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The 2017 clinical guidelines for obstetrical practice by the Japan Society of Obstetrics and Gynecology and the Japan Association of Obstetricians and Gynecologists were revised and published as the 2020 edition (in Japanese). The aim of these guidelines is to present appropriate standard obstetric diagnosis and management procedures that have reached consensus among Japanese obstetricians. The 2020...
Map retrieval, the problem of similarity search over a large collection of 2D pointset maps previously built by mobile robots, is crucial for autonomous navigation in indoor and outdoor environments. Bag-of-words (BoW) methods constitute a popular approach to map retrieval; however, these methods have extremely limited descriptive ability because they ignore the spatial layout information of the local...
In this study, we explore the use of deep convolutional neural network (DCNN) in visual place classification for robotic mapping and localization. An open question is how to partition the robot's workspace into places so as to maximize the performance (e.g., accuracy, precision & recall) of potential DCNN classifiers. This is a chicken and egg problem: If we had a well-trained DCNN classifier,...
Long-term visual SLAM, in familiar, semi-dynamic, and partially changing environments is an important area of research in robotics. The main problem we faced is the question of how to describe a scene discriminatively and compactly-both of which are necessary in order to cope with changes in appearance and a large amount of visual information. In this study, we address the above issues by mining visual...
Change detection, i.e., anomaly detection from local maps built by a mobile robot at multiple different times, is a challenging problem to solve in practice. Most previous work either cannot be applied to scenarios where the size of the map collection is large, or simply assumed that the robot self-location is globally known. In this paper, we tackle the problem of simultaneous self-localization and...
Map retrieval, the problem of similarity search over a large collection of 3D pointset maps previously built by mobile robots, is crucial for autonomous navigation in indoor and outdoor environments. Bag-of-words (BoW) methods constitute a popular approach to map retrieval; however, these methods have extremely limited descriptive ability because they ignore the spatial layout information of the local...
Loop closure detection, which is the task of identifying locations revisited by a robot in a sequence of odometry and perceptual observations, is typically formulated as a visual place recognition (VPR) task. However, even state-of-the-art VPR techniques generate a considerable number of false positives as a result of confusing visual features and perceptual aliasing. In this paper, we propose a robust...
Change detection, or anomaly detection, from street-view images acquired by an autonomous robot at multiple different times, is a major problem in robotic mapping and autonomous driving. Formulation as an image comparison task, which operates on a given pair of query and reference images is common to many existing approaches to this problem. Unfortunately, providing relevant reference images is not...
With the recent success of visual features from deep convolutional neural networks (DCNN) in visual robot self-localization, it has become important and practical to address more general self-localization scenarios. In this paper, we address the scenario of self-localization from images with small overlap. We explicitly introduce a localization difficulty index as a decreasing function of view overlap...
A novel visual image retrieval technique for fast cross-view UAV localization is presented in this paper. Our first contribution is to address the computational complexity of raw image matching, which can be time/space intractable due to the high dimensionality of raw image data. We propose to exploit raw image matching, not for the direct matching between query and database images, but for mining...
Map matching, the ability to match a local map built by a mobile robot to previously built maps, is crucial in robot vision applications. In this paper, we propose a novel map descriptor, to facilitate fast succinct text-based map matching. Unlike previous bag-of-words approaches that trade discriminativity for viewpoint invariance, we develop a holistic view descriptor that is view-dependent and...
We propose a discriminative compact scene descriptor for single-view cross-season place recognition. Unlike previous bag-of-words approaches which rely on a library of vector quantized visual features, the proposed scene descriptor is based on a library of raw image data (such as available visual experience, images shared by other colleague robots, and publicly available image data on the web) that...
In this study, we propose a novel scene descriptor for visual place recognition. Unlike popular bag-of-words scene descriptors which rely on a library of vector quantized visual features, our proposed descriptor is based on a library of raw image data, such as publicly available photo collections from Google StreetView and Flickr. The library images need not to be associated with spatial information...
Scene modeling is an important first stage in visual robot localization. In recent years, the bag-of-words (BoW) scene modeling approach has attracted considerable attention as a method for obtaining compact discriminative scene descriptors for map retrieval. However, a BoW scene descriptor alone cannot address partial view changes and often produces poor localization in practice. In this work, we...
While bag-of-words (BoW) scene descriptor has been widely used for scene retrieval applications, the BoW descriptor alone often fails to capture local details of a scene and produces poor results. In this paper, we address this issue by a simple effective approach, “un-supervised salient part discovery”, in which a set of salient parts are discovered via scene parsing and used as additional queries...
In this paper, we address the challenging problem of single-view cross-season place recognition. A new approach is proposed for compact discriminative scene descriptor that helps in coping with changes in appearance in the environment. We focus on a simple effective strategy that uses objects whose appearance remain the same across seasons as valid landmarks. Unlike popular bag-of-words (BoW) scene...
Map matching, the ability to match a local map built by a mobile robot to previously built maps, is crucial in many robotic mapping, self-localization, and simultaneous localization and mapping (SLAM) applications. In this paper, we propose a solution to the “map-to-text (M2T)” problem, which involves the generation of text descriptions of local map content based on scene understanding to facilitate...
We propose a discriminative and compact scene descriptor for single-view place recognition that facilitates long-term visual SLAM in familiar, semi-dynamic and partially changing environments. In contrast to popular bag-of-words scene descriptors, which rely on a library of vector quantized visual features, our proposed scene descriptor is based on a library of raw image data (such as an available...
Map matching is a fundamental task in many robot vision applications, including viewpoint localization, change detection, alignment, merging, segmentation of maps, and multi-robot mapping. Existing frameworks so far have concentrated on local feature-based approach, where discriminative local features are extracted from the maps and visual indexing and map database searched are performed to find correspondence...
We consider the task of long-term visual SLAM, i.e., simultaneous localization and mapping, in a partially changing environment (SLAM-PCE). The main problem we face is how to obtain discriminative and compact visual landmarks, which are necessary to cope with changes in appearance in an environment and with a large amount of visual information. We address this issue by proposing the use of common...
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