Showing papers in "Journal of Imaging in 2017"

Object Recognition in Aerial Images Using Convolutional Neural Networks

Abstract: There are numerous applications of unmanned aerial vehicles (UAVs) in the management of civil infrastructure assets. A few examples include routine bridge inspections, disaster management, power line surveillance and traffic surveying. As UAV applications become widespread, increased levels of autonomy and independent decision-making are necessary to improve the safety, efficiency, and accuracy of the devices. This paper details the procedure and parameters used for the training of convolutional neural networks (CNNs) on a set of aerial images for efficient and automated object recognition. Potential application areas in the transportation field are also highlighted. The accuracy and reliability of CNNs depend on the network’s training and the selection of operational parameters. This paper details the CNN training procedure and parameter selection. The object recognition results show that by selecting a proper set of parameters, a CNN can detect and classify objects with a high level of accuracy (97.5%) and computational efficiency. Furthermore, using a convolutional neural network implemented in the “YOLO” (“You Only Look Once”) platform, objects can be tracked, detected (“seen”), and classified (“comprehended”) from video feeds supplied by UAVs in real-time.

Early Yield Prediction Using Image Analysis of Apple Fruit and Tree Canopy Features with Neural Networks

Abstract: (1) Background: Since early yield prediction is relevant for resource requirements of harvesting and marketing in the whole fruit industry, this paper presents a new approach of using image analysis and tree canopy features to predict early yield with artificial neural networks (ANN); (2) Methods: Two back propagation neural network (BPNN) models were developed for the early period after natural fruit drop in June and the ripening period, respectively. Within the same periods, images of apple cv. “Gala” trees were captured from an orchard near Bonn, Germany. Two sample sets were developed to train and test models; each set included 150 samples from the 2009 and 2010 growing season. For each sample (each canopy image), pixels were segmented into fruit, foliage, and background using image segmentation. The four features extracted from the data set for the canopy were: total cross-sectional area of fruits, fruit number, total cross-section area of small fruits, and cross-sectional area of foliage, and were used as inputs. With the actual weighted yield per tree as a target, BPNN was employed to learn their mutual relationship as a prerequisite to develop the prediction; (3) Results: For the developed BPNN model of the early period after June drop, correlation coefficients (R2) between the estimated and the actual weighted yield, mean forecast error (MFE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were 0.81, −0.05, 10.7%, 2.34 kg/tree, respectively. For the model of the ripening period, these measures were 0.83, −0.03, 8.9%, 2.3 kg/tree, respectively. In 2011, the two previously developed models were used to predict apple yield. The RMSE and R2 values between the estimated and harvested apple yield were 2.6 kg/tree and 0.62 for the early period (small, green fruit) and improved near harvest (red, large fruit) to 2.5 kg/tree and 0.75 for a tree with ca. 18 kg yield per tree. For further method verification, the cv. “Pinova” apple trees were used as another variety in 2012 to develop the BPNN prediction model for the early period after June drop. The model was used in 2013, which gave similar results as those found with cv. “Gala”; (4) Conclusion: Overall, the results showed in this research that the proposed estimation models performed accurately using canopy and fruit features using image analysis algorithms.

An Overview of Infrared Remote Sensing of Volcanic Activity

Abstract: Volcanic activity consists of the transfer of heat from the interior of the Earth to the surface. The characteristics of the heat emitted relate directly to the geological processes underway and can be observed from space, using the thermal sensors present on many Earth-orbiting satellites. For over 50 years, scientists have utilised such sensors and are now able to determine the sort of volcanic activity being displayed without hazardous and costly field expeditions. This review will describe the theoretical basis of the discipline and then discuss the sensors available and the history of their use. Challenges and opportunities for future developments are then discussed.

Enhancing Face Identification Using Local Binary Patterns and K-Nearest Neighbors

Abstract: The human face plays an important role in our social interaction, conveying people’s identity. Using the human face as a key to security, biometric passwords technology has received significant attention in the past several years due to its potential for a wide variety of applications. Faces can have many variations in appearance (aging, facial expression, illumination, inaccurate alignment and pose) which continue to cause poor ability to recognize identity. The purpose of our research work is to provide an approach that contributes to resolve face identification issues with large variations of parameters such as pose, illumination, and expression. For provable outcomes, we combined two algorithms: (a) robustness local binary pattern (LBP), used for facial feature extractions; (b) k-nearest neighbor (K-NN) for image classifications. Our experiment has been conducted on the CMU PIE (Carnegie Mellon University Pose, Illumination, and Expression) face database and the LFW (Labeled Faces in the Wild) dataset. The proposed identification system shows higher performance, and also provides successful face similarity measures focus on feature extractions.

DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images

Abstract: Most digital libraries that provide user-friendly interfaces, enabling quick and intuitive access to their resources, are based on Document Image Analysis and Recognition (DIAR) methods. Such DIAR methods need ground-truthed document images to be evaluated/compared and, in some cases, trained. Especially with the advent of deep learning-based approaches, the required size of annotated document datasets seems to be ever-growing. Manually annotating real documents has many drawbacks, which often leads to small reliably annotated datasets. In order to circumvent those drawbacks and enable the generation of massive ground-truthed data with high variability, we present DocCreator, a multi-platform and open-source software able to create many synthetic image documents with controlled ground truth. DocCreator has been used in various experiments, showing the interest of using such synthetic images to enrich the training stage of DIAR tools.

Improving CNN-Based Texture Classification by Color Balancing

Abstract: Texture classification has a long history in computer vision. In the last decade, the strong affirmation of deep learning techniques in general, and of convolutional neural networks (CNN) in particular, has allowed for a drastic improvement in the accuracy of texture recognition systems. However, their performance may be dampened by the fact that texture images are often characterized by color distributions that are unusual with respect to those seen by the networks during their training. In this paper we will show how suitable color balancing models allow for a significant improvement in the accuracy in recognizing textures for many CNN architectures. The feasibility of our approach is demonstrated by the experimental results obtained on the RawFooT dataset, which includes texture images acquired under several different lighting conditions.

Characterization of Crystallographic Structures Using Bragg-Edge Neutron Imaging at the Spallation Neutron Source

Abstract: Over the past decade, wavelength-dependent neutron radiography, also known as Bragg-edge imaging, has been employed as a non-destructive bulk characterization method due to its sensitivity to coherent elastic neutron scattering that is associated with crystalline structures. Several analysis approaches have been developed to quantitatively determine crystalline orientation, lattice strain, and phase distribution. In this study, we report a systematic investigation of the crystal structures of metallic materials (such as selected textureless powder samples and additively manufactured (AM) Inconel 718 samples), using Bragg-edge imaging at the Oak Ridge National Laboratory (ORNL) Spallation Neutron Source (SNS). Firstly, we have implemented a phenomenological Gaussian-based fitting in a Python-based computer called iBeatles. Secondly, we have developed a model-based approach to analyze Bragg-edge transmission spectra, which allows quantitative determination of the crystallographic attributes. Moreover, neutron diffraction measurements were carried out to validate the Bragg-edge analytical methods. These results demonstrate that the microstructural complexity (in this case, texture) plays a key role in determining the crystallographic parameters (lattice constant or interplanar spacing), which implies that the Bragg-edge image analysis methods must be carefully selected based on the material structures.

Novel Low Cost 3D Surface Model Reconstruction System for Plant Phenotyping

Abstract: Accurate high-resolution three-dimensional (3D) models are essential for a non-invasive analysis of phenotypic characteristics of plants. Previous limitations in 3D computer vision algorithms have led to a reliance on volumetric methods or expensive hardware to record plant structure. We present an image-based 3D plant reconstruction system that can be achieved by using a single camera and a rotation stand. Our method is based on the structure from motion method, with a SIFT image feature descriptor. In order to improve the quality of the 3D models, we segmented the plant objects based on the PlantCV platform. We also deducted the optimal number of images needed for reconstructing a high-quality model. Experiments showed that an accurate 3D model of the plant was successfully could be reconstructed by our approach. This 3D surface model reconstruction system provides a simple and accurate computational platform for non-destructive, plant phenotyping.

Comparison of Small Unmanned Aerial Vehicles Performance Using Image Processing

Abstract: Precision agriculture is a farm management technology that involves sensing and then responding to the observed variability in the field. Remote sensing is one of the tools of precision agriculture. The emergence of small unmanned aerial vehicles (sUAV) have paved the way to accessible remote sensing tools for farmers. This paper describes the development of an image processing approach to compare two popular off-the-shelf sUAVs: 3DR Iris+ and DJI Phantom 2. Both units are equipped with a camera gimbal attached with a GoPro camera. The comparison of the two sUAV involves a hovering test and a rectilinear motion test. In the hovering test, the sUAV was allowed to hover over a known object and images were taken every quarter of a second for two minutes. For the image processing evaluation, the position of the object in the images was measured and this was used to assess the stability of the sUAV while hovering. In the rectilinear test, the sUAV was allowed to follow a straight path and images of a lined track were acquired. The lines on the images were then measured on how accurate the sUAV followed the path. The hovering test results show that the 3DR Iris+ had a maximum position deviation of 0.64 m (0.126 m root mean square RMS displacement) while the DJI Phantom 2 had a maximum deviation of 0.79 m (0.150 m RMS displacement). In the rectilinear motion test, the maximum displacement for the 3DR Iris+ and the DJI phantom 2 were 0.85 m (0.134 m RMS displacement) and 0.73 m (0.372 m RMS displacement). These results demonstrated that the two sUAVs performed well in both the hovering test and the rectilinear motion test and thus demonstrated that both sUAVs can be used for civilian applications such as agricultural monitoring. The results also showed that the developed image processing approach can be used to evaluate performance of a sUAV and has the potential to be used as another feedback control parameter for autonomous navigation.

Peach Flower Monitoring Using Aerial Multispectral Imaging

Abstract: One of the tools for optimal crop production is regular monitoring and assessment of crops. During the growing season of fruit trees, the bloom period has increased photosynthetic rates that correlate with the fruiting process. This paper presents the development of an image processing algorithm to detect peach blossoms on trees. Aerial images of peach (Prunus persica) trees were acquired from both experimental and commercial peach orchards in the southwestern part of Idaho using an off-the-shelf unmanned aerial system (UAS), equipped with a multispectral camera (near-infrared, green, blue). The image processing algorithm included contrast stretching of the three bands to enhance the image and thresholding segmentation method to detect the peach blossoms. Initial results showed that the image processing algorithm could detect peach blossoms with an average detection rate of 84.3% and demonstrated good potential as a monitoring tool for orchard management.

Computer Assisted Examination of Infrared and Near Infrared Spectra to Assess Structural and Molecular Changes in Biological Samples Exposed to Pollutants: A Case of Study

Abstract: We present a computer assisted method for the examination of the structural changes present in the probe organism Vicia faba exposed to inorganic arsenic, detected by means of Fourier transform infrared (FTIR) and Fourier transform near infrared (FTNIR) spectroscopy. Like the common ecotoxicological tests, the method is based on the comparison among control and exposed sample spectra of the organisms to detect structural changes caused by pollutants. Using FTIR spectroscopy, we measured and plotted the spectral changes related to the unsaturated to saturated lipid ratio changes (USL), the lipid to protein ratio changes (LPR), fatty and ester fatty acid content changes (FA), protein oxidation (PO) and denaturation, and DNA and RNA changes (DNA-RNA). Using FTNIR spectroscopy, we measured two spectral ranges that belonged to hydrogen bond interactions and aliphatic lipid chains called IntHCONH and Met1overt, respectively. The FTIR results showed that As modified the DNA-RNA ratio and also caused partial protein denaturation in the Vicia faba samples. The FTNIR results supported the FTIR results. The main advantage of the proposed computational method is that it does not require a skilled infrared or near infrared operator, lending support to conventional studies performed by toxicological testing.

Deep Learning vs. Conventional Machine Learning: Pilot Study of WMH Segmentation in Brain MRI with Absence or Mild Vascular Pathology

Abstract: In the wake of the use of deep learning algorithms in medical image analysis, we compared performance of deep learning algorithms, namely the deep Boltzmann machine (DBM), convolutional encoder network (CEN) and patch-wise convolutional neural network (patch-CNN), with two conventional machine learning schemes: Support vector machine (SVM) and random forest (RF), for white matter hyperintensities (WMH) segmentation on brain MRI with mild or no vascular pathology. We also compared all these approaches with a method in the Lesion Segmentation Tool public toolbox named lesion growth algorithm (LGA). We used a dataset comprised of 60 MRI data from 20 subjects in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, each scanned once every year during three consecutive years. Spatial agreement score, receiver operating characteristic and precision-recall performance curves, volume disagreement score, agreement with intra-/inter-observer reliability measurements and visual evaluation were used to find the best configuration of each learning algorithm for WMH segmentation. By using optimum threshold values for the probabilistic output from each algorithm to produce binary masks of WMH, we found that SVM and RF produced good results for medium to very large WMH burden but deep learning algorithms performed generally better than conventional ones in most evaluations.

Small Angle Scattering in Neutron Imaging—A Review

Abstract: Conventional neutron imaging utilizes the beam attenuation caused by scattering and absorption through the materials constituting an object in order to investigate its macroscopic inner structure. Small angle scattering has basically no impact on such images under the geometrical conditions applied. Nevertheless, in recent years different experimental methods have been developed in neutron imaging, which enable to not only generate contrast based on neutrons scattered to very small angles, but to map and quantify small angle scattering with the spatial resolution of neutron imaging. This enables neutron imaging to access length scales which are not directly resolved in real space and to investigate bulk structures and processes spanning multiple length scales from centimeters to tens of nanometers.

Neutron imaging facilities in a global context

Abstract: Neutron Imaging (NI) has been developed in the last decades from a film-based inspection method for non-destructive observations towards a powerful research tool with many new and competitive methods. The most important technical step forward has been the introduction and optimization of digital imaging detection systems. In this way, direct quantification of the transmission process became possible—the basis for all advanced methods like tomography, phase-contrast imaging and neutron microscopy. Neutron imaging facilities need to be installed at powerful neutron sources (reactors, spallation sources, other accelerator driven systems). High neutron intensity can be used best for either highest spatial, temporal resolution or best image quality. Since the number of such strong sources is decreasing world-wide due to the age of the reactors, the number of NI facilities is limited. There are a few installations with pioneering new concepts and versatile options on the one hand, but also relatively new sources with only limited performance thus far. It will be a challenge to couple the two parts of the community with the aim to install state-of-the-art equipment at the suitable beam ports and develop NI further towards a general research tool. In addition, sources with lower intensity should be equipped with modern installations in order to perform practical work best.

Deriving Quantitative Crystallographic Information from the Wavelength-Resolved Neutron Transmission Analysis Performed in Imaging Mode

Abstract: Current status of Bragg-edge/dip neutron transmission analysis/imaging methods is presented. The method can visualize real-space distributions of bulk crystallographic information in a crystalline material over a large area (~10 cm) with high spatial resolution (~100 μm). Furthermore, by using suitable spectrum analysis methods for wavelength-dependent neutron transmission data, quantitative visualization of the crystallographic information can be achieved. For example, crystallographic texture imaging, crystallite size imaging and crystalline phase imaging with texture/extinction corrections are carried out by the Rietveld-type (wide wavelength bandwidth) profile fitting analysis code, RITS (Rietveld Imaging of Transmission Spectra). By using the single Bragg-edge analysis mode of RITS, evaluations of crystal lattice plane spacing (d-spacing) relating to macro-strain and d-spacing distribution’s FWHM (full width at half maximum) relating to micro-strain can be achieved. Macro-strain tomography is performed by a new conceptual CT (computed tomography) image reconstruction algorithm, the tensor CT method. Crystalline grains and their orientations are visualized by a fast determination method of grain orientation for Bragg-dip neutron transmission spectrum. In this paper, these imaging examples with the spectrum analysis methods and the reliabilities evaluated by optical/electron microscope and X-ray/neutron diffraction, are presented. In addition, the status at compact accelerator driven pulsed neutron sources is also presented.

A Holistic Technique for an Arabic OCR System

Abstract: Analytical based approaches in Optical Character Recognition (OCR) systems can endure a significant amount of segmentation errors, especially when dealing with cursive languages such as the Arabic language with frequent overlapping between characters. Holistic based approaches that consider whole words as single units were introduced as an effective approach to avoid such segmentation errors. Still the main challenge for these approaches is their computation complexity, especially when dealing with large vocabulary applications. In this paper, we introduce a computationally efficient, holistic Arabic OCR system. A lexicon reduction approach based on clustering similar shaped words is used to reduce recognition time. Using global word level Discrete Cosine Transform (DCT) based features in combination with local block based features, our proposed approach managed to generalize for new font sizes that were not included in the training data. Evaluation results for the approach using different test sets from modern and historical Arabic books are promising compared with state of art Arabic OCR systems.

Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance

Abstract: A novel efficient method for content-based image retrieval (CBIR) is developed in this paper using both texture and color features. Our motivation is to represent and characterize an input image by a set of local descriptors extracted from characteristic points (i.e., keypoints) within the image. Then, dissimilarity measure between images is calculated based on the geometric distance between the topological feature spaces (i.e., manifolds) formed by the sets of local descriptors generated from each image of the database. In this work, we propose to extract and use the local extrema pixels as our feature points. Then, the so-called local extrema-based descriptor (LED) is generated for each keypoint by integrating all color, spatial as well as gradient information captured by its nearest local extrema. Hence, each image is encoded by an LED feature point cloud and Riemannian distances between these point clouds enable us to tackle CBIR. Experiments performed on several color texture databases including Vistex, STex, color Brodazt, USPtex and Outex TC-00013 using the proposed approach provide very efficient and competitive results compared to the state-of-the-art methods.

Preliminary Tests and Results Concerning Integration of Sentinel-2 and Landsat-8 OLI for Crop Monitoring

Abstract: The Sentinel-2 data by European Space Agency were recently made available for free. Their technical features suggest synergies with Landsat-8 dataset by NASA (National Aeronautics and Space Administration), especially in the agriculture context were observations should be as dense as possible to give a rather complete description of macro-phenology of crops. In this work some preliminary results are presented concerning geometric and spectral consistency of the two compared datasets. Tests were performed specifically focusing on the agriculture-devoted part of Piemonte Region (NW Italy). Geometric consistencies of Sentinel-2 and Landsat-8 datasets were tested “absolutely” (in respect of a selected reference frame) and “relatively” (one in respect of the other) by selecting, respectively, 160 and 100 well distributed check points. Spectral differences affecting at-the-ground reflectance were tested after images calibration performed by dark object subtraction approach. A special focus was on differences affecting derivable NDVI and NDWI spectral indices, being the most widely used in the agriculture remote sensing application context. Results are encouraging and suggest that this approach can successfully enter the ordinary remote sensing-supported precision farming workflow.

Automatic Detection and Distinction of Retinal Vessel Bifurcations and Crossings in Colour Fundus Photography

Abstract: The analysis of retinal blood vessels present in fundus images, and the addressing of problems such as blood clot location, is important to undertake accurate and appropriate treatment of the vessels. Such tasks are hampered by the challenge of accurately tracing back problems along vessels to their source. This is due to the unresolved issue of distinguishing automatically between vessel bifurcations and vessel crossings in colour fundus photographs. In this paper, we present a new technique for addressing this problem using a convolutional neural network approach to firstly locate vessel bifurcations and crossings and then to classifying them as either bifurcations or crossings. Our method achieves high accuracies for junction detection and classification on the DRIVE dataset and we show further validation on an unseen dataset from which no data has been used for training. Combined with work in automated segmentation, this method has the potential to facilitate: reconstruction of vessel topography, classification of veins and arteries and automated localisation of blood clots and other disease symptoms leading to improved management of eye disease.

3D Reconstructions Using Unstabilized Video Footage from an Unmanned Aerial Vehicle

Abstract: Structure from motion (SFM) is a methodology for automatically reconstructing three-dimensional (3D) models from a series of two-dimensional (2D) images when there is no a priori knowledge of the camera location and direction. Modern unmanned aerial vehicles (UAV) now provide a low-cost means of obtaining aerial video footage of a point of interest. Unfortunately, raw video lacks the required information for SFM software, as it does not record exchangeable image file (EXIF) information for the frames. In this work, a solution is presented to modify aerial video so that it can be used for photogrammetry. The paper then examines how the field of view effects the quality of the reconstruction. The input is unstabilized, and distorted video footage obtained from a low-cost UAV which is then combined with an open-source SFM system to reconstruct a 3D model. This approach creates a high quality reconstruction by reducing the amount of unknown variables, such as focal length and sensor size, while increasing the data density. The experiments conducted examine the optical field of view settings to provide sufficient overlap without sacrificing image quality or exacerbating distortion. The system costs less than e1000, and the results show the ability to reproduce 3D models that are of centimeter-level accuracy. For verification, the results were compared against millimeter-level accurate models derived from laser scanning.

Assessment of Geometric Distortion in Six Clinical Scanners Using a 3D-Printed Grid Phantom

Abstract: A cost-effective regularly structured three-dimensional (3D) printed grid phantom was developed to enable the quantification of machine-related magnetic resonance (MR) distortion. This phantom contains reference features, “point-like” objects, or vertices, which resulted from the intersection of mesh edges in 3D space. 3D distortions maps were computed by comparing the locations of corresponding features in both MR and computer tomography (CT) data sets using normalized cross correlation. Results are reported for six MRI scanners at both 1.5 T and 3.0 T field strengths within our institution. Mean Euclidean distance error for all MR volumes in this study, was less than 2 mm. The maximum detected error for the six scanners ranged from 2.4 mm to 6.9 mm. The conclusions in this study agree well with previous studies that indicated that MRI is quite accurate near the centre of the field but is more spatially inaccurate toward the edges of the magnetic field.

Performance of the Commercial PP/ZnS:Cu and PP/ZnS:Ag Scintillation Screens for Fast Neutron Imaging

Abstract: Fast neutron imaging has a great potential as a nondestructive technique for testing large objects. The main factor limiting applications of this technique is detection technology, offering relatively poor spatial resolution of images and low detection efficiency, which results in very long exposure times. Therefore, research on development of scintillators for fast neutron imaging is of high importance. A comparison of the light output, gamma radiation sensitivity and spatial resolution of commercially available scintillator screens composed of PP/ZnS:Cu and PP/ZnS:Ag of different thicknesses are presented. The scintillators were provided by RC Tritec AG company and the test performed at the NECTAR facility located at the FRM II nuclear research reactor. It was shown that light output increases and the spatial resolution decreases with the scintillator thickness. Both compositions of the scintillating material provide similar light output, while the gamma sensitivity of PP/ZnS:Cu is significantly higher as compared to PP/ZnS:Ag-based scintillators. Moreover, we report which factors should be considered when choosing a scintillator and what are the limitations of the investigated types of scintillators.

LaFiDa—A Laserscanner Multi-Fisheye Camera Dataset

Abstract: In this article, the Laserscanner Multi-Fisheye Camera Dataset (LaFiDa) for applying benchmarks is presented. A head-mounted multi-fisheye camera system combined with a mobile laserscanner was utilized to capture the benchmark datasets. Besides this, accurate six degrees of freedom (6 DoF) ground truth poses were obtained from a motion capture system with a sampling rate of 360 Hz. Multiple sequences were recorded in an indoor and outdoor environment, comprising different motion characteristics, lighting conditions, and scene dynamics. The provided sequences consist of images from three—by hardware trigger—fully synchronized fisheye cameras combined with a mobile laserscanner on the same platform. In total, six trajectories are provided. Each trajectory also comprises intrinsic and extrinsic calibration parameters and related measurements for all sensors. Furthermore, we generalize the most common toolbox for an extrinsic laserscanner to camera calibration to work with arbitrary central cameras, such as omnidirectional or fisheye projections. The benchmark dataset is available online released under the Creative Commons Attributions Licence (CC-BY 4.0), and it contains raw sensor data and specifications like timestamps, calibration, and evaluation scripts. The provided dataset can be used for multi-fisheye camera and/or laserscanner simultaneous localization and mapping (SLAM).

Olive Plantation Mapping on a Sub-Tree Scale with Object-Based Image Analysis of Multispectral UAV Data; Operational Potential in Tree Stress Monitoring

Abstract: The objective of this study was to develop a methodology for mapping olive plantations on a sub-tree scale. For this purpose, multispectral imagery of an almost 60-ha plantation in Greece was acquired with an Unmanned Aerial Vehicle. Objects smaller than the tree crown were produced with image segmentation. Three image features were indicated as optimum for discriminating olive trees from other objects in the plantation, in a rule-based classification algorithm. After limited manual corrections, the final output was validated by an overall accuracy of 93%. The overall processing chain can be considered as suitable for operational olive tree monitoring for potential stresses.

Neutron Imaging of Laser Melted SS316 Test Objects with Spatially Resolved Small Angle Neutron Scattering

Abstract: A novel neutron far field interferometer is explored for sub-micron porosity detection in laser sintered stainless steel alloy 316 (SS316) test objects. The results shown are images and volumes of the first quantitative neutron dark-field tomography at various autocorrelation lengths, ξ . In this preliminary work, the beam defining slits were adjusted to an uncalibrated opening of 0.5 mm horizontal and 5 cm vertical; the images are blurred along the vertical direction. In spite of the blurred attenuation images, the dark-field images reveal structural information at the micron-scale. The topics explored include: the accessible size range of defects, potentially 338 nm to 4.5 μ m, that can be imaged with the small angle scattering images; the spatial resolution of the attenuation image; the maximum sample dimensions compatible with interferometry optics and neutron attenuation; the procedure for reduction of the raw interferogram images into attenuation, differential phase contrast, and small angle scattering (dark-field) images; and the role of neutron far field interferometry in additive manufacturing to assess sub-micron porosity.

Exemplar-Based Face Colorization Using Image Morphing

Abstract: Colorization of gray-scale images relies on prior color information. Exemplar-based methods use a color image as source of such information. Then the colors of the source image are transferred to the gray-scale target image. In the literature, this transfer is mainly guided by texture descriptors. Face images usually contain few texture so that the common approaches frequently fail. In this paper, we propose a new method taking the geometric structure of the images rather their texture into account such that it is more reliable for faces. Our approach is based on image morphing and relies on the YUV color space. First, a correspondence mapping between the luminance Y channel of the color source image and the gray-scale target image is computed. This mapping is based on the time discrete metamorphosis model suggested by Berkels, Effland and Rumpf. We provide a new finite difference approach for the numerical computation of the mapping. Then, the chrominance U,V channels of the source image are transferred via this correspondence map to the target image. A possible postprocessing step by a variational model is developed to further improve the results. To keep the contrast special attention is paid to make the postprocessing unbiased. Our numerical experiments show that our morphing based approach clearly outperforms state-of-the-art methods.

Integrated Model of Image Protection Techniques

Abstract: We propose an integrated model of Block-Permutation-Based Encryption (BPBE) and Reversible Data Hiding (RDH). The BPBE scheme involves four processes for encryption, namely block scrambling, block-rotation/inversion, negative-positive transformation and the color component shuffling. A Histogram Shifting (HS) method is adopted for RDH in our model. The proposed scheme can be well suitable for the hierarchical access control system, where the data can be accessed with the different access rights. This scheme encrypts R, G and B components independently. Therefore, we can generate similar output images from different input images. Additionally, the key derivation scheme also provides the security according to the different access rights. Our scheme is also resilient against brute-force attacks and Jigsaw Puzzle Solvers (JPSs). Furthermore, the compression performance is also not severely degraded using a standard lossless compression method.

3D Imaging with a Sonar Sensor and an Automated 3-Axes Frame for Selective Spraying in Controlled Conditions

Abstract: Autonomous selective spraying could be a way for agriculture to reduce production costs, save resources, protect the environment and help to fulfill specific pesticide regulations. The objective of this paper was to investigate the use of a low-cost sonar sensor for autonomous selective spraying of single plants. For this, a belt driven autonomous robot was used with an attached 3-axes frame with three degrees of freedom. In the tool center point (TCP) of the 3-axes frame, a sonar sensor and a spray valve were attached to create a point cloud representation of the surface, detect plants in the area and perform selective spraying. The autonomous robot was tested on replicates of artificial crop plants. The location of each plant was identified out of the acquired point cloud with the help of Euclidian clustering. The gained plant positions were spatially transformed from the coordinates of the sonar sensor to the valve location to determine the exact irrigation points. The results showed that the robot was able to automatically detect the position of each plant with an accuracy of 2.7 cm and could spray on these selected points. This selective spraying reduced the used liquid by 72%, when comparing it to a conventional spraying method in the same conditions.

Nitrogen (N) Mineral Nutrition and Imaging Sensors for Determining N Status and Requirements of Maize

Abstract: Nitrogen (N) is one of the most limiting factors for maize (Zea mays L.) production worldwide. Over-fertilization of N may decrease yields and increase NO3− contamination of water. However, low N fertilization will decrease yields. The objective is to optimize the use of N fertilizers, to excel in yields and preserve the environment. The knowledge of factors affecting the mobility of N in the soil is crucial to determine ways to manage N in the field. Researchers developed several methods to use N efficiently relying on agronomic practices, the use of sensors and the analysis of digital images. These imaging sensors determine N requirements in plants based on changes in Leaf chlorophyll and polyphenolics contents, the Normalized Difference Vegetation Index (NDVI), and the Dark Green Color index (DGCI). Each method revealed limitations and the scope of future research is to draw N recommendations from the Dark Green Color Index (DGCI) technology. Results showed that more effort is needed to develop tools to benefit from DGCI.

Color Consistency and Local Contrast Enhancement for a Mobile Image-Based Change Detection System

Abstract: Mobile change detection systems allow for acquiring image sequences on a route of interest at different time points and display changes on a monitor. For the display of color images, a processing approach is required to enhance details, to reduce lightness/color inconsistencies along each image sequence as well as between corresponding image sequences due to the different illumination conditions, and to determine colors with natural appearance. We have developed a real-time local/global color processing approach for local contrast enhancement and lightness/color consistency, which processes images of the different sequences independently. Our approach combines the center/surround Retinex model and the Gray World hypothesis using a nonlinear color processing function. We propose an extended gain/offset scheme for Retinex to reduce the halo effect on shadow boundaries, and we employ stacked integral images (SII) for efficient Gaussian convolution. By applying the gain/offset function before the color processing function, we avoid color inversion issues, compared to the original scheme. Our combined Retinex/Gray World approach has been successfully applied to pairs of image sequences acquired on outdoor routes for change detection, and an experimental comparison with previous Retinex-based approaches has been carried out.