Sum And Difference Histograms Research Articles

Crop disease monitoring and recognizing system by soft computing and image processing models

In order to make the crop disease intelligent diagnosis system more cheap, convenient and efficient for common farmers, an effective and general monitoring system of crop diseases is constructed by Internet of Things (IoT). In the system, crop disease images are collected by IoT and passed to the Web server by wireless network. First, a crop disease image dataset is constructed. Second, a K-mean clustering algorithm is utilized to segment disease leaf images, and the sum and difference histogram (SADH) feature vector is extracted from each segmented defect image based on the intensity values of the neighboring pixels. Finally, a reasoning process is built on the reasoning decision tree for monitoring system. The results validates that the proposed system is benefit for monitoring and controlling crop diseases in practice.

Face image retrieval via sum and difference histograms of elliptical local ternary pattern

This paper presents a new feature extraction method called sum and difference histograms of elliptical local ternary pattern (SDH-ELTP) for face image retrieval. This technique first calculates sparse local ternary pattern (LTP) in an elliptical shaped neighborhood and then higher order statistical texture information is extracted via sum and difference histograms (SDH) of elliptical LTP features. In sparse elliptical LTP, a 4 point LTP from horizontal and vertical elliptical neighborhoods and 4 point LTP from simple diagonal neighborhood is considered. The SDH is calculated only in relevant directions. Since the sum and difference histogram provides higher order statistical information, the calculation of SDH of elliptical LTP features further enhances the discriminativeness of proposed descriptor. The SDH-ELTP is finally tested on two popular face image databases and the results are compared with several recent state of the art techniques. The SDH-ELTP is low dimensional and show the best retrieval results as compared to all other face image retrieval techniques.

Robust color texture descriptor for material recognition

This paper addresses the task of material and natural texture classification. We propose a new discriminant color texture descriptor based on local pattern encoding scheme using local maximum sum and difference histograms. This descriptor aims to improve the robustness of the sum and difference histograms (SDH) operator to noise and low quality images by using adaptive values of the pixels instead of their exact values. We also enhance the time and memory efficiency as well as the robustness to scale and rotation variations by rectifying the formula used to compute the SDH. Finally, we use three different encoding schemes in order to incorporate both color and texture information into the final descriptor. The proposed approach is evaluated on six popular and challenging datasets: Outex-TC13, New-BarkTex, FMD, KTH-TIPS2b, Outex-TC30 and Outex-TC32 databases. The experimental results show that the introduced operator significantly outperforms the state-of-the-art methods and enhance the performance of the basic SDH.

Textural fabric defect detection using statistical texture transformations and gradient search

The inspection of the fabric defects is an important problem, which highly affects both the quality and the cost in the textile industry. Because of consistency and accuracy problems, the inspection of the fabric defect by human experts is neither feasible nor efficient. This requires development and use of automated inspection techniques. Thus, in this study, a texture analysis method, which uses sum and difference histograms (SDH) conjointly with co-occurrence matrices, is proposed to introduce an objective criterion for defect detection. To accomplish the detection task with high accuracy, several features were extracted from SDH and then, a defect search technique, which was developed in the context of this study, was applied. Moreover, several experiments and parameter analysis were performed to carry out detection at feasible computation time and memory storage. The developed method was applied to 28 kinds of raw woven fabric defects and 27 of them (i.e. 93.1%) were successfully recognized by the proposed detection system. The quantitative results and qualitative discussions show the effectiveness of the developed strategy.

Assessing relationships between Radarsat-2 C-band and structural parameters of a degraded mangrove forest

Relationships were assessed between mangrove structural data (leaf area index (LAI), stem density, basal area, diameter at breast height (DBH)) collected from 61 stands located in a black mangrove (Avicennia germinans)-dominated forest and both single polarized ultra-fine (3 m) and multipolarized fine beam (8 m) Radarsat-2 C-band synthetic aperture radar (SAR) data. The stands examined included representatives from the four types of mangroves that typify this degraded system, specifically: predominantly dead mangrove, poor-condition mangrove, healthy dwarf mangrove, and tall healthy mangrove. The results indicate that the selection of the spatial resolution (3 m vs. 8 m) of the incidence angle (27–39°) and the polarimetric mode greatly influence the relationship between the SAR and mangrove structural data. Moreover, the extent of degradation, i.e. whether dead stands are considered, also determines the strength of the relationships between the various SAR and mangrove parameters. When dead stands are included, the strongest overall relationships between the ultra-fine backscatter (incidence angle of ∼32°) and the various structural parameters were found using the horizontal-horizontal (HH) polarization/horizontal-vertical (HV) polarization ratio. However, if the dead stands are not included, then significant relationships with the ultra-fine data were only calculated with the HH data. Similar results were observed using the corresponding incidence angle (∼33°) of the fine beam data. When a shallower incidence angle was considered (∼39°), fewer and weaker relationships were calculated. Moreover, no significant relationships were observed if the dead stands were excluded from the sample at this incidence angle. The highest correlation coefficients using the steepest incidence (∼27°) were found with the co-polarized (HH, vertical-vertical (VV) polarization) modes. Several polarimetric parameters (entropy, pedestal height, surface roughness, alpha angle) based on the decomposition of the scattering matrix of the fine beam mode at this incidence angle were also found to be significantly correlated to mangrove structural data. The highest correlation (R = 0.71) was recorded for entropy and LAI. When the dead stands were excluded, volume scattering was found to be the most significant polarimetric parameter. Finally, multiple regression models, based on texture measures derived from both the grey level co-occurrence matrix (GLCM) and the sum and difference histogram (SADH) of the ultra-fine data, were developed to estimate mangrove parameters. The results indicate that only models derived from the HH data are significant and that several of these were strong predictors of all but stem density.

Texture-Based Segmentation Using Image Fidelity Indexes

Texture analysis and segmentation is one of the first steps in modeling human visual system for machines. Through the use of texture descriptors, as Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Pattern (LBP) or Sum and Difference Histograms (SDH), this work applies image fidelity indexes to segment the textures of an image. This methodology was evaluated using a dataset of 1,452 images divided into 6 classes of different textures (242 images per class). It was also used to improve the segmentation results of Quadtree decomposition, where the results achieved were always better than the classical version. In another experiment, our proposal was applied to the segmentation of 18 texture mosaic images created from Brodatz textures. All the experiments produced satisfactory results, confirming the robustness of the approach.

Improvement of classification in urban areas by the use of textural features: The case study of Lucknow city, Uttar Pradesh

Investigations have been carried out for digital spectral and textural classification of an Indian urban environment using SPOT images with grey level co-occurrence matrix (GLCM), grey level difference histogram (GLDH), and sum and difference histogram (SADH) approaches. The results indicate that a combination of texture and spectral features significantly improves the classification accuracy compared with classification with pure spectral features only. This improvement is about 9% and 17% for an addition of one and two texture features, respectively. GLDH and SADH give statistically similar results to GLCM, and take less computing time than GLCM. Conventional separability measures like transformed divergence, Bhattacharya distance, etc. are not effective in feature selection when classification is carried out with spectral and texture features. An alternative approach using simple statistics such as average coefficient of variation, skewness, and kurtosis and correlation amongst feature sets has shown greater feature selection potential when a combination of spectral and texture features is used.

Cloud field classification based upon high spatial resolution textural features: 2. Simplified vector approaches

The huge volume of data being collected in global climate studies makes it necessary to develop efficient automatic data analysis methods. While most cloud classification algorithms are based upon multispectral signatures, there is growing use of textural features. The results given in Part 1 of this study demonstrate that textural features computed from the Gray Level Cooccurrence Matrix (GLCM) approach produce high cloud classification accuracies. The present study compares classification results derived from two vector approaches, Sum and Difference Histogram (SADH) and Gray Level Difference Vector (GLDV), with those from the GLCM approach. It is found that the SADH approach produces accuracies equivalent to those obtained using GLCM, but with greater ability to resolve error clusters; also, there is a 30% savings in run time and a 50% savings in storage requirements. The GLDV approach suffers a slight degradation in classification accuracy but has a 40% savings in run time and an 87% savings in storage requirements. Textural features are not highly sensitive to moderate variations in cloud threshold selection. However, the whole cloud, rather than only the brightest portions of the cloud, produce the highest classification accuracies. A very important result is that spatial information content and classification accuracy are preserved even at lower radiometric resolutions with effective gray levels of 16. means that significantly low resolution digitized versions of satellite imagery retain essentially the full spatial information content of the original digital data. Substitution of digitized imagery can significantly reduce the expense of many remote sensing studies.