Search

When an unmanned surface vehicle (USV) navigates in narrow waterway scenarios, its ability to detect vanishing points accurately and quickly is highly important for safeguarding its navigation safety and realizing automated navigation. We propose a novel approach for detecting vanishing points based on an improved lightweight AlexNet. First, a similarity evaluation calculation method based on image texture features is proposed, by which some scenarios are selected from the filtered Google Street Road Dataset (GSRD). These filtered scenarios, together with the USV Inland Dataset (USVID), compose the training dataset, which is manually labeled according to a non-uniformly distributed grid level. Next, the classical AlexNet was adjusted and optimized by constructing sequential connections of four convolutional layers and four pooling layers and incorporating the Inception A and Inception C structures in the first two convolutional layers. During model training, we formulate vanishing point detection as a classification problem using an output layer with 225 discrete possible vanishing point locations. Finally, we compare and analyze the labeled vanishing point with the detected vanishing point. The experimental results show that the accuracy of our method and the state-of-the-art algorithmic vanishing point detector improves, indicating that our improved lightweight AlexNet can be applied in narrow waterway navigation scenarios and can provide a technical reference for autonomous navigation of USVs. [ABSTRACT FROM AUTHOR]

Previous camera self-calibration methods have exhibited certain notable shortcomings. On the one hand, they either exclusively emphasized scene cues or solely focused on vehicle-related cues, resulting in a lack of adaptability to diverse scenarios and a limited number of effective features. Furthermore, these methods either solely utilized geometric features within traffic scenes or exclusively extracted semantic information, failing to comprehensively consider both aspects. This limited the comprehensive feature extraction from scenes, ultimately leading to a decrease in calibration accuracy. Additionally, conventional vanishing point-based self-calibration methods often required the design of additional edge-background models and manual parameter tuning, thereby increasing operational complexity and the potential for errors. Given these observed limitations, and in order to address these challenges, we propose an innovative roadside camera self-calibration model based on the Transformer architecture. This model possesses a unique capability to simultaneously learn scene features and vehicle features within traffic scenarios while considering both geometric and semantic information. Through this approach, our model can overcome the constraints of prior methods, enhancing calibration accuracy and robustness while reducing operational complexity and the potential for errors. Our method outperforms existing approaches on both real-world dataset scenarios and publicly available datasets, demonstrating the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

When an unmanned surface vehicle (USV) navigates in narrow waterway scenarios, its ability to detect vanishing points accurately and quickly is highly important for safeguarding its navigation safety and realizing automated navigation. We propose a novel approach for detecting vanishing points based on an improved lightweight AlexNet. First, a similarity evaluation calculation method based on image texture features is proposed, by which some scenarios are selected from the filtered Google Street Road Dataset (GSRD). These filtered scenarios, together with the USV Inland Dataset (USVID), compose the training dataset, which is manually labeled according to a non-uniformly distributed grid level. Next, the classical AlexNet was adjusted and optimized by constructing sequential connections of four convolutional layers and four pooling layers and incorporating the Inception A and Inception C structures in the first two convolutional layers. During model training, we formulate vanishing point detection as a classification problem using an output layer with 225 discrete possible vanishing point locations. Finally, we compare and analyze the labeled vanishing point with the detected vanishing point. The experimental results show that the accuracy of our method and the state-of-the-art algorithmic vanishing point detector improves, indicating that our improved lightweight AlexNet can be applied in narrow waterway navigation scenarios and can provide a technical reference for autonomous navigation of USVs.

Vanishing point (VP) detection in road images plays an important role in driving scenes for advanced driver assistance systems (ADAS) and autonomous vehicles. Because it is still a challenging problem to obtain expensive annotated datasets in deep-learning-based supervised training, in this paper, we propose a semi-supervised VP detection method in road images. Our proposed model first extracts high-resolution VP heatmaps of the road images by fusing multi-level and global–local contrastive learning. Then, we apply the π model as a semi-supervised learning framework and feed the whole global network with a small number of training samples to detect VPs. In the experiment, we used Kong's dataset for the unstructured road test and the KITTI-VP dataset for the structured road test. Compared with the state-of-the-art methods, our model achieved high accuracy and robustness in road VP detection. • As the first attempt at self-supervised VP detection, our approach uses contrastive learning for self supervised training, directly extracting road features without manually labeled data and applying the semi-supervised π -model for training with a small amount of labeled data. • Our approach combines multi-level and global–local feature extraction to improve the VP detection ability. • The proposed approach can solve the problem of both unstructured and structured road VP detection in complex road scenes, including different obstacles, lighting, extreme weather, shapes, etc. • We have built a structured road VP detection dataset KITTI-VP, including 6500 road images from the KITTI dataset. [ABSTRACT FROM AUTHOR]

Previous camera self-calibration methods have exhibited certain notable shortcomings. On the one hand, they either exclusively emphasized scene cues or solely focused on vehicle-related cues, resulting in a lack of adaptability to diverse scenarios and a limited number of effective features. Furthermore, these methods either solely utilized geometric features within traffic scenes or exclusively extracted semantic information, failing to comprehensively consider both aspects. This limited the comprehensive feature extraction from scenes, ultimately leading to a decrease in calibration accuracy. Additionally, conventional vanishing point-based self-calibration methods often required the design of additional edge-background models and manual parameter tuning, thereby increasing operational complexity and the potential for errors. Given these observed limitations, and in order to address these challenges, we propose an innovative roadside camera self-calibration model based on the Transformer architecture. This model possesses a unique capability to simultaneously learn scene features and vehicle features within traffic scenarios while considering both geometric and semantic information. Through this approach, our model can overcome the constraints of prior methods, enhancing calibration accuracy and robustness while reducing operational complexity and the potential for errors. Our method outperforms existing approaches on both real-world dataset scenarios and publicly available datasets, demonstrating the effectiveness of our approach.

To extract the centerline of fruit tree rows walked by orchard robots, a method of fruit tree row centerline extraction based on vanishing points is proposed. Firstly, a texture-based vanishing point detection algorithm is proposed to address the problems of large computation and low accuracy of existing vanishing point detection algorithms; then the detected vanishing point is used as a constraint to obtain a more accurate fruit tree row road boundary line; finally, the centerline of fruit tree rows is calculated to obtain the centerline of fruit tree rows. The results show that the method can accurately obtain the centerline of fruit tree rows, the accuracy rate is improved compared with the original algorithm, and the computing time is shortened to one-third of the original one. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

By detecting and identifying the vanishing point position in the image, it is able to assist mobile robots in mines roadways for autonomous navigation. The existing vanishing point detection methods have large errors in the mine roadway with poor lighting conditions and insufficient structured information. In order to solve the above problems, a vanishing point detection method in complex environment of mine roadways is proposed. Firstly, the image is pre-processed by reducing, filtering, graying, etc. This method can reduce the calculation amount significantly and the straight line characteristics can be better preserved. Then, the straight line detection algorithm is used to detect the straight line of the image. The straight line length threshold and the average gradient constraint are introduced to eliminate the interference line with small length and the interference line generated by shadows in the image respectively. Moreover, the block matching algorithm is used to generate the block motion trajectory straight line of the image. Finally, the straight lines after removing the interference and the block motion trajectory straight lines are converted into sample points in the parameter space. The outlier factor value of each sample point is calculated by the local anomaly factor algorithm, and the outlier factor value of the sample point and the length of the corresponding straight line are used as the criteria to measure the importance of the sample points. On this basis, the weight function of the weighted regression algorithm is designed to obtain the best estimate of the vanishing point. The experimental results on the mine roadway data set and public data set show that compared with the edge-based vanishing point detection method and the deep learning-based vanishing point detection method, the method in this paper has stronger robustness to light changes. It has higher accuracy in complex environment with poor lighting conditions and lack of straight line information, and has better real-time performance than the vanishing point detection method based on deep learning. This method can better meet the needs of mine roadway robot navigation.

Within the computer vision field, estimating image vanishing points has many applications regarding robotic navigation, camera calibration, image understanding, visual measurement, 3D reconstruction, among others. Different methods for detecting vanishing points relies on accumulator space techniques, while others employ a heuristic approach such as RANSAC. Nevertheless, these types of methods suffer from low accuracy or high computational cost. To explore a different technique, this paper focuses on improving the efficiency of the metaheuristic search for vanishing points by using a recently proposed population‐based method: The Teaching Learning Based Optimisation algorithm (TLBO). The TLBO algorithm is a metaheuristic technique inspired by the teaching–learning process. In our method, the TLBO algorithm is used after a line segment detection, to cluster line segments according to their more optimal vanishing point. Thus, our algorithm detects both orthogonal and nonorthogonal vanishing points in real images. To corroborate the performance of our proposed algorithm, different comparison and tests with other approaches were carried out. The results validate the accuracy and efficiency of our proposed method. Our approach had an average computational time of1.42 seconds and obtained a cumulative focal length error of 1 pixel, and cumulative angular error of 0.1°.

In modern railway systems, video surveillance and machine vision analysis have been widely used to detect perimeter intrusions. For pan-tilt-zoom (PTZ) cameras, the machine vision system needs to detect adjustments in PTZ cameras and then automatically determine the new alarm region in real time. In this paper, we propose a deep multi-task learning based algorithm for simultaneous vanishing point (VP) detection and rail segmentation, which can identify camera adjustment from changes in VP, and then automatically determine the alarm region from segmented rails. The multi-task based neural network consists of a feature extraction base network and three sub-task networks. The first sub-task network is a convolution regression network for VP detection. The second sub-task network utilizes an encoder-decoder structure for vanishing region (VR, a fixed region centered on VP) segmentation. The third sub-task network shares the encoder-decoder structure with the VR segmentation task and is used for rail segmentation. The VR segmentation task is activated only at the training stage, serving as an auxiliary task to enhance feature learning ability and increase VP detection accuracy. To further improve the accuracies of VP detection and rail segmentation, low-level features is modulated by high-level semantic information before feeding to the decoder stage. With the help of shared feature extraction and auxiliary training, the proposed VP prediction method needs very small training dataset and outperforms other methods in both efficiency and accuracy.

Copyright of Technisches Messen is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Interests of auto-calibration have been increased in several camera systems. This paper presents a novel self-calibration method using fast and accurate vanishing point detection algorithm that works in manmade environments. The proposed algorithm estimates focal length assuming that the principal point is the center of an image to satisfy the orthogonality of three vanishing points. By using proposed vanishing point detection algorithm and minimization of the proposed objective function, the proposed system detects accurate vanishing points with focal length outperforming other methods. The proposed vanishing point detection algorithm detects vanishing points by using J-linkage based method that is more delicate by fragmentation and re-merging strategies. The proposed objective function finally detects vanishing points that meets orthogonality among estimated hypotheses for vanishing points by checking several geometric relationships. We believe that the proposed method can be used for automatic camera calibration, localization of a camera in an autonomous navigation system, and three-dimensional reconstruction of a single-view image. [ABSTRACT FROM AUTHOR]

Within the computer vision field, estimating image vanishing points has many applications regarding robotic navigation, camera calibration, image understanding, visual measurement, 3D reconstruction, among others. Different methods for detecting vanishing points relies on accumulator space techniques, while others employ a heuristic approach such as RANSAC. Nevertheless, these types of methods suffer from low accuracy or high computational cost. To explore a different technique, this paper focuses on improving the efficiency of the metaheuristic search for vanishing points by using a recently proposed population‐based method: The Teaching Learning Based Optimisation algorithm (TLBO). The TLBO algorithm is a metaheuristic technique inspired by the teaching–learning process. In our method, the TLBO algorithm is used after a line segment detection, to cluster line segments according to their more optimal vanishing point. Thus, our algorithm detects both orthogonal and nonorthogonal vanishing points in real images. To corroborate the performance of our proposed algorithm, different comparison and tests with other approaches were carried out. The results validate the accuracy and efficiency of our proposed method. Our approach had an average computational time of1.42 seconds and obtained a cumulative focal length error of 1 pixel, and cumulative angular error of 0.1°. [ABSTRACT FROM AUTHOR]

With the development of the world economy and the accelerating process of urbanization, cars have brought great convenience to people's lives and activities, and have become an indispensable means of transportation. Intelligent vehicles have the important significance of reducing traffic accidents, improving transportation capacity and broad market prospects, and can lead the future development of the automotive industry, so they have received extensive attention. In the existing intelligent vehicle system, the laser radar is a well-deserved protagonist because of its excellent speed and precision. It is an indispensable part of achieving high-precision positioning, but to some extent, the price hindering its marketization is a major factor. Compared with lidar sensors, vision sensors have the advantages of fast sampling rate, light weight, low energy consumption and low price. Therefore, many domestic and foreign research institutions have listed them as the focus of research. However, the current vision-based intelligent vehicle environment sensing technology is also susceptible to factors such as illumination, climate and road type, resulting in insufficient accuracy and real-time performance of the algorithm. This paper takes the environment perception of intelligent vehicles as the research object, and conducts in-depth research on the existing problems in road recognition and obstacle detection algorithms, including road image vanishing point detection, road image segmentation problem, road scene based on binocular vision. Three-dimensional reconstruction and obstacle detection technology. [ABSTRACT FROM AUTHOR]

Robust vanishing point estimation has been widely applied to various applications in the field of computer vision and pattern recognition for robotics, advanced driver assistance systems, and autonomous driving vehicles. The major challenge for vanishing point detection lies in line segments, spurious vanishing candidate removal, and clustering for refinement. Recent vanishing point detection approaches have attempted to reduce the computational complexity involved with voting processes using optimized voter selection strategies to identify the vanishing point from line segments. This paper proposes a novel vanishing point detection method to select robust candidates, applying optimized minimum spanning tree-based clustering of the vanishing point candidates by analyzing the lines within a unit sphere domain. The proposed scheme was applied to an open database that included illumination, partial occlusion, and viewpoint changes to validate robustness without prior scene information. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Vanishing point detection is an essential component of vision-based autonomous navigation for unmanned ground vehicles and mobile robots. In this paper, we propose an accurate and robust vanishing point detection method for unstructured road scenes, where the road scenes lack clear road markings and include complex background interference. Since only the road region provides informative clues for vanishing point detection, we first introduce the manifold ranking method to estimate the road region based on background suppression. Then, we develop a series of principles for voter selection, and propose a dynamic adjustment strategy for the candidate selection that reduces the search scope of the vanishing point to perform candidate selection. Finally, we propose an effective voting strategy, in which the candidate that achieves the greatest number of votes in the voting space is considered to be the vanishing point. The experimental results on a large number of unstructured road images show that our proposed method is more accurate and robust than five existing methods. [ABSTRACT FROM AUTHOR]

The vanishing point provides a strong ability to infer the 3D structure of the scene. It finds great application in image composition analysis, lane detection, camera calibration and salience detection. Many methods have been proposed to predict the location of vanishing point. They are usually based on geometrical and structural features such as lines or contours. However, such methods suffer deteriorated accuracy due to the large number of outlier line segments in natural landscape images. In this paper, we propose a semantic-texture fusion network to detect the dominant vanishing point in the image. The proposed network includes two branches. The first branch is based on the Holistically-Nested Edge Detection Network which extracts textural features. The second branch aims to extract the semantic features. In order to boost the representational power of a network, we adopt the Squeeze-and-Excitation block to model the interdependencies between the semantic features and the textural features. Experimental results reveal a step forward against the state-of-the-art vanishing point detection methods in natural landscapes. Based on the detection results, we further demonstrate how the proposed model can be used to provide on-line guidance to amateur photographers. [ABSTRACT FROM AUTHOR]

In this paper, we propose a vanishing-point constrained Dijkstra road model for road detection in a stereo-vision paradigm. First, the stereo-camera is used to generate the u- and v-disparity maps of road image, from which the horizon can be extracted. With the horizon and ground region constraints, we can robustly locate the vanishing point of road region. Second, a weighted graph is constructed using all pixels of the image, and the detected vanishing point is treated as the source node of the graph. By computing a vanishing-point constrained Dijkstra minimum-cost map, where both disparity and gradient of gray image are used to calculate cost between two neighbor pixels, the problem of detecting road borders in image is transformed into that of finding two shortest paths that originate from the vanishing point to two pixels in the last row of image. The proposed approach has been implemented and tested over 2600 grayscale images of different road scenes in the KITTI data set. The experimental results demonstrate that this training-free approach can detect horizon, vanishing point, and road regions very accurately and robustly. It can achieve promising performance. [ABSTRACT FROM PUBLISHER]

The vision‐based lane detection is an important component of advanced driver assistance systems and it is essential for lane departure warning, lane keeping, and vehicle localisation. However, it is a challenging problem to improve the robustness of multi‐lane detection due to factors, such as perspective effect, possible low visibility of lanes, and partial occlusions. To deal with these issues, the authors propose an improved lane hypothesis generation method using a reliable binary blob analysis. Most existing top‐view based methods focused on the lane model fitting, but they neglected the reliability of hypothesis generation and the effectiveness in challenging conditions. To cope with these shortcomings, the authors carried out vanishing point detection and inverse perspective mapping to remove the perspective effect from the road images. Then two‐stage binary blob filtering and blob verification techniques using classification are introduced to improve the robustness of lane hypothesis generation for lane detection. The experimental results show that the average detection accuracy on a new challenging multi‐lane dataset is 97.7%. The performance of the proposed method outperforms that of the state‐of‐the‐art method by 1.6% in detection accuracy on the Caltech lane benchmark dataset. [ABSTRACT FROM AUTHOR]

In this paper, we focus on fully automatic traffic surveillance camera calibration, which we use for speed measurement of passing vehicles. We improve over a recent state-of-the-art camera calibration method for traffic surveillance based on two detected vanishing points. More importantly, we propose a novel automatic scene scale inference method. The method is based on matching bounding boxes of rendered 3D models of vehicles with detected bounding boxes in the image. The proposed method can be used from arbitrary viewpoints, since it has no constraints on camera placement. We evaluate our method on the recent comprehensive dataset for speed measurement BrnoCompSpeed. Experiments show that our automatic camera calibration method by detection of two vanishing points reduces error by 50% (mean distance ratio error reduced from 0.18 to 0.09) compared to the previous state-of-the-art method. We also show that our scene scale inference method is more precise, outperforming both state-of-the-art automatic calibration method for speed measurement (error reduction by 86 % – 7.98 km/h to 1.10 km/h) and manual calibration (error reduction by 19 % – 1.35 km/h to 1.10 km/h). We also present qualitative results of the proposed automatic camera calibration method on video sequences obtained from real surveillance cameras in various places, and under different lighting conditions (night, dawn, day). [ABSTRACT FROM AUTHOR]

The detection of multiple curved lane markings is still a challenge for advanced driver assistance systems today, due to interference such as road markings and shadows cast by roadside structures and vehicles. The vanishing point \mathbfVp contains the global information of the road image. Hence, \mathbfVp-based lane detection algorithms are quite insensitive to interference. When curved lanes are assumed, \mathbfVp shifts with respect to the rows of the image. In this paper, a \mathbfVp for each individual row of the image is estimated by first extracting a \mathbfVpy (vertical position of the \mathbfVp) for each individual row of the image from the v-disparity. Then, based on the estimated \mathbfVpy's, a 2-D \mathbfVpx (horizontal position of the \mathbfVp) accumulator is efficiently formed. Thus, by globally optimizing this 2-D \mathbfVpx accumulator, globally optimum \mathbfVp s for the road image are extracted. Then, estimated \mathbfVp s are utilized for multiple curved lane marking detection on nonflat road surfaces. The resultant system achieves a detection rate of 99% in 1862 frames of six stereo vision test sequences. [ABSTRACT FROM PUBLISHER]

Vanishing-point detection is an important component for the visual navigation system of an autonomous mobile robot. In this paper, we present a novel line space voting method for fast vanishing-point detection. First, the line segments are detected from the road image by the line segment detector (LSD) method according to the pixel's gradient and texture orientation computed by the Sobel operator. Then, the vanishing-point of the road is voted on by considering the points of the lines and their neighborhood spaces with weighting methods. Our algorithm is simple, fast, and easy to implement with high accuracy. It has been experimentally tested with over hundreds of structured and unstructured road images. The experimental results indicate that the proposed method is effective and can meet the real-time requirements of navigation for autonomous mobile robots and unmanned ground vehicles. [ABSTRACT FROM AUTHOR]

The multi-view perspectives are captured around a scene by camera array at the same time, and multi-view images cannot only keep two dimensions information but also has the depth architecture of a scene. Most of 3D displays support the multi-view contents, while there is a bottleneck of the 3D TV system to broadcast the larger storage and bandwidth for enduser. For saving bandwidth, a new TV broadcasting channel transfers third dimensional information, i.e., depth map has been utilized. The 3D display renders intermediate image and a depth map to form different views, as well as synthesizes these images into one image; this technology is the renowned Depth Image Based Rendering (DIBR). In other words, any 2D content can be transformed into multi-view images according to their corresponding depth map. Our system processes 2D images to capture a depth map of the intermediate view image during 3D contents capturing. In this paper, we utilize the vanishing point detection and color image segmentation technique to find the objects and deepest point (i.e., vanishing point) in the image, and then we assign depth value by comparing the vanishing point of object with image. After depth map generation, we propose the Vivid-DIBR system to imitate how human eyes see things. On the other hand, this system solves the holes (warping error points) problem by redistributing corresponding depth map. The design aims to convert 2D images into multi-view images which will be suitable for any interlacing 3D display by adjusting the focal plane location. [ABSTRACT FROM PUBLISHER]

In this paper, a novel approach for creating 3D models of building scenes is presented. The proposed method is fully automated and fast, and accurately reconstructs both outdoor images of a building and indoor scenes, with perspective cues in real-time, using only one image. It combines the extracted line segments to identify the vanishing points of the image, the orientation, the different planes that are depicted in the image and concludes whether the image depicts indoor or outdoor scenes. In addition, the proposed method efficiently eliminates the perspective distortion and produces an accurate 3D model of the scene without any intervention from the user. The main innovation of the method is that it uses only one image for the 3D reconstruction, while other state-of-the-art methods rely on the processing of multiple images. A website and a database of 100 images were created to prove the efficiency of the proposed method in terms of time needed for the 3D reconstruction, its automation and 3D model accuracy and can be used by anyone so as to easily produce user-generated 3D content: [ABSTRACT FROM AUTHOR]

In this paper, we propose a generalized Laplacian of Gaussian (LoG) (gLoG) filter for detecting general elliptical blob structures in images. The gLoG filter can not only accurately locate the blob centers but also estimate the scales, shapes, and orientations of the detected blobs. These functions can be realized by generalizing the common 3-D LoG scale-space blob detector to a 5-D gLoG scale-space one, where the five parameters are image-domain coordinates (x, y), scales (\sigmax, \sigmay), and orientation (\theta), respectively. Instead of searching the local extrema of the image's 5-D gLoG scale space for locating blobs, a more feasible solution is given by locating the local maxima of an intermediate map, which is obtained by aggregating the log-scale-normalized convolution responses of each individual gLoG filter. The proposed gLoG-based blob detector is applied to both biomedical images and natural ones such as general road-scene images. For the biomedical applications on pathological and fluorescent microscopic images, the gLoG blob detector can accurately detect the centers and estimate the sizes and orientations of cell nuclei. These centers are utilized as markers for a watershed-based touching-cell splitting method to split touching nuclei and counting cells in segmentation-free images. For the application on road images, the proposed detector can produce promising estimation of texture orientations, achieving an accurate texture-based road vanishing point detection method. The implementation of our method is quite straightforward due to a very small number of tunable parameters. [ABSTRACT FROM PUBLISHER]

Automatic road marking recognition is a key problem within the domain of automotive vision that lends support to both autonomous urban driving and augmented driver assistance such as situationally aware navigation systems. Here we propose an approach to this problem based on the extraction of robust road marking features via a novel pipeline of inverse perspective mapping and multi-level binarisation. A trained classifier combined with additional rule-based post-processing then facilitates the real-time delivery of road marking information as required. The approach is shown to operate successfully over a range of lighting, weather and road surface conditions. [ABSTRACT FROM AUTHOR]

Abstract: Vanishing point detection algorithms based on 2D histogramming techniques have been employed in a variety of computer vision systems. Previous algorithms achieved some good results but still failed to maintain a balanced performance in both accuracy and time. Recent research () shows that, vanishing point detection could be converted to a 1D histogram search problem, which largely accelerates the procedure. In this paper, we further improve this idea and propose a complete scheme for vanishing point detection from images of the so called “Manhattan world”. We test our algorithm and some commonly used vanishing point detection methods on public database YorkUrbanDB and our own implemented database PKUCampusDB. Our algorithm shows significant performance improvements. [Copyright &y& Elsevier]

We propose a novel way to augment a real-world scene with minimal user intervention on a mobile phone; the user only has to point the phone camera to the desired location of the augmentation. Our method is valid for horizontal or vertical surfaces only, but this is not a restriction in practice in manmade environments, and it avoids going through any reconstruction of the 3-D scene, which is still a delicate process on a resource-limited system like a mobile phone. Our approach is inspired by recent work on perspective patch recognition, but we adapt it for better performances on mobile phones. We reduce user interaction with real scenes by exploiting the phone accelerometers to relax the need for fronto-parallel views. As a result, we can learn a planar target in situ from arbitrary viewpoints and augment it with virtual objects in real-time on a mobile phone. [ABSTRACT FROM AUTHOR]

Given a single image of an arbitrary road, that may not be well-paved, or have clearly delineated edges, or some a priori known color or texture distribution, is it possible for a computer to find this road? This paper addresses this question by decomposing the road detection process into two steps: the estimation of the vanishing point associated with the main (straight) part of the road, followed by the segmentation of the corresponding road area based upon the detected vanishing point. The main technical contributions of the proposed approach are a novel adaptive soft voting scheme based upon a local voting region using high-confidence voters, whose texture orientations are computed using Gabor filters, and a new vanishing-point-constrained edge detection technique for detecting road boundaries. The proposed method has been implemented, and experiments with 1003 general road images demonstrate that it is effective at detecting road regions in challenging conditions. [ABSTRACT FROM AUTHOR]

A new automated approach for vanishing point detection in images of urban scenes is described. This method is based on the theorem of Thales. The main contribution of this paper is the automatic and simultaneous detection of all vanishing points of the image, achieved by converting this problem into the detection of circles in a complex cloud of points, in which each point corresponds to a segment and is associated with an uncertainty. This extraction of circles uses a RANSAC method, modified to improve its speed by using accumulation techniques (Hough transform or otherwise). This robust estimation is then refined by least squares error propagation using the individual variances of each segment. The algorithm is robust, its accuracy is optimised and it is entirely automatic. Its successful operation has been tested on a large number of images of varied urban scenes. [ABSTRACT FROM AUTHOR]

Abstract: In the literature, different approaches of artificial visual systems inspired to biological solutions can be found. In this paper a particular solution will be described with an experimental case study: the foveated retina approach. The solution can be considered as a proposal of ad-hoc hardware implementation of biological inspired solutions, but also as a model for the internal virtual representation of the real world structures. [Copyright &y& Elsevier]

In this paper, we present a robust road detection and tracking method based on a condensation particle filter for real-time video-based navigation applications. The image is divided into horizontal strips, and vanishing point (VP) detection is performed on each image strip. We propose a method for estimating the density of road boundary line segments in the image so that VP detection in an image strip takes into account the detection results in the neighboring image strips. This use of contextual information for VP detection leads to more accurate detection results. The estimated road parameters are then used to initialize the condensation tracker. Experiments using real road videos demonstrate the robustness of our method to difficult road conditions due to the presence of partial occlusion, shadows, and road signs. [ABSTRACT FROM PUBLISHER]