Comparison of Multiscale Morphology Approaches: PDE Implemented Via Curve Evolution Versus Chamfer Distance Transform

Author(s):  
Muhammad Akmal Butt ◽  
Petros Maragos
2017 ◽  
Vol 39 (1) ◽  
pp. 56 ◽  
Author(s):  
Sylvia K. Osterrieder ◽  
Iain M. Parnum ◽  
Chandra P. Salgado Kent ◽  
Randall W. Robinson

Individual identification is a beneficial tool in behavioural and ecological research. In mark–recapture studies, for example, it can improve abundance, residency and site fidelity estimates. Two non-invasive, photo-identification approaches, using whisker spot patterns, were tested to identify wild individual Australian sea lions (Neophoca cinerea). The Chamfer distance transform algorithm has shown promising results when applied to captive individuals. An alternative matching method using row/column locations of whisker spots, previously applied to lions (Panthera leo) was also tested. Resighting wild N. cinerea in this study proved unfeasible with both methods. Excessive variation between photographs of the same individual was found when applying the Chamfer distance transform, and similarity between photograph-pairs appeared to decrease with increasing time between photographs. Insufficient variation among N. cinerea row/column pattern was detected to successfully discriminate among individuals, averaging 39 mystacial spots (range 30–46, n = 20) in seven rows and 9–10 columns. Additionally, different observers marking the same photographs introduced considerable variation. Colour difference (red, green and blue colour levels) between the whisker spots and surrounding fur affected marking spot locations significantly, increasing uncertainty when contrast decreased. While other pattern-matching algorithms may improve performance, accurate identification of spot locations was the current limitation.


Author(s):  
Luis Fernando Segalla ◽  
Alexandre Zabot ◽  
Diogo Nardelli Siebert ◽  
Fabiano Wolf

Author(s):  
Ayyaz Hussain ◽  
, Mohammed Alawairdhi ◽  
Fayez Alazemi ◽  
Sajid Khan ◽  
Muhammad Ramzan

Bioimaging ◽  
1994 ◽  
Vol 2 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Karel C Strasters ◽  
Arnold W M Smeulders ◽  
Hans T M van der Voort

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1365
Author(s):  
Tao Zheng ◽  
Zhizhao Duan ◽  
Jin Wang ◽  
Guodong Lu ◽  
Shengjie Li ◽  
...  

Semantic segmentation of room maps is an essential issue in mobile robots’ execution of tasks. In this work, a new approach to obtain the semantic labels of 2D lidar room maps by combining distance transform watershed-based pre-segmentation and a skillfully designed neural network lidar information sampling classification is proposed. In order to label the room maps with high efficiency, high precision and high speed, we have designed a low-power and high-performance method, which can be deployed on low computing power Raspberry Pi devices. In the training stage, a lidar is simulated to collect the lidar detection line maps of each point in the manually labelled map, and then we use these line maps and the corresponding labels to train the designed neural network. In the testing stage, the new map is first pre-segmented into simple cells with the distance transformation watershed method, then we classify the lidar detection line maps with the trained neural network. The optimized areas of sparse sampling points are proposed by using the result of distance transform generated in the pre-segmentation process to prevent the sampling points selected in the boundary regions from influencing the results of semantic labeling. A prototype mobile robot was developed to verify the proposed method, the feasibility, validity, robustness and high efficiency were verified by a series of tests. The proposed method achieved higher scores in its recall, precision. Specifically, the mean recall is 0.965, and mean precision is 0.943.


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