scholarly journals Automatic Georeferencing of Topographic Map Sheets Using OpenCV and Tesseract

2021 ◽  
Vol 4 ◽  
pp. 1-4
Author(s):  
Mátyás Gede ◽  
Lola Varga
Keyword(s):  
Image Processing ◽  
Geometric Analysis ◽  
Corner Detection ◽  
Regular Expressions ◽  
Topographic Map ◽  
False Detection ◽  
Average Speed ◽  
Whole Process ◽  
Map Series ◽  
Recognition Success

Abstract. The authors developed a pipeline for the automatic georeferencing of older 1 : 25 000 topographic map sheets of Hungary. The first step is the detection of the corners of the map content, then the recognition of the sheet identifier. These maps depict geographic quadrangles whose extent can be derived from the sheet ID. The sheet corners are used as GCPs for the georeference.The whole process is implemented in Python, using various open source libraries: OpenCV for image processing, Tesseract for OCR and GDAL for georeferencing.1147 map sheets were processed with an average speed of 4 seconds per sheet. False detection of the corners is automatically filtered by geometric analysis of the detected GCPs, while the sheet IDs are validated using regular expressions. The error of corner detection is under 1% of the sheet size for 89% of the sheets, under 2% for 99%. The sheet ID recognition success rate is 75.9%.Although the system is finetuned to a specific map series, it can be easily adapted to any other map series having approximately rectangular frame.

IMAGE PROCESSING VIA CNN GENES WITH VON NEUMANN NEIGHBORHOODS

2005 ◽  
Vol 15 (12) ◽  
pp. 3999-4006 ◽  
Author(s):  
FENG-JUAN CHEN ◽  
FANG-YUE CHEN ◽  
GUO-LONG HE
Keyword(s):  
Image Processing ◽  
Computer Simulation ◽  
Edge Detection ◽  
Vertical Line ◽  
Corner Detection ◽  
Line Detection ◽  
Von Neumann ◽  
Center Point ◽  
Better Than

Some image processing research are restudied via CNN genes with five variables, and this include edge detection, corner detection, center point extraction and horizontal-vertical line detection. Although they were implemented with nine variables, the results of computer simulation show that the effect with five variables is identical to or better than that with nine variables.

scholarly journals A Corner Detection Algorithm Based on Regional Center of Mass in Imaging through Water Surface

2021 ◽  
Vol 2083 (3) ◽  
pp. 032090
Author(s):  
Changli Mai ◽  
Bijian Jian ◽  
Yongfa Ling
Keyword(s):  
Water Surface ◽  
Center Of Mass ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Random Fluctuation ◽  
False Detection ◽  
Detection Algorithms ◽  
Regional Center ◽  
Underwater Environment ◽  
Active Imaging

Abstract Structural light active imaging can obtain more information about the target scene, which is widely used in image registration,3D reconstruction of objects and motion detection. Due to the random fluctuation of water surface and complex underwater environment, the current corner detection algorithm has the problems of false detection and uncertainty. This paper proposes a corner detection algorithm based on the region centroid extraction. Experimental results show that, compared with the traditional detection algorithms, the proposed algorithm can extract the feature point information of the image in real time, which is of great significance to the subsequent image restoration.

On the Stability of Refined L-Curvature under Rotation Transformations

2010 ◽  
Vol 20-23 ◽  
pp. 401-406
Author(s):  
Bao Jiang Zhong
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Critical Role ◽  
Corner Detection ◽  
Curve Matching ◽  
Discrete Curvature ◽  
Efficient Measure ◽  
Curvature Measures ◽  
The Stability

Estimation of curvature on digital curves plays a critical role for various applications in the field of image processing and computer vision. The refined L-curvature has proven to be an efficient measure of discrete curvature for corner detection and curve matching. In this paper its stability under rotation transformations is evaluated. Experiments with comparisons to other existing curvature measures show that the refined L-curvature performs best.

Performance Evaluation of Visual Therapy Method Used for Cerebral Palsy Rehabilitation

2018 ◽  
Vol 8 (9) ◽  
pp. 1804-1818 ◽  
Author(s):  
P. Illavarason ◽  
J. Arokia Renjit ◽  
P. Mohan Kumar
Keyword(s):  
Image Processing ◽  
Cerebral Palsy ◽  
Performance Evaluation ◽  
Motor Impairment ◽  
Threshold Value ◽  
Processing Technique ◽  
Corner Detection ◽  
Image Processing Technique ◽  
Evaluation Tool ◽  
Systematic Understanding

Cerebral Palsy (CP) is a non progressive neurological disorders commonly associated with a spectrum of developmental disabilities such as strabismus (misalignment of eye). In this study, by quantitatively assess the performance analysis of Visual Therapy Method used for CP children. By capturing the Eye Movement of 25 children with CP (aged 3–15 years) with relatively mild motor-impairment and also analyzed the performance of CP children periodically. The Eye image are captured through camera, this make the quick diagnosis and examination the periodical assessment of CP kids. By Visual Therapy Function the CP children vision improvement develops as fast as or even faster. Proposed area of the research segments the eye image into Pupil, Iris, Eyelids and Eye corner detection using different image processing algorithms as well as measure the deviation position of pupil for abnormal eyes taking the normal eye as the reference or threshold value. To further enhance these compare deviation Position of the normal and the abnormal eyes and to find the severity affection of abnormal eye and to measure the performance improvement achievement by Visual Therapy Method for CP rehabilitation. The improvement analyzed for CP kids were maintained and recorded for the periodical month of Initial, 6th and 12th month. As a result, the Physicians can use this report to guide the CP kids in Rehabilitation Center and also this image processing technique offers the greater flexibility for the prospective subjects of improvement in CP Rehabilitation by Visual Therapy Method. In this context, Image processing techniques are being recommended as a performance evaluation tool in children with CP and each of these processes are suggesting method for developing a more systematic understanding of Oculomotor abnormalities.

scholarly journals Grapho-analytical Analysis of Video Frames of Supersonic Flow Around Two Solid Revolutions using the Digital Image Processing Method

TEM Journal ◽  
2020 ◽  
pp. 449-459
Author(s):  
Valery V. Bodryshev ◽  
Nikolay P. Korzhov ◽  
Lev N. Rabinskiy
Keyword(s):  
Shock Wave ◽  
Image Processing ◽  
Supersonic Flow ◽  
Shock Waves ◽  
Geometric Analysis ◽  
High Accuracy ◽  
Processing Method ◽  
Qualitative And Quantitative ◽  
Contact Points ◽  
Video Frames

The article is devoted to the study of the geometric laws of intersection of angle shock wave formed during supersonic flow around two axisymmetric solids. An analysis of such an experiment is given by the method of processing photographs (video frames) by the parameter of image intensity. This method allows determining with high accuracy the angle of inclination of shock waves and subsequently identifies the contact points of the shock wave with the surface of the models. The proposed approach to the geometric analysis of supersonic flow around two solids makes it possible to look differently at the interference of shock waves and evaluate the qualitative and quantitative processes that occur during their interaction.

scholarly journals A Real-Time Haze Removal & Mosaicing using Rapid Prototype Hardware

2020 ◽  
Vol 9 (10) ◽  
pp. 29-34
Keyword(s):  
Real Time ◽  
Rapid Prototype ◽  
Field Of View ◽  
Corner Detection ◽  
Dark Channel Prior ◽  
Low Contrast ◽  
Whole Process ◽  
Dark Channel ◽  
Information Fidelity ◽  
Initial Focus

In this paper, a pivotal technique was proposed that reduces the haze and combines the haze free image to increase the Field of View (FoV) in real-time with a rapid prototype hardware device. The Initial focus is to reduce the haze in an image with Dark Channel Prior Technique and the FSD method is utilized to mosaic the haze free images. Low contrast may occur due to the scattering light, air particles or fog in nature which results in a haze image that needs to be reduced and enhance the image for better vicinity. Haze reduction approach depends on entropy and information fidelity. Our Haze free algorithm executes multiple phases such as dark channel prior computation, estimation and refinement of transmission map and restoration of RGB values. The second technique is the mosaic process that improves the field of view of a scene and the phases that execute are corner detection, extraction, geometric computation and blending. Our experimental results have shown better when compared to the other algorithms. The whole process is executed in real-time with a standalone device called Intel compute stick.

scholarly journals Grape Berry Detection and Size Measurement Based on Edge Image Processing and Geometric Morphology

Machines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 233
Author(s):  
Lufeng Luo ◽  
Wentao Liu ◽  
Qinghua Lu ◽  
Jinhai Wang ◽  
Weichang Wen ◽  
...  
Keyword(s):  
Image Processing ◽  
Detection Method ◽  
Grape Berry ◽  
Corner Detection ◽  
Edge Image ◽  
Grape Berries ◽  
Berry Size ◽  
Average Accuracy ◽  
Different Types ◽  
Geometric Morphology

Counting grape berries and measuring their size can provide accurate data for robot picking behavior decision-making, yield estimation, and quality evaluation. When grapes are picked, there is a strong uncertainty in the external environment and the shape of the grapes. Counting grape berries and measuring berry size are challenging tasks. Computer vision has made a huge breakthrough in this field. Although the detection method of grape berries based on 3D point cloud information relies on scanning equipment to estimate the number and yield of grape berries, the detection method is difficult to generalize. Grape berry detection based on 2D images is an effective method to solve this problem. However, it is difficult for traditional algorithms to accurately measure the berry size and other parameters, and there is still the problem of the low robustness of berry counting. In response to the above problems, we propose a grape berry detection method based on edge image processing and geometric morphology. The edge contour search and the corner detection algorithm are introduced to detect the concave point position of the berry edge contour extracted by the Canny algorithm to obtain the best contour segment. To correctly obtain the edge contour information of each berry and reduce the error grouping of contour segments, this paper proposes an algorithm for combining contour segments based on clustering search strategy and rotation direction determination, which realizes the correct reorganization of the segmented contour segments, to achieve an accurate calculation of the number of berries and an accurate measurement of their size. The experimental results prove that our proposed method has an average accuracy of 87.76% for the detection of the concave points of the edge contours of different types of grapes, which can achieve a good edge contour segmentation. The average accuracy of the detection of the number of grapes berries in this paper is 91.42%, which is 4.75% higher than that of the Hough transform. The average error between the measured berry size and the actual berry size is 2.30 mm, and the maximum error is 5.62 mm, which is within a reasonable range. The results prove that the method proposed in this paper is robust enough to detect different types of grape berries.

scholarly journals Controlling Longitudinal Safe Distance Between Vehicles

2012 ◽  
Vol 21 (5) ◽  
pp. 303-310 ◽  
Author(s):  
Seyyed Mohammad Sadat Hoseini ◽  
Mahmmood Fathi ◽  
Manouchehr Vaziri
Keyword(s):  
Image Processing ◽  
Real Time ◽  
High Technology ◽  
Safe Distance ◽  
Main Concept ◽  
Average Speed ◽  
Image Processing Techniques ◽  
Online System ◽  
Police Agencies ◽  
Processing Techniques

Controlling the safe distances between vehicles on freeways can be used to prevent many accidents. In this research, image-processing techniques have been used to develop an online system that calculates the longitudinal distances between vehicles. This system facilitates controlling safe distances between vehicles without the need for high technology devices. Our approach is real-time and simple, but efficient operations have been used to reduce the image occlusion problem. The main concept of this system is using simple, quick, and effective algorithms for calculating the position of each vehicle in each image. In this way, traffic parameters like speed and distances between vehicles can be calculated for each vehicle in real time. In addition, aggregate parameters like average speed, density, and traffic flow can be calculated using gathered data of single vehicles. As an application of the developed system, controlling the safe distance between vehicles has been introduced. In this system, in case of a driver who does not observe the safe distance, the scene of violation is stored and can be used by the police agencies. KEY WORDS: image processing, traffic, longitudinal safe distance, real time, occlusion

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