Panoramic human structure maintenance based on invariant features of video frames

Chang, Shih Ming; Chang, Hon Hang; Yen, Shwu Huey; Shih, Timothy K.

doi:10.1186/2192-1962-3-14

Cited by 13 publications

(9 citation statements)

References 15 publications

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“…Several fault-state monitoring techniques have been proposed to improve robustness, reliability, and production capacity and reduce maintenance costs [4,8,[10][11][12][13][14][15][16][17][18][19] Lei et al proposed an intelligent classification method using WPT and EMD [10]. Dimensionless time-domain feature vectors were extracted from each of the original vibration signals and preprocessed to form a combined feature set.…”

Section: Related Workmentioning

confidence: 99%

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Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

Uddin

Kang

Nguyen

et al. 2014

Mathematical Problems in Engineering

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This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D) texture features and a multiclass support vector machine (MCSVM). The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns), and extracts these texture features by generating the dominant neighborhood structure (DNS) map. The principal component analysis (PCA) is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA) multiclass support vector machines (MCSVMs) to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.

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Section: Related Workmentioning

confidence: 99%

“…In the observation and testing process, we utilized SVM with a Gaussian radial basis function (RBF) kernel [17] because the RBF kernel shows an exceptional ability to…”

Section: Implementation Of the Proposed Modelmentioning

confidence: 99%

Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

Uddin

Kang

Nguyen

et al. 2014

Mathematical Problems in Engineering

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show abstract

“…In this paper, we propose a robust small target detection method that overcomes the disadvantages of the existing methods. Our proposed method searches for small target candidates based on morphology operations and detects the probability of a target by using a modified Gaussian distance coordinate function .…”

Section: Introductionmentioning

confidence: 99%

Small target detection using morphology and modified Gaussian distance function

Kim

Park

Ahn

et al. 2014

Security Comm Networks

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We propose a new small target detection system that detects small target candidates based on morphology operations and detects actual targets using a modified Gaussian distance function. To reduce clutter on the edges of clouds, a median filter is applied as preprocessing. Two kinds of images are calculated with closing and opening morphological operators, respectively. In the morphology operations, various sizes of structure elements that are used to consider the sizes of targets and candidate targets are extracted from different images between the two images in the closing and opening operations. With a modified Gaussian distance function, small targets are detected from the candidate targets. The proposed method is less sensitive to clutters than existing methods and has a detection rate of 98%. Copyright © 2014 John Wiley & Sons, Ltd.

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“…Furthermore, the movement of an NPC or a group of NPCs must be natural and realistic, and efficient planning of such path is an important task in developing games [1,2,[4][5][6][7]12,17].…”

mentioning

confidence: 99%

Efficient planning of NPC cluster movement in computer game environment

Kim

2014

Cluster Comput

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For natural group movement of non player characters (NPCs) in a computer game, an efficient planning algorithm is necessary. This paper presents a grid windowbased path planning algorithm for realistic group movement. The global path is planned in a static environment and the path is locally repaired to avoid dynamic obstacles during execution. The planning and execution processes are performed for the group, not for individual NPCs by using a 2D virtual grid window covering all the NPCs. The movement of each NPC is generated inside the grid window without complicated procedures to detect collisions. We implement and test the algorithm on a polygonal world modeling a real game background. The simulation results show that the proposed method provides natural group movement and dynamic obstacle avoidance, and the experimental result with various numbers of NPCs shows consistent performance.

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Panoramic human structure maintenance based on invariant features of video frames

Cited by 13 publications

References 15 publications

Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

Small target detection using morphology and modified Gaussian distance function

Efficient planning of NPC cluster movement in computer game environment

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