Linlin Zhu

V-disparity Based UGV Obstacle Detection in Rough Outdoor Terrain

This paper presents a fast obstacle detection system based on stereo vision for unmanned ground vehicle (UGV) navigation in unstructured environment.In order to make the UGV adaptable to more complex terrains,we propose a new estimation method of the main ground disparity (MGD) from the V-disparity images.Then,by comparing the disparity of the MGD with local 3D reconstruction,a coarse-to-fine method to find and localize obstacles is introduced in the paper.The obstacle detection system is tested practically on our UGV platform in some outdoor unstructured environments.The experimental results validate the effcacy of our system.

Skew detection in document images based on rectangular active contour

The digitalization processes of documents produce frequently images with small rotation angles. The skew angles in document images degrade the performance of optical character recognition (OCR) tools. Therefore, skew detection of document images plays an important role in automatic document analysis systems. In this paper, we propose a Rectangular Active Contour Model (RAC Model) for content region detection and skew angle calculation by imposing a rectangular shape constraint on the zero-level set in Chan–Vese Model (C-V Model) according to the rectangular feature of content regions in document images. Our algorithm differs from other skew detection methods in that it does not rely on local image features. Instead, it uses global image features and shape constraint to obtain a strong robustness in detecting skew angles of document images. We experimented on different types of document images. Comparing the results with other skew detection algorithms, our algorithm is more accurate in detecting the skews of the complex document images with different fonts, tables, illustrations, and layouts. We do not need to pre-process the original image, even if it is noisy, and at the same time the rectangular content region of a document image is also detected.

Outdoor shadow detection by combining tricolor attenuation and intensity

Shadow detection is of broad interest in computer vision. In this article, a new shadow detection method for single color images in outdoor scenes is proposed. Shadows attenuate pixel intensity, and the degrees of attenuation are different in the three RGB color channels. Previously, we proposed the Tricolor Attenuation Model (TAM) that describes the attenuation relationship between shadows and their non-shadow backgrounds in the three color channels. TAM can provide strong information on shadow detection; however, our previous study needs a rough segmentation as the pre-processing step and requires four thresholds. These shortcomings can be overcome by adding intensity information. This article addresses the problem of how to combine TAM and intensity and meanwhile to obtain a threshold for shadow segmentation. Simple and complicated shadow images are used to test the proposed method. The experimental results and comparisons validate its effectiveness.

A robust template tracking algorithm with weighted active drift correction

In this paper, we propose a novel algorithm for object template tracking and its drift correction. It can prevent the tracking drift effectively, and save the time of an additional correction tracking. In our algorithm, the total energy function consists of two terms: the tracking term and the drift correction term. We minimize the total energy function synchronously for template tracking and weighted active drift correction. The minimization of the active drift correction term is achieved by the inverse compositional algorithm with a weighted L2 norm, which is incorporated into traditional affine image alignment (AIA) algorithm. Its weights can be adaptively updated for each template. For diminishing the accumulative error in tracking, we design a new template update strategy that chooses a new template with the lowest matching error. Finally, we will present various experimental results that validate our algorithm. These results also show that our algorithm achieves better performance than the inverse compositional algorithm for drift correction.

The registration of UAV down-looking aerial images to satellite images with image entropy and edges

In this paper, we propose a novel and efficient image registration algorithm between high resolution satellite images and UAV down-looking aerial images. The algorithm is achieved by a composite deformable template matching. To overcome the limitations of environment changes and different sensors, and to remain image information, we fuse the image edge and entropy features as image representation. According to the altitude information of the UAV, we can get the scales of the down-looking aerial images relative to the satellite images. In the following, we perform an effective search strategy in the satellite images to find the best matching position. Different experimental results show that the proposed algorithm is effective and robust.

A double-side filter based power line recognition method for UAV vision system

Automatic power line recognition from cluttered background is an important and challenging task for a vision based unmanned aerial vehicles (UAVs) power line inspection system. In this paper, we propose a power line recognition method based on liner object enhancement and parallel lines constraint. A new double-side filter is proposed to enhance the power lines, and then the radon transform is used to find the parallel lines as the power line recognition results. Our experiments on real image data captured from UAV demonstrate our method is effective for automatic power line recognition.

Boundary Detection Using Open Spline Curve Based on Mumford-Shah Model

Abstract Inspired by Cremerss work, this paper proposes a novel method for detecting open boundaries, such as coastline and skyline in an image. This method is based on B-spline function, curve evolution, and the cartoon model of Mumford-Shah functional (M-S model). Because the object to be detected is an open curve in the image domain, two constraint equations are introduced into the M-S model. Thus, the problem of open boundary detection becomes a minimal partition problem. With the partial differential equations (PDEs) of control points and constraint equations, the curve will stop on the desired boundary. The method can be used to detect automatically a curve that separates an image into two distinct regions and is not necessarily defined by gradient, even if the image is very noisy. In addition, with two open curves, our model can be extended to detect belt-like objects, such as rivers and roads.

An active contour method for tubular object segmentation

There are many tubular objects in image segmentation problems, such as vessel, road, and river and so on. In this paper, an active contour method is proposed for tubular object segmentation. This method is inspired by capillarity and based on the tubular structure enhancement. We get the tubular structure response direction as well as the response intensity by analyzing the Hessian matrices, and the response result is incorporated in the active contour model to control the curves evolution just along the tubular objects extend direction. This active contour method is fit for tubular object segmentation, the experiments results valid our method.

A novel color based object detection and localization algorithm

We propose a novel and robust color object detection and localization algorithm. Without a priori information about the number of objects, our method can detect all the objects with similar color feature in template. An improved histogram backprojection algorithm is used to find the object candidate regions. The weighted histogram intersection is used to verify the presence of objects. With the color feature in template, our method can detect and locate the objects accurately, get the number of objects, estimate their scales and orientations. Our experimental results on outdoor images obtained under different environments verify the effectiveness of our algorithm

Two‐step active contour method based on gradient flow

Purpose - Active contour can describe targets silhouette accurately and has been widely used in image segmentation and target tracking. Its main drawback is huge computation that is still not well resolved. The purpose of this paper is to optimize the evolving path of active contour, to reduce the computation cost and to make the evolution effectively. Design/methodology/approach - The contour-evolution process is separated into two steps: global translation and local deformation. The contour global translation and local deformation are realized by average and normal gradient flow of the evolving contour curve, respectively. Findings - When a contour is far away from the object to be segmented or tracked, the effective way of contour evolution is that it moves to the object without deformation first and then it deforms into the shape of the object when it moves on the object. Originality/value - The method presented in this paper can optimize the curve evolving path effectively without complicated calculation, such as rebuilding a new inner product, and its computation cost is largely reduced.