Texture recognition by using GLCM and various aggregation functions

We discuss the problem of texture recognition based on the grey level co-occurrence matrix (GLCM). We performed a number of numerical experiments to establish whether the accuracy of classification is optimal when GLCM entries are aggregated into standard metrics like contrast, dissimilarity, homogeneity, entropy, etc., and compared these metrics to several alternative aggregation methods.We conclude that k nearest neighbors classification based on raw GLCM entries typically works better than classification based on the standard metrics for noiseless data, that metrics based on principal component analysis inprove classification, and that a simple change from the arithmetic to quadratic mean in calculating the standard metrics also improves classification. <br /

Image Retrieval in Mobiles using Signature based Approach

Abstract -Since camera based handheld devices are widely used in today&apos;s world, and we also tend to click pictures and store it. Hence there is a need for a system that could process the pictures clicked from a hand-held device and retrieve back similar images from a central image database along with the information tagged with it. Mobile phones have very limited display size and limited number of control keys, so most of these systems encounter serious difficulties for both presenting the query image and also showing the retrieval results. In this paper, we describe a way in which a captured image can be searched in the web using content based retrieval system

The role of texture information and data fusion in topographic objects extraction from satellite data

The growing availability of the satellite data has augmented the need of information extraction that can be utilized in various application including topographic map updation, city planning, pattern recognition and machine vision etc. The accurate information extraction from satellite images involves the integration of additional measures such as texture, shape etc. In this paper, investigation on extraction of topographic objects from satellite images by incorporating the texture information and data fusion has been made. The applicability of various texture measures based on the gray level co-occurrence matrix along with the effect of varying pixel window is also discussed. The classification results indicate that homogeneity texture image generated using 3*3 window size is best suitable for topographic objects extraction. The best classification results with overall accuracy 85.0% and kappa coefficient 0.80 are obtained when classification is performed on fused image (Multispectral + PAN + Texture)

Prepoznavanje građevina pogođenih potresom temeljem korelacijske detekcije promjena obilježja teksture na SAR snimkama

The detection of building damage due to earthquakes is crucial for disaster management and disaster relief activities. Change detection methodologies using satellite images, such as synthetic aperture radar (SAR) data, have being applied in earthquake damage detection. Information contained within SAR data relating to earthquake damage of buildings can be disturbed easily by other factors. This paper presents a multitemporal change detection approach intended to identify and evaluate information pertaining to earthquake damage by fully exploiting the abundant texture features of SAR imagery. The approach is based on two images, which are constructed through principal components of multiple texture features. An independent principal components analysis technique is used to extract multiple texture feature components. Then, correlation analysis is performed to detect the distribution information of earthquake-damaged buildings. The performance of the technique was evaluated in the town of Jiegu (affected by the 2010 Yushu earthquake) and in the Kathmandu Valley (struck by the 2015 Nepal earthquake) for which the overall accuracy of building detection was 87.8% and 84.6%, respectively. Cross-validation results showed the proposed approach is more sensitive than existing methods to the detection of damaged buildings. Overall, the method is an effective damage detection approach that could support post-earthquake management activities in future events.Detekcija oštećenja građevina uzrokovanih potresom od presudne je važnosti za upravljanje rizicima od katastrofa i aktivnostima prilikom elementarnih nepogoda. Metodologije detekcije promjena, koristeći satelitske snimke kao što su podaci radara sa sintetičkim otvorom antene (SAR), korištene su u detekciji oštećenja od potresa. Informacije sadržane unutar SAR podataka, koje se odnose na oštećenja građevina uzrokovana potresom, mogu lako sadržavati šumove zbog drugih faktora. Ovaj rad prikazuje viševremenski pristup detekciji promjena kako bi se identificirale i procijenile informacije koje se odnose na oštećenja od potresa koristeći u potpunosti značajke teksture SAR snimaka. Pristup se temelji na dvije snimke koje su izrađene kroz glavne komponente višestrukih osobina tekstura. Neovisna analiza glavnih komponenti koristi se kako bi se izdvojile komponente višestrukih tekstura. Nakon toga provodi se korelacijska analiza kako bi se detektirale informacije o distribuciji građevina oštećenih potresom. Učinkovitost ove tehnike ispitana je u gradu Jiegu (kojega je 2010. godine pogodio potres Yushu) te u dolini Kathmandu (koju je 2015. godine pogodio potres Nepal), u kojoj je ukupna točnost detektiranja građevina bila 87,8%, odnosno 84,6%. Rezultati međusobne provjere valjanosti pokazali su da je predloženi pristup osjetljiviji od postojećih metoda za detektiranje oštećenih građevina. Općenito govoreći, metoda je učinkovit pristup detektiranja oštećenja koji može u budućnosti pružati potporu u aktivnostima upravljanja nakon potresa

A Multi-Sensor Phenotyping System: Applications on Wheat Height Estimation and Soybean Trait Early Prediction

Phenotyping is an essential aspect for plant breeding research since it is the foundation of the plant selection process. Traditional plant phenotyping methods such as measuring and recording plant traits manually can be inefficient, laborious and prone to error. With the help of modern sensing technologies, high-throughput field phenotyping is becoming popular recently due to its ability of sensing various crop traits non-destructively with high efficiency. A multi-sensor phenotyping system equipped with red-green-blue (RGB) cameras, radiometers, ultrasonic sensors, spectrometers, a global positioning system (GPS) receiver, a pyranometer, a temperature and relative humidity probe and a light detection and ranging (LiDAR) was first constructed, and a LabVIEW program was developed for sensor controlling and data acquisition. Two studies were conducted focusing on system performance examination and data exploration respectively. The first study was to compare wheat height measurements from ultrasonic sensor and LiDAR. Canopy heights of 100 wheat plots were estimated five times over the season by the ground phenotyping system, and the results were compared to manual measurements. Overall, LiDAR provided the better estimations with root mean square error (RMSE) of 0.05 m and R2 of 0.97. Ultrasonic sensor did not perform well due to the style of our application. In conclusion LiDAR was recommended as a reliable method for wheat height evaluation. The second study was to explore the possibility of early predicting soybean traits through color and texture features of canopy images. Six thousand three hundred and eighty-three RGB images were captured at V4/V5 growth stage over 5667 soybean plots growing at four locations. One hundred and forty color features and 315 gray-level co-occurrence matrix (GLCM)-based texture features were derived from each image. Another two variables were also introduced to account for the location and timing difference between images. Cubist and Random Forests were used for regression and classification modelling respectively. Yield (RMSE=9.82, R2=0.68), Maturity (RMSE=3.70, R2=0.76) and Seed Size (RMSE=1.63, R2=0.53) were identified as potential soybean traits that might be early-predictable. Advisor: Yufeng G

A Multi-Sensor Phenotyping System: Applications on Wheat Height Estimation and Soybean Trait Early Prediction

Phenotyping is an essential aspect for plant breeding research since it is the foundation of the plant selection process. Traditional plant phenotyping methods such as measuring and recording plant traits manually can be inefficient, laborious and prone to error. With the help of modern sensing technologies, high-throughput field phenotyping is becoming popular recently due to its ability of sensing various crop traits non-destructively with high efficiency. A multi-sensor phenotyping system equipped with red-green-blue (RGB) cameras, radiometers, ultrasonic sensors, spectrometers, a global positioning system (GPS) receiver, a pyranometer, a temperature and relative humidity probe and a light detection and ranging (LiDAR) was first constructed, and a LabVIEW program was developed for sensor controlling and data acquisition. Two studies were conducted focusing on system performance examination and data exploration respectively. The first study was to compare wheat height measurements from ultrasonic sensor and LiDAR. Canopy heights of 100 wheat plots were estimated five times over the season by the ground phenotyping system, and the results were compared to manual measurements. Overall, LiDAR provided the better estimations with root mean square error (RMSE) of 0.05 m and R2 of 0.97. Ultrasonic sensor did not perform well due to the style of our application. In conclusion LiDAR was recommended as a reliable method for wheat height evaluation. The second study was to explore the possibility of early predicting soybean traits through color and texture features of canopy images. Six thousand three hundred and eighty-three RGB images were captured at V4/V5 growth stage over 5667 soybean plots growing at four locations. One hundred and forty color features and 315 gray-level co-occurrence matrix (GLCM)-based texture features were derived from each image. Another two variables were also introduced to account for the location and timing difference between images. Cubist and Random Forests were used for regression and classification modelling respectively. Yield (RMSE=9.82, R2=0.68), Maturity (RMSE=3.70, R2=0.76) and Seed Size (RMSE=1.63, R2=0.53) were identified as potential soybean traits that might be early-predictable. Advisor: Yufeng G

Use of aggregation functions in decision making

A key component of many decision making processes is the aggregation step, whereby a set of numbers is summarised with a single representative value. This research showed that aggregation functions can provide a mathematical formalism to deal with issues like vagueness and uncertainty, which arise naturally in various decision contexts

空間的なテクスチャ解析によるコンプレックスネットワークに基づくテクスチャ解析の改善

This thesis proposes a new texture analysis model which enhanced from traditional complex network-based model for texture characterization via spatial texture analysis. The conceptual framework of the proposed model is to synergize between pattern recognition and graph theory research areas. The results of experiment show that the proposed model can capture robust textural information under various uncontrolled environments using standard texture databases. Texture analysis has played an important role in the last few decades. There are a growing number of techniques described in the literature, one of new area research is a complex network for texture characterization, which has developed in recent years. Inspired by the human brain system, the relation among structure texture elements on an image can be derived using the complex network model. Compared to the task of texture classification, development of the original complex network model is required in order to improve classification performance in environment variations. To fulfill this requirement, the enhancing complex network by spatial texture analysis (i.e., spatial distribution and spatial relation) has been achieved in this thesis. The proposed approach addresses the above requirement by investigating and modifying the original complex network model by extracting more discriminative information. A new graph connectivity measurement has been devised, including local spatial pattern mapping, which is denoted as a LSPM, to encode and describe local spatial arrangement of pixels. To the best of the author\u27s knowledge, as investigated in this thesis, the encoding spatial information which has been adapted within the original complex network model presented here were first proposed and reported by the author. The essence of this proposed graph connectivity measurement describes the spatial structure of local image texture cause it can effectively capture and detect micro-structures (e.g., edges, lines, spots) information which is critical being used to distinguish various pattern structures and invariant uncontrolled environments. Moreover, the graph-based representation has been investigated for improving the performance of texture classification. Spatial vector property has been comprised of deterministic graph modeling which decomposing the two component of the magnitude and the direction. Then, the proposed hybrid-based complex network comprises the enhancing graph-based representation, and the new graph connectivity measurement has been devised as an enhancing complex network-based model for texture characterization in this thesis. The experiments are evaluated by using four standard texture databases include Brodatz, UIUC, KTH-TIPS, and UMD. The experimental results are presented in terms of classification rate in this thesis to demonstrate that: firstly, the proposed graph connectivity measurement (LSPM) approach achieved on-average 86.25%, 77.25%, 89.38% and 94.06% respectively based on four databases. Secondly, the proposed graph-based spatial property approach achieved on-average 90.92%, 87.92%, 96.56% and 92.65%, respectively; finally, the hybrid-based complex network model achieved on-average 88.92%, 85.46%, 95.14% and 95.52% respectively. Accordingly, this thesis has advanced the original complex network-based model for texture characterization.九州工業大学博士学位論文 学位記番号：生工博甲第329号 学位授与年月日：平成30年9月21日1 Introduction|2 Literature Review|3 Complex Network Model and Spatial Information|4 Graph-based Representation in Texture Analysis|5 Hybrid-based Complex Network Model|6 Conclusions九州工業大学平成30年

Study on Co-occurrence-based Image Feature Analysis and Texture Recognition Employing Diagonal-Crisscross Local Binary Pattern

In this thesis, we focus on several important fields on real-world image texture analysis and recognition. We survey various important features that are suitable for texture analysis. Apart from the issue of variety of features, different types of texture datasets are also discussed in-depth. There is no thorough work covering the important databases and analyzing them in various viewpoints. We persuasively categorize texture databases ? based on many references. In this survey, we put a categorization to split these texture datasets into few basic groups and later put related datasets. Next, we exhaustively analyze eleven second-order statistical features or cues based on co-occurrence matrices to understand image texture surface. These features are exploited to analyze properties of image texture. The features are also categorized based on their angular orientations and their applicability. Finally, we propose a method called diagonal-crisscross local binary pattern (DCLBP) for texture recognition. We also propose two other extensions of the local binary pattern. Compare to the local binary pattern and few other extensions, we achieve that our proposed method performs satisfactorily well in two very challenging benchmark datasets, called the KTH-TIPS (Textures under varying Illumination, Pose and Scale) database, and the USC-SIPI (University of Southern California ? Signal and Image Processing Institute) Rotations Texture dataset.九州工業大学博士学位論文 学位記番号:工博甲第354号　学位授与年月日:平成25年9月27日CHAPTER 1 INTRODUCTION|CHAPTER 2 FEATURES FOR TEXTURE ANALYSIS|CHAPTER 3 IN-DEPTH ANALYSIS OF TEXTURE DATABASES|CHAPTER 4 ANALYSIS OF FEATURES BASED ON CO-OCCURRENCE IMAGE MATRIX|CHAPTER 5 CATEGORIZATION OF FEATURES BASED ON CO-OCCURRENCE IMAGE MATRIX|CHAPTER 6 TEXTURE RECOGNITION BASED ON DIAGONAL-CRISSCROSS LOCAL BINARY PATTERN|CHAPTER 7 CONCLUSIONS AND FUTURE WORK九州工業大学平成25年

Exploring the relationship between spectral reflectance and tree species diversity in the savannah woodlands.

Doctor of Philosophy in Environmental Sciences. University of KwaZulu-Natal, Pietermaritzburg, 2018.Abstract available in PDF file