Research on detection of lane based on machine vision

To prevent a vehicle from departing the lane in assistant or automatic steering, real-time vision-based detection of lane is studied. The system architecture, detecting principle and lane model are described. Then the detecting algorithm of the lane image is discussed in detail. In this algorithm, several proper sub-windows in one image are first selected as the processing regions. To every sub-window, by means of such steps as appropriate pre-processing, edge detection and Hough transform, etc., the lane description features are extracted. Experimental results reveal that this detection method is of good real-time, high recognition reliability and strong robustness, etc., which can provide the decision-making foundation for the following automatic or assistant steering to some extent.

刍议小学古诗词教学

古诗词是传统文化的精华。古诗词在小学语文教学中占有重要地位。然而,古诗词的教学现状却不容乐观:传统教学以逐句理解为核心;象征性地谈感受,总结;学生缺乏想象,体悟不透;在教学中,如何培养学生的学习兴趣,以及提高学生学习的能力,进行以下几点探讨。

RIGHT INFERIOR FRONTAL GYRUS PROCESSING OF VISUAL EROTIC INFORMATION BASED ON fMRI

Visually-induced erotic arousal evoked by pornographic visual stimuli, such as films or photographs, is a common occurrence in human behavior. The brain activation associated with visual erotic stimuli in heterosexual right handed females is studied. Functional magnetic resonance imaging is used to investigate 15 female partici- panterotic arousal induced by visual stimuli in film and picture forms, respectively, performing three or more times during their menstrual cycle on a 3.0T magnetic resonance imaging scanner. There is activation of a set of bilateral brain areas, including the inferior lateral occipital cortex, the anterior supramarginal gyrus, the parietal operculum cortex, the superior parietal lobules, the right inferior frontal gyrus, the cerebellum, the hypothalamus, the thalamus, the hippocampus, and the mid-brain. From different regions, the brain activation is observed and the inferior frontal gyrus has found to be task-independent. Furthermore, the right inferior frontal gyrus has more activation than the left inferior frontal gyrus. The result shows that the right inferior frontal gyrus plays an important role in pornographic information processing rather than being activated stimuli property specific. It is presented for the first time that the functional laterization of the inferior frontal gyrus is bi-directional rather than single （left） directional.

Objective measurement for image defogging algorithms

Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations,three new methods for assessing the defogging algorithm were proposed.One was using synthetic foggy image simulated by image degradation model to assess the defogging algorithm in full-reference way.In this method,the absolute difference was computed between the synthetic image with and without fog.The other two were computing the fog density of gray level image or constructing assessment system of color image from human visual perception to assess the defogging algorithm in no-reference way.For these methods,an assessment function was defined to evaluate algorithm performance from the function value.Using the defogging algorithm comparison,the experimental results demonstrate the effectiveness and reliability of the proposed methods.

Global shape reconstruction of nano grid with singly fixed camera

Depth from defocus(DFD),as a typical shape reconstruction method,has been widely researched in most recent years.However,all the existing DFD algorithms require at least two defocused images with different camera parameters.Unfortunately,in micro/nano manipulation,any change on visual sensor's parameters is absolutely forbidden.Therefore,a novel DFD method to reconstruct the shape of a nano grid on micro/nano scale is researched in this paper.First,the blurring imaging model is constructed with the relative blurring and the diffusion equation.Second,the relationship between depth and blurring is discussed from four aspects.Subsequently,depth measurement problem is transformed into an optimization issue which is solved using the gradient flow algorithm.Finally,experiment results and error analysis are conducted to show the feasibility and effectiveness of the proposed method.

An area-based position and attitude estimation for unmanned aerial vehicle navigation

The paper aims to challenge non-GPS navigation problems by using visual sensors and geo-referenced images. An area-based method is proposed to estimate full navigation parameters(FNPs), including attitude, altitude and horizontal position, for unmanned aerial vehicle(UAV) navigation. Our method is composed of three main modules: geometric transfer function, local normalized sobel energy image(LNSEI) based objective function and simplex-simulated annealing(SSA) based optimization algorithm. The adoption of relatively rich scene information and LNSEI, makes it possible to yield a solution robustly even in the presence of very noisy cases, such as multi-modal and/or multi-temporal images that differ in the type of visual sensor, season, illumination, weather, and so on, and also to handle the sparsely textured regions where features are barely detected or matched. Simulation experiments using many synthetic images clearly support noise resistance and estimation accuracy, and experimental results using 2367 real images show the maximum estimation error of 5.16(meter) for horizontal position, 9.72(meter) for altitude and 0.82(degree) for attitude.

Robust water hazard detection for autonomous off-road navigation

Existing water hazard detection methods usually fail when the features of water surfaces are greatly changed by the surroundings, e.g., by a change in illumination. This paper proposes a novel algorithm to robustly detect different kinds of water hazards for autonomous navigation. Our algorithm combines traditional machine learning and image segmentation and uses only digital cameras, which are usually affordable, as the visual sensors. Active learning is used for automatically dealing with problems caused by the selection, labeling and classification of large numbers of training sets. Mean-shift based image segmentation is used to refine the final classification. Our experimental results show that our new algorithm can accurately detect not only ‘common’ water hazards, which usually have the features of both high brightness and low texture, but also ‘special’ water hazards that may have lots of ripples or low brightness.