Optimized Local Ternary Patterns: A New Texture Model With Set of Optimal Patterns for Texture Analysis

Raja, .

doi:10.3844/jcssp.2013.1.15

Cited by 12 publications

(4 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, LBPs are highly affected by noise and fluctuations in acoustic samples [1]. In order to further improve the discriminative power of LBP, LTPs were proposed for face recognition in 2010 [30], and later on applied in a number of works [31][32][33]. In contrast to the LBPs which encode the relationships of 'greater than' or 'less than' between the pixel and its neighbor, the LTPs reflect the 'greater than', 'equal to' or 'less than' relationships.…”

Section: -D Local Ternary Patternsmentioning

confidence: 99%

Automatic Scene Recognition through Acoustic Classification for Behavioral Robotics

et al. 2019

View full text Add to dashboard Cite

Classification of complex acoustic scenes under real time scenarios is an active domain which has engaged several researchers lately form the machine learning community. A variety of techniques have been proposed for acoustic patterns or scene classification including natural soundscapes such as rain/thunder, and urban soundscapes such as restaurants/streets, etc. In this work, we present a framework for automatic acoustic classification for behavioral robotics. Motivated by several texture classification algorithms used in computer vision, a modified feature descriptor for sound is proposed which incorporates a combination of 1-D local ternary patterns (1D-LTP) and baseline method Mel-frequency cepstral coefficients (MFCC). The extracted feature vector is later classified using a multi-class support vector machine (SVM), which is selected as a base classifier. The proposed method is validated on two standard benchmark datasets i.e., DCASE and RWCP and achieves accuracies of 97.38 % and 94.10 % , respectively. A comparative analysis demonstrates that the proposed scheme performs exceptionally well compared to other feature descriptors.

show abstract

Section: -D Local Ternary Patternsmentioning

confidence: 99%

Automatic Scene Recognition through Acoustic Classification for Behavioral Robotics

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Local Ternary Patterns [10] was introduced in 2010 by Tan and Triggs for face recognition. From then, several LTP methods were developed for improving the original LPT ( [40][41][42][43]). Original LTP used uses a group of ternary codes +1, 0, −1 { } to encode each pixel.…”

Section: Variantsmentioning

confidence: 99%

Effects of Challenging Weather and Illumination on Learning-Based License Plate Detection in Noncontrolled Environments

Río-Álvarez

Andres-Suarez

González-Rodríguez

et al. 2019

Scientific Programming

View full text Add to dashboard Cite

License Plate Detection (LPD) is one of the most important steps of an Automatic License Plate Recognition (ALPR) system because it is the seed of the entire recognition process. In indoor controlled environments, there are many effective methods for detecting license plates. However, outdoors LPD is still a challenge due to the large number of factors that may affect the process and the results obtained. It is an evidence that a complete training set of images including as many as possible license plates angles and sizes improves the performance of every classifier. On this line of work, numerous training sets contain images taken under different weather conditions. However, no studies tested the differences in the effectiveness of different descriptors for these different conditions. In this paper, various classifiers were trained with features extracted from a set of rainfall images using different kinds of texture-based descriptors. The accuracy of these specific trained classifiers over a test set of rainfall images was compared with the accuracy of the same descriptor-classifier pair trained with features extracted from an ideal conditions images set. In the same way, we repeat the experiment with images affected by challenging illumination. The research concludes, on one hand, that including images affected by rain, snow, or fog in the training sets does not improve the accuracy of the classifier detecting license plates over images affected by these weather conditions. Classifiers trained with ideal conditions images improve the accuracy of license plate detection in images affected by rainfalls up to 19% depending on the kind of extracted features. However, on the other hand, results evidence that including images affected by low illumination regardless of the kind of the selected feature increases the accuracy of the classifier up to 29%.

show abstract

“…In this paper, a method named ternary dynamic analysis (TDA) is proposed to extract the degradation features effectively. TDA combines the ideas of the Shannon entropy [28], local ternary pattern (LTP) [29], and optimized local ternary pattern (OLTP) [30]. State entropy (SE) and state transition entropy (STE) are obtained based on this method.…”

Section: Introductionmentioning

confidence: 99%

Degradation State Identification for Hydraulic Pumps Based on Multi-scale Ternary Dynamic Analysis, NSGA-II and SVM

Pei

2021

Measurement Science Review

View full text Add to dashboard Cite

Degradation state identification for hydraulic pumps is crucial to ensure system performance. As an important step, feature extraction has always been challenging. The non-stationary and non-Gaussian characteristics of the vibration signal are likely to weaken the performance of traditional features. In this paper, an efficient feature extraction algorithm named multi-scale ternary dynamic analysis (MTDA) is proposed. MTDA reconstructs the phase space based on the given signal and converts each embedding vector into a ternary pattern independently, which enhances its capacity of describing the details of non-stationary signals. State entropy (SE) and state transition entropy (STE) are calculated to estimate the dynamical changes and complexity of each signal sample. The excellent performance of SE and STE in detecting frequency changes, amplitude changes, and the development process of fault is verified with the use of four simulated signals. The proposed multi-scale analysis enables them to provide a more precise estimation of entropy. Furthermore, support vector machine (SVM) and nondominated sorting genetic algorithm II (NSGA-II) are introduced to conduct feature selection and state identification. NSGA-II and SVM can conduct the joint optimization of these two goals. The details of the method proposed in this paper are tested using simulated signals and experimental data, and some studies related to the fault diagnosis of rotating machinery are compared with our method. All the results show that our proposed method has better performance, which obtains higher recognition accuracy and lower feature set dimension.

show abstract

Optimized Local Ternary Patterns: A New Texture Model With Set of Optimal Patterns for Texture Analysis

Cited by 12 publications

References 28 publications

Automatic Scene Recognition through Acoustic Classification for Behavioral Robotics

Automatic Scene Recognition through Acoustic Classification for Behavioral Robotics

Effects of Challenging Weather and Illumination on Learning-Based License Plate Detection in Noncontrolled Environments

Degradation State Identification for Hydraulic Pumps Based on Multi-scale Ternary Dynamic Analysis, NSGA-II and SVM

Contact Info

Product

Resources

About