the RGB colour space is prominent as a colour representation and display scheme, although a number of other colour spaces have been developed over the years each with its own advantages and shortcomings with regard to...
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ISBN:
(纸本)3540263063
the RGB colour space is prominent as a colour representation and display scheme, although a number of other colour spaces have been developed over the years each with its own advantages and shortcomings with regard to its usefulness for colour/texture recognition. However, the recent advent of multiple classifier systems provides the unique opportunity to exploit the diverse information encapsulated in the different colour representations in a systematic fashion. In this paper we propose the use of classifier combination schemes which utilise information from different colour domains. We subsequently use suitable measures to investigate the diversity of the information infused by the different colour spaces. Experiments with two 40-class colour/texture datasets show the benefit of our multiple classifier approach, and reveal the existence of strong correlations between the accuracy achieved and the diversity measures. Finally, we illustrate, using quadratic regression, that there is significant scope to build and explore further (potentially causal) models of the observed relations between ensemble performance and diversity metrics. Our results point towards the use of diversity along with other statistical measures as possible predictors of the ensemble behaviour.
the proceedings contain 22 papers. the topics discussed include: tensor-patch-based discriminative marginalized least squares regression for membranous nephropathy hyperspectral data classification;quality perception ...
ISBN:
(纸本)9781510646896
the proceedings contain 22 papers. the topics discussed include: tensor-patch-based discriminative marginalized least squares regression for membranous nephropathy hyperspectral data classification;quality perception and discrimination thresholds in quantized triangle meshes;image classification based on self-attention convolutional neural network;flight trajectory prediction of point-conditioned time and altitudes;the sentiment analysis model with multi-head self-attention;improved error-correcting from extracted handwritings in Chinese;the generalized covariance union fusion approach for distributed sensors with different fields of view;grading and profiling for export quality coffee beans using red green blue analysis, blob analysis, Hu’s moments and back-propagation neural network;and contrastive learning for solar cell micro-crack detection.
the proceedings contain 24 papers. the special focus in this conference is on internationalworkshop on PRedictive Intelligence In MEdicine. the topics include: Deep Survival Analysis in60;Multiple Sclerosis;Federa...
ISBN:
(纸本)9783031460043
the proceedings contain 24 papers. the special focus in this conference is on internationalworkshop on PRedictive Intelligence In MEdicine. the topics include: Deep Survival Analysis in Multiple Sclerosis;Federated Multi-trajectory GNNs Under Data Limitations for Baby Brain Connectivity Forecasting;learning Task-Specific Morphological Representation for Pyramidal Cells via Mutual Information Minimization;dermoSegDiff: A Boundary-Aware Segmentation Diffusion Model for Skin Lesion Delineation;self-supervised Few-Shot Learning for Semantic Segmentation: An Annotation-Free Approach;imputing Brain Measurements Across Data Sets via graph Neural Networks;multi-input Vision Transformer with Similarity Matching;Federated Multi-domain GNN Network for Brain Multigraph Generation;an Ambient Intelligence-based Approach for Longitudinal Monitoring of Verbal and Vocal Depression Symptoms;Dynamic Depth-Supervised NeRF for Multi-view RGB-D Operating Room Videos;template-based Federated Multiview Domain Alignment for Predicting Heterogeneous Brain graph Evolution Trajectories from Baseline;Revisiting N-CNN for Clinical Practice;video-based Hand Pose Estimation for Remote Assessment of Bradykinesia in Parkinson’s Disease;more than Meets the Eye: Analyzing Anesthesiologists’ Visual Attention in the Operating Room Using Deep Learning Models;pose2Gait: Extracting Gait Features from Monocular Video of Individuals with Dementia;self-Supervised Learning of Gait-based Biomarkers;feature-based Transformer with Incomplete Multimodal Brain Images for Diagnosis of Neurodegenerative Diseases;repNet for Quantifying the Reproducibility of graph Neural Networks in Multiview Brain Connectivity Biomarker Discovery;synthA1c: Towards Clinically Interpretable Patient representations for Diabetes Risk Stratification;Confounding Factors Mitigation in Brain Age Prediction Using MRI with Deformation Fields;self-supervised Landmark Learning with Deformation Reconstruction and Cross-Subject Consistency Object
the ability to associate images is the basis for learning relationships involving vision, hearing, tactile sensation, and kinetic motion. A new architecture is described that has only local, recurrent connections, but...
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ISBN:
(纸本)9781424413270
the ability to associate images is the basis for learning relationships involving vision, hearing, tactile sensation, and kinetic motion. A new architecture is described that has only local, recurrent connections, but can directly form global image associations. this architecture has many similarities to the structure Of the neocortex, including the division into Brodmann areas, the distinct internal and external lamina, and the pattern of neuron interconnection. Analogous to the bits in an SR flip-flop, two arbitrary images can hold each other in place in an association processor and thereby form a short-term image memory. Overlay masks can focus attention on specific image regions. Spherically symmetric wavelets, identical to those found in the receptive fields of the retina, enable efficient image computations. Stability and noise reduction in reciprocal continuous wavelet transform representations can be achieved using an orthogonal projection based on the reproducing kernel.
Schizophrenia is a severe neural disorder that affects around 24 million individuals globally. In this context, Electroencephalogram (EEG) signal-based analysis and automated screening for Schizophrenia (SZ) have gain...
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ISBN:
(纸本)9783031451690;9783031451706
Schizophrenia is a severe neural disorder that affects around 24 million individuals globally. In this context, Electroencephalogram (EEG) signal-based analysis and automated screening for Schizophrenia (SZ) have gained importance. EEG-based Schizophrenia (SZ-EEG) analysis is traditionally done by extracting features from individual EEG electrodes' signals and utilizing these features for Machine Learning (ML)-based classification models. However, these methods do not exploit the Schizophrenia-induced alteration of functional brain connectivity between neuronal masses. the present study proposes a novel graph-signal (GS) representation of multi-channel SZ-EEG data that fully encompasses local brain activation and global interactions between brain regions. the proposed GS representation comprises the underlying connectivity network and the signal values on the network's vertices. Here, the EEG signal's entropy at each electrode is used as GS values, and a phase lag index (PLI)-based functional connectivity measure is utilized as the underlying connectivity network. Further, these connectivity-informed GSs are transformed to the spectral domain by the graph Fourier Transform (GFT), and relevant discriminatory features are extracted from them using the graph Signal Processing (GSP) technique. those features are fed to basic ML-based classification models. the efficacy of the proposed PLI-GSP framework is validated using a publicly available SZ-EEG dataset, and a 99.77% classification accuracy is achieved that outperforms most of the state-of-the-art models.
Examination of advantages and disadvantages of some not commonly used time-frequency representations of vibration signals has been the aim of the paper. the study has been mainly devoted to Wigner-Ville decomposition ...
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ISBN:
(纸本)0819454362
Examination of advantages and disadvantages of some not commonly used time-frequency representations of vibration signals has been the aim of the paper. the study has been mainly devoted to Wigner-Ville decomposition as well as instantaneous amplitude and frequency. these representations have been examined from the patternrecognition point of view. the Wigner-Ville decomposition was compared with short time Fourier transform taking into account its classification power. In the case of instantaneous amplitude and frequency representation a new method of feature extraction followed by classification using optimal neural classifier has been proposed. Results have been illustrated using a data set of signals measured by a laser vibrometer. they proved that the method proposed in the paper could be used for very fast classification based on vibration signals measured in transient state.
Chinese Named entity recognition is one of the most important tasks in NLP. Two kinds of Challenges we confront are how to improve the performance in one corpus and keep its performance in another different corpus. We...
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graphs are a powerful and versatile tool useful for representing patterns in various subfields of science and engineering. In many applications, for example, in patternrecognition and computer vision, it is required ...
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this paper proposes a general architecture to extract knowledge from graphic documents. the architecture consists of three major components. First, a set of modules able to extract descriptors that, combined with doma...
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ISBN:
(纸本)3540230602
this paper proposes a general architecture to extract knowledge from graphic documents. the architecture consists of three major components. First, a set of modules able to extract descriptors that, combined with domain-dependent knowledge and recognition strategies, allow to interpret a given graphical document. Second, a representation model based on a graph structure that allows to hierarchically represent the information of the document at different abstraction levels. Finally, the third component implements a calligraphic interface that allows the feedback between the user and the system. the second part of the paper describes an application scenario of the above platform. the scenario is a system for the interpretation of sketches of architectural plans. this is a tool to convert sketches to a CAD representation or to edit a given plan by a sketchy interface. the application scenario combines different symbol recognition algorithms stated in terms of document descriptors to extract building elements such as doors, windows, walls and furniture.
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