scholarly journals Practical alternative to estimate flow and a vehicle speed on an avenue, using Hough Transform and Morphological Operations

2019 ◽  
Author(s):  
Pedro Huamani-Navarrete
Keyword(s):  
Hough Transform ◽  
Morphological Operations ◽  
Vehicle Speed ◽  
Estimate Flow

The analysis of lane detection algorithms using histogram shapes and Hough transform

2015 ◽  
Vol 8 (3) ◽  
pp. 262-278 ◽  
Author(s):  
Mahasak Ketcham ◽  
Thittaporn Ganokratanaa
Keyword(s):  
Hough Transform ◽  
Color Image ◽  
Lane Detection ◽  
Morphological Operations ◽  
Grayscale Image ◽  
Video File ◽  
Content Type ◽  
Detection Analysis ◽  
Area Of Interest ◽  
Similarities And Differences

Purpose – The purpose of this paper is to develop a lane detection analysis algorithm by Hough transform and histogram shapes, which can effectively detect the lane markers in various lane road conditions, in driving system for drivers. Design/methodology/approach – Step 1: receiving image: the developed system is able to acquire images from video files. Step 2: splitting image: the system analyzes the splitting process of video file. Step 3: cropping image: specifying the area of interest using crop tool. Step 4: image enhancement: the system conducts the frame to convert RGB color image into grayscale image. Step 5: converting grayscale image to binary image. Step 6: segmenting and removing objects: using the opening morphological operations. Step 7: defining the analyzed area within the image using the Hough transform. Step 8: computing Houghline transform: the system operates the defined segment to analyze the Houghline transform. Findings – This paper presents the useful solution for lane detection by analyzing histogram shapes and Hough transform algorithms through digital image processing. The method has tested on video sequences filmed by using a webcam camera to record the road as a video file in a form of avi. The experimental results show the combination of two algorithms to compare the similarities and differences between histogram and Hough transform algorithm for better lane detection results. The performance of the Hough transform is better than the histogram shapes. Originality/value – This paper proposed two algorithms by comparing the similarities and differences between histogram shapes and Hough transform algorithm. The concept of this paper is to analyze between algorithms, provide a process of lane detection and search for the algorithm that has the better lane detection results.

scholarly journals Localization and segmentation of optic disc in retinal images using Circular Hough transform and Grow Cut algorithm

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2003 ◽  
Author(s):  
Muhammad Abdullah ◽  
Muhammad Moazam Fraz ◽  
Sarah A. Barman
Keyword(s):  
Optic Disc ◽  
Hough Transform ◽  
Retinal Image ◽  
Diagnostic System ◽  
Retinal Images ◽  
Computer Assisted ◽  
Morphological Operations ◽  
Circular Hough Transform ◽  
Disc Center ◽  
Unique Method

Automated retinal image analysis has been emerging as an important diagnostic tool for early detection of eye-related diseases such as glaucoma and diabetic retinopathy. In this paper, we have presented a robust methodology for optic disc detection and boundary segmentation, which can be seen as the preliminary step in the development of a computer-assisted diagnostic system for glaucoma in retinal images. The proposed method is based on morphological operations, the Circular Hough transform and the Grow Cut algorithm. The morphological operators are used to enhance the optic disc and remove the retinal vasculature and other pathologies. The optic disc center is approximated using the Circular Hough transform, and the Grow Cut algorithm is employed to precisely segment the optic disc boundary. The method is quantitatively evaluated on five publicly available retinal image databases DRIVE, DIARETDB1, CHASE_DB1, DRIONS-DB, Messidor and one local Shifa Hospital Database. The method achieves an optic disc detection success rate of 100% for these databases with the exception of 99.09% and 99.25% for the DRIONS-DB, Messidor, and ONHSD databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 78.6%, 85.12%, 83.23%, 85.1%, 87.93%, 80.1%, and 86.1%, respectively, for these databases. This unique method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc.

scholarly journals Towards Accurate Pupil Detection Based on Morphology and Hough Transform

2020 ◽  
Vol 17 (2) ◽  
pp. 0583
Author(s):  
Ebtesam AlShemmary et al.
Keyword(s):  
Hough Transform ◽  
Individual Recognition ◽  
Image Database ◽  
High Accuracy ◽  
Morphological Operations ◽  
Circular Area ◽  
Pupil Detection ◽  
Chinese Academy Of Sciences ◽  
Stable Performance ◽  
Academy Of Sciences

 Automatic recognition of individuals is very important in modern eras. Biometric techniques have emerged as an answer to the matter of automatic individual recognition. This paper tends to give a technique to detect pupil which is a mixture of easy morphological operations and Hough Transform (HT) is presented in this paper. The circular area of the eye and pupil is divided by the morphological filter as well as the Hough Transform (HT) where the local Iris area has been converted into a rectangular block for the purpose of calculating inconsistencies in the image. This method is implemented and tested on the Chinese Academy of Sciences (CASIA V4) iris image database 249 person and the IIT Delhi (IITD) iris database v1 using MATLAB 2017a. This method has high accuracy in the center and radius finding reaches 97% for 2268 iris on CASIA V4 image and 99.77% for 2240 iris images on IITD, the speed is acceptable compared to the real-time detection speed and stable performance.

scholarly journals Accurate Iris Localization Using Edge Map Generation and Adaptive Circular Hough Transform for Less Constrained Iris Images

2016 ◽  
Vol 6 (4) ◽  
pp. 1637 ◽  
Author(s):  
Vineet Kumar ◽  
Abhijit Asati ◽  
Anu Gupta
Keyword(s):  
Hough Transform ◽  
Near Infrared ◽  
Boundary Detection ◽  
Morphological Operations ◽  
Localization Algorithm ◽  
Iris Localization ◽  
Circular Hough Transform ◽  
Map Generation ◽  
Sobel Edge Detection ◽  
Binary Edge

This paper proposes an accurate iris localization algorithm for the iris images acquired under near infrared (NIR) illuminations and having noise due to eyelids, eyelashes, lighting reflections, non-uniform illumination, eyeglasses and eyebrow hair etc. The two main contributions in the paper are an edge map generation technique for pupil boundary detection and an adaptive circular Hough transform (CHT) algorithm for limbic boundary detection, which not only make the iris localization more accurate but faster also. The edge map for pupil boundary detection is generated on intersection (logical AND) of two binary edge maps obtained using thresholding, morphological operations and Sobel edge detection, which results in minimal false edges caused by the noise. The adaptive CHT algorithm for limbic boundary detection searches for a set of two arcs in an image instead of a full circle that counters iris-occlusions by the eyelids and eyelashes. The proposed CHT and adaptive CHT implementations for pupil and limbic boundary detection respectively use a two-dimensional accumulator array that reduces memory requirements. The proposed algorithm gives the accuracies of 99.7% and 99.38% for the challenging CASIA-Iris-Thousand (version 4.0) and CASIA-Iris-Lamp (version 3.0) databases respectively. The average time cost per image is 905 msec. The proposed algorithm is compared with the previous work and shows better results.

scholarly journals Vehicle plate localization and extraction based on hough transform and bilinear operations

2020 ◽  
Vol 20 (2) ◽  
pp. 1088
Author(s):  
Masar Abed Uthaib ◽  
Muayad Sadik Croock
Keyword(s):  
Hough Transform ◽  
High Efficiency ◽  
Digital Camera ◽  
Bilateral Filter ◽  
Morphological Operations ◽  
Connected Component ◽  
Bilinear Interpolation ◽  
Image Subtraction ◽  
The Third ◽  
Previous Step

<p>In general, the extraction of the vehicle plate is a previous step of plate recognition, and it actively studied for several decades. Plate localization is used in various security and traffic applications. In this paper, the proposed method is efficient to localize a plate for the multinational countries. The proposed method consists of three levels. The first level is the preprocessing<strong> </strong>that contains several steps. The digital camera capture images have been taken about twenty meters from the car with zooming two to three meters. Images are resampled using the zooming technique (bilinear interpolation) that makes the dimension of image (1024 x 768) pixels. The resampled images are resized to (940x 730). These images are converted to grayscale (green channel), and the bilateral filter is applied for removing noise. The second level is plate detection that adopts morphological operations, image subtraction, and vertical edge detection (Sobel). At the last Connected component analysis and Hough transform are used. The third level is the Deskew plate that notifies the plate may skew so that Hough transform is used to detect the largest line segment. Then, the images are rotated using bilinear interpolation. About 860 images are tested for different countries (Iraq, Belarus, Armenia, Hungary), and the accuracy is 98.99 % for extraction of the plate and 100% for the Deskew plate. Thus, the proposed system shows high efficiency in achievement.</p>

Accurate Iris Localization Using Edge Map Generation and Adaptive Circular Hough Transform for Less Constrained Iris Images

2016 ◽  
Vol 6 (4) ◽  
pp. 1637
Author(s):  
Vineet Kumar ◽  
Abhijit Asati ◽  
Anu Gupta
Keyword(s):  
Hough Transform ◽  
Near Infrared ◽  
Boundary Detection ◽  
Morphological Operations ◽  
Localization Algorithm ◽  
Iris Localization ◽  
Circular Hough Transform ◽  
Map Generation ◽  
Sobel Edge Detection ◽  
Binary Edge

This paper proposes an accurate iris localization algorithm for the iris images acquired under near infrared (NIR) illuminations and having noise due to eyelids, eyelashes, lighting reflections, non-uniform illumination, eyeglasses and eyebrow hair etc. The two main contributions in the paper are an edge map generation technique for pupil boundary detection and an adaptive circular Hough transform (CHT) algorithm for limbic boundary detection, which not only make the iris localization more accurate but faster also. The edge map for pupil boundary detection is generated on intersection (logical AND) of two binary edge maps obtained using thresholding, morphological operations and Sobel edge detection, which results in minimal false edges caused by the noise. The adaptive CHT algorithm for limbic boundary detection searches for a set of two arcs in an image instead of a full circle that counters iris-occlusions by the eyelids and eyelashes. The proposed CHT and adaptive CHT implementations for pupil and limbic boundary detection respectively use a two-dimensional accumulator array that reduces memory requirements. The proposed algorithm gives the accuracies of 99.7% and 99.38% for the challenging CASIA-Iris-Thousand (version 4.0) and CASIA-Iris-Lamp (version 3.0) databases respectively. The average time cost per image is 905 msec. The proposed algorithm is compared with the previous work and shows better results.

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