Human Face Classification Using Ultrasonic Sonar Imaging

Miao, Zhenwei; Ji, Wei; Xu, Yong; Jiang, Yang

doi:10.1143/jjap.48.07gc11

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…To demonstrate the implications of the proposed ATC detector, we evaluate it in the face recognition application [28], [29], [30]. Specifically, the ATC detector is compared with the SIFT [9], HR-A [8], HS-A [8], MSER [12] and ROLG [11] detectors.…”

Section: B Application To Face Recognitionmentioning

confidence: 99%

Interest Point Detection based on Adaptive Ternary Coding

Miao,

Yap,

Jiang

2019

Preprint

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In this paper, an adaptive pixel ternary coding mechanism is proposed and a contrast invariant and noise resistant interest point detector is developed on the basis of this mechanism. Every pixel in a local region is adaptively encoded into one of the three statuses: bright, uncertain and dark. The blob significance of the local region is measured by the spatial distribution of the bright and dark pixels. Interest points are extracted from this blob significance measurement. By labeling the statuses of ternary bright, uncertain, and dark, the proposed detector shows more robustness to image noise and quantization errors. Moreover, the adaptive strategy for the ternary cording, which relies on two thresholds that automatically converge to the median of the local region in measurement, enables this coding to be insensitive to the image local contrast. As a result, the proposed detector is invariant to illumination changes. The state-of-the-art results are achieved on the standard datasets, and also in the face recognition application.

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Section: B Application To Face Recognitionmentioning

confidence: 99%

Interest Point Detection based on Adaptive Ternary Coding

Miao,

Yap,

Jiang

2019

Preprint

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“…Over years, face recognition is always an active research topic [46,47,48]. Interest points are also applied to face recognition [17].…”

Section: Face Recognitionmentioning

confidence: 99%

Laplace gradient based Discriminative and Contrast Invertible descriptor

Miao¹,

Yap

Jiang

et al. 2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Local feature descriptors have been widely used in fine-grained visual object search thanks to their robustness in scale and rotation variation and cluttered background. However, the performance of such descriptors drops under severe illumination changes. In this paper, we proposed a Discriminative and Contrast Invertible (DCI) local feature descriptor. In order to increase the discriminative ability of the descriptor under illumination changes, a Laplace gradient based histogram is proposed. A robust contrast flipping estimate is proposed based on the divergence of a local region. Experiments on fine-grained object recognition and retrieval applications demonstrate the superior performance of DCI descriptor to others.

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“…With its advantages of high precision, broadband, high signal-to-noise ratio, and high quantity of information, the CTFM sonar is capable of detecting multiple targets, classifying primitive indoor targets [11], and even recognizing complex targets such as rough surfaces [12], leafy plants [13], and human faces [14]. However, most of the applications of a CTFM sonar are carried out under stationary conditions.…”

Section: Introductionmentioning

confidence: 99%

Continuous Transmission Frequency Modulation Detection under Variable Sonar-Target Speed Conditions

Wang

Yang

2013

Sensors

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As a ranging sensor, a continuous transmission frequency modulation (CTFM) sonar with its ability for range finding and range profile formation works effectively under stationary conditions. When a relative velocity exists between the target and the sonar, the echo signal is Doppler-shifted. This situation causes the output of the sensor to deviate from the actual target range, thus limiting its applications to stationary conditions only. This work presents an approach for correcting such a deviation. By analyzing the Doppler effect during the propagation process, the sensor output can be corrected by a Doppler factor. To obtain this factor, a conventional CTFM system is slightly modified by adding a single tone signal with a frequency that locates out-of-sweep range of the transmitted signal. The Doppler factor can be extracted from the echo. Both verification experiments and performance tests are carried out. Results indicate the validity of the proposed approach. Moreover, ranging precision under different processing setups is discussed. For adjacent multiple targets, the discrimination ability is influenced by displacement and velocity. A discrimination boundary is provided through an analysis.

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Human Face Classification Using Ultrasonic Sonar Imaging

Cited by 5 publications

References 11 publications

Interest Point Detection based on Adaptive Ternary Coding

Interest Point Detection based on Adaptive Ternary Coding

Laplace gradient based Discriminative and Contrast Invertible descriptor

Continuous Transmission Frequency Modulation Detection under Variable Sonar-Target Speed Conditions

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