Synthetic aperture radar ( SAR ) imagery has been used as a promising data source for monitoring maritime activities , and its application for oil and ship detection has been the focus of many previous research studies . Many object detection methods ranging from traditional to deep learning approaches have been proposed . However , majority of them are computationally intensive and have accuracy problems . We train an agent to automatically choose a sequence of actions for a human annotator to produce a bounding box in a minimal amount of time . Specifically , we consider two actions : box verification [ 34 ] , where the annotator verifies a box generated by an object detector , and manual box drawing . We explore two kinds of agents , one based on predicting the probability that a box will be positively verified , and the other based on reinforcement learning . Unfortunately , such approaches might result with erroneous annotations . In this work , different types of annotation errors for object detection problem are simulated and the performance of a popular state-of-the-art object detector , YOLOv3 , with erroneous annotations during training and testing stages is examined . Moreover , some inevitable annotation errors in Anti-UAV Challenge dataset is also examined in this manner , while proposing a solution to correct such annotation errors of this valuable data set . An object detection device ( 10 ) is equipped with : an image conversion unit ( 20 ) that converts an input image produced by photographing the vicinity of a vehicle into a characteristic image for which image characteristics have been extracted from the input image ; a per-distance image extraction-combination unit ( 30 ) which , on the basis of the distance from the vehicle , extracts from the characteristic image different regions , which differ for each distance , and which combines these regions ; a first object detection unit ( 40 ) that scans the combined image and performs a first object detection process ; an object candidate position designation unit ( 50 ) that obtains an object candidate position from the result of the first object detection process ; a second object detection unit ( 60 ) that performs a second object detection process with respect to the object candidate position ; and an object position identification unit ( 70 ) that identifies the object position from the result of the second object detection process . Thus , objects in the vicinity of a vehicle can be detected quickly without a decrease in detection capability . To reduce the value of noise pixel or not-connected pixel in this binary image , then will do an erosion operation as a morphology function . The object detection process in this research is using template matching method by get a mean value of every each image then get a location of heart object for further . The result from trial use 60 image , which 20 are test image , 20 train image , and 20 are not trained image . The present application relates to an object detecting method , object detecting apparatus and an image pickup apparatus , the object detecting method comprising : an object detection step of detecting a specific object in the image by a specific object detector ; scene model acquiring step of acquiring images of a specific characterization background of the scene model object ; and a filtration step , the scene model object with a detection result of the object detecting step is filtered to determine the specific object . The present invention relates to an object detecting system for delivering power to the vehicle ( 1 ) on the surface ( 2 ) in that , in particular path ( 3 ) on the object detecting system for the inductive power transfer system , the inductive power delivery system includes a primary winding structure and a secondary comprises a winding structure , the inductive inductive power transfer system for electric power transmission is assigned a charging unit ( CV ) , the object detection system includes at least one sensing device , the sensing device has a detecting section 10 , at least one sensing device , the detector 10 is arranged to align so as to be disposed entirely on the outside of the charging or or the detector 10 comprises only the edge portion of the charging section ( CV ) . Furthermore , the invention relates to a method , an inductive power transfer system , and the vehicle for detecting an extraneous object . However , this is not a feasible solution since image generation would require training examples of these rare occurrences to begin with . Another solution is to generate all possible occlusions and deformations and train object detectors from them . However , since the space of deformations and occlusions is huge , this is not a scalable solution . Simplex algorithm has been successfully used over the years to deliver self-optimization to systems requiring maximum output . This work proposes that integrating Simplex algorithm into Condor 's resource management framework can instill self-optimizing capabilities in the HTC . The results not only improve the performance with a higher throughput , but also lead to the greater user satisfaction . Natural and expressive body motions allow the user to specify the parameters which define the curves and surfaces . Results are demonstrated using the HTC Vive VR system . The co-acquisition of EEG in a virtual environment ( VE ) would give researchers and clinicians the opportunity to acquire EEG data with millisecond-level temporal resolution while participants performed VE activities . This study integrated Advanced Brain Monitoring ’s ( ABM ) X-24 t EEG hardware with the HTC Vive VR headset and investigated EEG differences in tasks delivered in two modalities : VE and a desktop computer . We have named the extension of the BOINC software infrastructure as BOINC@TACC , and are using it to route * qualified * HTC jobs from the supercomputers at the Texas Advanced Computing Center ( TACC ) to not only the typically volunteered devices but also to the cloud computing resources such as Jetstream and Chameleon . BOINC@TACC can be extremely useful for those researchers/scholars who are running low on allocations of compute-cycles on the supercomputers , or are interested in reducing the turnaround time of their HTC jobs when the supercomputers are over-subscribed . We have also developed a web-application for TACC users so that , through the convenience of their web-browser , they can submit their HTC jobs for running on the resources volunteered by the community . A Glove for Illusionary Touch Feedback when Grasping Virtual Objects . When touching virtual objects , we lack haptic feedback using commonly and commercially available VR controllers , such as the HTC Vive Controller or Oculus Touch . Haptic Illusion Glove is a wearable device used to create the haptic illusion when grasping objects . Navigation is common in VR environments . The traditional game controller for consoles is still a common choice but only now companies are just beginning to introduce new concepts ( for example the HTC Vive Controller ) . In this research we explore the development of an alternative input controller to support users ' navigation activities . To address this , Queensland Genomics commissioned a pilot program for the establishment of a local Australian SRA Cache . Utilizing the hosting capabilities of the NeCTAR Research Cloud , QRIScloud 's HTC infrastructure and the MeDiCI data fabric as a storage solution , and the software stack of Cromwell for workflow management , PostgreSQL database for sample and job metadata , and a coordinator Python Flask application , a local cache of seventeen bacterial species was established . Furthermore , the workflow capabilities of Cromwell were leveraged to provide analysis solutions for cached sample data , including quality control and taxonomic profiling , and individual and multiple sample analysis . Purpose : To utilize a High Throughput Computing ( HTC ) system to provide distributed computing on a network of computer workstations for a metaheuristic , Nested Partitions ( NP ) , for beam angle selection ( BAS ) in IMRT.Method and Materials : An important feature of the NP approach is that its partition/sampling processes are naturally parallelized , allowing the integration of this powerful optimization methodology with high‐performance distributed environments such as the Condor system . Condor is a specialized workload management system that can used on existing computer networks for compute‐intensive jobs , allowing the submission of many jobs at the same time , and performing these jobs by locating and utilizing idle computers in the network . The CNC lathe is elaborated on the functional module and structural module . A CNC machine tool company ’s HTC Series CNC lathes modular design is illustrated as an example . High Throughput Computing ( HTC ) environments strive " to provide large amounts of processing capacity to customers over long periods of time by exploiting existing resources on the network " according to Basney and Livny [ 1 ] . A single Blue Gene/L rack can provide thousands of CPU resources into HTC environments . The results obtained are compared with the corresponding flat categorization techniques applied to leaf level categories of the considered hierarchies . While kNN-based flat text categorization produced slightly better results than kNN-based HTC on the largest TanCorpHier and 20NGHier datasets , SVM-based HTC results do not considerably differ from the corresponding flat techniques , due to shallow hierarchies ; still , they outperform both kNN-based flat and hierarchical categorization on all corpora except the smallest Reuters-Hier1 and Reuters-Hier2 datasets . Formal evaluation confirmed that the proposed techniques obtained state-of-the-art results . However , this method is inefficient and of low precision due to limited personal sensing and experience . This article proposes a hybrid cascade approach with an improved level set and a random forest to locate and automatically classify defective joints in microelectronics packaging . A grayscale variance-based pixel neighborhood is introduced to accurately locate the joint , and an improved gray projection is used to remove the redundant nonjoint area . Using a motivating case study , the general problem of resource usage maximization is addressed . We show how the issues can be leveraged by employing a hybrid task model for anytime algorithms , which is supported by recent advances in fixed priority scheduling for imprecise computation . In particular , with a novel scheduling scheme based on Dual Priority Scheduling , hard tasks are guaranteed by schedulability analysis and scheduled in favor of optional and anytime components which are executed whenever possible for enhancing system utility . As a consequence , the system behaves in a non-periodic manner , and the real performance is degraded with respect to the expected response . This paper proposes a hybrid task model to reduce the impact of the scheduling on the control performance . For a large batch of typical plants , we analyze how sensitive the control system is to jitter when the sampling rate is slow or fast compared to the bandwidth of the system In exploring a more effective approach , we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation . In this work , we propose a new framework , Hybrid Task Cascade ( HTC ) , which differs in two important aspects : ( 1 ) instead of performing cascaded refinement on these two tasks separately , it interweaves them for a joint multi-stage processing ; ( 2 ) it adopts a fully convolutional branch to provide spatial context , which can help distinguishing hard foreground from cluttered background . Overall , this framework can learn more discriminative features progressively while integrating complementary features together in each stage . As lung cancer continues to threaten human health , Computer-Aided Diagnostic ( CAD ) plays an increasingly significant role in lung cancer diagnosis , and convolutional neural networks ( CNNs ) have shown the outstanding performance in image segmentation . In this work , Hybrid Task Cascade ( HTC ) is used to segment lung nodules that are difficult to find in CT images . Considering that lung nodules are usually quite small , this study integrates Feature Pyramid Network ( FPN ) into ResNet-50 to make full use of multi-scale feature and improve the segmentation accuracy of small target nodules . A multisensor scheduling algorithm based on the hybrid task decomposition and modified binary particle swarm optimization ( MBPSO ) is proposed . Firstly , aiming at the complex relationship between sensor resources and tasks , a hybrid task decomposition method is presented , and the resource scheduling problem is decomposed into subtasks ; then the sensor resource scheduling problem is changed into the match problem of sensors and subtasks . Secondly , the resource match optimization model based on the sensor resources and tasks is established , which considers several factors , such as the target priority , detecting benefit , handover times , and resource load . The invention discloses a project management application interface based on a hybrid task board and a critical path method ( CPM ) . A seamless interaction between a task board and an underlying project management application interface based on the critical path method ( CPM ) is provided , where users are enabled to move tasks on an user interface of a task board across columns or summary task ( " story " ) rows . We aim to develop an autonomous navigation pipeline by a combination of both approaches . Using DeepLabv3 + for Semantic Segmentation we achieve an mIOU of 68.58 % , and using Hybrid Task Cascade we achieve an Average Recall of 56.5 % . Instance Segmentation is an interesting yet challenging task in computer vision . In this paper , we conduct a series of refinements with the Hybrid Task Cascade ( HTC ) Network , and empirically evaluate their impact on the final model performance through ablation studies . By taking all the refinements , we achieve 0.47 on the COCO test-dev dataset and 0.47 on the COCO test-challenge dataset . In this paper , we propose a novel parallelism scheme , called ‘ Hybrid partitioning ’ , for mapping a H.264 high definition ( HD ) decoder onto REMUS-II , a CGRA systemon-chip ( SoC ) . Combining good features of data partitioning and task partitioning , our methodology mainly consists of three levels from top to bottom : ( 1 ) hybrid task pipeline based on slice and macroblock ( MB ) level ; ( 2 ) MB row-level data parallelism ; ( 3 ) sub-MB level parallelism method . Further , on the sub-MB level , we propose a few mapping strategies such as hybrid variable block size motion compensation ( Hybrid VBSMC ) for MC , 2D-wave for intra 4 × 4 , parallel processing order for deblocking . The traditional scheduling algorithms for real-time system are not suitable for these characteristics . A new hybrid-tasks system model is presented in this paper and the heuristic best-first search ( BFS ) algorithm is introduced to schedule tasks of this model . The BFS algorithm searches the state node matching all constraint conditions and minimizing ( maximizing ) the value of heuristic evaluation function in the state-space tree , so it solves the schedule problem with an optimal solution . Its effectiveness is validated through extensive experiments on multiple image restoration tasks : image denoising , demosaicing , compression artifact reduction , and super resolution . Without any bells and whistles , our PANet ( pyramid attention module with simple network backbones ) can produce state-of-the-art results with superior accuracy and visual quality . This will make the attention prediction for a particular person not accurate enough . In this work , we present the novel idea of personalized attention prediction and develop Personalized Attention Network ( PANet ) , a convolutional network that predicts saliency in images with personal preference . The model consists of two streams which share common feature extraction layers , and one stream is responsible for saliency prediction , while the other is adapted from the detection model and used to fit user preference .
Synthetic aperture radar ( SAR ) imagery has been used as a promising data source for monitoring maritime activities , and its application for oil and ship detection has been the focus of many previous research studies . Many object detection methods ranging from traditional to deep learning approaches have been proposed . However , majority of them are computationally intensive and have accuracy problems .
We train an agent to automatically choose a sequence of actions for a human annotator to produce a bounding box in a minimal amount of time . Specifically , we consider two actions : box verification [ 34 ] , where the annotator verifies a box generated by an object detector , and manual box drawing . We explore two kinds of agents , one based on predicting the probability that a box will be positively verified , and the other based on reinforcement learning .
Unfortunately , such approaches might result with erroneous annotations . In this work , different types of annotation errors for object detection problem are simulated and the performance of a popular state-of-the-art object detector , YOLOv3 , with erroneous annotations during training and testing stages is examined . Moreover , some inevitable annotation errors in Anti-UAV Challenge dataset is also examined in this manner , while proposing a solution to correct such annotation errors of this valuable data set .
An object detection device ( 10 ) is equipped with : an image conversion unit ( 20 ) that converts an input image produced by photographing the vicinity of a vehicle into a characteristic image for which image characteristics have been extracted from the input image ; a per-distance image extraction-combination unit ( 30 ) which , on the basis of the distance from the vehicle , extracts from the characteristic image different regions , which differ for each distance , and which combines these regions ; a first object detection unit ( 40 ) that scans the combined image and performs a first object detection process ; an object candidate position designation unit ( 50 ) that obtains an object candidate position from the result of the first object detection process ; a second object detection unit ( 60 ) that performs a second object detection process with respect to the object candidate position ; and an object position identification unit ( 70 ) that identifies the object position from the result of the second object detection process . Thus , objects in the vicinity of a vehicle can be detected quickly without a decrease in detection capability .
To reduce the value of noise pixel or not-connected pixel in this binary image , then will do an erosion operation as a morphology function . The object detection process in this research is using template matching method by get a mean value of every each image then get a location of heart object for further . The result from trial use 60 image , which 20 are test image , 20 train image , and 20 are not trained image .
The present application relates to an object detecting method , object detecting apparatus and an image pickup apparatus , the object detecting method comprising : an object detection step of detecting a specific object in the image by a specific object detector ; scene model acquiring step of acquiring images of a specific characterization background of the scene model object ; and a filtration step , the scene model object with a detection result of the object detecting step is filtered to determine the specific object .
The present invention relates to an object detecting system for delivering power to the vehicle ( 1 ) on the surface ( 2 ) in that , in particular path ( 3 ) on the object detecting system for the inductive power transfer system , the inductive power delivery system includes a primary winding structure and a secondary comprises a winding structure , the inductive inductive power transfer system for electric power transmission is assigned a charging unit ( CV ) , the object detection system includes at least one sensing device , the sensing device has a detecting section 10 , at least one sensing device , the detector 10 is arranged to align so as to be disposed entirely on the outside of the charging or or the detector 10 comprises only the edge portion of the charging section ( CV ) . Furthermore , the invention relates to a method , an inductive power transfer system , and the vehicle for detecting an extraneous object .
However , this is not a feasible solution since image generation would require training examples of these rare occurrences to begin with . Another solution is to generate all possible occlusions and deformations and train object detectors from them . However , since the space of deformations and occlusions is huge , this is not a scalable solution .
Simplex algorithm has been successfully used over the years to deliver self-optimization to systems requiring maximum output . This work proposes that integrating Simplex algorithm into Condor 's resource management framework can instill self-optimizing capabilities in the HTC . The results not only improve the performance with a higher throughput , but also lead to the greater user satisfaction .
Natural and expressive body motions allow the user to specify the parameters which define the curves and surfaces . Results are demonstrated using the HTC Vive VR system .
The co-acquisition of EEG in a virtual environment ( VE ) would give researchers and clinicians the opportunity to acquire EEG data with millisecond-level temporal resolution while participants performed VE activities . This study integrated Advanced Brain Monitoring ’s ( ABM ) X-24 t EEG hardware with the HTC Vive VR headset and investigated EEG differences in tasks delivered in two modalities : VE and a desktop computer .
We have named the extension of the BOINC software infrastructure as BOINC@TACC , and are using it to route * qualified * HTC jobs from the supercomputers at the Texas Advanced Computing Center ( TACC ) to not only the typically volunteered devices but also to the cloud computing resources such as Jetstream and Chameleon . BOINC@TACC can be extremely useful for those researchers/scholars who are running low on allocations of compute-cycles on the supercomputers , or are interested in reducing the turnaround time of their HTC jobs when the supercomputers are over-subscribed . We have also developed a web-application for TACC users so that , through the convenience of their web-browser , they can submit their HTC jobs for running on the resources volunteered by the community .
A Glove for Illusionary Touch Feedback when Grasping Virtual Objects . When touching virtual objects , we lack haptic feedback using commonly and commercially available VR controllers , such as the HTC Vive Controller or Oculus Touch . Haptic Illusion Glove is a wearable device used to create the haptic illusion when grasping objects .
Navigation is common in VR environments . The traditional game controller for consoles is still a common choice but only now companies are just beginning to introduce new concepts ( for example the HTC Vive Controller ) . In this research we explore the development of an alternative input controller to support users ' navigation activities .
To address this , Queensland Genomics commissioned a pilot program for the establishment of a local Australian SRA Cache . Utilizing the hosting capabilities of the NeCTAR Research Cloud , QRIScloud 's HTC infrastructure and the MeDiCI data fabric as a storage solution , and the software stack of Cromwell for workflow management , PostgreSQL database for sample and job metadata , and a coordinator Python Flask application , a local cache of seventeen bacterial species was established . Furthermore , the workflow capabilities of Cromwell were leveraged to provide analysis solutions for cached sample data , including quality control and taxonomic profiling , and individual and multiple sample analysis .
Purpose : To utilize a High Throughput Computing ( HTC ) system to provide distributed computing on a network of computer workstations for a metaheuristic , Nested Partitions ( NP ) , for beam angle selection ( BAS ) in IMRT.Method and Materials : An important feature of the NP approach is that its partition/sampling processes are naturally parallelized , allowing the integration of this powerful optimization methodology with high‐performance distributed environments such as the Condor system . Condor is a specialized workload management system that can used on existing computer networks for compute‐intensive jobs , allowing the submission of many jobs at the same time , and performing these jobs by locating and utilizing idle computers in the network .
The CNC lathe is elaborated on the functional module and structural module . A CNC machine tool company ’s HTC Series CNC lathes modular design is illustrated as an example .
High Throughput Computing ( HTC ) environments strive " to provide large amounts of processing capacity to customers over long periods of time by exploiting existing resources on the network " according to Basney and Livny [ 1 ] . A single Blue Gene/L rack can provide thousands of CPU resources into HTC environments .
The results obtained are compared with the corresponding flat categorization techniques applied to leaf level categories of the considered hierarchies . While kNN-based flat text categorization produced slightly better results than kNN-based HTC on the largest TanCorpHier and 20NGHier datasets , SVM-based HTC results do not considerably differ from the corresponding flat techniques , due to shallow hierarchies ; still , they outperform both kNN-based flat and hierarchical categorization on all corpora except the smallest Reuters-Hier1 and Reuters-Hier2 datasets . Formal evaluation confirmed that the proposed techniques obtained state-of-the-art results .
However , this method is inefficient and of low precision due to limited personal sensing and experience . This article proposes a hybrid cascade approach with an improved level set and a random forest to locate and automatically classify defective joints in microelectronics packaging . A grayscale variance-based pixel neighborhood is introduced to accurately locate the joint , and an improved gray projection is used to remove the redundant nonjoint area .
Using a motivating case study , the general problem of resource usage maximization is addressed . We show how the issues can be leveraged by employing a hybrid task model for anytime algorithms , which is supported by recent advances in fixed priority scheduling for imprecise computation . In particular , with a novel scheduling scheme based on Dual Priority Scheduling , hard tasks are guaranteed by schedulability analysis and scheduled in favor of optional and anytime components which are executed whenever possible for enhancing system utility .
As a consequence , the system behaves in a non-periodic manner , and the real performance is degraded with respect to the expected response . This paper proposes a hybrid task model to reduce the impact of the scheduling on the control performance . For a large batch of typical plants , we analyze how sensitive the control system is to jitter when the sampling rate is slow or fast compared to the bandwidth of the system
In exploring a more effective approach , we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation . In this work , we propose a new framework , Hybrid Task Cascade ( HTC ) , which differs in two important aspects : ( 1 ) instead of performing cascaded refinement on these two tasks separately , it interweaves them for a joint multi-stage processing ; ( 2 ) it adopts a fully convolutional branch to provide spatial context , which can help distinguishing hard foreground from cluttered background . Overall , this framework can learn more discriminative features progressively while integrating complementary features together in each stage .
As lung cancer continues to threaten human health , Computer-Aided Diagnostic ( CAD ) plays an increasingly significant role in lung cancer diagnosis , and convolutional neural networks ( CNNs ) have shown the outstanding performance in image segmentation . In this work , Hybrid Task Cascade ( HTC ) is used to segment lung nodules that are difficult to find in CT images . Considering that lung nodules are usually quite small , this study integrates Feature Pyramid Network ( FPN ) into ResNet-50 to make full use of multi-scale feature and improve the segmentation accuracy of small target nodules .
A multisensor scheduling algorithm based on the hybrid task decomposition and modified binary particle swarm optimization ( MBPSO ) is proposed . Firstly , aiming at the complex relationship between sensor resources and tasks , a hybrid task decomposition method is presented , and the resource scheduling problem is decomposed into subtasks ; then the sensor resource scheduling problem is changed into the match problem of sensors and subtasks . Secondly , the resource match optimization model based on the sensor resources and tasks is established , which considers several factors , such as the target priority , detecting benefit , handover times , and resource load .
The invention discloses a project management application interface based on a hybrid task board and a critical path method ( CPM ) . A seamless interaction between a task board and an underlying project management application interface based on the critical path method ( CPM ) is provided , where users are enabled to move tasks on an user interface of a task board across columns or summary task ( " story " ) rows .
We aim to develop an autonomous navigation pipeline by a combination of both approaches . Using DeepLabv3 + for Semantic Segmentation we achieve an mIOU of 68.58 % , and using Hybrid Task Cascade we achieve an Average Recall of 56.5 % .
Instance Segmentation is an interesting yet challenging task in computer vision . In this paper , we conduct a series of refinements with the Hybrid Task Cascade ( HTC ) Network , and empirically evaluate their impact on the final model performance through ablation studies . By taking all the refinements , we achieve 0.47 on the COCO test-dev dataset and 0.47 on the COCO test-challenge dataset .
In this paper , we propose a novel parallelism scheme , called ‘ Hybrid partitioning ’ , for mapping a H.264 high definition ( HD ) decoder onto REMUS-II , a CGRA systemon-chip ( SoC ) . Combining good features of data partitioning and task partitioning , our methodology mainly consists of three levels from top to bottom : ( 1 ) hybrid task pipeline based on slice and macroblock ( MB ) level ; ( 2 ) MB row-level data parallelism ; ( 3 ) sub-MB level parallelism method . Further , on the sub-MB level , we propose a few mapping strategies such as hybrid variable block size motion compensation ( Hybrid VBSMC ) for MC , 2D-wave for intra 4 × 4 , parallel processing order for deblocking .
The traditional scheduling algorithms for real-time system are not suitable for these characteristics . A new hybrid-tasks system model is presented in this paper and the heuristic best-first search ( BFS ) algorithm is introduced to schedule tasks of this model . The BFS algorithm searches the state node matching all constraint conditions and minimizing ( maximizing ) the value of heuristic evaluation function in the state-space tree , so it solves the schedule problem with an optimal solution .
Its effectiveness is validated through extensive experiments on multiple image restoration tasks : image denoising , demosaicing , compression artifact reduction , and super resolution . Without any bells and whistles , our PANet ( pyramid attention module with simple network backbones ) can produce state-of-the-art results with superior accuracy and visual quality .
This will make the attention prediction for a particular person not accurate enough . In this work , we present the novel idea of personalized attention prediction and develop Personalized Attention Network ( PANet ) , a convolutional network that predicts saliency in images with personal preference . The model consists of two streams which share common feature extraction layers , and one stream is responsible for saliency prediction , while the other is adapted from the detection model and used to fit user preference .
Ship Detection Based on YOLOv2 for SAR Imagery Learning Intelligent Dialogs for Bounding Box Annotation Effect of Annotation Errors on Drone Detection with YOLOv3 Object detection device, object detection method, and object detection program Segmentasi objek jantung pada citra X-Ray thorax menggunakan metode template matching Object detecting method, object detecting apparatus and an image pickup apparatus Object detection system and method for detecting foreign objects in an inductive power transfer system A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection Self-optimizing resource management in High Throughput Computing Systems Interactive swept surface modeling in virtual reality with motion-tracked controllers EEG Acquisition During the VR Administration of Resting State, Attention, and Image Recognition Tasks: A Feasibility Study Scalable Software Infrastructure for Integrating Supercomputing with Volunteer Computing and Cloud Computing Haptic Illusion Glove: A Glove for Illusionary Touch Feedback when Grasping Virtual Objects VRMController: an input device for navigation activities in virtual reality environments SRA Down Under: Cache and Analysis Platform for Infectious Disease. SU‐FF‐T‐242: High‐Throughput Computing in Condor Using a Nested Partitions Framework for IMRT Beam and Dose Optimization Study on Module Partition Method for CNC Lathe Design Asynchronous task dispatch for high throughput computing for the eServer IBM Blue Gene® Supercomputer Hierarchical vs. flat n-gram-based text categorization: Can we do better? Cascaded Approach to Defect Location and Classification in Microelectronic Bonded Joints: Improved Level Set and Random Forest Supporting Deliberative Real-Time AI Systems: A Fixed Priority Scheduling Approach Jitter Evaluation of Real-Time Control Systems Hybrid Task Cascade for Instance Segmentation Modified Hybrid Task Cascade for Lung Nodules Segmentation in CT Images with Guided Anchoring Multisensors Cooperative Detection Task Scheduling Algorithm Based on Hybrid Task Decomposition and MBPSO Hybrid task board and critical path method based project management application interface Complete Scene Parsing for Autonomous Navigation in Unstructured Environments Towards Good Practices for Instance Segmentation Parallelism Analysis of H.264 Decoder and Realization on a Coarse-Grained Reconfigurable SoC A heuristic search algorithm based on hybrid-tasks system model for scheduling tasks of NC system Pyramid Attention Networks for Image Restoration Where's YOUR focus: Personalized Attention
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CVPR 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) SBIM@Expressive HCI (34) Mensch &amp; Computer VRCAI CSISE (1) IPDPS ECRTS RTCSA ICMLC
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