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Automated diagnosis of tuberculosis (TB) from chest X-Rays (CXR) has been tackled with either hand-crafted algorithms or machine learning approaches such as support vector machines (SVMs) and convolutional neural networks (CNNs). Most deep neural network applied to the task of tuberculosis diagnosis have been adapted from natural image classification. These models have a large number of parameters as well as high hardware requirements, which makes them prone to overfitting and harder to deploy in mobile settings. We propose a simple convolutional neural network optimized for the problem which is faster and more efficient than previous models but preserves their accuracy. Moreover, the visualization capabilities of CNNs have not been fully investigated. We test saliency maps and grad-CAMs as tuberculosis visualization methods, and discuss them from a radiological perspective.

3D object detection using LiDAR point clouds is a fundamental task in the fields of computer vision, robotics, and autonomous driving. However, existing 3D detectors heavily rely on annotated datasets, which are both time-consuming and prone to errors during the process of labeling 3D bounding boxes. In this paper, we propose a Scene Completion Pre-training (SCP) method to enhance the performance of 3D object detectors with less labeled data. SCP offers three key advantages: (1) Improved initialization of the point cloud model. By completing the scene point clouds, SCP effectively captures the spatial and semantic relationships among objects within urban environments. (2) Elimination of the need for additional datasets. SCP serves as a valuable auxiliary network that does not impose any additional efforts or data requirements on the 3D detectors. (3) Reduction of the amount of labeled data for detection. With the help of SCP, the existing state-of-the-art 3D detectors can achieve comparable performance while only relying on 20% labeled data.

ChemCam is a remote sensing instrument suite on board the “Curiosity” rover (NASA) that uses Laser-Induced Breakdown Spectroscopy (LIBS) to provide the elemental composition of soils and rocks at the surface of Mars from a distance of 1.3 to 7 m, and a telescopic imager to return high resolution context and micro-images at distances greater than 1.16 m. We describe five analytical capabilities: rock classification, quantitative composition, depth profiling, context imaging, and passive spectroscopy. They serve as a toolbox to address most of the science questions at Gale crater. ChemCam consists of a Mast-Unit (laser, telescope, camera, and electronics) and a Body-Unit (spectrometers, digital processing unit, and optical demultiplexer), which are connected by an optical fiber and an electrical interface. We then report on the development, integration, and testing of the Mast-Unit, and summarize some key characteristics of ChemCam. This confirmed that nominal or better than nominal performances were achieved for critical parameters, in particular power density (>1 GW/cm 2 ). The analysis spot diameter varies from 350 μm at 2 m to 550 μm at 7 m distance. For remote imaging, the camera field of view is 20 mrad for 1024×1024 pixels. Field tests demonstrated that the resolution (∼90 μrad) made it possible to identify laser shots on a wide variety of images. This is sufficient for visualizing laser shot pits and textures of rocks and soils. An auto-exposure capability optimizes the dynamical range of the images. Dedicated hardware and software focus the telescope, with precision that is appropriate for the LIBS and imaging depths-of-field. The light emitted by the plasma is collected and sent to the Body-Unit via a 6 m optical fiber. The companion to this paper (Wiens et al. this issue ) reports on the development of the Body-Unit, on the analysis of the emitted light, and on the good match between instrument performance and science specifications.

Background and Aims Vine balance is defined as a relation between vegetative (mass of dormant pruning wood) and generative (yield) growth. For grapevine breeding, emphasis is usually placed on the evaluation of individual seedlings. In this study, we calculated the mass of dormant pruning wood with the assistance of an automated image‐based method for estimating the pixel area of dormant pruning wood. The evaluation of digital images in combination with depth map calculation and image segmentation is a new and non‐invasive tool for objective data acquisition. Methods and Results The proposed method was tested on a set of seedlings planted at the Institute for Grapevine Breeding Geilweilerhof, Germany. All images taken in the field were geo‐referenced, and the automated method was validated by manual segmentation. Together with additional yield parameters, the vine balance indices can be used to classify seedlings for breeding purposes. Conclusion The computed pruning mass obtained using image‐based methods is an accurate, inexpensive and easy method to estimate pruning mass compared with the manual time‐consuming measurements. Together with the yield parameters, it is a suitable method for seedling evaluation and can also be used in precision viticulture. Significance of the Study This study demonstrates an image‐based evaluation of the pruning mass to be a highly valuable tool for grapevine research and grapevine breeding. Moreover, the tool might be used by industry to monitor vine balance. The key findings reported have the potential to increase grapevine breeding efficiency by using an accurate and objective phenotyping method.

To improve the quality of algorithms for automatic generation of Digital Surface Models (DSM) from optical stereo data in the remote sensing community, the Working Group 4 of Commission I: Geometric and Radiometric Modeling of Optical Airborne and Spaceborne Sensors provides on its website http://www2.isprs.org/commissions/comm1/wg4/benchmark-test.html a benchmark dataset for measuring and comparing the accuracy of dense stereo algorithms. The data provided consists of several optical spaceborne stereo images together with ground truth data produced by aerial laser scanning. In this paper we present our latest work on this benchmark, based upon previous work. As a first point, we noticed that providing the abovementioned test data as geo-referenced satellite images together with their corresponding RPC camera model seems too high a burden for being used widely by other researchers, as a considerable effort still has to be made to integrate the test datas camera model into the researchers local stereo reconstruction framework. To bypass this problem, we now also provide additional rectified input images, which enable stereo algorithms to work out of the box without the need for implementing special camera models. Care was taken to minimize the errors resulting from the rectification transformation and the involved image resampling. We further improved the robustness of the evaluation method against errors in the orientation of the satellite images (with respect to the LiDAR ground truth). To this end we implemented a point cloud alignment of the DSM and the LiDAR reference points using an Iterative Closest Point (ICP) algorithm and an estimation of the best fitting transformation. This way, we concentrate on the errors from the stereo reconstruction and make sure that the result is not biased by errors in the absolute orientation of the satellite images. The evaluation of the stereo algorithms is done by triangulating the resulting (filled) DSMs and computing for each LiDAR point the nearest Euclidean distance to the DSM surface. We implemented an adaptive triangulation method minimizing the second order derivative of the surface in a local neighborhood, which captures the real surface more accurate than a fixed triangulation. As a further advantage, using our point-to-surface evaluation, we are also able to evaluate non-uniformly sampled DSMs or triangulated 3D models in general. The latter is for example needed when evaluating building extraction and data reduction algorithms. As practical example we compare results from three different matching methods applied to the data available within the benchmark data sets. These results are analyzed using the above mentioned methodology and show advantages and disadvantages of the different methods, also depending on the land cover classes.

Improving estimates of carbon inventories in soils is currently hindered by lack of a rapid analysis method for total soil carbon. A rapid, accurate, and precise method that could be used in the field would be a significant benefit to researchers investigating carbon cycling in soils and dynamics of soil carbon in global change processes. We tested a new analysis method for predicting total soil carbon using laser‐induced breakdown spectroscopy (LIBS). We determined appropriate spectral signatures and calibrated the method using measurements from dry combustion of a Mollisol from a cultivated plot. From this calibration curve we predicted carbon concentrations in additional samples from the same soil and from an Alfisol collected in a semiarid woodland and compared these predictions with additional dry combustion measurements. Our initial tests suggest that the LIBS method rapidly and efficiently measures soil carbon with excellent detection limits (∼300 mg/kg), precision (4–5%), and accuracy (3–14%). Initial testing shows that LIBS measurements and dry combustion analyses are highly correlated (adjusted r2 = 0.96) for soils of distinct morphology, and that a sample can be analyzed by LIBS in less than one minute. The LIBS method is readily adaptable to a field‐portable instrument, and this attribute—in combination with rapid and accurate sample analysis—suggests that this new method offers promise for improving measurement of total soil carbon. Additional testing of LIBS is required to understand the effects of soil properties such as texture, moisture content, and mineralogical composition (i.e., silicon content) on LIBS measurements.

We present a novel variational approach for segmenting the image plane into a set of regions of parametric motion on the basis of two consecutive frames from an image sequence. Our model is based on a conditional probability for the spatio-temporal image gradient, given a particular velocity model, and on a geometric prior on the estimated motion field favoring motion boundaries of minimal length. Exploiting the Bayesian framework, we derive a cost functional which depends on parametric motion models for each of a set of regions and on the boundary separating these regions. The resulting functional can be interpreted as an extension of the Mumford-Shah functional from intensity segmentation to motion segmentation. In contrast to most alternative approaches, the problems of segmentation and motion estimation are jointly solved by continuous minimization of a single functional. Minimizing this functional with respect to its dynamic variables results in an eigenvalue problem for the motion parameters and in a gradient descent evolution for the motion discontinuity set. We propose two different representations of this motion boundary: an explicit spline-based implementation which can be applied to the motion-based tracking of a single moving object, and an implicit multiphase level set implementation which allows for the segmentation of an arbitrary number of multiply connected moving objects. Numerical results both for simulated ground truth experiments and for real-world sequences demonstrate the capacity of our approach to segment objects based exclusively on their relative motion.[PUBLICATION ABSTRACT]

Beginning with the fundamentals, and moving through to a thorough discussion of equipment, methods, and techniques, this book provides a unique reference source that will be of value for years to come for this important new analysis method. --

High-detail semantic 3D building models are frequently utilized in robotics, geoinformatics, and computer vision. One key aspect of creating such models is employing 2D conflict maps that detect openings’ locations in building facades. Yet, in reality, these maps are often incomplete due to obstacles encountered during laser scanning. To address this challenge, we introduce FacaDiffy, a novel method for inpainting unseen facade parts by completing conflict maps with a personalized Stable Diffusion model. Specifically, we first propose a deterministic ray analysis approach to derive 2D conflict maps from existing 3D building models and corresponding laser scanning point clouds. Furthermore, we facilitate the inpainting of unseen facade objects into these 2D conflict maps by leveraging the potential of personalizing a Stable Diffusion model. To complement the scarcity of real-world training data, we also develop a scalable pipeline to produce synthetic conflict maps using random city model generators and annotated facade images. Extensive experiments demonstrate that FacaDiffy achieves state-of-the-art performance in conflict map completion compared to various inpainting baselines and increases the detection rate by 22% when applying the completed conflict maps for high-definition 3D semantic building reconstruction. The code is be publicly available in the corresponding GitHub repository: https://github.com/ThomasFroech/InpaintingofUnseenFacadeObjects

We present a modification of the Mumford-Shah functional and its cartoon limit which facilitates the incorporation of a statistical prior on the shape of the segmenting contour. By minimizing a single energy functional, we obtain a segmentation process which maximizes both the grey value homogeneity in the separated regions and the similarity of the contour with respect to a set of training shapes. We propose a closed-form, parameter-free solution for incorporating invariance with respect to similarity transformations in the variational framework. We show segmentation results on artificial and real-world images with and without prior shape information. In the cases of noise, occlusion or strongly cluttered background the shape prior significantly improves segmentation. Finally we compare our results to those obtained by a level set implementation of geodesic active contours.[PUBLICATION ABSTRACT]