Wavelet-based texture analysis for SAR image classification

Chumsamrong, W.; Thitimajshima, Punya; Rangsanseri, Yuttapong

doi:10.1109/igarss.1999.772020

Cited by 5 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…15 In these experiments SWT (Fig. 2) has been used resulting in the decomposed image being of the same size after decomposition ensuring the translational invariance.…”

Section: Discrete Stationary Wavelet Transformmentioning

confidence: 99%

“…This MHI representation of hand actions is based on moment based features of wavelet sub-images resulting from the decomposition of MHI using stationary wavelet transform (SWT), and classification using neural networks. [13][14][15] This technique combines the use of geometrical moments and wavelet transforms and is computationally inexpensive and is not sensitive to inter and intra subject variation of speed of movement. The results of classification accuracy of SWT with moment-based features are compared for the classification accuracy between the earlier works of authors for hand gestures classification of similar hand movements using Hu-moments [24][25][26] and stationary wavelet transform with approximate wavelet sub-images.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Visual Hand Gestures Classification Using Wavelet Transform and Moment Based Features

Kumar

2005

Int. J. Wavelets Multiresolut Inf. Process.

View full text Add to dashboard Cite

This paper presents a novel technique for classifying human hand gestures based on stationary wavelet transform (SWT) and classification based on geometrical based moments and compares the results with the classification based on Hu-moments and wavelet approximate images. The technique uses view-based approach for representation of hand actions, and uses a cumulative image-difference technique where the time between the sequences of images is implicitly captured in the representation of action resulting in Motion History Images (MHI). These MHIs are decomposed into wavelet sub-images using SWT. Translation and scaling invariant moments are then computed from the resulting high scale low pass residue of the wavelet sub-images. These geometrical moments are used as the global image descriptors and are fed to Artificial Neural Network (ANN) for classification. The recognition criterion is established using backpropagation based multilayer perceptron (MLP). The experiments show that such a system can classify human hand gestures with a classification accuracy of 97%.

show abstract

“…15 In these experiments SWT (Fig. 2) has been used resulting in the decomposed image being of the same size after decomposition ensuring the translational invariance.…”

Section: Discrete Stationary Wavelet Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Visual Hand Gestures Classification Using Wavelet Transform and Moment Based Features

Kumar

2005

Int. J. Wavelets Multiresolut Inf. Process.

View full text Add to dashboard Cite

show abstract

“…More precisely, for level 1, the DWT for a given image can be obtained by convolving the signal with the pair of low pass filter (H) and a high pass filter (G) and then down sampling by 2 along both rows and columns. SWT is similar to DWT and can be obtained by convolving the image with pair of low pass filter (H) and a high pass filter (G) but without down sampling along rows and columns [26]. In these experiments SWT (Figure 1) has been used resulting in the decomposed image being of the same size after decomposition ensuring the translational invariance.…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Biometrics Identification Based on Visual Hand Movements Using Wavelet Transform

Kumar¹,

Kumar²,

Mclachalan³

2005

Proceedings of the 1st International Workshop on Biosignal Processing and Classification

View full text Add to dashboard Cite

This work presents a novel technique of biometric identification based on the temporal history templates (THTs) of visual hand movements. The technique uses view-based approach for representation of hand movements, and uses a cumulative image-difference technique where the time between the sequences of images is implicitly captured in the representation of action. The low level representation of the action collapses the temporal structure of the motion from the video sequences of the hand movements while removing any static content from the video sequences to generate temporal history templates (THTs) of the hand movement. THTs of different individuals present distinctive 2-D motion patterns, where each pixel describes the function of temporal history of motion in that sequence. This THT are further subdivided into four sub-images an average and three detailed images using multi resolution wavelet transforms. The approximate wavelet sub-image is considered as the feature for recognition. The recognition criterion is established using KNN nearest neighbor technique using Mahalanobis distance. The accuracy of accepting an enrolled subject (AAES %) and accuracy of rejecting an imposter (ARI %) are the indicators of identification performance of the technique. The experimental results from 5-different individual indicate that the THT based technique achieves high identification rate when subject specific movements are assigned to the subjects during enrolment.

show abstract

“…In the texture segmentation scheme of [3], a 4-dimension feature vector was formed from the sub-bands at the ½ ×Ø or ¾ Ò levels of a pyramid decomposition was used in a minimum distance classification scheme, see Figure 2. …”

Section: Multi-scale Texture Analysismentioning

confidence: 99%

“…This technique has been applied to SAR texture analysis using the 4 sub-bands at each scale of a pyramid decomposition to form the feature vectors [3]. Laine [4] introduced a new approach to characterise textures at multiple scales based on a full wavelet packet decomposition, in which all the sub-bands at each scale are selected for further decomposition.…”

Section: Introductionmentioning

confidence: 99%

Minimum distance texture classification of SAR images using wavelet packets

Fletcher

Evans

IEEE International Geoscience and Remote Sensing Symposium

View full text Add to dashboard Cite

A multi-scale texture segmentation algorithm for SAR images based on the discrete wavelet transform is presented. Responses from different sub-bands are used to form a feature vector for each pixel position that is the input to the classification scheme. To further improve the classification results, the tree-structured wavelet packet transform is used to automatically identify a suitable sub-set of elements from the feature vector. Results show that this approach is effective for both the redundant and non-redundant wavelet transforms.

show abstract

Wavelet-based texture analysis for SAR image classification

Cited by 5 publications

References 4 publications

Visual Hand Gestures Classification Using Wavelet Transform and Moment Based Features

Visual Hand Gestures Classification Using Wavelet Transform and Moment Based Features

Biometrics Identification Based on Visual Hand Movements Using Wavelet Transform

Minimum distance texture classification of SAR images using wavelet packets

Contact Info

Product

Resources

About