Segmentation and detection of contrast objects and their application in robot navigation

Kiy, K. I.

doi:10.1134/s1054661815020145

Cited by 11 publications

(18 citation statements)

References 5 publications

(12 reference statements)

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“…A detailed description of the geometrized histograms method can be found in [10][11][12][13]. To construct the geometrized histogram for a color or grayscale image, this image is divided into strips St i , i = 1, …, n, of the same small width W with sides parallel to the horizontal (or vertical) axis Os of the image plane.…”

Section: A Brief Description Of the Geometrized Histograms Methodsmentioning

confidence: 99%

“…Here, the I component is equal to Y in (Y, C b , C r ). Using the methods developed in [10][11][12][13], we can attach to each color image the graph of color bunches STG (STructural Graph). Suppose that we deal with horizontal strips.…”

Section: A Brief Description Of the Geometrized Histograms Methodsmentioning

confidence: 99%

“…Moreover, the technique will provide tools for distinguishing temporary and permanent road markings when they are both present on the road. Since any connected real object in the image generates a corresponding formal object in STG [10][11][12][13], we will investigate images of real road makings in this graph. For this purpose, the geometrized histogram of the color image and the geometrized histogram of its grayscale component will be employed.…”

Section: Problem Statement and Facilities For Its Solutionmentioning

confidence: 99%

“…At the second stage, continuous sequences of the found local objects located in different adjacent strips are constructed. The construction of continuous sequences of local objects is based on a modification of the concept of left and right contrast curves (germs of left and right global objects) introduced in [11,12]. The main specific feature of the method is that we construct the image of road markings in the graph STG.…”

Section: Problem Statement and Facilities For Its Solutionmentioning

confidence: 99%

“…It becomes especially clear when the road marking starts rather far from the bottom of the frame on its left or right boundary (this may occur in sharp turns). In this connection, it seems to be reasonable to apply the real-time geometrized histograms method for segmentation and description of color images [10][11][12][13] for finding and distinguishing road markings. This method makes it possible to find both white road marking of arbitrary shape and colored road making (for example, temporary one) and provides the technique for distinguishing them even under difficult illumination condition.…”

mentioning

confidence: 99%

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A new real-time method for finding temporary and permanent road marking and its applications

Dosaev¹,

Kiy²

2016

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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In this paper, a new real-time method for finding temporary and permanent road marking is proposed. The method is based on the geometrized histograms method for segmenting and describing color images. This method is able to deal with both rectilinear and curvilinear marking, as well as with color temporary and permanent road marking. It also makes it possible to distinguish temporary road marking from white permanent road marking. The developed method is stable under illumination and is able to work even for partially disappearing road marking, typical for late winter and early spring. In contrast to many other methods, this method does not require any information about camera parameters and calibration and is able to find road marking in images taken under unknown conditions. The proposed method has been implemented by a program written in C++, operating under Windows and Linux. The program operation has been tested on video records shot on typical Russian roads during different seasons and under diverse whether and illumination conditions. The processing speed is about 20 fps for a standard modern computer. Using parallel computing, this speed is reduced considerably. The results of program operation are presented and discussed. The developed program is a part of the computer vision component of the control system of the AvtoNiva pilotless vehicle. IntroductionThe problem of finding road markings is very important due to applications in advanced driver assistance systems (ADAS) and vision systems of autonomous vehicles. Efficient algorithms for finding road marking implemented in ADAS can prevent many road accidents and save many lives. The first efficient methods for real-time lane (road marking) detection were proposed and implemented by E.D. Dickmanns and his colleagues [1, 2]. These methods were dated back to the late 1980s and employed the so-called 4D approach proposed by E.D. Dickmanns [1]. Many methods based on Hough transform, RANSAC algorithm, and learning approaches, including deep learning of convolutional networks, have been proposed (see [3][4][5][6][7] and the references in these papers) since then. The most recent review of the results obtained in this area can be found in [6]. In spite of the serious progress in investigations on this topic, vital problems connected with lane marking, disappearing in early spring and late winter, wet places and occlusion still take place. It deserves special mentioning that temporary lane markings may have extremely high curvature, which may pose additional problems to the existing methods. Despite of the impressing results reached by application of convolutional networks, there are certain problems that arise in using them, such as overfitting, a large

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Section: A Brief Description Of the Geometrized Histograms Methodsmentioning

confidence: 99%