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The physical mechanisms controlling deep-focus earthquakes, or those observed at depths greater than 300 km, remain enigmatic. The leading processes by which deep-focus earthquakes are thought to occur include transformational faulting, thermal runaway and dehydration embrittlement, but distinguishing observations in support of one or more prevailing mechanisms are needed. In this study, we use a modified back-projection method, data recorded by the Hi-net array in Japan and a 3-D velocity model to produce source images of 19 deep-focus earthquakes within the Izu-Bonin subduction zone. We find that the rupture properties and fault plane orientations of imaged events separate according to reported moment magnitude, indicating the distinct operation of two moment-dependent causal mechanisms of deep-focus earthquakes in this region. We discuss these results in the context of previous observational, laboratory and numerical studies and emphasize the importance of continued research to validate the dual-mechanism hypothesis both in and outside Izu-Bonin. Such work may not only improve our understanding of the nucleation and propagation of deep-focus earthquakes, but also help clarify slab structure and subduction zone dynamics. [ABSTRACT FROM AUTHOR]

Pavement management has traditionally relied on human-based decisions. In many countries, however, the pavement stock has recently increased, while the number of management experts has declined, posing the challenge of how to efficiently manage the larger stock with fewer resources. Compared to efficient computer-based techniques, human-based methods are more prone to errors that compromise analysis and decisions. This research built a robust probabilistic pavement management model with a safety metric output using inputs from image processing tested against the judgment of experts. The developed model optimized road pavement safety. The study explored image processing techniques considering the trade-off between processing cost and output accuracy, with annotation precision and intersection over union (IoU) set objectively. The empirical applicability of the model is shown for selected roads in Japan. [ABSTRACT FROM AUTHOR]

In Japan, the final tightening of bolts in the bolt-tightening operations is guaranteed to have been performed correctly by visually determining the change in markings during the temporary tightening operation performed by the technician. However, the engineer must confirm many bolts; further, the amount of time needed for the confirmation work and the inability to keep an objective record of the confirmation results present problems. To solve these problems, we developed a system for automating the final tightening of bolts using deep learning-based image-processing technology. The proposed system takes as input videos of bolt fastening points, extracts individual bolts, extracts markings on the extracted bolts, and makes fastening decisions based on the markings. In the judgment stage, the system processes information on each bolt where a marking is detected; thus, it is possible to leave this information as objective data. In this paper, we evaluated the accuracy of each automated step using an actual bridge video. We also compared the confirmation time with human confirmation. As a result of the confirmation, our proposed method reduces the confirmation time by about 33% in comparison to human confirmation. [ABSTRACT FROM AUTHOR]

In recent years, Japan's agricultural industry has faced a number of challenges, including a decline in production due to a decrease in farmland area, a shortage of labor due to a decrease in the number of producers, and an aging population. Therefore, in recent years, smart agriculture using robots and IoT has been studied. A caliper is often used to analyze the growth of tomatoes in a plant factory, but this method may damage the stems and is also hard on the measurer. We developed a system that detects them through image analysis and measures the thickness of stems and the length between flower clusters and growing points. The camera device developed in this study costs about USD 150 and once installed, it does not need to be moved unless it malfunctions. The camera device reduces the effort required to analyze crop growth by about 80%. [ABSTRACT FROM AUTHOR]

Early Japanese books, classical humanities resources in Japan, have great historical and cultural value. However, Kuzushi-ji, the old character in early Japanese books, is scratched, faded ink, and lost due to weathering and deterioration over the years. The restoration of deteriorated early Japanese books has tremendous significance in cultural revitalization. In this paper, we introduce augmented identity loss and propose enhanced CycleGAN for deteriorated character restoration, which combines domain discriminators and augmented identity loss. This enhanced CycleGAN makes it possible to restore multiple levels of deterioration in the early Japanese books. It obtains the high readability of the actual deteriorated characters, which is proved by higher structural similarity(SSIM) and accuracy of deep learning models than standard CycleGAN and traditional image processing. In particular, SSIM increases by 8.72%, and the accuracy of ResNet50 for damaged characters improves by 1.1% compared with the competitive CycleGAN. Moreover, we realize the automatic restoration of pages of early Japanese books written about 300 years ago. [ABSTRACT FROM AUTHOR]

Benefiting from rich agricultural land, easy transport and fishing, etc., more and more people are moving to live in coastal areas, with more than 200 million people now living in coastal areas that are vulnerable to extreme sea level events. Sea level information is useful for coastal societies. Image measurement is rapidly developing as a new type of measurement tool. A multicamera-based sea level monitoring system along Japan's coast near the Pacific is proposed in this paper, and a long-distance sea wave matching method for this system is described. The whole system employs multiple binocular vision systems to take sea surface images and obtain the sea level height based on the disparity between the field of views of the left and right cameras, forming a local measurement and overall analysis monitoring system. Sea level monitoring requires a high processing accuracy and speed to realize a timely response to extreme events. Thus, the paper extracts sea waves and integrates a sea wave's appearance features as feature points and descriptors and pioneers the idea of searching deterministic features for fast image processing. The average stereo matching precision of the proposed method is up to 89.9% with a running time smaller than 40 ms for most pairs of images. Experiments on various real sea surface image pairs are conducted to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Iterative least-squares reverse time migration (LSRTM) is the state-of-the-art linearized waveform inversion method to obtain quantitative subsurface parameters. The main drawback of such an iterative imaging scheme is the significant computational expense of many modelling/adjoint cycles through iterations. In the context of the extended domain, an interesting alternative to LSRTM is the asymptotic linearized direct waveform inversion, providing quantitative results with only a single iteration. This approach was first proposed for constant-density acoustics and recently extended to the variable-density case. The former is based on the application of the asymptotic inverse Born operator, whereas the latter has two more extra steps: building an angle-dependent response of the asymptotic inverse Born operator and then solving a weighted least-squares approach for simultaneous inversion of two acoustic parameters. To examine the importance of accounting for density variations, we compare constant- and variable-density linearized direct waveform inversion techniques applied to a marine real data set from the Eastern Nankai Trough, offshore Japan. The inversion results confirm the efficiency of the asymptotic linearized direct waveform inversion in estimating quantitative parameters within a single iteration. The variable-density direct inversion yields subsurface images that (1) exhibit a superior resolution and (2) better reconstruct the field data than does the constant-density approach, even if the data set does not contain large enough surface offset to fully decompose velocity and density perturbations. [ABSTRACT FROM AUTHOR]

In recent years, countries around Japan and especially in Southeast Asia, white spot disease (WSD) is highly infectious and severely damages shrimp aquaculture. At the same time, the various diseases are occurring in shrimp farms. In the early stages of infection, shrimp shows three abnormal behaviors: (1) they appear in the shallow waters of the farm, (2) they do not move and do not eat even when feeding, and (3) they suddenly stop moving. Currently, infected shrimps are found by visual inspection, which places a burden on the farmers and delays the discovery. Therefore, in this paper, we proposed a system for detecting infected shrimp by using image processing technology in order to eliminate the delay of discovery and reduce the burden of farmers. According to our experimental results, the proposed system has 95% precision, 100% recall rate and an accuracy of 96.4% by using hold-out evaluation method. [ABSTRACT FROM AUTHOR]

Phenotyping is a critical process in plant breeding, especially when there is an increasing demand for streamlining a selection process in a breeding program. Since manual phenotyping has limited efficiency, high-throughput phenotyping methods are recently popularized owing to progress in sensor and image processing technologies. However, in a size-limited breeding field, which is common in Japan and other Asian countries, it is challenging to introduce large machinery in the field or fly unmanned aerial vehicles over the field. In this study, we developed a ground-based high-throughput field phenotyping rover that could be easily introduced to a field regardless of the scale and location of the field even without special facilities. We also made the field rover open-source hardware, making its system available to public for easy modification, so that anyone can build one for their own use at a low cost. The trial run of the field rover revealed that it allowed the collection of detailed remote-sensing images of plants and quantitative analyses based on the images. The results suggest that the field rover developed in this study could allow efficient phenotyping of plants especially in a small breeding field. [ABSTRACT FROM AUTHOR]

The historical castles (castellated walls), which are cultural heritages in Japan, require regular maintenance, and it is necessary to record the arrangement of individual wall stones in the maintenance work. Recently, image processing techniques are practiced to optimize maintenance and management of the infrastructure assets. In the previous study, we proposed an automatic method for efficiently extracting individual wall stone polygons by improved multiscale image segmentation technique. However, the problem has remained that wall stone polygons could not be extracted properly when there were no clear gaps or boundaries between stones. To address this problem, we improved the multiscale image segmentation technique used in our previous studies. The first improvement is that in the region growing process, selecting the best combination of a plurality of objects instead of two. The second improvement is the modification of the shape criterion to be used. Besides, we proposed three-stage Stacked cGAN for wall stone edge detection that enables us to complement areas with weak or broken boundaries of stone edges. This approach is composed of a coarse-to-fine based image-to-edges translation network. The edge images derived from this method are used as the additional channel in multiscale image segmentation with a higher weight compared to the other RGB channels. It was confirmed that the separation performance of individual wall stone polygons was improved by the proposed method. Furthermore, the proposed method is highly effective to reduce the difficulty in setting of the scale parameter, which is usually sensitive to segmentation results and requires trial and error. [ABSTRACT FROM AUTHOR]

The use of bisphosphonates for osteoporosis patients has markedly decreased the incidence of femoral neck or trochanteric fractures. However, anti‐osteoporosis drugs have been reported to increase the incidence of atypical femoral fractures, which involve stress fractures in the subtrochanteric region or the proximal diaphysis. In this study, the morphological characteristics of the cortical bone in human femoral diaphysis samples were analyzed from individuals who lived before bisphosphonate drugs were available in Japan. A total of 90 right femoral bones were arbitrarily selected (46 males and 44 females) from modern Japanese skeletal specimens. Full‐length images of these femurs were acquired using a computed tomography scanner. An image processing method for binarization was used to calculate the threshold values of individual bones for determining their contours. The range between the lower end of the lesser trochanter and the adductor tubercle of each femur was divided at regular intervals to obtain 10 planes. The mean value of cortical bone thickness, periosteal border length, and the cortical cross‐sectional area was evaluated for all planes. Moreover, the ratio of the area of the cortical bone to the total area of cross‐section at the mid‐diaphysis was calculated. A comparison between males and females demonstrated that most females had lower cortical bone area ratios at the mid‐diaphysis. The femoral outer shape did not differ markedly according to age or sex; however, substantial individual differences were observed in the shape of the inner surface of the cortical bone. The cortical bone thickness and the cross‐sectional area decreased with age in the femoral diaphysis; furthermore, in females, the decrease was higher for the former than for the latter. This may be due to a compensatory increase in the circumference of the femoral diaphysis. In addition, in about half of the subjects there was a discrepancy between the region with maximal value of the cortical bone thickness and that of the total cross‐sectional area. Biological responses to mechanical stresses to the femoral diaphysis are thought not to be uniform. Bisphosphonates inhibit bone resorption and may promote non‐physiological bone remodeling. Thus, a nonhomogeneous decrease in cortical thickness may be related to the fracture occurrence in the femoral diaphysis in some cases. Thus, long‐term administration of bisphosphonates in patients with morphological vulnerability in the femoral cortical bones may increase the occurrence of atypical femoral fractures. [ABSTRACT FROM AUTHOR]

The objective of this study was to establish an automated system for the recognition of banknote serial numbers by developing a deep learning (DL)-based optical character recognition framework. An integrated serial number recognition model for the banknotes of four countries (South Korea (KRW), the United States (USD), India (INR), and Japan (JPY)) was developed. One-channel image data obtained from banknote counters were used in this study. The dataset used for the multi-currency integrated serial number recognition contains about 150,000 images. The class imbalance problem and model accuracy were improved through data augmentation based on geometric transforms that consider the range of errors that occur when a bill is inserted into the counter. In addition, by fine-tuning the recognition network, it was confirmed that the performance was improved when the serial numbers of the banknotes of four countries were recognized instead of the serial number of a banknote from each country from a single-currency dataset, and the generalization performance was improved by training the model to recognize the diverse serial numbers of multiple currencies. Therefore, the proposed method shows that real-time processing of less than 30 ms per image and character recognition with 99.99% accuracy are possible, even though there is a tradeoff between inference speed and serial number recognition accuracy when data augmentation based on the characteristics of banknote counters and a 1-stage object detector for banknote serial number recognition is used.

The Convallaria keiskei , a plant species indigenous to Japan, is on the verge of extinction. In the past, they have been manually protected and managed. Unmanned aerial vehicles (UAVs), which are already being applied in various fields, such as agriculture, surveying, and logistics, can be applied to automate this task. Image processing and machine learning techniques applied on images obtained from UAVs can automate Convallaria keiskei classification, help to estimate the increase in colony numbers, and reduce the detection cost. In a previous study, a flower number estimation method that combines image processing and a convolutional neural network (CNN) was proposed. However, leaf regions similar to flower regions were misidentified as flower regions, and the accuracy was reduced. Therefore, in this study, a method was investigated to reduce the number of false positives by excluding areas similar to the flower regions. Specifically, a novel detection method combining image processing, CNN, and fuzzy c-means is proposed. To validate the proposed method, it was compared with the previous method as well as the method in which k-means was used instead of fuzzy c-means. All results were evaluated using flower distribution maps marked by field workers. The proposed method improved the F-measure by up to 22.0% compared with the previous method. Application of the proposed method to orthorectified images facilitates the understanding of flower populations over a wide range of areas, which can contribute to the conservation and management of the species. • Detection method for Convallaria keiskei (CK) colonies by combining CNN and FCM. • Detection CK colonies with reducion the number of false positives. • Combining method of CNN and FCM can improve the accuracy of CK colonies detection. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) and global positioning system (GPS) have obvious deficiencies for monitoring surface deformations, for example, 1-D line-of-sight (LOS) measurements for InSAR and spatially very sparse observations for GPS. In this paper, InSAR and GPS measurements are integrated to derive spatially high-resolution 3-D coseismic surface displacement fields of the 2011 Mw 9.0 Tohoku-Oki, Japan earthquake. A unified simultaneous least squares (USLS) approach is developed to minimize the inconsistence between the InSAR results from adjacent paths. 3-D ground displacements are then derived by integrating the InSAR and the GPS measurements with the method of weighted least squares (WLS). Comparisons with independent GPS measurements show that the root mean square errors (RMSEs) of the derived 3-D displacements are 6.30, 4.57 and 1.29 cm for the vertical, east and north components, respectively. The 3-D coseismic displacement map shows that the Honshu Island moved eastwards towards the epicentre and subsided in the eastern part. The maximal displacements in the vertical, east and north directions are −1.5, 5.0 and −2.0 m, respectively. The effects of the density of GPS sites on the InSAR/GPS integration are also investigated. The experimental results reveal that lower to 70 km spatial resolution's GPS observations are adequate to guarantee the accuracies of the 3-D displacements for the Tohoku-Oki earthquake. This demonstrates the applicability of the developed WLS-based InSAR/GPS integration method, as in general the GPS observations are not as dense as those in this study area. Based on the spatially high-resolution 3-D surface displacement fields, we estimate the high-resolution and 3-D strain of the Tohoku-Oki earthquake. The preliminary results show that the Honshu Island suffers from an evident dilatation and shear during the seismic event. [ABSTRACT FROM AUTHOR]

Abstract: Vegetation phenology such as the onset of green-up and senescence is strongly controlled by climate and other environmental factors, and in turn affects the terrestrial carbon balance. Therefore, phenological observation is important as an indicator of global warming and for estimation of the terrestrial carbon balance. Because phenological responses differ from species to species, precise monitoring from the species scale to the global scale is required. In this study, we analyzed images from digital cameras, which have proliferated in recent years, to investigate their utility as remote sensors. We collected daily images taken by digital cameras in national parks across Japan over 8years in wetland mixed deciduous forest, and evergreen broadleaved forest. Values of red, green, and blue (RGB) channels in each pixel within images were extracted, and a vegetation green excess index (2G-RBi) was calculated to detect phenology. The time series of 2G-RBi showed clear phenological patterns of each vegetation type in each year at the species or community scale. Even physiological damage due to a typhoon was detected. The dates of green-up were estimated easily and objectively from the second derivative of 2G-RBi, and a trend in yearly green-up dates of various types of vegetation was demonstrated. Furthermore, a strong correlation between interannual variations in green-up dates and local spring temperature was found, and the sensitivity of green-up date to temperature was revealed. The results suggest the utility of digital cameras for phenological observations at precise temporal and spatial resolutions, despite a year-to-year drift of color balance of camera as a technical device. As a form of near-surface remote sensing, digital cameras could obtain significant ecological information. Establishing camera networks could help us understand phenological responses at a wide range of scales. [ABSTRACT FROM AUTHOR]

The challenges in developing a fruit harvesting robot are recognizing the fruit in the foliage and detaching the fruit from the tree without damaging either the fruit or the tree. The objectives of this study were to develop a real-time fruit detection system using machine vision and a laser ranging sensor and to develop an end effector capable of detaching the fruit in a way similar to manual pick. The Fuji apple variety was used in this study. In the detection of the fruit, machine vision was combined with a laser ranging sensor. The machine vision recognized the fruit and the laser ranging sensor determined the distance. The system detected a single fruit with 100% accuracy in both front and back lighted scenes with ±3 mm accuracy in distance measurement. To detach the fruit from the tree, an end effector was developed with a peduncle holder and a wrist; the peduncle holder pinches the peduncle of the fruit and the wrist rotates the peduncle holder to detach the fruit. Field test results of the end effector showed more than 90% success rate in detaching the fruit with average time use of 7.1 seconds. [ABSTRACT FROM AUTHOR]

The reconstruction of surface waves from the cross-correlation of random wavefields has recently been extensively inspected by theoretical and experimental approaches. However, the reconstruction of body waves has not been extensively studied. In this study, we present a method for extracting body waves, that is, direct P and S waves, and reflected waves from the Philippine Sea slab. We use the cross-correlation of the wavefield generated by the teleseismic S waves observed by Hi-net tiltmeters with a passband of 0.07–0.5 Hz. To enhance the contribution of the S coda, we reduced the source-time function and the deterministic phases sS, ScS and SS, and the surface wave in S coda. The records of the processed S coda observed at every station-pair are cross-correlated for each teleseismic event. The cross-correlation functions (CCFs) of different earthquakes are stacked to retrieve wavefields propagating between the station-pairs. As a result, direct P, S and the reflected waves could be extracted when the stacked CCFs are aligned as a function of distance of separation between two stations. The gradients of the traveltime curves for the direct P and S waves are approximately 5–7 and 3 km s−1, respectively. These velocities correspond to the seismic velocities of the crust or the P-wave velocity of the uppermost mantle. In order to enhance the reflected waves, we searched for the reflection points by assuming that the later phases in the CCFs are SS reflections and map the amplitudes onto the depth sections. As a result, the negative phases dipping to the north can be traced right below the region of hypocentre distribution. These negative phases probably correspond to the oceanic Moho within the Philippine Sea slab. We also show that the oceanic Moho can also be traced by assuming PP reflections. These results indicate that the CCFs plausibly contain information regarding both P and S waves propagating between the two receivers, and are capable of detecting reflected phases in addition to the direct waves. [ABSTRACT FROM AUTHOR]

The generation of map units is a fundamental step for an appropriate assessment of landslide susceptibility. Recent studies have indicated that the terrain relief-based slope units perform better in homogeneity compared with the grid units. However, it is difficult at present to generate high-precision and high-matching slope units by traditional methods. The problem commonly concentrates in the plain areas without obvious terrain reliefs and the junction of sudden changes in terrain. In this paper, we propose a novel object-oriented segmentation method for generating homogeneous slope units. Herein, the multi-resolution segmentation algorithm in the image processing field is introduced, enabling the integration of terrain boundary conditions and image segmentation conditions in slope units. In order to illustrate the performances of the proposed method, Kitakyushu region in Japan is selected as a case study. The results show that the proposed method generates satisfactory slope units that satisfactorily reproduce the actual terrain relief, with the best within-unit and between-unit homogeneities compared with the previous methods, in particular at the plain areas. We also verify the effectiveness of the presented method through the sensitivity analysis using different resolutions of digital elevation models (DEMs) data of the region. It is reported that the presented approach is notably advanced in the requirements of the quality of DEM data, as the presented approach is less sensitive to DEM spatial resolution compared with other available methods. [ABSTRACT FROM AUTHOR]

Reconstruction of 3-D objects from multiaspect high-resolution synthetic aperture radar (SAR) images is of great importance for SAR technology applications. In this paper, simple building objects are modeled as cuboids, and an approach for automatic reconstruction of 3-D building objects from multiaspect SAR images in meter resolution is developed. The edge detector of constant false alarm rate and a Hough transform technique for parallel line segment pairs are first employed to extract the parallelogram-like image of the building walls in SAR images. A set of probability density functions is presented to describe the object images and their multiaspect coherence. The maximum-likelihood estimation of an object is then derived from its multiaspect object images. A hybrid priority criterion is defined to evaluate the reliability of the reconstruction result. An automatic reconstruction algorithm is further developed to match object images of different aspects and, finally, to reconstruct the building objects. Besides, an iterative method is proposed for the coregistration of multiaspect building images. Four-aspect simulated images of a virtual scene and four-aspect Pi-SAR images over the campus of Tohoku University, Japan, are investigated. Reconstruction of building objects from their multiaspect images shows the fidelity of the whole process chain and the feasibility of 3-D objects automatic reconstruction from multiaspect SAR images. At last, a practical application that is based on spaceborne meter-resolution SAR is proposed. [ABSTRACT FROM AUTHOR]

In recent years, remote-sensing (RS) technologies have been used together with image processing and traditional techniques in various disaster-related works. Among these is detecting building damage from orthophoto imagery that was inflicted by earthquakes. Automatic and visual techniques are considered as typical methods to produce building damage maps using RS images. The visual technique, however, is time-consuming due to manual sampling. The automatic method is able to detect the damaged building by extracting the defect features. However, various design methods and widely changing real-world conditions, such as shadow and light changes, cause challenges to the extensive appointing of automatic methods. As a potential solution for such challenges, this research proposes the adaption of deep learning (DL), specifically convolutional neural networks (CNN), which has a high ability to learn features automatically, to identify damaged buildings from pre- and post-event RS imageries. Since RS data revolves around imagery, CNNs can arguably be most effective at automatically discovering relevant features, avoiding the need for feature engineering based on expert knowledge. In this work, we focus on RS imageries from orthophoto imageries for damaged-building detection, specifically for (i) background, (ii) no damage, (iii) minor damage, and (iv) debris classifications. The gist is to uncover the CNN architecture that will work best for this purpose. To this end, three CNN models, namely the twin model, fusion model, and composite model, are applied to the pre- and post-orthophoto imageries collected from the 2016 Kumamoto earthquake, Japan. The robustness of the models was evaluated using four evaluation metrics, namely overall accuracy (OA), producer accuracy ( PA ), user accuracy ( UA ), and F1 score. According to the obtained results, the twin model achieved higher accuracy (OA = 76.86%; F1 score = 0.761) compare to the fusion model (OA = 72.27%; F1 score = 0.714) and composite (OA = 69.24%; F1 score = 0.682) models. [ABSTRACT FROM AUTHOR]

Due to the population aging in Japan, more elderly people are retaining their driver's licenses and the increase in the number of car accidents by elderly drivers is a social problem. To address this problem, an objective data-based method to evaluate whether elderly drivers can continue driving is needed. In this paper, we propose a car driving evaluation system based on gaze as calculated by eye and head angles. We used an eye tracking device (TalkEye Lite) made by the Takei Scientific Instruments Cooperation. For our image processing technique, we propose a gaze fixation condition using deep learning (YOLOv2-tiny). By using an eye tracking device and the proposed gaze fixation condition, we built a system where drivers could be evaluated during actual car operation. We describe our system in this paper. In order to evaluate our proposed method, we conducted experiments from November 2017 to November 2018 where elderly people were evaluated by our system while driving an actual car. The subjects were 22 general drivers (two were 80–89 years old, four were 70–79 years old, six were 60–69 years old, three were 50–59 years old, five were 40–49 years old and two were 30–39 years old). We compared the subjects' gaze information with the subjective evaluation by a professional driving instructor. As a result, we confirm that the subjects' gaze information is related to the subjective evaluation by the instructor. [ABSTRACT FROM AUTHOR]

The Body Condition Score (BCS) for cows indicates their energy reserves, the scoring for which ranges from very thin to overweight. These measurements are especially useful during calving, as well as early lactation. Achieving a correct BCS helps avoid calving difficulties, losses and other health problems. Although BCS can be rated by experts, it is time-consuming and often inconsistent when performed by different experts. Therefore, the aim of our system is to develop a computerized system to reduce inconsistencies and to provide a time-saving solution. In our proposed system, the automatic body condition scoring system is introduced by using a 3D camera, image processing techniques and regression models. The experimental data were collected on a rotary parlor milking station on a large-scale dairy farm in Japan. The system includes an application platform for automatic image selection as a primary step, which was developed for smart monitoring of individual cows on large-scale farms. Moreover, two analytical models are proposed in two regions of interest (ROI) by extracting 3D surface roughness parameters. By applying the extracted parameters in mathematical equations, the BCS is automatically evaluated based on measurements of model accuracy, with one of the two models achieving a mean absolute percentage error (MAPE) of 3.9%, and a mean absolute error (MAE) of 0.13. [ABSTRACT FROM AUTHOR]

Nowadays, for numerous reasons, smart farming systems focus on the use of image processing technologies and 5G communications. In this paper, we propose a tracking system for individual cows using an ear tag visual analysis. By using ear tags, the farmers can track specific data for individual cows such as body condition score, genetic abnormalities, etc. Specifically, a four-digit identification number is used, so that a farm can accommodate up to 9999 cows. In our proposed system, we develop an individual cow tracker to provide effective management with real-time upgrading enforcement. For this purpose, head detection is first carried out to determine the cow's position in its related camera view. The head detection process incorporates an object detector called You Only Look Once (YOLO) and is then followed by ear tag detection. The steps involved in ear tag recognition are (1) finding the four-digit area, (2) digit segmentation using an image processing technique, and (3) ear tag recognition using a convolutional neural network (CNN) classifier. Finally, a location searching system for an individual cow is established by entering the ID numbers through the application's user interface. The proposed searching system was confirmed by performing real-time experiments at a feeding station on a farm at Hokkaido prefecture, Japan. In combination with our decision-making process, the proposed system achieved an accuracy of 100% for head detection, and 92.5% for ear tag digit recognition. The results of using our system are very promising in terms of effectiveness. [ABSTRACT FROM AUTHOR]

This presentation examined a machine learning with image processing techniques for classifying land cover conditions in UAV (Unmanned Aerial Vehicle) images of a riverine landscape. The UAV images were taken in a river course of Kurobe River, Japan in both November 2017 and 2018, where it had a braided channel with well-vegetated gravel beds. The UAV image analyzed was composed of a RGB, NDVI (Normalized Difference Vegetation Index), and a DSM (Digital Surface Model) of the river geomorphology made from an SfM (Structure from Motion) image processing of the UAV images. In addition, as a preprocessing of a machine learning, the DSM was decomposed into the low/high wavenumber components by a wavelet transform, and further its edges were extracted by a Laplacian filter for effectively utilizing the height difference information in DSM. These characteristic values from UAV images were all used in combination to examine their effectiveness in machine learning for the riverine land cover classification. The machine learning techniques examined in this presentation was RF (Random Forest), and the true values of trees, grasses, bare gravel/sand bed, and water surface in part of the river course were given as a training dataset. The result of the machine learning with the image preprocessing treatment showed that the F-measure, which is consist of precision and recall rates, had high enough above 0.91 in the dataset including all the characteristic values from RGB, DMS, and NDVI into the machine learning algorithm. As for the comparison of effectiveness in each characteristic value in riverine land cover classification, the combination of DSM high wavenumber and DSM edge values made the F-measure higher in trees/grasses classification. Furthermore, the applicability of the machine learning with the image processing techniques examined here were discussed in detail in terms of the calculation time and the quality of input images for accurate classification. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common invasive cancer among women and its incidence is increasing. Risk assessment is valuable and recent methods are incorporating novel biomarkers such as mammographic density. Artificial neural networks (ANN) are adaptive algorithms capable of performing pattern-to-pattern learning and are well suited for medical applications. They are potentially useful for calibrating full-field digital mammography (FFDM) for quantitative analysis. This study uses ANN modeling to estimate volumetric breast density (VBD) from FFDM on Japanese women with and without breast cancer. ANN calibration of VBD was performed using phantom data for one FFDM system. Mammograms of 46 Japanese women diagnosed with invasive carcinoma and 53 with negative findings were analyzed using ANN models learned. ANN-estimated VBD was validated against phantom data, compared intra-patient, with qualitative composition scoring, with MRI VBD, and inter-patient with classical risk factors of breast cancer as well as cancer status. Phantom validations reached an R 2 of 0.993. Intra-patient validations ranged from R 2 of 0.789 with VBD to 0.908 with breast volume. ANN VBD agreed well with BI-RADS scoring and MRI VBD with R 2 ranging from 0.665 with VBD to 0.852 with breast volume. VBD was significantly higher in women with cancer. Associations with age, BMI, menopause, and cancer status previously reported were also confirmed. ANN modeling appears to produce reasonable measures of mammographic density validated with phantoms, with existing measures of breast density, and with classical biomarkers of breast cancer. FFDM VBD is significantly higher in Japanese women with cancer.