scholarly journals SFE2D: A Hybrid Tool for Spatial and Spectral Feature Extraction

2021 ◽  
Author(s):  
Bahman Abbassi ◽  
Li Zhen Cheng
Keyword(s):  
Feature Extraction ◽  
Image Interpretation ◽  
Spectral Feature ◽  
Geological Mapping ◽  
High Dimensional ◽  
Mathematical Tool ◽  
Multiple Sources ◽  
Color Processing ◽  
Geological Features ◽  
Highly Correlated

A crucial task for integrated geoscientific image (geo-image) interpretation is the relevant geological representation of multiple geo-images, which demands high-dimensional techniques for extracting latent geological features from high-dimensional geo-images. A standalone mathematical tool called SFE2D (spatiospectral feature extraction in two-dimension) is developed based on independent component analysis (ICA), continuous wavelet transform (CWT), k-means clustering segmentation, and RGB color processing that iteratively separates, extracts, clusters, and visualizes the highly correlated and overlapped geological features from multiple sources of geo-images. The SFE2D offers spatial feature extraction and wavelet-based spectral feature extraction for further extraction of frequency-dependent features. We show that the SFE2D is a robust tool for automated pattern recognition, fast pseudo-geological mapping, and detection of regions of interest with a wide range of applications in different scales, from regional geophysical surveys to the interpretation of microscopic images.

Pattern Recognition for Classification and Matching of Car Tires

2005 ◽  
Vol 33 (1) ◽  
pp. 2-17 ◽  
Author(s):  
D. Colbry ◽  
D. Cherba ◽  
J. Luchini
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Pattern Matching ◽  
Fourier Transforms ◽  
Two Dimensions ◽  
Multiple Sources ◽  
Matching Process ◽  
Visual Searches ◽  
New Feature ◽  
2D Fft

Abstract Commercial databases containing images of tire tread patterns are currently used by product designers, forensic specialists and product application personnel to identify whether a given tread pattern matches an existing tire. Currently, this pattern matching process is almost entirely manual, requiring visual searches of extensive libraries of tire tread patterns. Our work explores a first step toward automating this pattern matching process by building on feature analysis techniques from computer vision and image processing to develop a new method for extracting and classifying features from tire tread patterns and automatically locating candidate matches from a database of existing tread pattern images. Our method begins with a selection of tire tread images obtained from multiple sources (including manufacturers' literature, Web site images, and Tire Guides, Inc.), which are preprocessed and normalized using Two-Dimensional Fast Fourier Transforms (2D-FFT). The results of this preprocessing are feature-rich images that are further analyzed using feature extraction algorithms drawn from research in computer vision. A new, feature extraction algorithm is developed based on the geometry of the 2D-FFT images of the tire. The resulting FFT-based analysis allows independent classification of the tire images along two dimensions, specifically by separating “rib” and “lug” features of the tread pattern. Dimensionality of (0,0) indicates a smooth treaded tire with no pattern; dimensionality of (1,0) and (0,1) are purely rib and lug tires; and dimensionality of (1,1) is an all-season pattern. This analysis technique allows a candidate tire to be classified according to the features of its tread pattern, and other tires with similar features and tread pattern classifications can be automatically retrieved from the database.

scholarly journals Spectral–Spatial Feature Partitioned Extraction Based on CNN for Multispectral Image Compression

2020 ◽  
Vol 13 (1) ◽  
pp. 9
Author(s):  
Fanqiang Kong ◽  
Kedi Hu ◽  
Yunsong Li ◽  
Dan Li ◽  
Shunmin Zhao
Keyword(s):  
Feature Extraction ◽  
Image Compression ◽  
Multispectral Imaging ◽  
Rapid Development ◽  
Spectral Feature ◽  
Multispectral Image ◽  
Landsat 8 ◽  
Spatial Feature ◽  
Spatial Features ◽  
Multispectral Image Compression

Recently, the rapid development of multispectral imaging technology has received great attention from many fields, which inevitably involves the image transmission and storage problem. To solve this issue, a novel end-to-end multispectral image compression method based on spectral–spatial feature partitioned extraction is proposed. The whole multispectral image compression framework is based on a convolutional neural network (CNN), whose innovation lies in the feature extraction module that is divided into two parallel parts, one is for spectral and the other is for spatial. Firstly, the spectral feature extraction module is used to extract spectral features independently, and the spatial feature extraction module is operated to obtain the separated spatial features. After feature extraction, the spectral and spatial features are fused element-by-element, followed by downsampling, which can reduce the size of the feature maps. Then, the data are converted to bit-stream through quantization and lossless entropy encoding. To make the data more compact, a rate-distortion optimizer is added to the network. The decoder is a relatively inverse process of the encoder. For comparison, the proposed method is tested along with JPEG2000, 3D-SPIHT and ResConv, another CNN-based algorithm on datasets from Landsat-8 and WorldView-3 satellites. The result shows the proposed algorithm outperforms other methods at the same bit rate.

scholarly journals A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Chenchen Huang ◽  
Wei Gong ◽  
Wenlong Fu ◽  
Dongyu Feng
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Recognition Rate ◽  
Original Method ◽  
Speech Emotion Recognition ◽  
High Dimensional ◽  
Svm Classifier ◽  
Multiple Classifier System ◽  
Classifier System ◽  
Multiple Classifier

Feature extraction is a very important part in speech emotion recognition, and in allusion to feature extraction in speech emotion recognition problems, this paper proposed a new method of feature extraction, using DBNs in DNN to extract emotional features in speech signal automatically. By training a 5 layers depth DBNs, to extract speech emotion feature and incorporate multiple consecutive frames to form a high dimensional feature. The features after training in DBNs were the input of nonlinear SVM classifier, and finally speech emotion recognition multiple classifier system was achieved. The speech emotion recognition rate of the system reached 86.5%, which was 7% higher than the original method.

Research on a feature extraction method for local faults in planetary gearboxes based on improved dynamic time warping

2021 ◽  
Vol 63 (8) ◽  
pp. 465-471
Author(s):  
Shang Zhiwu ◽  
Yu Yan ◽  
Geng Rui ◽  
Gao Maosheng ◽  
Li Wanxiang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Extraction Method ◽  
Dynamic Time Warping ◽  
Spectral Feature ◽  
Time Warping ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Dynamic Time

Aiming at the local fault diagnosis of planetary gearbox gears, a feature extraction method based on improved dynamic time warping (IDTW) is proposed. As a calibration matching algorithm, the dynamic time warping method can detect the differences between a set of time-domain signals. This paper applies the method to fault diagnosis. The method is simpler and more intuitive than feature extraction methods in the frequency domain and the time-frequency domain, avoiding their limitations and disadvantages. Due to the shortcomings of complex calculation, singularity and poor robustness, the paper proposes an improved method. Finally, the method is verified by envelope spectral feature analysis and the local fault diagnosis of gears is realised.

Color Processing in Macaque Striate Cortex: Electrophysiological Properties

2002 ◽  
Vol 87 (6) ◽  
pp. 3138-3151 ◽  
Author(s):  
Carole E. Landisman ◽  
Daniel Y. Ts'O
Keyword(s):  
Optical Imaging ◽  
Cytochrome Oxidase ◽  
Striate Cortex ◽  
Color Patch ◽  
Color Processing ◽  
Color Properties ◽  
Electrophysiological Properties ◽  
Color Opponency ◽  
One To One ◽  
Highly Correlated

We have shown in the accompanying paper that optical imaging of macaque striate cortex reveals patches that are preferentially activated by equiluminant chromatic gratings compared with luminance gratings. These imaged color patches are highly correlated, although not always in one-to-one correspondence, with the cytochrome-oxidase (CO) blobs. In the present study, we have investigated the electrophysiological properties of neurons in the imaged color patches and the CO blobs. Our results indicate that individual blobs tend to contain cells of only one type of color opponency: either red/green or blue/yellow. Individual imaged color patches, however, can bridge blobs of similar opponency or differing opponency. When imaged color patches contain two blobs of differing opponency, the cells in the bridge region exhibit mixed color properties that are not opponent along the two cardinal color axes (either red/green or blue/yellow). Two blobs within a single imaged color patch receive input from the same eye or from different eyes. In the latter case, the bridge region between blobs contains binocular cells that are color selective. Because the cells recorded in imaged color patches were more color selective and unoriented than cells outside of color patches, color properties appear to be organized in a clustered and segregated fashion in primate V1.

Feature Extraction in Remote Sensing High-Dimensional Image Data

2007 ◽  
Vol 4 (1) ◽  
pp. 107-111 ◽  
Author(s):  
Maciel Zortea ◽  
Victor Haertel ◽  
Robin Clarke
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Image Data ◽  
High Dimensional ◽  
Dimensional Image
Sign in / Sign up

Export Citation Format

Share Document