With the rapid development of Urban Rail Transit Signal Technology, the demand of signal power supply system for signal equipment is higher and higher. The signal intelligent power supply panel is the main component of the urban rail traffic signal power supply system. Whether the intelligent power supply panel working or not is directly related to traffic safety. The maintenance of intelligent signal power supply panel is particularly important. Line 7 of Shanghai Metro adopts PMZG Signal Intelligent Power Supply Panel, which is produced by Beijing Jinyujiaxin Polytron Technologies Inc. Maintenance of power supply system mainly includes routine maintenance and troubleshooting. This article will make clear the routine maintenance contents of PMZG Signal Intelligent Power Supply Panel, and put forward the common fault information and troubleshooting methods of PMZG Signal Intelligent Power Supply Panel. In accordance with the steps of fault handling, the faults can be eliminated in the shortest possible time, and PMZG Signal Intelligent Power Supply Panel can be quickly restored to normal working state.
Techniques of remote sensing have been improved incredibly in recent years and very accurate results and high resolution images can be acquired. There exist possible ways to use such data to reconstruct railroads. In this paper, an automated railroad reconstruction method from remote sensing images based on Gabor filter was proposed. The method is divided in three steps. Firstly, the edge-oriented railroad characteristics (such as line features) in a remote sensing image are detected using Gabor filter. Secondly, two response images with the filtering orientations perpendicular to each other are fused to suppress the noise and acquire a long stripe smooth region of railroads. Thirdly, a set of smooth regions can be extracted by firstly computing global threshold for the previous result image using Otsu's method and then converting it to a binary image based on the previous threshold. This workflow is tested on a set of remote sensing images and was found to deliver very accurate results in a quickly and highly automated manner.
The condition detection of rails in high-speed railway is one of the important means to ensure the safety of railway transportation. In order to replace the traditional manual inspection, save manpower and material resources, and improve the detection speed and accuracy, it is of great significance to develop a machine vision system for locating and identifying defects on rails automatically. Rail defects exhibit different properties and are divided into various categories related to the type and position of flaws on the rail. Several kinds of interrelated factors cause rail defects such as type of rail, construction conditions, and speed and/or frequency of trains using the rail. Rail corrugation is a particular kind of defects that produce an undulatory deformation on the rail heads. In high speed train, the corrugation induces harmful vibrations on wheels and its components and reduces the lifetime of rails. This type of defects should be detected to avoid rail fractures. In this paper, a novel method for fast rail corrugation detection based on texture filtering was proposed.
Edges and contours of an object contain a lot of information, so the detection and extraction of saliency edges and contours in the image become one of the most active issues in the research field of automatic target recognition. Weak edge enhancement plays an important role in contour detection. Based on psychophysical and physiological findings, a contour detection method which focuses on weak edge enhancement and inspired by the visual mechanism in the primary visual cortex (V1) is proposed in this paper. The method is divided in three steps. Firstly, the response of every single visual neuron in V1 is computed by local energy. Secondly, the local contrast which corresponds to the CRF is computed. If the local contrast in the image is below the low contrast threshold, expand NCRF to change the spatially modulatory range by increasing the NCRF radius. Thirdly, the facilitation and suppression (the contextual influence) on a neuron through horizontal interactions are obtained by using a spatially unified modulating function. We tested it on synthetic images and encouraging results were acquired.
Many challenging computer vision problems have been proven to benefit from the incorporation of depth information, to name a few, semantic labellings, pose estimations and even contour detection. Different objects have different depths from a single monocular image. The depth information of one object is coherent and the depth information of different objects may vary discontinuously. Meanwhile, there exists a broad non-classical receptive field (NCRF) outside the classical receptive field (CRF). The response of the central neuron is affected not only by the stimulus inside the CRF, but also modulated by the stimulus surrounding it. The contextual modulation is mediated by horizontal connections across the visual cortex. Based on the findings and researches, a biological-inspired contour detection model which combined with depth information is proposed in this paper.
Automatic gender recognition based on face images plays an important role in computer vision and machine vision. In
this paper, a novel and simple gender recognition method based on face geometric features is proposed. The method is
divided in three steps. Firstly, Pre-processing step provides standard face images for feature extraction. Secondly, Active
Shape Model (ASM) is used to extract geometric features in frontal face images. Thirdly, Adaboost classifier is chosen
to separate the two classes (male and female). We tested it on 2570 pictures (1420 males and 1150 females) downloaded
from the internet, and encouraging results were acquired. The comparison of the proposed geometric feature based
method and the full facial image based method demonstrats its superiority.
Outside the classical receptive field (CRF), there exists a broad non-classical receptive field (NCRF). The response of
the central neuron is affected not only by the stimulus inside the CRF, but also modulated by the stimulus surrounding it.
The contextual modulation is mediated by horizontal connections across the visual cortex. In this paper, a contour
detection method inspired by the visual mechanism in the primary visual cortex (V1) is proposed. The method is divided
in three steps. Firstly, the response of every single visual neuron in V1 is computed by local energy. Secondly, the
facilitation and suppression (the contextual influence) on a neuron through horizontal interactions are obtained by
constructing a two neighbor modulating functions. Finally, the total output response of one neuron to complex visual
stimuli is acquired by combing the influence of local visual context on the neuron and energy response by itself. We
tested it on natural image and encouraging results were acquired.
Monitoring urbanization may help government agencies and urban region planners in updating land maps and forming
long-term plans accordingly. In this paper, a novel method for fast extracting residential area from remote sensing
images based on log-Gabor filter was proposed. The method is divided in three steps. Firstly, we detect the edge-oriented
urban characteristics in a remote sensing image using log-Gabor filter. Secondly, with the filtering orientations
perpendicular to each other, we choose two log-Gabor filter response images to suppress the noise and acquire a smooth
spatial region. Thirdly, a set of smooth regions served as residential areas can be extracted using Otsu's method. We
tested it on diverse aerial and satellite images and encouraging results were acquired. The comparison of our method
with the classical texture analyzing method of co-occurrence matrix demonstrated its superiority.
Comprehensive quantitative evaluation of tumor segmentation technique on large scale clinical data sets is crucial
for routine clinical use of CT based tumor volumetry for cancer diagnosis and treatment response evaluation.
In this paper, we present a systematic validation study of a semi-automatic image segmentation technique for
measuring tumor volume from CT images. The segmentation algorithm was tested using clinical data of 200
tumors in 107 patients with liver, lung, lymphoma and other types of cancer. The performance was evaluated
using both accuracy and reproducibility. The accuracy was assessed using 7 commonly used metrics that can
provide complementary information regarding the quality of the segmentation results. The reproducibility was
measured by the variation of the volume measurements from 10 independent segmentations. The effect of
disease type, lesion size and slice thickness of image data on the accuracy measures were also analyzed. Our
results demonstrate that the tumor segmentation algorithm showed good correlation with ground truth for all
four lesion types (<i>r</i> = 0.97, 0.99, 0.97, 0.98, <i>p</i> < 0.0001 for liver, lung, lymphoma and other respectively). The
segmentation algorithm can produce relatively reproducible volume measurements on all lesion types (coefficient
of variation in the range of 10-20%). Our results show that the algorithm is insensitive to lesion size (coefficient
of determination close to 0) and slice thickness of image data(<i>p</i> > 0.90). The validation framework used in this
study has the potential to facilitate the development of new tumor segmentation algorithms and assist large scale
evaluation of segmentation techniques for other clinical applications.
This paper developed a new model of region extraction based on salient region detection and scale-space primal sketch. In the proposed model, we extract the region of interest (ROI) in two steps. Firstly, we estimate the extent of object by means of region detection, which considers the feature that contributes most to the saliency map. Secondly, we use the scale-space primal sketch to acquire an explicit representation of the significant image structure which gives a qualitative description of the scales and regions of interest. Finally, we combine the results from the two steps. Applications to extract ROI showed that this new model could lead to better results which can be used for guiding later stage processing.