A robust segmentation algorithm for branch structure and its implementation

Flood fill mean shift: A robust segmentation algorithm

International Journal of Control Automation and Systems ◽

10.1007/s12555-010-0617-6 ◽

2010 ◽

Vol 8 (6) ◽

pp. 1313-1319 ◽

Cited By ~ 10

Author(s):

Jayong Lee ◽

Hoon Kang

Keyword(s):

Mean Shift ◽

Segmentation Algorithm ◽

Robust Segmentation ◽

Flood Fill

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A Multi-Sensor Matched Filter Approach to Robust Segmentation of Assisted Gait

Sensors ◽

10.3390/s18092970 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2970 ◽

Cited By ~ 3

Author(s):

Satinder Gill ◽

Nitin Seth ◽

Erik Scheme

Keyword(s):

Matched Filter ◽

Angular Rate ◽

Peak Detection ◽

Segmentation Algorithm ◽

Sensor Data ◽

Assistive Device ◽

Mobility Impairments ◽

Non Invasive ◽

Robust Segmentation ◽

Filter Approach

Individuals with mobility impairments related to age, injury, or disease, often require the help of an assistive device (AD) such as a cane to ambulate, increase safety, and improve overall stability. Instrumenting these devices has been proposed as a non-invasive way to proactively monitor an individual’s reliance on the AD while also obtaining information about behaviors and changes in gait. A critical first step in the analysis of these data, however, is the accurate processing and segmentation of the sensor data to extract relevant gait information. In this paper, we present a highly accurate multi-sensor-based gait segmentation algorithm that is robust to a variety of walking conditions using an AD. A matched filtering approach based on loading information is used in conjunction with an angular rate reversal and peak detection technique, to identify important gait events. The algorithm is tested over a variety of terrains using a hybrid sensorized cane, capable of measuring loading, mobility, and stability information. The reliability and accuracy of the proposed multi-sensor matched filter (MSMF) algorithm is compared with variations of the commonly employed gyroscope peak detection (GPD) algorithm. Results of an experiment with a group of 30 healthy participants walking over various terrains demonstrated the ability of the proposed segmentation algorithm to reliably and accurately segment gait events.

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Fuzzy C-Means Cluster Segmentation Algorithm Based on Bacterial Colony Chemotaxis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.421.465 ◽

2011 ◽

Vol 421 ◽

pp. 465-469

Author(s):

Yan Ling Li ◽

Gang Li

Keyword(s):

Image Segmentation ◽

Optimal Solution ◽

Segmentation Algorithm ◽

Bacterial Colony ◽

Fcm Algorithm ◽

Fuzzy C Means ◽

Robust Segmentation ◽

Initial Cluster ◽

Local Optimal Solution ◽

Stochastic Selection

Fuzzy C-Means(FCM) algorithm is one of the most popular methods for image segmentation, but it is in essence a technology of searching local optimal solution. The algorithm’s initial clustering centers are stochastic selection which causes it to depend on the selection of the initial cluster centers excessively. For this reason, fuzzy C-means cluster segmentation algorithm based on bacterial colony chemotaxis (BCC) is proposed in this paper. Firstly, initial cluster centers of FCM algorithm is get by BCC algorithm. Then, the images are segmented using FCM algorithm. Experimental results show that the proposed algorithm used for image segmentation can segment images more effectively and can provide more robust segmentation results.

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