scholarly journals Fault-Diagnosing Deep-Visual-SLAM for 3D Change Object Detection

2021 ◽  
Vol 25 (3) ◽  
pp. 356-364
Author(s):  
Kanji Tanaka ◽  
Keyword(s):  
Large Scale ◽  
Color Image ◽  
State Of The Art ◽  
Three Dimensional ◽  
Background Modeling ◽  
Original Form ◽  
Detection Accuracy ◽  
Time Space ◽  
Localization And Mapping

Although image change detection (ICD) methods provide good detection accuracy for many scenarios, most existing methods rely on place-specific background modeling. The time/space cost for such place-specific models is prohibitive for large-scale scenarios, such as long-term robotic visual simultaneous localization and mapping (SLAM). Therefore, we propose a novel ICD framework that is specifically customized for long-term SLAM. This study is inspired by the multi-map-based SLAM framework, where multiple maps can perform mutual diagnosis and hence do not require any explicit background modeling/model. We extend this multi-map-based diagnosis approach to a more generic single-map-based object-level diagnosis framework (i.e., ICD), where the self-localization module of SLAM, which is the change object indicator, can be used in its original form. Furthermore, we consider map diagnosis on a state-of-the-art deep convolutional neural network (DCN)-based SLAM system (instead of on conventional bag-of-words or landmark-based systems), in which the blackbox nature of the DCN complicates the diagnosis problem. Additionally, we consider a three-dimensional point cloud (PC)-based (instead of typical monocular color image-based) SLAM and adopt a state-of-the-art scan context PC descriptor for map diagnosis for the first time.

scholarly journals Fault-Diagnosing DCN-SLAM for 3D Change Object Detection: A Method based on Masking Input Images

2020 ◽  
Author(s):  
kanji tanaka
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Background Modeling ◽  
Original Form ◽  
Detection Accuracy ◽  
Time Space ◽  
Localization And Mapping ◽  
First Time ◽  
Localization Systems

Although image change detection (ICD) methods provide good detection accuracy for many scenarios, most of the existing methods rely on place-specific background modeling. The time/space cost for such place-specific models becomes prohibitive for large-scale scenarios, such as long-term robotic visual simultaneous localization and mapping (SLAM). Therefore, we propose a novel ICD framework that is specifically tailored for long-term SLAM. This study is inspired by the multi-map-based SLAM framework, where N multiple localizers are capable of mutual diagnosis, thus not requiring any explicit background modeling/model. We extend this multi-map diagnosis approach toward a more generic single-map-based object-level diagnosis framework (i.e., ICD), where state-of-the-art self-localization systems can be used in their original form, which is as the change object indicator. The available single localizer is extended to different N localizers by introducing different N masked input images. Further, we also consider map diagnosis on a state-of-the-art deep-visual-SLAM system (rather than on conventional bag-of-words or landmark -based systems) in which the blackbox nature of the deep convolutional neural network (DCN) complicates the diagnosis problem. We also consider a 3D point cloud (PC) -based SLAM, and for the first time (to the best of our knowledge) adopt the state-of-the-art scan context PC descriptor for the purpose of map diagnosis.

scholarly journals Documentary data and the study of past droughts: a global state of the art

2018 ◽  
Vol 14 (12) ◽  
pp. 1915-1960 ◽  
Author(s):  
Rudolf Brázdil ◽  
Andrea Kiss ◽  
Jürg Luterbacher ◽  
David J. Nash ◽  
Ladislava Řezníčková
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Drought Indices ◽  
Documentary Evidence ◽  
Climatic Trends ◽  
Instrumental Observations ◽  
Spatio Temporal ◽  
Epigraphic Evidence ◽  
Administrative Evidence

Abstract. The use of documentary evidence to investigate past climatic trends and events has become a recognised approach in recent decades. This contribution presents the state of the art in its application to droughts. The range of documentary evidence is very wide, including general annals, chronicles, memoirs and diaries kept by missionaries, travellers and those specifically interested in the weather; records kept by administrators tasked with keeping accounts and other financial and economic records; legal-administrative evidence; religious sources; letters; songs; newspapers and journals; pictographic evidence; chronograms; epigraphic evidence; early instrumental observations; society commentaries; and compilations and books. These are available from many parts of the world. This variety of documentary information is evaluated with respect to the reconstruction of hydroclimatic conditions (precipitation, drought frequency and drought indices). Documentary-based drought reconstructions are then addressed in terms of long-term spatio-temporal fluctuations, major drought events, relationships with external forcing and large-scale climate drivers, socio-economic impacts and human responses. Documentary-based drought series are also considered from the viewpoint of spatio-temporal variability for certain continents, and their employment together with hydroclimate reconstructions from other proxies (in particular tree rings) is discussed. Finally, conclusions are drawn, and challenges for the future use of documentary evidence in the study of droughts are presented.

Bistability of somatic pattern memories: stochastic outcomes in bioelectric circuits underlying regeneration

2021 ◽  
Vol 376 (1821) ◽  
pp. 20190765 ◽  
Author(s):  
Giovanni Pezzulo ◽  
Joshua LaPalme ◽  
Fallon Durant ◽  
Michael Levin
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
State Of The Art ◽  
Memory Representation ◽  
Theme Issue ◽  
Evolutionary Innovation ◽  
New Interpretation ◽  
The Brain

Nervous systems’ computational abilities are an evolutionary innovation, specializing and speed-optimizing ancient biophysical dynamics. Bioelectric signalling originated in cells' communication with the outside world and with each other, enabling cooperation towards adaptive construction and repair of multicellular bodies. Here, we review the emerging field of developmental bioelectricity, which links the field of basal cognition to state-of-the-art questions in regenerative medicine, synthetic bioengineering and even artificial intelligence. One of the predictions of this view is that regeneration and regulative development can restore correct large-scale anatomies from diverse starting states because, like the brain, they exploit bioelectric encoding of distributed goal states—in this case, pattern memories. We propose a new interpretation of recent stochastic regenerative phenotypes in planaria, by appealing to computational models of memory representation and processing in the brain. Moreover, we discuss novel findings showing that bioelectric changes induced in planaria can be stored in tissue for over a week, thus revealing that somatic bioelectric circuits in vivo can implement a long-term, re-writable memory medium. A consideration of the mechanisms, evolution and functionality of basal cognition makes novel predictions and provides an integrative perspective on the evolution, physiology and biomedicine of information processing in vivo . This article is part of the theme issue ‘Basal cognition: multicellularity, neurons and the cognitive lens’.

scholarly journals Deep-Learning-Based Road Crack Detection Frameworks for Dashcam-captured Images under Different Illumination Conditions

2021 ◽  
Author(s):  
Da-Ren Chen ◽  
Wei-Min Chiu
Keyword(s):  
Object Detection ◽  
Large Scale ◽  
Crack Detection ◽  
State Of The Art ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
The Road ◽  
Art Object

Abstract Machine learning techniques have been used to increase detection accuracy of cracks in road surfaces. Most studies failed to consider variable illumination conditions on the target of interest (ToI), and only focus on detecting the presence or absence of road cracks. This paper proposes a new road crack detection method, IlumiCrack, which integrates Gaussian mixture models (GMM) and object detection CNN models. This work provides the following contributions: 1) For the first time, a large-scale road crack image dataset with a range of illumination conditions (e.g., day and night) is prepared using a dashcam. 2) Based on GMM, experimental evaluations on 2 to 4 levels of brightness are conducted for optimal classification. 3) the IlumiCrack framework is used to integrate state-of-the-art object detecting methods with CNN to classify the road crack images into eight types with high accuracy. Experimental results show that IlumiCrack outperforms the state-of-the-art R-CNN object detection frameworks.

Hepatocyte Aggregate Culture Technique for Bioreactors in Hybrid Liver Support Systems

1993 ◽  
Vol 16 (12) ◽  
pp. 843-846 ◽  
Author(s):  
J.C. Gerlach ◽  
K. Klöppel ◽  
C. MÜller ◽  
N. Schnoy ◽  
M.D. Smith ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Support Systems ◽  
Three Dimensional ◽  
Culture Technique ◽  
Dimensional Structure ◽  
Cell Aggregates ◽  
Liver Support ◽  
Aggregate Culture

Utilizing a modified culture technique for hepatocytes, a high performance suspension culture is possible in which hepatocytes spontaneously form cell aggregates. The aggregates of 20-100 cells have been histologically confirmed to hold a three-dimensional structure, they show a long-term external metabolism and a survival time comparable with standard adhesion cultures. This technique has several advantages in the construction of large scale bioreactors for hybrid liver support systems.

scholarly journals Documentary data and the study of the past droughts: an overview of the state of the art worldwide

2018 ◽  
Author(s):  
Rudolf Brázdil ◽  
Andrea Kiss ◽  
Jürg Luterbacher ◽  
David J. Nash ◽  
Ladislava Řezníčková
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Drought Indices ◽  
Documentary Evidence ◽  
Instrumental Observations ◽  
Spatio Temporal ◽  
Epigraphic Evidence ◽  
Administrative Evidence

Abstract. The use of documentary evidence to investigate past climatic trends and events has become a recognised approach in recent decades. This contribution presents the state of the art in its application to droughts. The range of documentary evidence is very wide, including: general annals, chronicles, and memoirs, diaries kept by missionaries, travellers and those specifically interested in the weather, the records kept by administrators tasked with keeping accounts and other financial and economic records, legal-administrative evidence, religious sources, letters, marketplace and shopkeepers' songs, newspapers and journals, pictographic evidence, chronograms, epigraphic evidence, early instrumental observations, society commentaries, compilations and books, and historical-climatological databases. These come from many parts of the world. This variety of documentary information is evaluated with respect to the reconstruction of hydroclimatic conditions (precipitation, drought frequency and drought indices). Documentary-based drought reconstructions are then addressed in terms of long-term spatio-temporal fluctuations, major drought events, relationships with external forcing and large-scale climate drivers, socio-economic impacts and human responses. Documentary-based drought series are also discussed from the viewpoint of spatio-temporal variability for certain continents, and their employment together with hydroclimate reconstructions from other proxies (in particular tree-rings) is discussed. Finally, conclusions are drawn and challenges for the future use of documentary evidence in the study of droughts are presented.

scholarly journals GlobalTrack: A Simple and Strong Baseline for Long-Term Tracking

2020 ◽  
Vol 34 (07) ◽  
pp. 11037-11044
Author(s):  
Lianghua Huang ◽  
Xin Zhao ◽  
Kaiqi Huang
Keyword(s):  
Online Learning ◽  
Success Rate ◽  
Large Scale ◽  
State Of The Art ◽  
Temporal Consistency ◽  
Post Processing ◽  
Two Stage ◽  
Multi Scale ◽  
Instance Search

A key capability of a long-term tracker is to search for targets in very large areas (typically the entire image) to handle possible target absences or tracking failures. However, currently there is a lack of such a strong baseline for global instance search. In this work, we aim to bridge this gap. Specifically, we propose GlobalTrack, a pure global instance search based tracker that makes no assumption on the temporal consistency of the target's positions and scales. GlobalTrack is developed based on two-stage object detectors, and it is able to perform full-image and multi-scale search of arbitrary instances with only a single query as the guide. We further propose a cross-query loss to improve the robustness of our approach against distractors. With no online learning, no punishment on position or scale changes, no scale smoothing and no trajectory refinement, our pure global instance search based tracker achieves comparable, sometimes much better performance on four large-scale tracking benchmarks (i.e., 52.1% AUC on LaSOT, 63.8% success rate on TLP, 60.3% MaxGM on OxUvA and 75.4% normalized precision on TrackingNet), compared to state-of-the-art approaches that typically require complex post-processing. More importantly, our tracker runs without cumulative errors, i.e., any type of temporary tracking failures will not affect its performance on future frames, making it ideal for long-term tracking. We hope this work will be a strong baseline for long-term tracking and will stimulate future works in this area.

scholarly journals 3D SURFACE RECONSTRUCTION FROM MULTI-DATE SATELLITE IMAGES

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 313-320
Author(s):  
S. Bullinger ◽  
C. Bodensteiner ◽  
M. Arens
Keyword(s):  
Large Scale ◽  
Satellite Images ◽  
Stereo Matching ◽  
State Of The Art ◽  
Satellite Image ◽  
Three Dimensional ◽  
Point Clouds ◽  
Reconstruction Algorithms ◽  
Depth Maps ◽  
Median Error

Abstract. The reconstruction of accurate three-dimensional environment models is one of the most fundamental goals in the field of photogrammetry. Since satellite images provide suitable properties for obtaining large-scale environment reconstructions, there exist a variety of Stereo Matching based methods to reconstruct point clouds for satellite image pairs. Recently, a Structure from Motion (SfM) based approach has been proposed, which allows to reconstruct point clouds from multiple satellite images. In this work, we propose an extension of this SfM based pipeline that allows us to reconstruct not only point clouds but watertight meshes including texture information. We provide a detailed description of several steps that are mandatory to exploit state-of-the-art mesh reconstruction algorithms in the context of satellite imagery. This includes a decomposition of finite projective camera calibration matrices, a skew correction of corresponding depth maps and input images as well as the recovery of real-world depth maps from reparameterized depth values. The paper presents an extensive quantitative evaluation on multi-date satellite images demonstrating that the proposed pipeline combined with current meshing algorithms outperforms state-of-the-art point cloud reconstruction algorithms in terms of completeness and median error. We make the source code of our pipeline publicly available.

scholarly journals A High-Capacity Ammonium Vanadate Cathode for Zinc-Ion Battery

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Qifei Li ◽  
Xianhong Rui ◽  
Dong Chen ◽  
Yuezhan Feng ◽  
Ni Xiao ◽  
...  
Keyword(s):  
Large Scale ◽  
Three Dimensional ◽  
High Capacity ◽  
Cycling Performance ◽  
Zinc Ion ◽  
First Principles Calculations ◽  
Diffusion Channel ◽  
Ion Storage ◽  
Ion Intercalation

AbstractGiven the advantages of being abundant in resources, environmental benign and highly safe, rechargeable zinc-ion batteries (ZIBs) enter the global spotlight for their potential utilization in large-scale energy storage. Despite their preliminary success, zinc-ion storage that is able to deliver capacity > 400 mAh g−1 remains a great challenge. Here, we demonstrate the viability of NH4V4O10 (NVO) as high-capacity cathode that breaks through the bottleneck of ZIBs in limited capacity. The first-principles calculations reveal that layered NVO is a good host to provide fast Zn2+ ions diffusion channel along its [010] direction in the interlayer space. On the other hand, to further enhance Zn2+ ion intercalation kinetics and long-term cycling stability, a three-dimensional (3D) flower-like architecture that is self-assembled by NVO nanobelts (3D-NVO) is rationally designed and fabricated through a microwave-assisted hydrothermal method. As a result, such 3D-NVO cathode possesses high capacity (485 mAh g−1) and superior long-term cycling performance (3000 times) at 10 A g−1 (~ 50 s to full discharge/charge). Additionally, based on the excellent 3D-NVO cathode, a quasi-solid-state ZIB with capacity of 378 mAh g−1 is developed.

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