Search results for: texture and pixel

Authors: R. Srinivas, K. V. Ramana

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This paper presents a novel method called VESTAL to label vertebrae and inter vertebral discs. Each vertebra has certain statistical features properties. To label vertebrae and discs, a new equation to model the path of spinal cord is derived using statistical properties of the spinal canal. VESTAL uses this equation for labeling vertebrae and discs. For each vertebrae and inter vertebral discs both posterior, interior width, height are measured. The calculated values are compared with real values which are measured using venires calipers and the comparison produced 95% efficiency and accurate results. The VESTAL is applied on 50 patients 350 MR images and obtained 100% accuracy in labeling.

Keywords: spine, vertebrae, inter vertebral disc, labeling, statistics, texture, disc

Procedia PDF Downloads 363

Authors: Ahmed Khalil, Hatem M. El-Desoky

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The present paper deals with the petrography and geochemistry of the Basic Dokhan Volcanics, Eastern Desert, Egypt. The basalts from Gabal Wassif, Atalla volcanics and Gabal Esh Mellaha were tested for use as aggregates in concrete mixes. The representative twelve samples were collected from areas. These samples were examined by using a petrographic microscope to evaluate sample texture, degree of alteration and the presence of volcanic glass in the matrix. The results obtained indicate that basalt can be used successfully for preparing concrete, but some attention should be paid to the choice of the suitable types of basalt. A general improvement in concrete mix properties has been found by using basalt aggregates in the mix.

Keywords: basic Dokhan volcanics, petrography, geochemistry, petrogenesis and concrete aggregates

Procedia PDF Downloads 519

Authors: Jennifer Buz, Alvin Spivey

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The most common greenhouse gas emitted through human activities, carbon dioxide (CO2), is naturally consumed by plants during photosynthesis. This process is actively being monetized by companies wishing to offset their carbon dioxide emissions. For example, companies are now able to purchase protections for vegetated land due-to-be clear cut or purchase barren land for reforestation. Therefore, by actively preventing the destruction/decay of plant matter or by introducing more plant matter (reforestation), a company can theoretically offset some of their emissions. One of the biggest issues in the carbon credit market is validating and verifying carbon offsets. There is a need for a system that can accurately and frequently ensure that the areas sold for carbon credits have the vegetation mass (and therefore for carbon offset capability) they claim. Traditional techniques for measuring vegetation mass and determining health are costly and require many person-hours. Orbital Sidekick offers an alternative approach that accurately quantifies carbon mass and assesses vegetation health through satellite hyperspectral imagery, a technique which enables us to remotely identify material composition (including plant species) and condition (e.g., health and growth stage). How much carbon a plant is capable of storing ultimately is tied to many factors, including material density (primarily species-dependent), plant size, and health (trees that are actively decaying are not effectively storing carbon). All of these factors are capable of being observed through satellite hyperspectral imagery. This abstract focuses on speciation. To build a species classification model, we matched pixels in our remote sensing imagery to plants on the ground for which we know the species. To accomplish this, we collaborated with the researchers at the Teakettle Experimental Forest. Our remote sensing data comes from our airborne “Kato” sensor, which flew over the study area and acquired hyperspectral imagery (400-2500 nm, 472 bands) at ~0.5 m/pixel resolution. Coverage of the entire teakettle experimental forest required capturing dozens of individual hyperspectral images. In order to combine these images into a mosaic, we accounted for potential variations of atmospheric conditions throughout the data collection. To do this, we ran an open source atmospheric correction routine called ISOFIT1 (Imaging Spectrometer Optiman FITting), which converted all of our remote sensing data from radiance to reflectance. A database of reflectance spectra for each of the tree species within the study area was acquired using the Teakettle stem map and the geo-referenced hyperspectral images. We found that a wide variety of machine learning classifiers were able to identify the species within our images with high (>95%) accuracy. For the most robust quantification of carbon mass and the best assessment of the health of a vegetated area, speciation is critical. Through the use of high resolution hyperspectral data, ground-truth databases, and complex analytical techniques, we are able to determine the species present within a pixel to a high degree of accuracy. These species identifications will feed directly into our carbon mass model.

Keywords: hyperspectral, satellite, carbon, imagery, python, machine learning, speciation

Procedia PDF Downloads 131

Authors: Bhuyain Mobarok Hossain

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Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).

Keywords: image classification, object detection, localization, particle filter

Procedia PDF Downloads 306

Authors: Yuan Zheng

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3D model-based vehicle matching provides a new way for vehicle recognition, localization and tracking. Its key is to construct an evaluation function, also called fitness function, to measure the degree of vehicle matching. The existing fitness functions often poorly perform when the clutter and occlusion exist in traffic scenarios. In this paper, we present a practical and efficient fitness function. Unlike the existing evaluation functions, the proposed fitness function is to study the vehicle matching problem from both local and global perspectives, which exploits the pixel gradient information as well as the silhouette information. In view of the discrepancy between 3D vehicle model and real vehicle, a weighting strategy is introduced to differently treat the fitting of the model’s wireframes. Additionally, a normalization operation for the model’s projection is performed to improve the accuracy of the matching. Experimental results on real traffic videos reveal that the proposed fitness function is efficient and robust to the cluttered background and partial occlusion.

Keywords: 3D-2D matching, fitness function, 3D vehicle model, local image gradient, silhouette information

Procedia PDF Downloads 399

Authors: I. Gusti A. A. Rai Asmiwyati, Robert J. Corner, Ashraf M. Dewan

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Change on land use/cover (LULC) dominantly affects spatial structure and function. It can have such impacts by disrupting social culture practice and disturbing physical elements. Thus, it has become essential to understand of the dynamics in time and space of LULC as it can be used as a critical input for developing sustainable LULC. This study was an attempt to map and monitor the LULC change in Bali Indonesia from 2003 to 2013. Using object based classification to improve the accuracy, and change detection, multi temporal land use/cover data were extracted from a set of ASTER satellite image. The overall accuracies of the classification maps of 2003 and 2013 were 86.99% and 80.36%, respectively. Built up area and paddy field were the dominant type of land use/cover in both years. Patch increase dominantly in 2003 illustrated the rapid paddy field fragmentation and the huge occurring transformation. This approach is new for the case of diverse urban features of Bali that has been growing fast and increased the classification accuracy than the manual pixel based classification.

Keywords: land use/cover, urban, Bali, ASTER

Procedia PDF Downloads 542

Authors: Mallikarjuna Rao Gundavarapu, Ch. Mallikarjuna Rao, K. Anuradha Bai

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Real Time Video Tracking is a challenging task for computing professionals. The performance of video tracking techniques is greatly affected by background detection and elimination process. Local regions of the image frame contain vital information of background and foreground. However, pixel-level processing of local regions consumes a good amount of computational time and memory space by traditional approaches. In our approach we have explored the concurrent computational ability of General Purpose Graphic Processing Units (GPGPU) to address this problem. The Gaussian Mixture Model (GMM) with adaptive weighted kernels is used for detecting the background. The weights of the kernel are influenced by local regions and are updated by inter-frame variations of these corresponding regions. The proposed system has been tested with GPU devices such as GeForce GTX 280, GeForce GTX 280 and Quadro K2000. The results are encouraging with maximum speed up 10X compared to sequential approach.

Keywords: connected components, embrace threads, local weighted kernel, structuring elements

Procedia PDF Downloads 442

Authors: Tsedekech Gebremeskel Weldmichael, Tamas Szegi, Lubangakene Denish, Ravi Kumar Gangwar, Erika Micheli, Barbara Simon

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Following the 1992 Earth Summit in Rio de Janeiro, soil biodiversity has been recognized globally as a crucial player in guaranteeing the functioning of soil and a provider of several ecosystem services essential for human well-being. The microbial fraction of the soil is a vital component of soil fertility as soil microbes play key roles in soil aggregate formation, nutrient cycling, humification, and degradation of pollutants. Soil fauna, such as earthworms, have huge impacts on soil organic matter dynamics, nutrient cycling, and infiltration and distribution of water in the soil. Currently, land-use change has been a global concern as evidence accumulates that it adversely affects soil biodiversity and the associated ecosystem goods and services. In this study, we examined the patterns of soil microbial respiration (SMR) and earthworm (abundance, biomass, and species richness) across three land-use types (grassland, arable land, and forest) in Hungary. The objectives were i) to investigate whether there is a significant difference in SMR and earthworm (abundance, biomass, and species richness) among land-use types. ii) to determine the key soil properties that best predict the variation in SMR and earthworm communities. Soil samples, to a depth of 25 cm, were collected from the surrounding areas of seven soil profiles. For physicochemical parameters, soil organic matter (SOM), pH, CaCO₃, E₄/E₆, available nitrogen (NH₄⁺-N and NO₃⁻-N), potassium (K₂O), phosphorus (P₂O₅), exchangeable Ca²⁺, Mg²⁺, soil moisture content (MC) and bulk density were measured. The analysis of SMR was determined by basal respiration method, and the extraction of earthworms was carried out by hand sorting method as described by ISO guideline. The results showed that there was no statistically significant difference among land-use types in SMR (p > 0.05). However, the highest SMR was observed in grassland soils (11.77 mgCO₂ 50g⁻¹ soil 10 days⁻¹) and lowest in forest soils (8.61 mgCO₂ 50g⁻¹ soil 10 days⁻¹). SMR had strong positive correlations with exchangeable Ca²⁺ (r = 0.80), MC (r = 0.72), and exchangeable Mg²⁺(r = 0.69). We found a pronounced variation in SMR among soil texture classes (p < 0.001), where the highest value in silty clay loam soils and the lowest in sandy soils. This study provides evidence that agricultural activities can negatively influence earthworm communities, in which the arable land had significantly lower earthworm communities compared to forest and grassland respectively. Overall, in our study, land use type had minimal effects on SMR whereas, earthworm communities were profoundly influenced by land-use type particularly agricultural activities related to tillage. Exchangeable Ca²⁺, MC, and texture were found to be the key drivers of the variation in SMR.

Keywords: earthworm community, land use, soil biodiversity, soil microbial respiration, soil property

Procedia PDF Downloads 142

Authors: A. A Warra, S. Fatima

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The experimental investigation revealed that the hexane extract of onion seed oil has acid value, iodine value, peroxide value, saponification value, relative density and refractive index of 0.03±0.01 mgKOH/g, 129.80±0.21 gI2/100g, 3.00± 0.00 meq H2O2 203.00±0.71 mgKOH/g, 0.82±0.01and 1.44±0.00 respectively. The percentage yield was 50.28±0.01%. The colour of the oil was light green. We restricted our GC-MS spectra interpretation to compounds identification, particularly fatty acids and they are identified as palmitic acid, linolelaidic acid, oleic acid, stearic acid, behenic acid, linolenic acid and eicosatetraenoic acid. The pH , foam ability (cm³), total fatty matter, total alkali and percentage chloride of the onion oil soap were 11.03± 0.02, 75.13±0.15 (cm³), 36.66 ± 0.02 %, 0.92 ± 0.02% and 0.53 ± 0.15 % respectively. The texture was soft and the colour was lighter green. The results indicated that the hexane extract of the onion seed oil has potential for cosmetic industries.

Keywords: onion seeds, soxhlet extraction, physicochemical, GC-MS, cold saponification

Procedia PDF Downloads 318

Authors: Toshiyuki Sato, Kenta Iitani, Koji Toma, Takahiro Arakawa, Kohji Mitsubayashi

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A 2-dimensional imaging system (Sniff-camera) for gaseous ethanol emissions from a human palm skin was constructed and demonstrated. This imaging system measures gaseous ethanol concentrations as intensities of chemiluminescence (CL) by luminol reaction induced by alcohol oxidase and luminol-hydrogen peroxide system. A conversion of ethanol distributions and concentrations to 2-dimensional CL was conducted on an enzyme-immobilized mesh substrate in a dark box, which contained a luminol solution. In order to visualize ethanol emissions from human palm skin, we developed highly sensitive and selective imaging system for transpired gaseous ethanol at sub ppm-levels. High sensitivity imaging allows us to successfully visualize the emissions dynamics of transdermal gaseous ethanol. The intensity of each pixel on the palm shows the reflection of ethanol concentrations distributions based on the metabolism of oral alcohol administration. This imaging system is significant and useful for the assessment of ethanol measurement of the palmar skin.

Keywords: sniff-camera, gas-imaging, ethanol vapor, human body gas

Procedia PDF Downloads 371

Authors: Endalkachew Abebe Kebede, Bojin Bojinov, Andon Vasilev Andonov, Orhan Dengiz

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Remote sensing has a potential application in assessing and monitoring the plants' biophysical properties using the spectral responses of plants and soils within the electromagnetic spectrum. However, only a few reports compare the performance of different remote sensing sensors against in-situ field spectral measurement. The current study assessed the potential applications of open data source satellite images (Sentinel 2 and Landsat 9) in estimating the biophysical properties of the wheat crop on a study farm found in the village of OvchaMogila. A Landsat 9 (30 m resolution) and Sentinel-2 (10 m resolution) satellite images with less than 10% cloud cover have been extracted from the open data sources for the period of December 2021 to April 2022. An Unmanned Aerial Vehicle (UAV) has been used to capture the spectral response of plant leaves. In addition, SpectraVue 710s Leaf Spectrometer was used to measure the spectral response of the crop in April at five different locations within the same field. The ten most common vegetation indices have been selected and calculated based on the reflectance wavelength range of remote sensing tools used. The soil samples have been collected in eight different locations within the farm plot. The different physicochemical properties of the soil (pH, texture, N, P₂O₅, and K₂O) have been analyzed in the laboratory. The finer resolution images from the UAV and the Leaf Spectrometer have been used to validate the satellite images. The performance of different sensors has been compared based on the measured leaf spectral response and the extracted vegetation indices using the five sampling points. A scatter plot with the coefficient of determination (R2) and Root Mean Square Error (RMSE) and the correlation (r) matrix prepared using the corr and heatmap python libraries have been used for comparing the performance of Sentinel 2 and Landsat 9 VIs compared to the drone and SpectraVue 710s spectrophotometer. The soil analysis revealed the study farm plot is slightly alkaline (8.4 to 8.52). The soil texture of the study farm is dominantly Clay and Clay Loam.The vegetation indices (VIs) increased linearly with the growth of the plant. Both the scatter plot and the correlation matrix showed that Sentinel 2 vegetation indices have a relatively better correlation with the vegetation indices of the Buteo dronecompared to the Landsat 9. The Landsat 9 vegetation indices somewhat align better with the leaf spectrometer. Generally, the Sentinel 2 showed a better performance than the Landsat 9. Further study with enough field spectral sampling and repeated UAV imaging is required to improve the quality of the current study.

Keywords: landsat 9, leaf spectrometer, sentinel 2, UAV

Procedia PDF Downloads 108

Authors: Joe Amalraj, M. Arunkumar

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Haze is an atmospheric phenomenon that signicantly degrades the visibility of outdoor scenes. This is mainly due to the atmosphere particles that absorb and scatter the light. This paper introduces a novel single image approach that enhances the visibility of such degraded images. In this method is a fusion-based strategy that derives from two original hazy image inputs by applying a white balance and a contrast enhancing procedure. To blend effectively the information of the derived inputs to preserve the regions with good visibility, we filter their important features by computing three measures (weight maps): luminance, chromaticity, and saliency. To minimize artifacts introduced by the weight maps, our approach is designed in a multiscale fashion, using a Laplacian pyramid representation. This paper demonstrates the utility and effectiveness of a fusion-based technique for de-hazing based on a single degraded image. The method performs in a per-pixel fashion, which is straightforward to implement. The experimental results demonstrate that the method yields results comparative to and even better than the more complex state-of-the-art techniques, having the advantage of being appropriate for real-time applications.

Keywords: single image de-hazing, outdoor images, enhancing, DSP

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Authors: Ali Abdrhman M. Ukasha

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Data security needed in data transmission, storage, and communication to ensure the security. The color image is decomposed into red, green, and blue channels. The blue and green channels are compressed using 3-levels discrete wavelet transform. The Arnold transform uses to changes the locations of red image channel pixels as image scrambling process. Then all these channels are encrypted separately using a key image that has same original size and is generating using private keys and modulo operations. Performing the X-OR and modulo operations between the encrypted channels images for image pixel values change purpose. The extracted contours of color image recovery can be obtained with accepted level of distortion using Canny edge detector. Experiments have demonstrated that proposed algorithm can fully encrypt 2D color image and completely reconstructed without any distortion. It has shown that the color image can be protected with a higher security level. The presented method has easy hardware implementation and suitable for multimedia protection in real time applications such as wireless networks and mobile phone services.

Keywords: color image, wavelet transform, edge detector, Arnold transform, lossy image encryption

Procedia PDF Downloads 486

Authors: Stephen A. Akinlabi, Esther T. Akinlabi

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The drip irrigation system is one of the important areas that affect the livelihood of farmers directly. The use of drip irrigation system has been the most efficient system compared to the other types of irrigations systems because the drip irrigation helps to save water and increase the productivity of crops. But like any other system, it can be considered inefficient when the filters and the emitters get clogged while in operation. The efficiency of the entire system is reduced when the emitters are clogged and blocked. This consequently impact and affect the farm operations which may result in scarcity of farm products and increase the demand. This design work focuses on how to overcome some of the challenges affecting drip irrigation system through the design of an efficient filtration and flushing system.

Keywords: drip irrigation system, filters, soil texture, mechanical engineering design, analysis

Procedia PDF Downloads 386

Authors: Wael A. Aboutaleb, Ahmed M. A. El Naggar, Heba M. Gobara

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This research work reports the photocatalytic production of hydrogen from water-methanol mixture using three different 15% ceria/iron oxide catalysts. The catalysts were prepared by physical mixing, precipitation, and ultrasonication methods and labeled as catalysts A-C. The structural and texture properties of the obtained catalysts were confirmed by X-ray diffraction (XRD), BET-surface area analysis and transmission electron microscopy (TEM). The photocatalytic activity of the three catalysts towards hydrogen generation was then tested. Promising hydrogen productivity was obtained by the three catalysts however different gases compositions were obtained by each type of catalyst. Specifically, catalyst A had produced hydrogen mixed with CO₂ while the composite structure (catalyst B) had generated only pure H₂. In the case of catalyst C, syngas made of H₂ and CO was revealed, as a novel product, for the first time, in such process.

Keywords: hydrogen production, water splitting, photocatalysts, clean energy

Procedia PDF Downloads 242

Authors: Huy Hieu Pham, Houssam Salmane, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio Velastin

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We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from color video sequences. Our approach proceeds along two stages. In the first, we run a real-time 2D pose detector to determine the precise pixel location of important key points of the body. A two-stream neural network is then designed and trained to map detected 2D keypoints into 3D poses. In the second, we deploy the Efficient Neural Architecture Search (ENAS) algorithm to find an optimal network architecture that is used for modeling the Spatio-temporal evolution of the estimated 3D poses via an image-based intermediate representation and performing action recognition. Experiments on Human3.6M, Microsoft Research Redmond (MSR) Action3D, and Stony Brook University (SBU) Kinect Interaction datasets verify the effectiveness of the proposed method on the targeted tasks. Moreover, we show that our method requires a low computational budget for training and inference.

Keywords: human action recognition, pose estimation, D-CNN, deep learning

Procedia PDF Downloads 146

Authors: Chien-Chun Huang, P. W. Yuan

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Banana is one of the important fruits which constitute a valuable source of energy, vitamins and minerals and an important food component throughout the world. The fruit ripening and maturity standards vary from country to country depending on the expected shelf life of market. During ripening there are changes in appearance, texture and chemical composition of banana. The changes of component of banana during ethylene-induced ripening are categorized as nutritive values and commercial utilization. The objectives of this study were to investigate the changes of chemical composition and physicochemical properties of banana during ethylene-induced ripening. Green bananas were harvested and ripened by ethylene gas at low temperature (15℃) for seven stages. At each stage, banana was sliced and freeze-dried for banana flour preparation. The changes of total starch, resistant starch, chemical compositions, physicochemical properties, activity of amylase, polyphenolic oxidase (PPO) and phenylalanine ammonia lyase (PAL) of banana were analyzed each stage during ripening. The banana starch was isolated and analyzed for gelatinization properties, pasting properties and microscopic appearance each stage of ripening. The results indicated that the highest total starch and resistant starch content of green banana were 76.2% and 34.6%, respectively at the harvest stage. Both total starch and resistant starch content were significantly declined to 25.3% and 8.8%, respectively at the seventh stage. Soluble sugars content of banana increased from 1.21% at harvest stage to 37.72% at seventh stage during ethylene-induced ripening. Swelling power of banana flour decreased with the progress of ripening stage, but solubility increased. These results strongly related with the decreases of starch content of banana flour during ethylene-induced ripening. Both water insoluble and alcohol insoluble solids of banana flour decreased with the progress of ripening stage. Both activity of PPO and PAL increased, but the total free phenolics content decreased, with the increases of ripening stages. As ripening stage extended, the gelatinization enthalpy of banana starch significantly decreased from 15.31 J/g at the harvest stage to 10.55 J/g at the seventh stage. The peak viscosity and setback increased with the progress of ripening stages in the pasting properties of banana starch. The highest final viscosity, 5701 RVU, of banana starch slurry was found at the seventh stage. The scanning electron micrograph of banana starch showed the shapes of banana starch appeared to be round and elongated forms, ranging in 10-50 μm at the harvest stage. As the banana closed to ripe status, some parallel striations were observed on the surface of banana starch granular which could be caused by enzyme reaction during ripening. These results inferred that the highest resistant starch was found in the green banana could be considered as a potential application of healthy foods. The changes of chemical composition and physicochemical properties of banana could be caused by the hydrolysis of enzymes during the ethylene-induced ripening treatment.

Keywords: maturation of banana, appearance, texture, soluble sugars, resistant starch, enzyme activities, physicochemical properties of banana starch

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Authors: Sher Muhammad, Mirza Muhammad Waqar

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It is necessary to monitor and identify mangroves types and spatial extent near coastal areas because it plays an important role in coastal ecosystem and environmental protection. This research aims at identifying and mapping mangroves types along Karachi coast ranging from 24.79 to 24.85 degree in latitude and 66.91 to 66.97 degree in longitude using hyperspectral remote sensing data and techniques. Image acquired during February, 2012 through Hyperion sensor have been used for this research. Image preprocessing includes geometric and radiometric correction followed by Minimum Noise Fraction (MNF) and Pixel Purity Index (PPI). The output of MNF and PPI has been analyzed by visualizing it in n-dimensions for end-member extraction. Well-distributed clusters on the n-dimensional scatter plot have been selected with the region of interest (ROI) tool as end members. These end members have been used as an input for classification techniques applied to identify and map mangroves species including Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF), and Spectral Information Diversion (SID). Only two types of mangroves namely Avicennia Marina (white mangroves) and Avicennia Germinans (black mangroves) have been observed throughout the study area.

Keywords: mangrove, hyperspectral, hyperion, SAM, SFF, SID

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Authors: Piyush Samant, Ravinder Agarwal

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Complementary and Alternative Medicine (CAM) techniques are quite popular and effective for chronic diseases. Iridology is more than 150 years old CAM technique which analyzes the patterns, tissue weakness, color, shape, structure, etc. for disease diagnosis. The objective of this paper is to validate the use of iridology for the diagnosis of the diabetes. The suggested model was applied in a systemic disease with ocular effects. 200 subject data of 100 each diabetic and non-diabetic were evaluated. Complete procedure was kept very simple and free from the involvement of any iridologist. From the normalized iris, the region of interest was cropped. All 63 features were extracted using statistical, texture analysis, and two-dimensional discrete wavelet transformation. A comparison of accuracies of six different classifiers has been presented. The result shows 89.66% accuracy by the random forest classifier.

Keywords: complementary and alternative medicine, classification, iridology, iris, feature extraction, disease prediction

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Authors: Caio Marcelo Nunes, Anderson da Silva Soares, Gustavo Teodoro Laureano, Clarimar Jose Coelho

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This paper presents a methodology to pixel recognition in aerial images using fuzzy $c$-means algorithm. This algorithm is a alternative to recognize areas considering uncertainties and inaccuracies. Traditional clustering technics are used in recognizing of multispectral images of earth's surface. This technics recognize well-defined borders that can be easily discretized. However, in the real world there are many areas with uncertainties and inaccuracies which can be mapped by clustering algorithms that use fuzzy sets. The methodology presents in this work is applied to multispectral images obtained from Landsat-5/TM satellite. The pixels are joined using the $c$-means algorithm. After, a classification process identify the types of surface according the patterns obtained from spectral response of image surface. The classes considered are, exposed soil, moist soil, vegetation, turbid water and clean water. The results obtained shows that the fuzzy clustering identify the real type of the earth's surface.

Keywords: aerial images, fuzzy clustering, image processing, pattern recognition

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Authors: L. Mouzai, M. Bouhadef

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Rills begin to be generated once overland flow shear capacity overcomes the soil surface resistance. This resistance depends on soil texture, the arrangement of soil particles and on chemical and physical properties. The rill density could affect soil erosion, especially when the distance between the rills (interrill) contributes to the variation of the rill characteristics, and consequently on sediment concentration. To investigate this point, agricultural sandy soil, a soil tray of 0.2x1x3m³ and a piece of hardwood rectangular in shape to build up rills were the base of this work. The results have shown that small lines have been developed between the rills and the flow acceleration increased in comparison to the flow on the flat surface (interrill). Sediment concentration increased with increasing rill number (density).

Keywords: artificial rainfall, experiments, rills, soil erosion, transport capacity

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Authors: Moein Izadi, Ali Mohammadzadeh

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Detection of damaged parts of roads after earthquake is essential for coordinating rescuers. In this study, an approach is presented for the semi-automatic detection of damaged roads in a city using pre-event vector maps and both pre- and post-earthquake QuickBird satellite images. Damage is defined in this study as the debris of damaged buildings adjacent to the roads. Some spectral and texture features are considered for SVM classification step to detect damages. Finally, the proposed method is tested on QuickBird pan-sharpened images from the Bam City earthquake and the results show that an overall accuracy of 81% and a kappa coefficient of 0.71 are achieved for the damage detection. The obtained results indicate the efficiency and accuracy of the proposed approach.

Keywords: SVM classifier, disaster management, road damage detection, quickBird images

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Authors: Mohit Batra, Noel Sarkar, Jayeeta Mitra

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In this study silica nanoparticles were synthesized using different methods and different silica sources namely Tetraethyl ortho silicate (TEOS), Sodium Silicate, Rice husk while chitosan nanoparticles were prepared with ionic gelation method using Sodium tripolyphosphate (TPP). Size and texture of silica nanoparticles were studied using field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM) along with the effect of change in concentration of various reagents in different synthesis processes. Size and dispersion of Silica nanoparticles prepared from TEOS using stobber’s method were found better than other methods while nanoparticles prepared using rice husk were cheaper than other ones. Catalyst found to play a very significant role in controlling the size of nanoparticles in all methods.

Keywords: silica nanoparticles, gelatin, bio-nanocomposites, SEM, TEM, chitosan

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Authors: Thanh Noi Phan, Martin Kappas, Jan Degener

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The currently launched Sentinel 2 (S2) satellite (June, 2015) bring a great potential and opportunities for land use/cover map applications, due to its fine spatial resolution multispectral as well as high temporal resolutions. So far, there are handful studies using S2 real data for land cover classification. Especially in northern Vietnam, to our best knowledge, there exist no studies using S2 data for land cover map application. The aim of this study is to provide the preliminary result of land cover classification using Sentinel -2 data with a rising state – of – art classifier, Random Forest. A case study with heterogeneous land use/cover in the eastern of Hanoi Capital – Vietnam was chosen for this study. All 10 spectral bands of 10 and 20 m pixel size of S2 images were used, the 10 m bands were resampled to 20 m. Among several classified algorithms, supervised Random Forest classifier (RF) was applied because it was reported as one of the most accuracy methods of satellite image classification. The results showed that the red-edge and shortwave infrared (SWIR) bands play an important role in land cover classified results. A very high overall accuracy above 90% of classification results was achieved.

Keywords: classify algorithm, classification, land cover, random forest, sentinel 2, Vietnam

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Authors: Shreya Pare, Parvin Akhter

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Remotely sensing image classification becomes a very challenging task owing to the high dimensionality of hyperspectral images. The pixel-wise classification methods fail to take the spatial structure information of an image. Therefore, to improve the performance of classification, spatial information can be integrated into the classification process. In this paper, the multilevel thresholding algorithm based on a modified fuzzy entropy function is used to perform the segmentation of hyperspectral images. The fuzzy parameters of the MFE function have been optimized by using a new meta-heuristic algorithm based on the Tree-Search algorithm. The segmented image is classified by a large distribution machine (LDM) classifier. Experimental results are shown on a hyperspectral image dataset. The experimental outputs indicate that the proposed technique (MFE-TSA-LDM) achieves much higher classification accuracy for hyperspectral images when compared to state-of-art classification techniques. The proposed algorithm provides accurate segmentation and classification maps, thus becoming more suitable for image classification with large spatial structures.

Keywords: classification, hyperspectral images, large distribution margin, modified fuzzy entropy function, multilevel thresholding, tree search algorithm, hyperspectral image classification using tree search algorithm

Procedia PDF Downloads 180

Authors: Saleh Gorji, Sahil Gulati, Ana Pakzad

Abstract:

Continuous electron diffraction tomography, also known as microcrystal electron diffraction (MicroED) or three-dimensional electron diffraction (3DED), is a powerful technique, which in combination with cryo-electron microscopy (cryo-ED), can provide atomic-scale 3D information about the crystal structure and composition of different classes of crystalline materials such as proteins, peptides, and small molecules. Unlike the well-established X-ray crystallography method, 3DED does not require large single crystals and can collect accurate electron diffraction data from crystals as small as 50 – 100 nm. This is a critical advantage as growing larger crystals, as required by X-ray crystallography methods, is often very difficult, time-consuming, and expensive. In most cases, specimens studied via 3DED method are electron beam sensitive, which means there is a limitation on the maximum amount of electron dose one can use to collect the required data for a high-resolution structure determination. Therefore, collecting data using a conventional scintillator-based fiber coupled camera brings additional challenges. This is because of the inherent noise introduced during the electron-to-photon conversion in the scintillator and transfer of light via the fibers to the sensor, which results in a poor signal-to-noise ratio and requires a relatively higher and commonly specimen-damaging electron dose rates, especially for protein crystals. As in other cryo-EM techniques, damage to the specimen can be mitigated if a direct detection camera is used which provides a high signal-to-noise ratio at low electron doses. In this work, we have used two classes of such detectors from Gatan, namely the K3® camera (a monolithic active pixel sensor) and Stela™ (that utilizes DECTRIS hybrid-pixel technology), to address this problem. The K3 is an electron counting detector optimized for low-dose applications (like structural biology cryo-EM), and Stela is also a counting electron detector but optimized for diffraction applications with high speed and high dynamic range. Lastly, data collection workflows, including crystal screening, microscope optics setup (for imaging and diffraction), stage height adjustment at each crystal position, and tomogram acquisition, can be one of the other challenges of the 3DED technique. Traditionally this has been all done manually or in a partly automated fashion using open-source software and scripting, requiring long hours on the microscope (extra cost) and extensive user interaction with the system. We have recently introduced Latitude® D in DigitalMicrograph® software, which is compatible with all pre- and post-energy-filter Gatan cameras and enables 3DED data acquisition in an automated and optimized fashion. Higher quality 3DED data enables structure determination with higher confidence, while automated workflows allow these to be completed considerably faster than before. Using multiple examples, this work will demonstrate how to direct detection electron counting cameras enhance 3DED results (3 to better than 1 Angstrom) for protein and small molecule structure determination. We will also show how Latitude D software facilitates collecting such data in an integrated and fully automated user interface.

Keywords: continuous electron diffraction tomography, direct detection, diffraction, Latitude D, Digitalmicrograph, proteins, small molecules

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Authors: Reza Mohammadi, Mahmod R. Sahebi, Mehrnoosh Omati, Milad Vahidi

Abstract:

Classification of high resolution polarimetric Synthetic Aperture Radar (PolSAR) images plays an important role in land cover and land use management. Recently, classification algorithms based on Bag of Visual Words (BOVW) model have attracted significant interest among scholars and researchers in and out of the field of remote sensing. In this paper, BOVW model with pixel based low-level features has been implemented to classify a subset of San Francisco bay PolSAR image, acquired by RADARSAR 2 in C-band. We have used segment-based decision-making strategy and compared the result with the result of traditional Support Vector Machine (SVM) classifier. 90.95% overall accuracy of the classification with the proposed algorithm has shown that the proposed algorithm is comparable with the state-of-the-art methods. In addition to increase in the classification accuracy, the proposed method has decreased undesirable speckle effect of SAR images.

Keywords: Bag of Visual Words (BOVW), classification, feature extraction, land cover management, Polarimetric Synthetic Aperture Radar (PolSAR)

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Authors: Adnan A. Y. Mustafa

Abstract:

Image matching is a fundamental problem that arises frequently in many aspects of robot and computer vision. It can become a time-consuming process when matching images to a database consisting of hundreds of images, especially if the images are big. One approach to reducing the time complexity of the matching process is to reduce the search space in a pre-matching stage, by simply removing dissimilar images quickly. The Probabilistic Matching Model for Binary Images (PMMBI) showed that dissimilarity detection between binary images can be accomplished quickly by random pixel mapping and is size invariant. The model is based on the gamma binary similarity distance that recognizes an image and its inverse as containing the same scene and hence considers them to be the same image. However, in many applications, an image and its inverse are not treated as being the same but rather dissimilar. In this paper, we present a comparative analysis of dissimilarity detection between PMMBI based on the gamma binary similarity distance and a modified PMMBI model based on a similarity distance that does distinguish between an image and its inverse as being dissimilar.

Keywords: binary image, dissimilarity detection, probabilistic matching model for binary images, image mapping

Procedia PDF Downloads 155

Authors: C. Casini, V. Da Deppo, P. Zuppella, P. Chioetto, A. Slemer, F. Frassetto, M. Romoli, F. Landini, M. Pancrazzi, V. Andretta, E. Antonucci, A. Bemporad, M. Casti, Y. De Leo, M. Fabi, S. Fineschi, F. Frassati, C. Grimani, G. Jerse, P. Heinzel, K. Heerlein, A. Liberatore, E. Magli, G. Naletto, G. Nicolini, M.G. Pelizzo, P. Romano, C. Sasso, D. Spadaro, M. Stangalini, T. Straus, R. Susino, L. Teriaca, M. Uslenghi, A. Volpicelli

Abstract:

Solar Orbiter, launched on February 9^th 2020, is an ESA/NASA mission conceived to study the Sun. The payload is composed of 10 instruments, among which there is the Metis coronagraph. A coronagraph aims at taking images of the solar corona: the occulter element simulates a total solar eclipse. This work presents some of the results obtained in the visible light band (580-640 nm) using a flat field panel source. The flat field panel gives a uniform illumination; consequently, it has been used during the on-ground calibration for several purposes: evaluating the response of each pixel of the detector (linearity); and characterizing the Field of View of the coronagraph. As a conclusion, a major result is the verification that the requirement for the Field of View (FoV) of Metis is fulfilled. Some investigations are in progress in order to verify that the performance measured on-ground did not change after launch.

Keywords: solar orbiter, Metis, coronagraph, flat field panel, calibration, on-ground, performance

Procedia PDF Downloads 106

Authors: Salim Çalışkan, Hakan Akyüz

Abstract:

Digital image correlation has been accustomed as a versatile and efficient method for measuring displacements on the article surfaces by comparing reference subsets in undeformed images with the define target subset in the distorted image. The theoretical model points out that the accuracy of the digital image correlation displacement data can be exactly anticipated based on the divergence of the image noise and the sum of the squares of the subset intensity gradients. The digital image correlation procedure locates each subset of the original image in the distorted image. The software then determines the displacement values of the centers of the subassemblies, providing the complete displacement measures. In this paper, the effect of the speckle distribution and its effect on displacements measured out plane displacement data as a function of the size of the subset was investigated. Nine groups of speckle patterns were used in this study: samples are sprayed randomly by pre-manufactured patterns of three different hole diameters, each with three coverage ratios, on a computer numerical control punch press. The resulting displacement values, referenced at the center of the subset, are evaluated based on the average of the displacements of the pixel’s interior the subset.

Keywords: digital image correlation, speckle pattern, experimental mechanics, tensile test, aluminum alloy

Procedia PDF Downloads 75