Volcanic Ash ◽  
Detection Method ◽  
Low Cost ◽  
Signal Strength ◽  
Distributed Sensor ◽  
Additional Processing ◽  
Signal Path ◽  
Ash Plume ◽  
Processing Steps

AbstractThe presence of volcanic ash in the signal path between a GPS satellite and a ground-based receiver strongly correlates with a decrease in GPS signal strength. This effect has been seen in data collected from GPS sites located near active volcanoes; however, the sparse placement of existing GPS sites limits the applicability of this technique as an ash plume detection method to relatively few well-instrumented volcanoes. This deficiency has motivated the development of a low-cost distributed sensor system based on navigation-grade GPS receivers, which can take advantage of attenuated GPS signals to increase the quality and availability of real-time ash plume observations during an eruption. This GPS-based system has been designed specifically to meet remote sensing needs while operating autonomously in difficult conditions and minimizing on-site infrastructure requirements. Prototypes of this system have undergone long-term testing and the data collected from this testing have been used to develop the additional processing steps necessary to account for the different behavior of navigation grade GPS equipment compared to the geodetic equipment used at existing GPS sites.

INTEGRATING EDGE DETECTION AND THRESHOLDING APPROACHES TO SEGMENTING FEMORA AND PATELLAE FROM MAGNETIC RESONANCE IMAGES

2005 ◽  
Vol 17 (01) ◽  
pp. 1-11 ◽  
Author(s):  
JIANN-SHU LEE ◽  
YI-NUNG CHUNG
Keyword(s):  
Magnetic Resonance ◽  
Edge Detection ◽  
Knee Pain ◽  
Pathological Condition ◽  
Region Of Interest ◽  
Magnetic Resonance Images ◽  
Initial Guess ◽  
Joint Analysis ◽  
Edge Based ◽  
The Moment

Anterior knee pain (AKP) is a common pathological condition. The most obvious problem causing knee pain is the abnormal patellar tracking mechanism. For computerized knee joint analysis, how to successfully segment the knee bones is an import issue. This paper presents a simple while effective algorithm for fully automatic femur and patella segmentation for magnetic resonance (MR) knee images through integrating edge detection and thresholding approaches. Based on consideration of computational complexity and accuracy, we develop a compound approach to segment the MR knee images. The moment preserving thresholding is first utilized to gather the bone-outline information employed to estimate the region of interest (ROI). An ROI based wavelet enhancement is proposed to restrict the contrast improvement only around the bone edges. The restriction makes both the adhesion separation of bone and surrounding tissues and the bone contour conservation become possible and avoid harsh thresholding resulting from the global based wavelet enhancement. Cooperating with the morphology operation, stable initial guess of the bone regions can be achieved. To overwhelm the main drawback of the existing edge based segmentation methods, i.e. the necessity of complicated post-processing, a new approach - FLoG is proposed to provide a feasible solution. It converts the edge detection results using LoG into a region-based format through the flow fill operation. The developed onion-growing algorithm can properly combine the initial guess of bone regions with the FLoG outputs in an efficient way. The experimental study shows our method is superior to the conventional ones in meeting the requirement of physicians. This is because our method can perform well in dealing with the tougher conditions, i.e. the partial volume and the soft tissue adhesion conditions. Because of the integration of the thresholding approach with the FLoG edge detector, our algorithm is even robust to unsatisfactory imaging conditions. Hence, our method lends itself to assisting the clinical diagnosis of knee functions.

A Fuzzy-Like Approach for Smoothing and Edge Detection in Color Images

1998 ◽  
Vol 12 (06) ◽  
pp. 801-816 ◽  
Author(s):  
A. Moghaddamzadeh ◽  
D. Goldman ◽  
N. Bourbakis
Keyword(s):  
Edge Detection ◽  
Image Understanding ◽  
Detection Algorithm ◽  
Color Images ◽  
Color Contrast ◽  
Edge Detection Algorithm ◽  
Edge Contrast ◽  
Hsi Space ◽  
Fuzzy Like ◽  
Processing Steps

Edge detection is one of the most important image processing steps towards image understanding. It is desired that edges be continuous and that the resultant regions or segments be completely isolated from their neighbors. Initially, images must first be smoothed to remove noise. In this paper, a novel fuzzy-like smoother algorithm is presented which removes camera noise and enhances edge contrast. The edge detection algorithm, which is applied on the smoothed image, is then presented. In this algorithm normalized hue in HSI space and color contrast in RGB space are combined using an aggregate operator. Pixels considered to be at least "nearly" locally maximum (defined within) are then found for all edge directions and the results are combined.

Comparison of Isothermal Anneal Techniques for be or Si Implanted S.I. InP

1985 ◽  
Vol 52 ◽  
Author(s):  
B. Molnar ◽  
H. B. Dietrich
Keyword(s):  
Activation Energy ◽  
Maximum Temperature ◽  
Rapid Thermal Anneal ◽  
Temperature Range ◽  
Isothermal Anneal ◽  
Additional Processing ◽  
Activation Data ◽  
Inp Substrates ◽  
Processing Steps

ABSTRACTThis paper presents a study of the annealing of Be and Si implants into InP. It compares rapid thermal anneal (RTA) and furnace anneal (FA) techniques over a temperature range of 600-;900° C. The results demonstrate that RTA results in activation and mobilities as good as those obtained by FA for both Si and Be implant. The background Fe concentration of S.I. InP substrates lead to substantial differences in activation. Arrhenius fit of optimal activation data of Si indicates an activation energy of 1.8 eV. The Si implants display no redistribution during either type of annealing, while the Be implants display more than one type of redistribution. Moreover, the complete description of the Be redistribution requires the knowledge of both the atomic and the electronic profiles. Capless annealing eliminates the additional processing steps of capping but it also sets a limit on the maximum temperature and time of the annealing.

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