One of the most basic tasks for any autonomous mobile robot is that of safely navigating from one point to another (e.g. service robots should be able to find their way in different kinds of environments). Typically, ...
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ISBN:
(纸本)9783319192642;9783319192635
One of the most basic tasks for any autonomous mobile robot is that of safely navigating from one point to another (e.g. service robots should be able to find their way in different kinds of environments). Typically, vision is used to find landmarks in that environment to help the robot localise itself reliably. However, some environments may lack of these landmarks and the robot would need to be able to find its way in a featureless environment. this paper presents a topological vision-based approach for navigating through a featureless maze-like environment using a NAO humanoid robot, where all processing is performed by the robot's embedded computer. We show how our approach allows the robot to reliably navigate in this kind of environment in real-time.
the proceedings contain 34 papers. the special focus in this conference is on patternrecognition, Artificial Intelligent Techniques, Image Processing, Robotics and computervision. the topics include: Recommendation ...
ISBN:
(纸本)9783319192635
the proceedings contain 34 papers. the special focus in this conference is on patternrecognition, Artificial Intelligent Techniques, Image Processing, Robotics and computervision. the topics include: Recommendation of process discovery algorithms through event log classification;a new method based on graph transformation for FAS mining in multi-graph collections;classification of hand movements from non-invasive brain signals using lattice neural networks with dendritic processing;a different approach for pruning micro-clusters in data stream clustering;computing constructs by using typical testor algorithms;multitask reinforcement learning in nondeterministic environments;prototype selection for graph embedding using instance selection;correlation of resampling methods for contrast pattern based classifiers;boosting the permutation based index for proximity searching;similarity analysis of archaeological potsherds using 3D surfaces;automatic detection of clouds from aerial photographs of snowy volcanoes;a comparative study of robust segmentation algorithms for iris verification system of high reliability;vision-based humanoid robot navigation in a featureless environment;evaluation of local descriptors for vision-based localization of humanoid robots;sampled weighted min-hashing for large-scale topic mining;patterns used to identify relations in corpus using formal concept analysis;improving information retrieval through a global term weighting scheme;sentiment groups as features of a classification model using a Spanish sentiment lexicon;modified binary inertial particle swarm optimization for gene selection in DNA microarray data;encoding polysomnographic signals into spike firing rate for sleep staging and patrolling routes optimization using ant colonies.
In this paper, we address the problem of appearance-based localization of humanoid robots in the context of robot navigation using a visual memory. this problem consists in determining the most similar image belonging...
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ISBN:
(纸本)9783319192642;9783319192635
In this paper, we address the problem of appearance-based localization of humanoid robots in the context of robot navigation using a visual memory. this problem consists in determining the most similar image belonging to a previously acquired set of key images (visual memory) to the current view of the monocular camera carried by the robot. the robot is initially kidnapped and the current image has to be compared withthe visual memory. We tackle the problem by using a hierarchical visual bag of words approach. the main contribution of the paper is a comparative evaluation of local descriptors to represent the images. Real-valued, binary and color descriptors are compared using real datasets captured by a small-size humanoid robot. A specific visual vocabulary is proposed to deal with issues generated by the humanoid locomotion: blurring and rotation around the optical axis.
the estimation of multiple homographies between two piecewise planar views of a rigid scene is often assumed to be a solved problem. We show that contrary to popular opinion various crucial aspects of the task have no...
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Both parametric and non-parametric approaches have demonstrated encouraging performances in the human parsing task, namely segmenting a human image into several semantic regions (e.g., hat, bag, left arm, face). In th...
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ISBN:
(纸本)9781467369640
Both parametric and non-parametric approaches have demonstrated encouraging performances in the human parsing task, namely segmenting a human image into several semantic regions (e.g., hat, bag, left arm, face). In this work, we aim to develop a new solution withthe advantages of both methodologies, namely supervision from annotated data and the flexibility to use newly annotated (possibly uncommon) images, and present a quasi-parametric human parsing model. Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict the matching confidence and displacements of the best matched region in the testing image for a particular semantic region in one KNN image. Given a testing image, we first retrieve its KNN images from the annotated/manually-parsed human image corpus. then each semantic region in each KNN image is matched with confidence to the testing image using M-CNN, and the matched regions from all KNN images are further fused, followed by a superpixel smoothing procedure to obtain the ultimate human parsing result. the M-CNN differs from the classic CNN [12] in that the tailored cross image matching filters are introduced to characterize the matching between the testing image and the semantic region of a KNN image. the cross image matching filters are defined at different convolutional layers, each aiming to capture a particular range of displacements. Comprehensive evaluations over a large dataset with 7,700 annotated human images well demonstrate the significant performance gain from the quasi-parametric model over the state-of-the-arts [29, 30], for the human parsing task.
this book constitutes the refereed proceedings of the 10thchineseconference on Advances in Image and Graphics Technologies, IGTA 2015, held in Beijing, China, in June 2015. the 50 papers presented were carefully rev...
ISBN:
(数字)9783662477915
ISBN:
(纸本)9783662477908;9783662477915
this book constitutes the refereed proceedings of the 10thchineseconference on Advances in Image and Graphics Technologies, IGTA 2015, held in Beijing, China, in June 2015. the 50 papers presented were carefully reviewed and selected from 138 submissions. they provide a forum for sharing new aspects of the progresses in the areas of image processing technology, image analysis und understanding, computervision and patternrecognition, big data mining, computer graphics and VR, image technology application.
In order to achieve the accurate and sensitive detection of flaw on the panel from the reuse cars, a machine vision measurement method based on pattern-light was applied by using an industrial camera. this method was ...
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the proceedings contain 92 papers. the special focus in this conference is on patternrecognition, Machine Learning, computervision, Signal Processing and Medical Image. the topics include: Spatiotemporal stacked seq...
ISBN:
(纸本)9783319193892
the proceedings contain 92 papers. the special focus in this conference is on patternrecognition, Machine Learning, computervision, Signal Processing and Medical Image. the topics include: Spatiotemporal stacked sequential learning for pedestrian detection;social signaling descriptor for group behaviour analysis;a new smoothing method for lexicon-based handwritten text keyword spotting;empirical evaluation of different feature representations for social circles detection;on the impact of distance metrics in instance-based learning algorithms;object discovery using CNN features in egocentric videos;prototype generation on structural data using dissimilarity space representation;human centered scene understanding based on 3D long-term tracking data;fast head pose estimation for human-computer interaction;structured light system calibration for perception in underwater tanks;scene recognition invariant to symmetrical reflections and illumination conditions in robotics;system for medical mask detection in the operating room through facial attributes;noise decomposition using polynomial approximation;color correction for image stitching by monotone cubic spline interpolation;escaping path approach with extended neighborhood for speckle noise reduction;unsupervised approximation of digital planar curves;on the modification of binarization algorithms to retain grayscale information for handwritten text recognition;improving the minimum description length inference of phrase-based translation models;a kinect-based system to assess lymphedema impairments in breast cancer patients;word-graph based applications for handwriting documents;spatial-dependent similarity metric supporting multi-atlas MRI segmentation and color detection in dermoscopy images based on scarce annotations.
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