A new Bayesian model improves dynamic texture segmentation.
problem Automatic selection of DTs in video sequences.
method Joint Dirichlet process mixture and GDTM approach with VBEM and RTSS.
result The proposed algorithm outperforms previous methods in efficiency and accuracy.
Algorithm segments glandular structures in colon histology images for cancer grading.
problem Manual gland segmentation is time-consuming and risky for patients.
method Local intensity and texture features, Random Forest classifier, multilevel approach.
result Fast, accurate automatic gland segmentation for clinical use.
This work improves texture segmentation by automatically tuning hyperparameters for Total-Variation.
problem The challenge is to automatically select hyperparameters for Total-Variation texture segmentation.
method The approach involves extending Stein's unbiased gradient estimator to handle correlated Gaussian noise, leading to an automatic tuning method.
result The method provides an automatic way to select hyperparameters for Total-Variation texture segmentation.
A framework uses complex networks for image segmentation.
problem Over-segmentation in image segmentation.
method Initial segmentation, adaptive network construction, community detection.
result The proposed framework improves segmentation performance.
Enhanced rotation prediction improves SSL models by capturing both shape and texture information.
problem Rotation prediction misses texture information, limiting model performance.
method Introduces image enhanced rotation prediction (IE-Rot) that combines rotation and image enhancement tasks.
result IE-Rot models outperform Rotation on various benchmarks.
Improves instance segmentation accuracy by integrating low-level features.
problem Low-level features enhance instance localization accuracy.
method Integrates low-level features into all layers of a feature pyramid network.
result Consistent improvement in precision on COCO Dataset.
Deep learning improves plaque prediction for coronary artery health.
problem Predicting coronary artery plaque health from CT scans.
method 3D RCNN, 2D multi-view ensemble, 2.5D approach.
result Improved prediction accuracy for revascularization decisions.
Lazy labels enable deep learning for microscopy segmentation without full annotation.
problem Lack of pixel-wise annotations limits fully supervised learning for bioimage segmentation.
method Lazy labels combined with coarse labels for training a deep neural network.
result Model achieves accurate segmentation with minimal pixel-wise annotations.
This paper advocates a novel framework for segmenting a dataset in a Riemannian manifold M into clusters lying around low-dimensional submanifolds of M. Important examples of M, for which the proposed clustering algorithm is computationally efficient, are the sphere, the set of positive definite matrices, and the…
DeepIrisNet2 learns iris codes without iris segmentation or normalization.
problem Iris recognition under non-ideal conditions.
method Deep learning framework with spatial transformer layers and dual CNN segmentation.
result Significantly improves iris recognition performance.
Quantitative CT predicts ILD patterns and prognosis.
problem Diagnosing and predicting prognosis of fibrosing ILD patterns.
method High-resolution CT texture features, TM model for classification and survival analysis.
result TM model outperforms histogram-based model in distinguishing UIP from non-UIP patterns and allows for survival group partitioning.
Enhances uncertainty estimation in medical image segmentation.
problem Frequency-related noise in medical imaging leads to biased uncertainty estimates.
method Extends MC-Dropout to the frequency domain for better uncertainty estimation.
result MC-Frequency Dropout improves calibration and uncertainty in semantic segmentation.
A machine learning method predicts rock permeability from 3D images.
problem Efficiently predict permeability of heterogeneous rocks for planetary and robotic applications.
method Machine learning guided 3D properties recognition of rock morphology from 3D micro CT and MRI images.
result The morphology decoder method accurately predicts permeability from 3D images.
Generative model creates user-specified textures from datasets.
problem Creating detailed textures from raw data.
method Generative adversarial networks with user control and adversarial loss.
result Model generates descriptive texture manifolds and 3D textures.
Paper proposes TBSD for efficient anomaly detection in textured images.
problem Challenges in anomaly detection for textured images, especially in manufacturing systems.
method Texture basis integrated smooth decomposition (TBSD) approach.
result TBSD surpasses benchmarks with less misidentification and superior performance.
A new histogram layer improves texture analysis performance.
problem Extracting features for texture analysis from local spatial regions.
method Directly computes local spatial distribution of features during backpropagation.
result Improves performance on three material/texture datasets.
Generative adversarial networks (GANs) are a recent approach to train generative models of data, which have been shown to work particularly well on image data. In the current paper we introduce a new model for texture synthesis based on GAN learning. By extending the input noise distribution space from a single vector …
Framework automates microstructure image analysis for materials science.
problem Complex microstructures in materials require automated analysis.
method Combines unsupervised and supervised learning for classification and segmentation.
result Framework can automatically segment and classify micrographs.
ReDO extracts object masks from images without annotations.
problem Solving object segmentation with supervised learning requires large datasets and pixel-level annotations.
method ReDO uses an adversarial architecture where a generator redraws objects in images without annotations.
result ReDO successfully extracts high-quality object masks from various datasets.
This paper presents a novel framework for generating texture mosaics with convolutional neural networks. Our method is called GANosaic and performs optimization in the latent noise space of a generative texture model, which allows the transformation of a content image into a mosaic exhibiting the visual properties of t…
This paper introduces a novel approach to texture synthesis based on generative adversarial networks (GAN) (Goodfellow et al., 2014). We extend the structure of the input noise distribution by constructing tensors with different types of dimensions. We call this technique Periodic Spatial GAN (PSGAN). The PSGAN has sev…
TMTF improves time series visualization by separating dynamic regimes.
problem Misleading global transition matrix in time series analysis.
method Temporal chunking, local transition matrices, and image assembly.
result Temporal segmentation reveals distinct transition dynamics.
Paper learns dynamic generator models for video sequences.
problem Modeling spatial-temporal processes like dynamic textures and actions.
method Alternating back-propagation through time algorithm to learn latent state vectors and generator model.
result Trains realistic models for dynamic textures and actions.
GOTEX synthesizes textures by optimizing feature distributions using optimal transport.
problem Texture synthesis with control over feature distributions.
method Optimal transport for statistical feature distribution comparison, semi-dual formulation, minimax optimization.
result GOTEX produces high-quality textures with various features, outperforming state-of-the-art methods.
Improves CNN robustness by reducing texture bias.
problem CNNs' reliance on local texture over global shape.
method Inspired by human vision, InfoDrop decorrelates model output from local texture.
result Enhanced robustness across various scenarios.
We apply the spike-and-slab Restricted Boltzmann Machine (ssRBM) to texture modeling. The ssRBM with tiled-convolution weight sharing (TssRBM) achieves or surpasses the state-of-the-art on texture synthesis and inpainting by parametric models. We also develop a novel RBM model with a spike-and-slab visible layer and bi…
Few-shot image classification is improved by correcting CNNs' texture bias.
problem Few-shot image classification performance is hindered by CNNs' texture bias.
method Corrected CNNs' texture bias using a simpler method than state-of-the-art approaches.
result State-of-the-art performance on miniImageNet task achieved.
Improved texture synthesis using wavelet-based statistics with rectifier non-linearity.
problem Improving texture synthesis quality using wavelet representations.
method Proposes a family of statistics based on non-linear wavelet representations with a generalized rectifier non-linearity.
result Significantly improves visual quality of texture synthesis compared to classical wavelet-based models.
Currently, Markov-Gibbs random field (MGRF) image models which include high-order interactions are almost always built by modelling responses of a stack of local linear filters. Actual interaction structure is specified implicitly by the filter coefficients. In contrast, we learn an explicit high-order MGRF structure b…
Macrocanonical models generate textures matching input features.
problem Generating textures that match specific features.
method Use Gibbs measures and minimize a convex function for sampling.
result Macrocanonical models can be applied to real-valued images under certain conditions.
A new liquid crystalline texture is proposed using gnomonic projection of the Hopf fibration.
problem Creating bend-free textures in flat space from 3-sphere Hopf fibration.
method Geodesic-preserving gnomonic projection of the Hopf fibration.
result A new liquid crystalline phase with only splay and twist.
Model for directed synthesis of audio textures using multi-scale RNNs.
problem Challenges in modeling complex audio textures with traditional methods.
method Combining multi-scale RNNs with a conditioning strategy for user-directed synthesis.
result Demonstrated improved performance on various audio texture datasets.
Using movement primitive libraries is an effective means to enable robots to solve more complex tasks. In order to build these movement libraries, current algorithms require a prior segmentation of the demonstration trajectories. A promising approach is to model the trajectory as being generated by a set of Switching L…
Proposes a new model for better speech segmentation.
problem Improving speech segmentation accuracy.
method Integrates recurrent explicit duration variables into rSLDS and uses Pólya-gamma augmentation for inference.
result Demonstrates improved segmentation on various datasets.
Object-centric learning improves generalization and robustness in multi-object scenes.
problem Improving generalization and robustness in neural networks for scenes with multiple objects.
method Training state-of-the-art unsupervised models on multi-object datasets and evaluating segmentation metrics and downstream tasks.
result Object-centric representations are useful for downstream tasks and generally robust to most distribution shifts affecting objects, but less so for less structured shifts.
The paper proposes a mixture model with segmentation for heterogeneous functional data.
problem Heterogeneity in time and population for functional data.
method Mixture model with segmentation of time, maximum likelihood estimator, EM algorithm with dynamic programming.
result The method is consistent and identifiable, and illustrated on simulated and real datasets.
The paper classifies soil texture using 1D CNNs on hyperspectral data.
problem Classifying soil texture from hyperspectral data.
method Developed and implemented three 1D CNNs: LucasCNN, LucasResNet, and LucasCoordConv.
result LucasCoordConv achieves the best performance in accuracy.
System classifies lung CT scans into normal or COVID-19 using machine learning.
problem Detecting COVID-19 infection in lung CT scans.
method MLS with CBA+KE thresholding, feature extraction, selection, and classification.
result SVM with FFV achieved 89.80% detection accuracy.
Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on…
Machine learning and complexity-entropy methods estimate liquid crystal properties from textures.
problem Extracting physical properties from liquid crystal textures.
method Combining permutation entropy, statistical complexity, and machine learning.
result Significant precision in predicting physical properties of liquid crystals.
Segmental structure is a common pattern in many types of sequences such as phrases in human languages. In this paper, we present a probabilistic model for sequences via their segmentations. The probability of a segmented sequence is calculated as the product of the probabilities of all its segments, where each segment …
Single network with segmented training prevents forgetting in dynamic systems.
problem Catastrophic forgetting in continual learning for dynamic systems.
method Single Net with Progressive Segmented Training (PST). Model parameters are divided into frozen and secondary groups.
result Achieves state-of-the-art accuracy in single-head evaluation on CIFAR-10 and CIFAR-100 datasets.
Deep learning and radiomics methods assess coronary artery plaque from CT scans.
problem Improving patient management and clinical outcomes by assessing coronary artery plaque.
method Three machine learning approaches: radiomics, deep learning, and fusion of both.
result Methods achieve AUC scores of 0.84-0.88, comparable to FFR measurements.
Stable deep models learn dynamical systems with formal stability guarantees.
problem Difficulties in making formal claims about stability of deep network dynamics models.
method Jointly learning a dynamics model and Lyapunov function to ensure non-expansiveness.
result Proposes an approach for stable deep learning of dynamical systems.
Optimal switching regret for all segmentations in online convex optimisation.
problem Non-stationary online convex optimisation problems.
method Developed an efficient algorithm to achieve optimal switching regret on every possible segmentation.
result Achieved asymptotically optimal switching regret on every possible segmentation simultaneously.
This study improves user segmentation for online news recommendation systems.
problem Challenges in building modern recommender systems due to dynamic environments and data sparsity.
method Trend-responsive unsupervised user segmentation using multi-armed bandits.
result Significant improvements in online A/B tests compared to global-optimization algorithms.
Single image super-resolution is the task of inferring a high-resolution image from a single low-resolution input. Traditionally, the performance of algorithms for this task is measured using pixel-wise reconstruction measures such as peak signal-to-noise ratio (PSNR) which have been shown to correlate poorly with the …
New method detects changes by maximizing cross-entropy, outperforming existing techniques.
problem Detecting abrupt changes in data streams without labeled examples.
method Maximizes cross-entropy between segments to find change points, using dynamic programming.
result Outperforms three state-of-the-art approaches on challenging datasets.