scholarly journals DEVELOPING A METHOD TO GENERATE INDOORGML DATA FROM THE OMNI-DIRECTIONAL IMAGE

2015 ◽  
Vol XL-2/W4 ◽  
pp. 17-19 ◽  
Author(s):  
M. Kim ◽  
J. Lee
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Information Service ◽  
Indoor Navigation ◽  
Classification Method ◽  
Indoor Space ◽  
Edge Detection Method ◽  
Navigation Service ◽  
Space Data ◽  
Directional Image

Recently, many applications for indoor space are developed. The most realistic way to service an indoor space application is on the omni-directional image so far. Due to limitations of positioning technology and indoor space modelling, however, indoor navigation service can’t be implemented properly. In 2014, IndoorGML is approved as an OGC’s standard. This is an indoor space data model which is for the indoor navigation service. Nevertheless, the IndoorGML is defined, there is no method to generate the IndoorGML data except manually. This paper is aimed to propose a method to generate the IndoorGML data semi-automatically from the omni-directional image. In this paper, image segmentation and classification method are adopted to generate the IndoorGML data. The edge detection method is used to extract the features from the image. After doing the edge detection method, image classification method with ROI is adopted to find the features that we want. The following step is to convert the extracted area to the point which is regarded as state and connect to shooting point’s state. This is the IndoorGML data at the shooting point. It can be expanded to the floor’s IndoorGML data by connecting the each shooting points after repeating the process. Also, IndoorGML data of building can be generated by connecting the floor’s IndoorGML data. The proposed method is adopted at the testbed, and the IndoorGML data is generated. By using the generated IndoorGML data, it can be applied to the various applications for indoor space information service.

scholarly journals Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 590
Author(s):  
Zhenqian Zhang ◽  
Ruyue Cao ◽  
Cheng Peng ◽  
Renjie Liu ◽  
Yifan Sun ◽  
...  
Keyword(s):  
Machine Vision ◽  
Edge Detection ◽  
Detection Method ◽  
Region Of Interest ◽  
Average Error ◽  
Vertical Projection ◽  
Cut Edge ◽  
Fitting Method ◽  
Combine Harvester ◽  
Edge Detection Method

A cut-edge detection method based on machine vision was developed for obtaining the navigation path of a combine harvester. First, the Cr component in the YCbCr color model was selected as the grayscale feature factor. Then, by detecting the end of the crop row, judging the target demarcation and getting the feature points, the region of interest (ROI) was automatically gained. Subsequently, the vertical projection was applied to reduce the noise. All the points in the ROI were calculated, and a dividing point was found in each row. The hierarchical clustering method was used to extract the outliers. At last, the polynomial fitting method was used to acquire the straight or curved cut-edge. The results gained from the samples showed that the average error for locating the cut-edge was 2.84 cm. The method was capable of providing support for the automatic navigation of a combine harvester.

Research on Improvement Algorithm of Image Edge Detection

2014 ◽  
Vol 539 ◽  
pp. 141-145
Author(s):  
Shui Li Zhang
Keyword(s):  
Cognitive Psychology ◽  
Edge Detection ◽  
High Frequency ◽  
Detection Method ◽  
Low Frequency ◽  
Frequency Information ◽  
Image Edge Detection ◽  
Improvement Algorithm ◽  
Image Edge ◽  
Edge Detection Method

This paper presents new theorems Stevens edge detection method based on cognitive psychology on. Firstly, based on the number of the image is decomposed into high-frequency and low-frequency information, and the high-frequency information extracted by subtracting the maximum number of images to the image after the filter, then the amount of high frequency information into psychological cognitive psychology based on Stevenss theorem. The algorithm suppression refined edge after the non-minimum, applications Pillar K-means algorithm to extract image edge. Experimental results show that: the brightness of the image is converted to the amount of psychological edge can better unify under different brightness values.

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