Probabilistic segmentation using edge detection and region growing

1992 ◽  
Author(s):  
Russell R. Stringham ◽  
William A. Barrett ◽  
David C. Taylor
Keyword(s):  
Edge Detection ◽  
Region Growing

scholarly journals A Vision-Based Approach for Sidewalk and Walkway Trip Hazards Assessment

2020 ◽  
Vol 17 (22) ◽  
pp. 8438
Author(s):  
Rachel Cohen ◽  
Geoff Fernie ◽  
Atena Roshan Fekr
Keyword(s):  
Environmental Factors ◽  
Edge Detection ◽  
Gaussian Process ◽  
Regression Models ◽  
Gaussian Process Regression ◽  
Region Growing ◽  
Cost Effective ◽  
Second Phase ◽  
Cost Effective Method ◽  
Regression Techniques

Tripping hazards on the sidewalk cause many falls annually, and the inspection and repair of these hazards cost cities millions of dollars. Currently, there is not an efficient and cost-effective method to monitor the sidewalk to identify any possible tripping hazards. In this paper, a new portable device is proposed using an Intel RealSense D415 RGB-D camera to monitor the sidewalks, detect the hazards, and extract relevant features of the hazards. This paper first analyzes the effects of environmental factors contributing to the device’s error and compares different regression techniques to calibrate the camera. The Gaussian Process Regression models yielded the most accurate predictions with less than 0.09 mm Mean Absolute Errors (MAEs). In the second phase, a novel segmentation algorithm is proposed that combines the edge detection and region-growing techniques to detect the true tripping hazards. Different examples are provided to visualize the output results of the proposed method.

scholarly journals Discriminating figure from ground: the role of edge detection and region growing.

1987 ◽  
Vol 84 (20) ◽  
pp. 7354-7358 ◽  
Author(s):  
D. Mumford ◽  
S. M. Kosslyn ◽  
L. A. Hillger ◽  
R. J. Herrnstein
Keyword(s):  
Edge Detection ◽  
Region Growing

VISUAL TARGET TRACKING, EXTRACTION AND RECOGNITION FROM A SEQUENCE OF IMAGES USING THE LG GRAPH APPROACH

2002 ◽  
Vol 11 (04) ◽  
pp. 513-529 ◽  
Author(s):  
NIKOLAOS G. BOURBAKIS
Keyword(s):  
Feature Extraction ◽  
Edge Detection ◽  
Target Tracking ◽  
Visual Target ◽  
Region Growing ◽  
Geometric Approach ◽  
Moving Target ◽  
Extraction Region ◽  
Fuzzy Like ◽  
Visual Target Tracking

This paper presents a methodology for visually tracking, extracting and recognizing targets from a sequence of images (video). The methodology is based on the local-global (LG) graph as a combination of algorithms, such as fuzzy-like segmentation, edge detection, thinning, region growing, fractals, feature extraction, region-graph with attributes, etc., appropriately used for tracking, extracting and recognizing targets under various conditions, such as moving target - still camera, still camera - moving target, moving target - moving camera. The main contribution of this paper is the real-time combination of algorithms that provides a human-like feedback geometric approach of processing low resolution information in a sequence of consecutive images. Simulated results of the metholodology are presented for synthetic and real images.

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