Bok-Suk Shin

Visual lane analysis and higher-order tasks: a concise review

Lane detection, lane tracking, or lane departure warning have been the earliest components of vision-based driver assistance systems. At first (in the 1990s), they have been designed and implemented for situations defined by good viewing conditions and clear lane markings on highways. Since then, accuracy for particular situations (also for challenging conditions), robustness for a wide range of scenarios, time efficiency, and integration into higher-order tasks define visual lane detection and tracking as a continuing research subject. The paper reviews past and current work in computer vision that aims at real-time lane or road understanding under a comprehensive analysis perspective, for moving on to higher-order tasks combined with various lane analysis components, and introduces related work along four independent axes as shown in Fig.xa02. This concise review provides not only summarizing definitions and statements for understanding key ideas in related work, it also presents selected details of potentially applicable methods, and shows applications for illustrating progress. This review helps to plan future research which can benefit from given progress in visual lane analysis. It supports the understanding of newly emerging subjects which combine lane analysis with more complex road or traffic understanding issues. The review should help readers in selecting suitable methods for their own targeted scenario.

Accurate and Robust Line Segment Extraction Using Minimum Entropy With Hough Transform

The Hough transform is a popular technique used in the field of image processing and computer vision. With a Hough transform technique, not only the normal angle and distance of a line but also the line-segments length and midpoint (centroid) can be extracted by analysing the voting distribution around a peak in the Hough space. In this paper, a method based on minimum-entropy analysis is proposed to extract the set of parameters of a line segment. In each column around a peak in Hough space, the voting values specify probabilistic distributions. The corresponding entropies and statistical means are computed. The line-segments normal angle and length are simultaneously computed by fitting a quadratic polynomial curve to the voting entropies. The line-segments midpoint and normal distance are computed by fitting and interpolating a linear curve to the voting means. The proposed method is tested on simulated images for detection accuracy by providing comparative results. Experimental results on real-world images verify the method as well. The proposed method for line-segment detection is both accurate and robust in the presence of quantization error, background noise, or pixel disturbances.

A superparticle filter for lane detection

We extend previously defined particle filters for lane detection by using a more general lane model supporting the use of two independent particle filters for detecting left and right lane borders separately, by combining multiple particles, traditionally used for identifying a winning particle in one image row, into one superparticle, and by using local linear regression for adjusting detected border points. The combination of multiple particles makes it possible to extend the traditional emphasis of particle-filter-based lane detectors (on identifying sequences of isolated border points) towards a local approximation of lane borders by polygonal or smooth curves further detailed in our local linear regression. The paper shows by experimental studies that results, obtained by the proposed novel lane detection procedure, improve compared to previously achieved particle-filter-based results especially for challenging lane detection situations. The presentation of several methods for comparative performance evaluation is another contribution of this paper. HighlightsExtends previously defined particle filters for lane detection.Introduces a more general lane model.Applies two independent particle filters for left and right lane borders.Combines multiple row-particles into one superparticle.Provides an extensive comparative performance evaluation.

Effective feature extraction by trace transform for insect footprint recognition

The paper discusses insect footprint recognition. Footprint segments are extracted from scanned footprints, and appropriate features are calculated for those segments (or cluster of segments) in order to discriminate species of insects. The selection or identification of such features is crucial for this classification process. This paper proposes methods for automatic footprint segmentation and feature extraction. First, we use a morphological method in order to extract footprint regions by clustering footprint patterns. Second, an improved SOM algorithm and an ART2 algorithm of automatic threshold selection are applied to extract footprint segments by clustering footprint regions regardless of footprint size or stride. Third, we use a trace transform technique in order to find out appropriate features for the segments extracted by the above methods. The trace transform builds a new type of data structure from the segmented images, by defining functions based on parallel trace lines. This new type of data structure has characteristics invariant to translation, rotation and reflection of images. This data structure is converted into triple features by using diametric and circus functions; the triple features are finally used for discriminating patterns of insect footprints. In this paper, we show that the triple features found by applying the proposed methods are sufficient to distinguish species of insects to a specified degree.

A Statistical Method for Peak Localization in Hough Space by Analysing Butterflies

The Hough transform is an efficient method for extracting lines in images. Precision of detection relies on how to find and locate accurately the peak in Hough space after the voting process. In this paper, a statistical method is proposed to improve peak localization by considering quantization error and image noise, and by considering the coordinate origin selection. The proposed peak localization is based on butterfly analysis: statistical standard variances and statistical means are computed and used as parameters of fitting and interpolation processes. We show that accurate peak parameters are achieved. Experimental results compare our results with those provided by other peak detection methods. In summary, we show that the proposed peak localization method for the Hough transform is both accurate and robust in the presence of quantization error and image noise.

Closed form line-segment extraction using the Hough transform

This paper proposes a novel closed-form solution to complete line-segment extraction. Given a voting angle in image space, the voting distribution is analyzed and two functional relationships are deduced. Regarding the corresponding column in Hough space, voting along the distance axis is considered as being a random variable, and voting values in cells are considered as forming a probability distribution. Statistical characteristics of this distribution are used to fit a quadratic polynomial curve and a linear curve. Direction, length, and width of a line segment are simultaneously computed in a closed form based on coefficients of fitted quadratic polynomial curves. The midpoint of a line segment is determined based on the fitted linear curve. The method is tested on simulated and real-world images; results show that the proposed closed-form solution is feasible in the presence of quantization errors or image noise. HighlightsWe proposed a method for obtaining the complete set of line-segment parameters.The direction, length and width of a line-segment are extracted simul- taneously in a closed form.We provided a complete theoretical derivation of voting variance with respect to voting angle.The midpoint of a line-segment is determined by the coefficients of the fitted linear curve.Parallel, crossing, and aligned line-segments are discussed by analysing events in image space and Hough space.

Evaluation of two stereo matchers on long real-world video sequences

The paper evaluates iterative semi-global matching (iSGM) and linear belief-propagation matching (linBPM), both using a census data-cost function, which are two of the currently top-ranked stereo matchers. The evaluation is on long real-world video sequences where disparity ground-truth is not available. The paper applies two alternative (or mutually supporting) techniques for performance evaluation: the previously known third-eye method, and a few new data measures on video sequences. The main contribution of the paper is on answering the questions, how to evaluate stereo matchers on long real-world sequences if disparity ground truth is not available, and how to compare evaluation measures relatively to each other. The two stereo matchers used are illustrating the discussed evaluation measures; they could be replaced by other matchers, but evaluation results for those two matchers are also of interest on its own, by illustrating correlations in the behavior of those two basically very different matchers (defined by dynamic programming or by belief propagation optimization, respectively) on data sequences recorded in different traffic situations. HighlightsWe evaluate iterative semi-global matching and linear belief-propagation matching.The evaluation is on long real-world video sequences where disparity ground-truth is not available.We apply the third-eye method and new data measures on video sequences.We propose how to compare evaluation measures relatively to each other.We suggest an evaluation based on data measures only.

Line Segment Detection with Hough Transform Based on Minimum Entropy

The Hough transform is a popular technique used in the field of image processing. In this paper, fitting and interpolation techniques are employed to compute high-accuracy peak parameters by considering peak spreading. The entropy is selected to measure the scatter-degree of voting. The voting in each column is considered as a random variable and voting values are considered as a probabilistic distribution. The corresponding entropies are computed and used to estimate the peak parameters. Endpoint coordinates of a line segment are computed by fitting a sine curve with more cells. It is more accurate and robust compared to solving directly two equations. The proposed method is tested on simulated and real images.

Segmentation of scanned insect footprints using ART2 for threshold selection

In a process of insect footprint recognition, footprint segments need to be extracted from scanned insect footprints in order to find out appropriate features for classification. In this paper, we use a clustering method in a preprocessing stage for extraction of insect footprint segments. In general, sizes and strides of footprints may be different according to type and size of an insect for recognition. Therefore we propose a method for insect footprint segment extraction using an improved ART2 algorithm regardless of size and stride of footprint pattern. In the improved ART2 algorithm, an initial threshold value for clustering is determined automatically using the contour shape of the graph created by accumulating distances between all the spots within a binarized footprint pattern image. In the experimental results, applying the proposed method to two kinds of insect footprint patterns, we illustrate that clustering is accomplished correctly.

A statistical method for line segment detection

Voting in each column around an initial peak is considered to be a random variable.The optimal ? is determined by fitting and minimizing a 2nd-order curve.The optimal ? is determined by fitting and interpolating a sine curve.We calculate voting boundaries instead of searching for non-zero voting cells.The endpoint coordinates are determined by fitting instead of by solving equations. Line segment detection is a fundamental procedure in computer vision, pattern recognition, or image analysis applications. This paper proposes a statistical method based on the Hough transform for line segment detection by considering quantization error, image noise, pixel disturbance, and peak spreading, also taking the choice of the coordinate origin into account.A random variable is defined in each column in a peak region. Statistical means and statistical variances are calculated; the statistical non-zero cells are analyzed and computed. The normal angle is determined by minimizing the function which fits the statistical variances; the normal distance is calculated by interpolating the function which fits the statistical means. Endpoint coordinates of a detected line segment are determined by fitting a sine curve (rather than searching for the first and last non-zero voting cells, and solving equations containing coordinates of such cells).Experimental results on simulated data and real world images validate the performance of the proposed method for line segment detection.