This paper describes an ongoing research project aimed at implementing a trademark retrieval system using an associative memory neural network . The novel aspect presented in this paper is the proposed integrated framework for image retrieval using multiple representations of images based on gestalt principles . In this paper we summarise the methods we followed in extracting local perceptual features as well as features of the closed figures of images . Wine label retrieval is key to automatic wine brand search through the web or mobile phone in our daily life . In comparison with the general image retrieval tasks , it is a rather challenging problem with a huge number of unbalanced wine brand images . In this paper , we propose a CNN-SIFT Consecutive Searching and Matching ( CSCSM ) framework for wine label retrieval . Now , we will show you the new habit that , actually it 's a very old habit to do that can make your life more qualified . When feeling bored of always chatting with your friends all free time , you can find the book enPDF symbolic projection for image information retrieval and spatial reasoning theory applications and systems for image information retrieval and spatial reasoning and then read it . Our representations are created deterministically , can be applied to any image dataset irrespective of the task at hand , and are amenable to standard NLP tools developed for tree-based structures . We show that we can apply syntax-based SMT and tree kernel methods in order to build models for image description generation and image-based retrieval . Experimental results on real-world images demonstrate the effectiveness of the framework . Reading text in natural images is a challenging task under active research . It is driven by many real - world applications , such as Photo OCR , geo - location , and image retrieval . In a text reading system , text detection , i.e. localizing text with bounding boxes of words or text lines , is usually the first step of great significance . Attributes provide one such space ; they relate different classes through well - known and shared characteristics of objects . Attributes , which are often collected manually , have shown promising results in various applications , i.e. caption generation , face recognition , image retrieval , action recognition and image classification . The main challenge of attribute - based zero - shot learning arises on more challenging fine - grained data collections , in which categories may visually differ only subtly . Medical image application in clinical diagnosis and treatment is becoming more and more widely , How to use a large number of images in the image management system and it is a very important issue how to assist doctors to analyze and diagnose . This paper studies the medical image retrieval based on multi-layer resampling template under the thought of the wavelet decomposition , the image retrieval method consists of two retrieval process which is coarse and fine retrieval . Coarse retrieval process is the medical image retrieval process based on the image contour features . We also combine the ranked lists generated using both text and image indexes to further improve the retrieval performance . We have achieved the best overall performance for the medical image retrieval task in CLEF 2005 . These experimental results show that while most queries are better handled by the text query processing as most semantic information are contained in the medical text cases , both textual and visual ontology dimensions are complementary in improving the results during media fusion Our approach uses a segment-based model that incorporates the spatial relationship between a pixel and its causal neighbours . We apply the technique in a medical image retrieval problem , where items in a database of brain SPECT scans have to be ranked according to their similarities to a query scan . In our experiments , the resulting similarity ranking correlates well with visual inspection . for years , researchers in medical image retrieval area have been representing and recognizing medical images based on Local Binary Patterns ( LBP ) . Compared to Gabor wavelets , the LBP features can be extracted faster in a single scan through the raw image and lie in a lower dimensional space , whilst still retaining image information efficiently . Based on these semantic descriptions , our system facilitates knowledge base construction using a machine learning scheme . The result would be useful for other researchers in medical image retrieval area , who can take advantage of both text-based queries and image queries . To develop this , content based image Retrieval method has played an important role in the field of image retrieval . This paper aims to provide an efficient medical image data Retrieval from a huge content of medical database using one of the images content such as image shape , because , efficient content-based image Retrieval in the medical domain is still a challenging problem . The main objective of this paper is to provide an efficient tool which is used for efficient medical image retrieval from a huge content of medical image database and which is used for further medical diagnosis purposes . This chapter is intended to disseminate the knowledge of the CBIR approach to the applications of medical image management and to attract greater interest from various research communities to rapidly advance research in this field . IDEA GROUP PUBLISHING This paper appears in the book , Database Modeling for Industrial Data Management : Emerging Technologies and Applications edited by Zongmin Ma © 2006 , Idea Group Inc. 701 E. Chocolate Avenue , Suite 200 , Hershey PA 17033 - 1240 , USA Tel : 717/533 - 8845 ; Fax 717/533 - 8661 ; URL-http://www.idea-group.com ITB11974 A Content-Based Approach to Medical Image Database Retrieval 259 Copyright © 2006 , Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited . Introduction John Doe , a radiologist in a university hospital , takes X-rays and MRI scans for patients producing hundreds of digital images each day . This textual information , which is stored within the DICOM header , is currently the only means to access and retrieve medical images from the PACS archive . Since an image tells more than a thousand words , recall and precision of this type of medical image information retrieval is limited in general [ 1,2 ] . In this work , remote rendering technology is employed to implement the post-processing of medical images instead of local rendering , and a service protocol is developed to standardize the communication between the render server and mobile client . In order to make mobile devices with different platforms be able to access post-processing of medical images , the Extensible Markup Language is taken to describe this protocol , which contains four main parts : user authentication , medical image query/ retrieval , 2D post-processing ( e.g. window leveling , pixel values obtained ) and 3D post-processing ( e.g. maximum intensity projection , multi-planar reconstruction , curved planar reformation and direct volume rendering ) . And then an instance is implemented to verify the protocol . In the medical image retrieval literature , there are two main approaches : content-based retrieval using the visual information contained in the image itself and context-based retrieval using the metadata and the labels associated with the images . We present a work that fits in the context-based category , where queries are composed of medical keywords and the documents are metadata that succinctly describe the medical images . Dealing with the difficulties of integrating various medical image viewing and processing technologies with a variety of clinical and departmental information systems and , in the meantime , overcoming the performance constraints in transferring and processing large-scale and ever-increasing image data in healthcare enterprise , we design and implement a flexible , usable and high-performance architecture for medical image workstations . This architecture is not developed for radiology only , but for any workstations in any application environments that may need medical image retrieving , viewing , and post-processing . This architecture contains an infrastructure named Memory PACS and different kinds of image applications built on it . document : Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes During the last half decade , convolutional neural networks ( CNNs ) have triumphed over semantic segmentation , which is a core task of various emerging industrial applications such as autonomous driving and medical imaging . However , to train CNNs requires a huge amount of data , which is difficult to collect and laborious to annotate . This is particularly the case of infant brain MRIs . Therefore , both active and semi / weakly supervised learning frameworks are currently attracting substantial interest in medical image analysis . For instance , in the recent study in , Yang et al . proposed an active learning framework , and showed its potential in the context of segmenting glands in histology images and lymph nodes in ultrasound . section : Acknowledgments This work is supported by the National Science and Engineering Research Council of Canada ( NSERC ) , discovery grant program , and by the ETS Research Chair on Artificial Intelligence in Medical Imaging . section : References It would be interesting , however , to investigate structured ways for incorporating such existing information as priors into the network ’s feature space , which should simplify the optimization problem while letting a specialist guide the network towards an optimal solution . Although neural networks seem promising for medical image analysis , making the inference process more interpretable is required . This would allow understanding when the network fails , an important aspect in biomedical applications . Results reported on the 2013 BRATS test dataset reveal that our architecture improves over the currently published state - of - the - art while being over 30 times faster . [ t1 ] Accepted in Medical Image Analysis . [ fn1 ] mohammad.havaei@gmail.com Although CNNs first appeared over two decades ago , they have recently become a mainstay of the computer vision community due to their record - shattering performance in the ImageNet Large - Scale Visual Recognition Challenge . While CNNs have also been successfully applied to segmentation problems , most of the previous work has focused on non - medical tasks and many involve architectures that are not well suited to medical imagery or brain tumor segmentation in particular . Our preliminary work on using convolutional neural networks for brain tumor segmentation together with two other methods using CNNs was presented in BRATS‘14 workshop . Like our work , other recent work has exploited convolution operations in the final layer of a network to extend traditional CNN architectures for semantic scene segmentation . In the medical imaging domain in general there has been comparatively less work using CNNs for segmentation . However , some notable recent work by has used CNNs to predict the boundaries of neural tissue in electron microscopy images . Two advanced techniques including cross - entropy loss and dice similarity are introduced for efficient training of classification and segmentation tasks in [ reference ][ reference ] . Furthermore , in medical image processing , global localization and context modulation is very often applied for localization tasks . Each pixel is assigned a class label with a desired boundary that is related to the contour of the target lesion in identification tasks . First , we consider the task of identifying spans in abstracts that describe the respective PICO elements ( Section [ reference ] ) . This would , e.g. , improve medical literature search and retrieval systems . Next , we outline the problem of extracting structured information from abstracts ( Section [ reference ] ) . Furthermore , multiple auxiliary navigation paths that form a navigation envelope are used for data augmentation to make the system adaptable to real-life deployment scenarios . The approach is suitable for automating drone navigation in applications that exhibit regular trips or visits to same locations such as environmental and desertification monitoring , parcel/aid delivery and drone-based wireless internet delivery . In this case , the proposed algorithm replaces human operators , enhances accuracy of GPS-based map navigation , alleviates problems related to GPS-spoofing and enables navigation in GPS-denied environments . This paper presents an innovative integrated navigation unit developed for small unmanned aircraft systems , i.e. drones . It exploits an original redundant configuration of " consumer grade " Micro Electro-Mechanical Sensors to generate measurements that are compliant with specifications for drone navigation , both in terms of accuracy and integrity . To this aim , six boards equipped with inertial sensors are arranged in a cube containing triaxial gyros , accelerometers and magnetometers . For relatively brief GPS outage periods , a tactical-grade INS serves to navigate with acceptable accuracy through the outage . For longer outages , INS/GPS data obtained before the outage enables on-the-fly calibration of dead reckoning ( DR ) sensors ( such as a vehicle odometer and compass ) , which in turn enables better DR navigation ( and pointing ) performance than is obtainable with unaided DR . This type of system provides performance ( and cost ) midway between systems employing expensive navigation grade INS systems and those that use no INS at all . The main goal of the research presented here is to develop theoretical foundations and implementation algorithms , which integrate GPS , micro-electro-mechanical inertial measurement unit ( MEMS IMU ) , digital barometer , electronic compass , and human pedometry to provide navigation and tracking of military and rescue ground personnel . This paper discusses the design , implementation and the initial performance analyses of the personal navigator prototype1 , with a special emphasis on dead-reckoning ( DR ) navigation supported by the human locomotion model . To facilitate this functionality , the adaptive knowledge system , based on the Artificial Neural Networks ( ANN ) and Fuzzy Logic , is trained during the GPS signal reception and used to maintain navigation under GPS-denied conditions . Motivated by drone autonomous navigation applications we address a novel problem of velocity-aided attitude estimation by combining two linear velocity components measured in a body-fixed frame and a linear velocity component measured in an inertial frame with the measurements of an Inertial Measurement Unit ( IMU ) . The main contributions of the present paper are the design of a Riccati nonlinear observer , which may be viewed as deterministic versions of an Extended Kalman filter ( EKF ) , and an analysis of observability conditions under which local exponential stability of the observer is achieved . Motivated by drone autonomous navigation applications we address a novel problem of velocity-aided attitude estimation by combining two linear velocity components measured in a body-fixed frame and a linear velocity component measured in an inertial frame with the measurements of an Inertial Measurement Unit ( IMU ) . The main contributions of the present paper are the design of Riccati nonlinear observers , which may be viewed as deterministic versions of an Extended Kalman filter ( EKF ) , and an analysis of observability conditions under which local exponential stability of the observer is achieved . The target is able to move and the Drone must be able to plan a path and reach the target object . The experiment result shows that the proposed method is effective to be used as drone path planning in a dynamic environment . Drone-assisted inventory management is attractive for companies with large warehouses and factories . Additionally , there is an interest in a novel method that automatically detects target barcodes using a IR-based camera , which enables efficient drone path planning and results in reducing power consumption . In this paper , we propose an efficient detection framework which determines the localizations of 2D barcodes . This topic is particularly important in a wide variety of aerospace applications , which often rely on multiple imaging sensors whose information should be merged , or on imaging devices carried by manned or unmanned vehicles . Such applications range from flying object detection to tridimensional reconstruction by using aerial or satellite images to drone automatic navigation , to change detection for area monitoring to georegistration by ground-to-aerial image matching . A generic strategy that handles the navigation of the drone on a grid for completion of the allotted task is also developed . Based on the simulation and real-life experiments , the final developments on the drone localization and navigation in a structured environment are discussed . A significant body of research has already been performed with the aim of improving the autonomy of UAV missions , particularly in the areas of navigation and target identification . In this work , we review current approaches to drone navigation and exploration for planetary missions , with a focus on Mars and the main autonomy levels/techniques employed to achieve these levels . Recognising the importance of astrobiology in Mars exploration , we highlight progress in the area of autonomous biosignature detection capabilities trialed on Earth , and discuss the objectives and challenges in relation to future missions to Mars . Additional features include circuit-based solutions for obstacle blockage avoidance and gravity . The efficacy of the proposed ASIC is evaluated on a drone navigation application , 3-D Voronoi diagrams , and a physical optics experiment . The chip is twice as energy efficient as prior 2-D work while containing < inline-formula > < tex-math notation="LaTeX">$5\times $ < /tex-math></inline-formula > more vertices and < inline-formula > < tex-math notation="LaTeX">$7.5\times $ < /tex-math></inline-formula > additional edge connections . The recorded data is then used to train a neural network which maps interaction forces to acceleration commands . The system is also reinforced with a human path prediction module to improve the drone ’s navigation , as well as , a collision detection module to completely avoid possible impacts . Moreover , a performance metric is defined which allows us to numerically evaluate and compare the fulfillment of the different learned policies . Finally , an energy-aware mapping scheme is adopted to assign clusters onto tile-based NoCs . We validate this approach with a drone self-navigation application and the experimental results show that we can achieve up to 8.4x energy reduction and 10.5x performance speedup . To our knowledge , University-1652 is the first drone-based geo-localization dataset and enables two new tasks , i.e. , drone-view target localization and drone navigation . As the name implies , drone-view target localization intends to predict the location of the target place via drone-view images . On the other hand , given a satellite-view query image , drone navigation is to drive the drone to the area of interest in the query .
This paper describes an ongoing research project aimed at implementing a trademark retrieval system using an associative memory neural network . The novel aspect presented in this paper is the proposed integrated framework for image retrieval using multiple representations of images based on gestalt principles . In this paper we summarise the methods we followed in extracting local perceptual features as well as features of the closed figures of images .
Wine label retrieval is key to automatic wine brand search through the web or mobile phone in our daily life . In comparison with the general image retrieval tasks , it is a rather challenging problem with a huge number of unbalanced wine brand images . In this paper , we propose a CNN-SIFT Consecutive Searching and Matching ( CSCSM ) framework for wine label retrieval .
Now , we will show you the new habit that , actually it 's a very old habit to do that can make your life more qualified . When feeling bored of always chatting with your friends all free time , you can find the book enPDF symbolic projection for image information retrieval and spatial reasoning theory applications and systems for image information retrieval and spatial reasoning and then read it .
Our representations are created deterministically , can be applied to any image dataset irrespective of the task at hand , and are amenable to standard NLP tools developed for tree-based structures . We show that we can apply syntax-based SMT and tree kernel methods in order to build models for image description generation and image-based retrieval . Experimental results on real-world images demonstrate the effectiveness of the framework .
Reading text in natural images is a challenging task under active research . It is driven by many real - world applications , such as Photo OCR , geo - location , and image retrieval . In a text reading system , text detection , i.e. localizing text with bounding boxes of words or text lines , is usually the first step of great significance .
Attributes provide one such space ; they relate different classes through well - known and shared characteristics of objects . Attributes , which are often collected manually , have shown promising results in various applications , i.e. caption generation , face recognition , image retrieval , action recognition and image classification . The main challenge of attribute - based zero - shot learning arises on more challenging fine - grained data collections , in which categories may visually differ only subtly .
Medical image application in clinical diagnosis and treatment is becoming more and more widely , How to use a large number of images in the image management system and it is a very important issue how to assist doctors to analyze and diagnose . This paper studies the medical image retrieval based on multi-layer resampling template under the thought of the wavelet decomposition , the image retrieval method consists of two retrieval process which is coarse and fine retrieval . Coarse retrieval process is the medical image retrieval process based on the image contour features .
We also combine the ranked lists generated using both text and image indexes to further improve the retrieval performance . We have achieved the best overall performance for the medical image retrieval task in CLEF 2005 . These experimental results show that while most queries are better handled by the text query processing as most semantic information are contained in the medical text cases , both textual and visual ontology dimensions are complementary in improving the results during media fusion
Our approach uses a segment-based model that incorporates the spatial relationship between a pixel and its causal neighbours . We apply the technique in a medical image retrieval problem , where items in a database of brain SPECT scans have to be ranked according to their similarities to a query scan . In our experiments , the resulting similarity ranking correlates well with visual inspection .
for years , researchers in medical image retrieval area have been representing and recognizing medical images based on Local Binary Patterns ( LBP ) . Compared to Gabor wavelets , the LBP features can be extracted faster in a single scan through the raw image and lie in a lower dimensional space , whilst still retaining image information efficiently .
Based on these semantic descriptions , our system facilitates knowledge base construction using a machine learning scheme . The result would be useful for other researchers in medical image retrieval area , who can take advantage of both text-based queries and image queries .
To develop this , content based image Retrieval method has played an important role in the field of image retrieval . This paper aims to provide an efficient medical image data Retrieval from a huge content of medical database using one of the images content such as image shape , because , efficient content-based image Retrieval in the medical domain is still a challenging problem . The main objective of this paper is to provide an efficient tool which is used for efficient medical image retrieval from a huge content of medical image database and which is used for further medical diagnosis purposes .
This chapter is intended to disseminate the knowledge of the CBIR approach to the applications of medical image management and to attract greater interest from various research communities to rapidly advance research in this field . IDEA GROUP PUBLISHING This paper appears in the book , Database Modeling for Industrial Data Management : Emerging Technologies and Applications edited by Zongmin Ma © 2006 , Idea Group Inc. 701 E. Chocolate Avenue , Suite 200 , Hershey PA 17033 - 1240 , USA Tel : 717/533 - 8845 ; Fax 717/533 - 8661 ; URL-http://www.idea-group.com ITB11974 A Content-Based Approach to Medical Image Database Retrieval 259 Copyright © 2006 , Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited . Introduction John Doe , a radiologist in a university hospital , takes X-rays and MRI scans for patients producing hundreds of digital images each day .
This textual information , which is stored within the DICOM header , is currently the only means to access and retrieve medical images from the PACS archive . Since an image tells more than a thousand words , recall and precision of this type of medical image information retrieval is limited in general [ 1,2 ] .
In this work , remote rendering technology is employed to implement the post-processing of medical images instead of local rendering , and a service protocol is developed to standardize the communication between the render server and mobile client . In order to make mobile devices with different platforms be able to access post-processing of medical images , the Extensible Markup Language is taken to describe this protocol , which contains four main parts : user authentication , medical image query/ retrieval , 2D post-processing ( e.g. window leveling , pixel values obtained ) and 3D post-processing ( e.g. maximum intensity projection , multi-planar reconstruction , curved planar reformation and direct volume rendering ) . And then an instance is implemented to verify the protocol .
In the medical image retrieval literature , there are two main approaches : content-based retrieval using the visual information contained in the image itself and context-based retrieval using the metadata and the labels associated with the images . We present a work that fits in the context-based category , where queries are composed of medical keywords and the documents are metadata that succinctly describe the medical images .
Dealing with the difficulties of integrating various medical image viewing and processing technologies with a variety of clinical and departmental information systems and , in the meantime , overcoming the performance constraints in transferring and processing large-scale and ever-increasing image data in healthcare enterprise , we design and implement a flexible , usable and high-performance architecture for medical image workstations . This architecture is not developed for radiology only , but for any workstations in any application environments that may need medical image retrieving , viewing , and post-processing . This architecture contains an infrastructure named Memory PACS and different kinds of image applications built on it .
document : Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes During the last half decade , convolutional neural networks ( CNNs ) have triumphed over semantic segmentation , which is a core task of various emerging industrial applications such as autonomous driving and medical imaging . However , to train CNNs requires a huge amount of data , which is difficult to collect and laborious to annotate .
This is particularly the case of infant brain MRIs . Therefore , both active and semi / weakly supervised learning frameworks are currently attracting substantial interest in medical image analysis . For instance , in the recent study in , Yang et al . proposed an active learning framework , and showed its potential in the context of segmenting glands in histology images and lymph nodes in ultrasound .
section : Acknowledgments This work is supported by the National Science and Engineering Research Council of Canada ( NSERC ) , discovery grant program , and by the ETS Research Chair on Artificial Intelligence in Medical Imaging . section : References
It would be interesting , however , to investigate structured ways for incorporating such existing information as priors into the network ’s feature space , which should simplify the optimization problem while letting a specialist guide the network towards an optimal solution . Although neural networks seem promising for medical image analysis , making the inference process more interpretable is required . This would allow understanding when the network fails , an important aspect in biomedical applications .
Results reported on the 2013 BRATS test dataset reveal that our architecture improves over the currently published state - of - the - art while being over 30 times faster . [ t1 ] Accepted in Medical Image Analysis . [ fn1 ] mohammad.havaei@gmail.com
Although CNNs first appeared over two decades ago , they have recently become a mainstay of the computer vision community due to their record - shattering performance in the ImageNet Large - Scale Visual Recognition Challenge . While CNNs have also been successfully applied to segmentation problems , most of the previous work has focused on non - medical tasks and many involve architectures that are not well suited to medical imagery or brain tumor segmentation in particular . Our preliminary work on using convolutional neural networks for brain tumor segmentation together with two other methods using CNNs was presented in BRATS‘14 workshop .
Like our work , other recent work has exploited convolution operations in the final layer of a network to extend traditional CNN architectures for semantic scene segmentation . In the medical imaging domain in general there has been comparatively less work using CNNs for segmentation . However , some notable recent work by has used CNNs to predict the boundaries of neural tissue in electron microscopy images .
Two advanced techniques including cross - entropy loss and dice similarity are introduced for efficient training of classification and segmentation tasks in [ reference ][ reference ] . Furthermore , in medical image processing , global localization and context modulation is very often applied for localization tasks . Each pixel is assigned a class label with a desired boundary that is related to the contour of the target lesion in identification tasks .
First , we consider the task of identifying spans in abstracts that describe the respective PICO elements ( Section [ reference ] ) . This would , e.g. , improve medical literature search and retrieval systems . Next , we outline the problem of extracting structured information from abstracts ( Section [ reference ] ) .
Furthermore , multiple auxiliary navigation paths that form a navigation envelope are used for data augmentation to make the system adaptable to real-life deployment scenarios . The approach is suitable for automating drone navigation in applications that exhibit regular trips or visits to same locations such as environmental and desertification monitoring , parcel/aid delivery and drone-based wireless internet delivery . In this case , the proposed algorithm replaces human operators , enhances accuracy of GPS-based map navigation , alleviates problems related to GPS-spoofing and enables navigation in GPS-denied environments .
This paper presents an innovative integrated navigation unit developed for small unmanned aircraft systems , i.e. drones . It exploits an original redundant configuration of " consumer grade " Micro Electro-Mechanical Sensors to generate measurements that are compliant with specifications for drone navigation , both in terms of accuracy and integrity . To this aim , six boards equipped with inertial sensors are arranged in a cube containing triaxial gyros , accelerometers and magnetometers .
For relatively brief GPS outage periods , a tactical-grade INS serves to navigate with acceptable accuracy through the outage . For longer outages , INS/GPS data obtained before the outage enables on-the-fly calibration of dead reckoning ( DR ) sensors ( such as a vehicle odometer and compass ) , which in turn enables better DR navigation ( and pointing ) performance than is obtainable with unaided DR . This type of system provides performance ( and cost ) midway between systems employing expensive navigation grade INS systems and those that use no INS at all .
The main goal of the research presented here is to develop theoretical foundations and implementation algorithms , which integrate GPS , micro-electro-mechanical inertial measurement unit ( MEMS IMU ) , digital barometer , electronic compass , and human pedometry to provide navigation and tracking of military and rescue ground personnel . This paper discusses the design , implementation and the initial performance analyses of the personal navigator prototype1 , with a special emphasis on dead-reckoning ( DR ) navigation supported by the human locomotion model . To facilitate this functionality , the adaptive knowledge system , based on the Artificial Neural Networks ( ANN ) and Fuzzy Logic , is trained during the GPS signal reception and used to maintain navigation under GPS-denied conditions .
Motivated by drone autonomous navigation applications we address a novel problem of velocity-aided attitude estimation by combining two linear velocity components measured in a body-fixed frame and a linear velocity component measured in an inertial frame with the measurements of an Inertial Measurement Unit ( IMU ) . The main contributions of the present paper are the design of a Riccati nonlinear observer , which may be viewed as deterministic versions of an Extended Kalman filter ( EKF ) , and an analysis of observability conditions under which local exponential stability of the observer is achieved .
Motivated by drone autonomous navigation applications we address a novel problem of velocity-aided attitude estimation by combining two linear velocity components measured in a body-fixed frame and a linear velocity component measured in an inertial frame with the measurements of an Inertial Measurement Unit ( IMU ) . The main contributions of the present paper are the design of Riccati nonlinear observers , which may be viewed as deterministic versions of an Extended Kalman filter ( EKF ) , and an analysis of observability conditions under which local exponential stability of the observer is achieved .
The target is able to move and the Drone must be able to plan a path and reach the target object . The experiment result shows that the proposed method is effective to be used as drone path planning in a dynamic environment .
Drone-assisted inventory management is attractive for companies with large warehouses and factories . Additionally , there is an interest in a novel method that automatically detects target barcodes using a IR-based camera , which enables efficient drone path planning and results in reducing power consumption . In this paper , we propose an efficient detection framework which determines the localizations of 2D barcodes .
This topic is particularly important in a wide variety of aerospace applications , which often rely on multiple imaging sensors whose information should be merged , or on imaging devices carried by manned or unmanned vehicles . Such applications range from flying object detection to tridimensional reconstruction by using aerial or satellite images to drone automatic navigation , to change detection for area monitoring to georegistration by ground-to-aerial image matching .
A generic strategy that handles the navigation of the drone on a grid for completion of the allotted task is also developed . Based on the simulation and real-life experiments , the final developments on the drone localization and navigation in a structured environment are discussed .
A significant body of research has already been performed with the aim of improving the autonomy of UAV missions , particularly in the areas of navigation and target identification . In this work , we review current approaches to drone navigation and exploration for planetary missions , with a focus on Mars and the main autonomy levels/techniques employed to achieve these levels . Recognising the importance of astrobiology in Mars exploration , we highlight progress in the area of autonomous biosignature detection capabilities trialed on Earth , and discuss the objectives and challenges in relation to future missions to Mars .
Additional features include circuit-based solutions for obstacle blockage avoidance and gravity . The efficacy of the proposed ASIC is evaluated on a drone navigation application , 3-D Voronoi diagrams , and a physical optics experiment . The chip is twice as energy efficient as prior 2-D work while containing < inline-formula > < tex-math notation="LaTeX">$5\times $ < /tex-math></inline-formula > more vertices and < inline-formula > < tex-math notation="LaTeX">$7.5\times $ < /tex-math></inline-formula > additional edge connections .
The recorded data is then used to train a neural network which maps interaction forces to acceleration commands . The system is also reinforced with a human path prediction module to improve the drone ’s navigation , as well as , a collision detection module to completely avoid possible impacts . Moreover , a performance metric is defined which allows us to numerically evaluate and compare the fulfillment of the different learned policies .
Finally , an energy-aware mapping scheme is adopted to assign clusters onto tile-based NoCs . We validate this approach with a drone self-navigation application and the experimental results show that we can achieve up to 8.4x energy reduction and 10.5x performance speedup .
To our knowledge , University-1652 is the first drone-based geo-localization dataset and enables two new tasks , i.e. , drone-view target localization and drone navigation . As the name implies , drone-view target localization intends to predict the location of the target place via drone-view images . On the other hand , given a satellite-view query image , drone navigation is to drive the drone to the area of interest in the query .
Trademark image retrieval using multiple features CNN-SIFT Consecutive Searching and Matching for Wine Label Retrieval Symbolic projection for image information retrieval and spatial reasoning Understanding visual scenes Detecting Oriented Text in Natural Images by Linking Segments Evaluation of output embeddings for fine-grained image classification Medical Image Retrieval Based on Multi-Layer Resampling Template Combining Textual and Visual Ontologies to Solve Medical Multimodal Queries A Similarity Measure Based on Causal Neighbours and Mutual Information Medical Image Retrieval Using Local Binary Patterns with Image Euclidean Distance Automatic extraction of semantic concepts in medical images Content based Image Retrieval for Medical Images using Canny Edge Detection Algorithm A Content-Based Approach to Medical Image Database Retrieval A Content-Based Approach to Image Retrieval in Medical Applications A service protocol for post-processing of medical images on the mobile device Document/query expansion based on selecting significant concepts for context based retrieval of medical images An open architecture for medical image workstation Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation Brain tumor segmentation with Deep Neural Networks Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature Deep Convolutional Neural Network-Based Autonomous Drone Navigation Redundant configuration of low-cost inertial sensors for advanced navigation of small unmanned aerial systems Benefits of Low-Cost INS/GPS to Augment Land Navigation Pedestrian Tracking and Navigation Using Neural Networks and Fuzzy Logic Riccati nonlinear observer for velocity-aided attitude estimation of accelerated vehicles using coupled velocity measurements Riccati observers for velocity-aided attitude estimation of accelerated vehicles using coupled velocity measurements The Use of Direction Matrix to Determine Autonomous Quad-Copter Drone Path when Facing Obstacle 2D Barcode Detection using Images for Drone-assisted Inventory Management Tutorial: Dealing with rotation matrices and translation vectors in image-based applications: A tutorial Grid-based Localization Stack for Inspection Drones towards Automation of Large Scale Warehouse Systems A Review of Current Approaches for UAV Autonomous Mission Planning for Mars Biosignatures Detection A Shortest Path Finding Time-Based Accelerator Core With Built-in Gravity Control and Buffer Zone for Smooth 3-D Navigation Teaching a Drone to Accompany a Person from Demonstrations using Non-Linear ASFM Design Methodology for Energy Efficient Unmanned Aerial Vehicles University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization
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