Detection of landmines and underground utilities from acoustic and GPR images with a cepstral approach

Authors:
Umar S. Khan;Waleed Al-Nuaimy;Fathi E. Abd El-Samie
Affiliations:
Department of Electrical Engineering and Electronics, University of Liverpool, UK;Department of Electrical Engineering and Electronics, University of Liverpool, UK;Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt
Venue:
Journal of Visual Communication and Image Representation
Year:
2010

Citing 8
Cited 0

Fundamentals of digital image processing

Fundamentals of digital image processing
Robust and efficient techniques for speech recognition in noise

Robust and efficient techniques for speech recognition in noise
Local feature extraction for robust speech recognition in the presence of noise

Local feature extraction for robust speech recognition in the presence of noise
Neural Network Theory

Neural Network Theory
Neural Networks

Neural Networks
Auditory-Based Wavelet Packet Filterbank for Speech Recognition Using Neural Network

ADCOM '07 Proceedings of the 15th International Conference on Advanced Computing and Communications
Face active appearance modeling and speech acoustic information to recover articulation

IEEE Transactions on Audio, Speech, and Language Processing - Special issue on multimodal processing in speech-based interactions
Robust Feature Extraction for Continuous Speech Recognition Using the MVDR Spectrum Estimation Method

IEEE Transactions on Audio, Speech, and Language Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a cepstral approach for the automatic detection of landmines and underground utilities from acoustic and ground penetrating radar (GPR) images. This approach is based on treating the problem as a pattern recognition problem. Cepstral features are extracted from a group of images, which are transformed first to 1-D signals by lexicographic ordering. Mel-frequency cepstral coefficients (MFCCs) and polynomial shape coefficients are extracted from these 1-D signals to form a database of features, which can be used to train a neural network with these features. The target detection can be performed by extracting features from any new image with the same method used in the training phase. These features are tested with the neural network to decide whether a target exists or not. The different domains are tested and compared for efficient feature extraction from the lexicographically ordered 1-D signals. Experimental results show the success of the proposed cepstral approach for landmine detection from both acoustic and GPR images at low as well as high signal to noise ratios (SNRs). Results also show that the discrete cosine transform (DCT) is the most appropriate domain for feature extraction.