TY - JOUR
T1 - Foldover Features for Dynamic Object Behaviour Description in Microscopic Videos
AU - Li, Xialin
AU - Li, Chen
AU - Kulwa, Frank
AU - Rahaman, Md Mamunur
AU - Zhao, Wenwei
AU - Wang, Xue
AU - Xue, Dan
AU - Yao, Yudong
AU - Cheng, Yilin
AU - Li, Jindong
AU - Qi, Shouliang
AU - Jiang, Tao
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - A behavior description helps analyze tiny objects, similar objects, objects with weak visual information, and objects with similar visual information. It plays a fundamental role in the identification and classification of dynamic objects in microscopic videos. To this end, we propose foldover features to describe the behavior of dynamic objects. Foldover is defined as: Each frame of an object's motion is superimposed on the same spatial plane in the spacetime order of the motion, the result of the superposition is the foldover of the object's motion. Foldover of an object contains temporal information, spatial information, behavior features and static features. Therefore, the features extracted based on the foldover of the object are the foldover features. In this work, we first generate foldover for each object in microscopic videos in X, Y and Z directions, respectively. Then, we extract foldover features from the X, Y and Z directions with statistical methods, respectively. The core content of this paper is to construct the foldovers and extract the foldover features. Through these two steps, the temporal information, spatial information, behavior features and static features of the object are enhanced and included in the foldover features. Furthermore, the description of the behavior of dynamic objects by the foldover features is strengthened. Finally, we use four different classifiers to test the effectiveness of the proposed foldover features. In the experiment, we use a microscopic sperm video dataset to evaluate the proposed foldover features, including three types of 1374 sperms, and obtain the highest classification accuracy of 96.5%.
AB - A behavior description helps analyze tiny objects, similar objects, objects with weak visual information, and objects with similar visual information. It plays a fundamental role in the identification and classification of dynamic objects in microscopic videos. To this end, we propose foldover features to describe the behavior of dynamic objects. Foldover is defined as: Each frame of an object's motion is superimposed on the same spatial plane in the spacetime order of the motion, the result of the superposition is the foldover of the object's motion. Foldover of an object contains temporal information, spatial information, behavior features and static features. Therefore, the features extracted based on the foldover of the object are the foldover features. In this work, we first generate foldover for each object in microscopic videos in X, Y and Z directions, respectively. Then, we extract foldover features from the X, Y and Z directions with statistical methods, respectively. The core content of this paper is to construct the foldovers and extract the foldover features. Through these two steps, the temporal information, spatial information, behavior features and static features of the object are enhanced and included in the foldover features. Furthermore, the description of the behavior of dynamic objects by the foldover features is strengthened. Finally, we use four different classifiers to test the effectiveness of the proposed foldover features. In the experiment, we use a microscopic sperm video dataset to evaluate the proposed foldover features, including three types of 1374 sperms, and obtain the highest classification accuracy of 96.5%.
KW - Foldover feature extraction
KW - content-based microscopic image analysis
KW - dynamic object behavior
KW - microscopic videos
UR - http://www.scopus.com/inward/record.url?scp=85087901563&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85087901563&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.3003993
DO - 10.1109/ACCESS.2020.3003993
M3 - Article
AN - SCOPUS:85087901563
VL - 8
SP - 114519
EP - 114540
JO - IEEE Access
JF - IEEE Access
M1 - 9121989
ER -