Improvement of CNN-Based Road Extraction from Satellite Images via Morphological Image Processing

2020 ◽  
Author(s):  
Heeji Im ◽  
Hoeseok Yang
Keyword(s):  
Image Processing ◽  
Satellite Images ◽  
Road Extraction ◽  
Morphological Image Processing ◽  
Morphological Image

scholarly journals Extraction of urban buildings using color image processing and morphological operations.

2021 ◽  
Vol 11 (1) ◽  
pp. 45-66
Author(s):  
Mete Durlu ◽  
Ozan Eski ◽  
Emre Sumer
Keyword(s):  
Image Processing ◽  
Satellite Images ◽  
Color Image ◽  
Color Image Segmentation ◽  
Morphological Operations ◽  
Morphological Image Processing ◽  
Urban Buildings ◽  
Colour Image ◽  
High Resolution Satellite Images

In many geospatial applications, automated detection of buildings has become a key concern in recent years. Determination of building locations provides great benefits for numerous geospatial applications such as urban planning, disaster management, infrastructure planning, environmental monitoring. The study  aims to present a practical technique for extracting the buildings from high-resolution satellite images using color image segmentation and binary morphological image processing. The proposed method is implemented on satellite images of 4 different selected study areas of the city of Batikent, Ankara.  According to experiments conducted on the study areas, overall accuracy, sensitivity, and F1 values were computed to be on average, respectively. After applying morphological operations, the same metrics are calculated . The results show that the determination of urban buildings can be done more successfully with the suitable combination of morphological operations using rectangular structuring element. Keywords: Building Extraction; Colour Image Processing;Colour space conversion; Image Morphology; Remote Sensing        

scholarly journals Robotic grasp detection based on image processing and random forest

2019 ◽  
Vol 79 (3-4) ◽  
pp. 2427-2446 ◽  
Author(s):  
Jiahao Zhang ◽  
Miao Li ◽  
Ying Feng ◽  
Chenguang Yang
Keyword(s):  
Image Processing ◽  
Random Forest ◽  
Main Idea ◽  
Morphological Image Processing ◽  
Segmentation Approach ◽  
Novel Objects ◽  
Set Up ◽  
The One ◽  
Objects Detection ◽  
Morphological Image

AbstractReal-time grasp detection plays a key role in manipulation, and it is also a complex task, especially for detecting how to grasp novel objects. This paper proposes a very quick and accurate approach to detect robotic grasps. The main idea is to perform grasping of novel objects in a typical RGB-D scene view. Our goal is not to find the best grasp for every object but to obtain the local optimal grasps in candidate grasp rectangles. There are three main contributions to our detection work. Firstly, an improved graph segmentation approach is used to do objects detection and it can separate objects from the background directly and fast. Secondly, we develop a morphological image processing method to generate candidate grasp rectangles set which avoids us to search grasp rectangles globally. Finally, we train a random forest model to predict grasps and achieve an accuracy of 94.26%. The model is mainly used to score every element in our candidate grasps set and the one gets the highest score will be converted to the final grasp configuration for robots. For real-world experiments, we set up our system on a tabletop scene with multiple objects and when implementing robotic grasps, we control Baxter robot with a different inverse kinematics strategy rather than the built-in one.

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