Highway Lane Detection Based on a New Gray Method

2014 ◽  
Vol 1044-1045 ◽  
pp. 1553-1557
Author(s):  
Yan Zhou Peng ◽  
Hong Feng Gao
Keyword(s):  
Hough Transform ◽  
Color Image ◽  
Lane Detection ◽  
Gray Level ◽  
The Novel ◽  
Threshold Values ◽  
Transform Method ◽  
Illumination Changes ◽  
Canny Edge ◽  
Gray Level Image

a new gray method is provided in this paper for highway lane detection. Firstly, the novel gray method transform an RGB color image to a gray-level image based on a new gray vector. To deal with illumination changes, the new gray vector is updated on real-time.Secondly,the canny edge detector’s threshold values are decided adaptive.Lastly,Hough transform method realizes the detection of lanes. For different time in a day, experiments indicate that the proposed algorithm has good results.

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.

Optimum Gray Level Image Thresholding using a Quantum Inspired Genetic Algorithm

2016 ◽  
pp. 349-377 ◽  
Author(s):  
Sandip Dey ◽  
Siddhartha Bhattacharyya ◽  
Ujjwal Maulik
Keyword(s):  
Genetic Algorithm ◽  
Chaotic Map ◽  
Population Diversity ◽  
Gray Level ◽  
The Novel ◽  
Image Thresholding ◽  
Gray Level Image ◽  
Quantum Operators ◽  
Pass Through ◽  
Gray Level Images

In this article, a genetic algorithm inspired by quantum computing is presented. The novel algorithm referred to as quantum inspired genetic algorithm (QIGA) is applied to determine optimal threshold of two gray level images. Different random chaotic map models exhibit the inherent interference operation in collaboration with qubit and superposition of states. The random interference is followed by three different quantum operators viz., quantum crossover, quantum mutation and quantum shifting produce population diversity. Finally, the intermediate states pass through the quantum measurement for optimization of image thresholding. In the proposed algorithm three evaluation metrics such as Brinks's, Kapur's and Pun's algorithms have been applied to two gray level images viz., Lena and Barbara. These algorithms have been applied in conventional GA and Han et al.'s QEA. A comparative study has been made between the proposed QIGA, Han et al.'s algorithm and conventional GA that indicates encouraging avenues of the proposed QIGA.

The Multi-Scale Hough Transform Lane Detection Method Based on the Algorithm of Otsu and Canny

2014 ◽  
Vol 1042 ◽  
pp. 126-130 ◽  
Author(s):  
Yu Chai ◽  
Su Jing Wei ◽  
Xin Chun Li
Keyword(s):  
Hough Transform ◽  
Detection Method ◽  
Lane Detection ◽  
Transform Method ◽  
Canny Operator ◽  
The Road ◽  
Multi Scale ◽  
Lane Departure ◽  
Lane Line

In order to improve the accuracy of detecting lane for automatic vehicle driving, a method for detecting the straight part of Lane is proposed, which is the Multi-Scale Hough transform method for lane detection based on the algorithm of Otsu and Canny. First of all, by the methods of Otsu to segment image and use the morphology operation of erode and dilate to wipe off the information of roadside trees and fences to strengthen the road boundary characteristics.Then the lane edge and feature is gained by the canny operator. At last, using Standard Hough Transform, Progressiveness Probabilities Hough Transform and Multi-Scale Hough Transform complete the detection of lane’s straight part. The experimental results show that, Multi-Scale Hough Transform method can accurately detect the lane line and provide the reliable basis for the path planning, automatic follow-up vehicle driving and lane departure warning.

scholarly journals Vision Based Driver Alertness System for Lane Detection

2019 ◽  
Vol 8 (6) ◽  
pp. 1394-1397
Keyword(s):  
Cross Product ◽  
Lane Detection ◽  
Threshold Values ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Ip Camera ◽  
Canny Edge ◽  
Left And Right ◽  
Assistance Systems ◽  
Edge Detection Method

This paper presents lane detection used by IP camera. By using the Haugh transform, the lane is detected and gives the notification to the driver. It is also gives the left and right lane marking. In preprocessing, Gaussian filter is apply for smoothing and Canny edge detection method is used as it gives better response than the Sobel, Robert and Marr-Hildreth. Firstly, detect the red edge as left side lane and yellow edge which is for right side lane. The line sharpening is carried out with masking operation. The masking of the image for lane defines the threshold values specifically for Hue, Saturation, and Value (HSV). HSV values are different for red and yellow lane detection. Indication of direction needs to find the vanishing point, and the points are found by using cross product. This paper present display an overview of Hough Transform (HT) calculation as well as utilize it on a path location framework, whereas automobile and mechanical automaton depends upon MATLAB. This paper utilized Internet Protocol (IP) based driving path location framework to accomplish the objective of Advanced Driver Assistance Systems (ADAS). This system is used in car for driverless system

Color in Image Watermarking

2010 ◽  
pp. 36-56 ◽  
Author(s):  
Gaël Chareyron ◽  
Jérôme Da Rugna ◽  
Alain Trémeau
Keyword(s):  
Color Image ◽  
State Of The Art ◽  
Image Watermarking ◽  
The State ◽  
Color Images ◽  
Gray Level ◽  
Color Image Watermarking ◽  
Color Channel ◽  
Multimedia Contents ◽  
Gray Level Image

This chapter summarizes the state-of-the-art color techniques used in the emerging field of image watermarking. It is now well understood that a color approach is required when it comes to deal with security, steganography and watermarking applications of multimedia contents. Indeed, consumers and business expectations are focused on the protection of their contents, which are here color images and videos. In the past few years, several gray-level image watermarking schemes have been proposed but their application to color image is often inadequate since they usually work with the luminance or with individual color channel. Unfortunately, color cannot be considered as a simple RGB decomposition and all of its intrinsic information must be integrated in the watermarking process. Therefore, it is the chapter objective to present, first, the major difficulties associated with the treatment of color images, and second, the state-of-the-art methods used in the field of color image watermarking.

Proper Enhancement and Segmentation of the Overexposed Color Skin Cancer Image

2018 ◽  
pp. 240-258 ◽  
Author(s):  
Krishna Gopal Dhal ◽  
Swarnajit Ray ◽  
Mandira Sen ◽  
Sanjoy Das
Keyword(s):  
Skin Cancer ◽  
Color Image ◽  
Histogram Equalization ◽  
Input Image ◽  
Color Model ◽  
Gray Level ◽  
Saturation Intensity ◽  
Enhancement Method ◽  
Gray Level Image ◽  
Clustering Approach

Proper enhancement and segmentation of the overexposed color skin cancer images is a great challenging task in medical image processing field. Computer-aided diagnosis (CAD) facilitates quantitative analysis of digital images with a high throughput processing rate. But, analysis of CAD purely depends on the input image quality. Therefore, in this study, overexposed and washed out skin cancer images are enhanced properly with the help of exact hue-saturation-intensity (eHSI) color model and contrast limited adaptive histogram equalization (CLAHE) method which is applied through this model. eHSI color model is hue preserving and gamut problem free. Any gray level image enhancement method can be easily employed for color image through this eHSI model. The segmentation of these enhanced color images has been done by employing one unsupervised clustering approach with the assistance of seven different gray level thresholding methods. Comparison of the segmentation efficiency of gray level thresholding methods has been done in the cases of overexposed as well as for enhanced images.

scholarly journals An algorithm for extracting groove rail area based on improved Hough transform

2021 ◽  
Vol 336 ◽  
pp. 02025
Author(s):  
Zhongbin Fang ◽  
Xiaojie Huang ◽  
Kangquan Ye ◽  
Jing Ji ◽  
Qiantong Wu ◽  
...  
Keyword(s):  
Hough Transform ◽  
Line Length ◽  
Transform Method ◽  
Straight Line ◽  
Extraction Algorithm ◽  
Region Extraction ◽  
Actual Environment ◽  
Speed Up ◽  
Canny Edge ◽  
Edge Detection Method

In order to improve the accuracy and real-time performance of the automatic cleaning of groove rails in modern trams, this paper proposes a groove rail region extraction algorithm based on improved Hough transform. First, in order to speed up the detection and remove noise, the algorithm performs a series of pre-processing on the images collected by the camera, and then use the Canny edge detection method to extract the edge feature information of the groove rail. Finally, the algorithm is improved on the basis of the traditional Hough transform method according to the actual environment. The algorithm proposes three constraints from the straight line length, the slope of the straight line and the distance between the left and right edges, making the algorithm more feasible and accurate in extracting groove rail area. The extraction accuracy reached 97.9%, and the average extraction speed was 0.1903s, laying the foundation for the automatic cleaning of trough rails of modern trams.

scholarly journals Road Lane Detection using Hough Transform Method (Daylight Condition)

2019 ◽  
Vol 14 (18) ◽  
pp. 6642-6649
Author(s):  
H. Tia Amelia ◽  
Agus Virgono ◽  
Randy Erfa Saputra
Keyword(s):  
Hough Transform ◽  
Lane Detection ◽  
Transform Method

scholarly journals Nystagmus Estimation for Dizziness Diagnosis by Pupil Detection and Tracking Using Mexican-Hat-Type Ellipse Pattern Matching

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 885
Author(s):  
Yoanda Alim Syahbana ◽  
Yokota Yasunari ◽  
Morita Hiroyuki ◽  
Aoki Mitsuhiro ◽  
Suzuki Kanade ◽  
...  
Keyword(s):  
Pattern Matching ◽  
Hough Transform ◽  
University Hospital ◽  
Transform Method ◽  
Data Set ◽  
Medical Specialists ◽  
Detection And Tracking ◽  
Mexican Hat ◽  
Pupil Detection ◽  
In The Wild

The detection of nystagmus using video oculography experiences accuracy problems when patients who complain of dizziness have difficulty in fully opening their eyes. Pupil detection and tracking in this condition affect the accuracy of the nystagmus waveform. In this research, we design a pupil detection method using a pattern matching approach that approximates the pupil using a Mexican hat-type ellipse pattern, in order to deal with the aforementioned problem. We evaluate the performance of the proposed method, in comparison with that of a conventional Hough transform method, for eye movement videos retrieved from Gifu University Hospital. The performance results show that the proposed method can detect and track the pupil position, even when only 20% of the pupil is visible. In comparison, the conventional Hough transform only indicates good performance when 90% of the pupil is visible. We also evaluate the proposed method using the Labelled Pupil in the Wild (LPW) data set. The results show that the proposed method has an accuracy of 1.47, as evaluated using the Mean Square Error (MSE), which is much lower than that of the conventional Hough transform method, with an MSE of 9.53. We conduct expert validation by consulting three medical specialists regarding the nystagmus waveform. The medical specialists agreed that the waveform can be evaluated clinically, without contradicting their diagnoses.

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