The Hilbert map's image is discussed, showing when it's surjective.
problem Understanding when the Hilbert map is surjective.
method Analyzing the Hilbert map's properties to determine surjectivity.
result Necessary and sufficient conditions for the Hilbert map to be surjective.
Study on images and singularities of pseudoholomorphic maps.
problem Characterize images and singularities of pseudoholomorphic maps.
method Analyzes pseudoholomorphic maps in domains and targets of dimension four.
result Proves properties of images and singularities of pseudoholomorphic maps.
SR-NAM maps low-res images to multiple high-res images realistically.
problem Mapping low-resolution images to multiple high-resolution images realistically.
method SR-NAM using Non-Adversarial Mapping (NAM) technique and a degradation model.
result Realistic degradation and down-sampling of high-resolution images.
Open and discrete maps with specific branch set images are equivalent to PL branched covers.
problem Understanding the equivalence of open and discrete maps and PL branched covers.
method Demonstrated that an open and discrete map f:SnoSn with a specific branch set image is equivalent to a PL branched cover up to homeomorphism. result Open and discrete maps with a specific branch set image are equivalent to PL branched covers.
We give some results concerning the smoothness of the image of a real-analytic submanifold in complex space under the action of a finite holomorphic mapping. For instance, if the submanifold is not contained in a proper complex subvariety, we give a necessary and sufficient condition guaranteeing that its image is smoo…
NAM maps images between domains without adversarial learning, achieving better quality and stability.
problem Translating images between domains without correspondences and adversarial learning.
method NAM separates generative modeling and cross-domain mapping, using a pre-trained target model.
result Higher quality and resolution image translations with simpler and more stable training.
An ODE variational calculation shows that an image principle curvature ratio factor can raise the lower bound, 2(Image Area), on energy of a harmonic map of a surface into Rn. In certain situations, including all radially symmetry harmonic maps, equality is achieved.
New variational model preserves image contrasts and features using Weingarten map minimization.
problem Image reconstruction with preservation of contrasts and features.
method Variational model with L1 norm of Weingarten map, ADMM algorithm, gradient descent. result The proposed models preserve image contrasts and features efficiently.
SMAPGAN generates styled map tiles from remote sensing images.
problem Generating timely updated map tiles from remote sensing images is challenging.
method Semi-supervised GAN model with gradient loss and ESSI metric.
result SMAPGAN outperforms state-of-the-art methods in quality metrics and human perception.
New method for image super-resolution using MAP inference with neural networks.
problem Underdetermined image super-resolution problem with blurry outputs.
method Amortised MAP inference using convolutional neural networks.
result GAN-based approach performs best on real image data.
The study examines the topology of map germs and their images.
problem Understanding the topology of map germs and their images.
method Using the topology of the link to analyze the normal and non-normal images.
result Normal images of map germs are quotient singularities.
The Gauss Image Measure uniquely identifies dual convex bodies up to dilation.
problem Identifying dual convex bodies based on their Gauss Image Measure.
method Analyzing the Gauss Image Measure and its properties to establish the uniqueness of dual bodies.
result Dual convex bodies are equal up to a dilation on each path-connected component of the support of the measure.
BCD-Net uses identical CNN structures for image recovery in undersampled imaging.
problem Challenges in obtaining accurate images from undersampled or noisy measurements.
method Incorporates image mapping CNN into BCD signal recovery method using alternating direction method of multipliers.
result Significantly more accurate image recovery compared to existing methods.
Generative Map learns interpretable neural network maps for camera localization.
problem Creating interpretable maps for neural network-based camera localization.
method Combining generative models with Kalman filters and incorporating additional sensor information.
result Generative Map predicts images closely resembling the true scene and achieves comparable localization performance.
Non-polynomial growth harmonic maps from the complex plane to the hyperbolic space are studied. Some non-surjectivity results are obtained. Moreover, images of such harmonic maps are investigated with reference to their Hopf differentials.
Localized curvature bounds ensure harmonic maps are constant.
problem Ensuring harmonic maps are constant under localized curvature constraints.
method Localized Bochner-type rigidity theorem for harmonic maps with image-dependent curvature bounds.
result Harmonic maps are constant if minimal Ricci curvature dominates image-dependent curvature bounds.
New real algebraic maps with prescribed images and compositions are constructed locally like moment maps.
problem Constructing real algebraic maps with specific properties and compositions.
method Explicit construction of real algebraic hypersurfaces and maps with prescribed images and compositions.
result Explicit families of functions represented as compositions of constructed maps with canonical projections.
A novel method compresses point cloud attributes by folding them onto a 2D grid.
problem Efficiently compressing point cloud attributes for storage and transmission.
method Interpreting point clouds as 2D manifolds, folding onto a grid, and mapping attributes to the grid using optimized methods.
result The proposed folding-based approach achieves performance comparable to state-of-the-art codecs.
We obtain conditions on the Lee form under which a holomorphic map between almost Hermitian manifolds is a harmonic map or morphism. Then we discuss under what conditions (i) the image of a holomorphic map from a cosymplectic manifold is also cosymplectic, (ii) a holomophic map with Hermitian image defines a Hermitian …
Neural network classifies breast cancer lesions using global and local image features.
problem Classifying breast cancer lesions in medical images with high resolution and small regions of interest.
method Proposes a neural network that combines global saliency maps and local patches for pixel-level saliency maps.
result Achieves radiologist-level performance in screening mammography interpretation.
Johnson has defined a surjective homomorphism from the Torelli subgroup of the mapping class group of the surface of genus g with one boundary component to ∧3H, the third exterior product of the homology of the surface. Morita then extended Johnson's homomorphism to a homomorphism from the entire mapping cla…
Image visibility graphs map images into graphs for processing and classification.
problem Mapping image structures into graphs for processing and classification.
method Introduced image visibility graphs (IVGs) and explored their use in image processing and classification.
result IVGs encapsulate relevant image structure information and are computationally efficient.
Study on cold and freezing sets in digital images.
problem Properties of cold sets in digital images.
method Analysis of properties and relationships between cold and freezing sets.
result Examined relationships between cold and freezing sets.
ICAM creates interpretable feature attribution maps for brain images.
problem Challenges in predicting class relevance from brain images due to heterogeneity and background variation.
method A VAE-GAN framework for disentangling class relevance from background features.
result FA maps generated by ICAM outperform baseline methods and support phenotype variation exploration.
A new method improves robustness in image translation by modeling uncertainty.
problem Performance degradation in image translation models due to lack of robustness to outliers and uncertainty.
method UGAC method based on Uncertainty-aware Generalized Adaptive Cycle Consistency, modeling per-pixel residual with generalized Gaussian distribution.
result Our method exhibits stronger robustness towards unseen perturbations in test data.
Study on the parity of fold map singular points, showing non-invariance for odd-dimensional manifolds.
problem Parity of connected components of fold map singular points for odd-dimensional manifolds.
method Constructive proofs using open book decompositions, round fold maps, and allowable moves.
result Parity of connected components is not a homotopy invariant for odd-dimensional manifolds.
We show that Cannon-Thurston maps exist for degenerate free groups without parabolics, i.e. for handlebody groups. Combining these techniques with earlier work proving the existence of Cannon-Thurston maps for surface groups, we show that Cannon-Thurston maps exist for arbitrary finitely generated Kleinian groups witho…
Paper estimates intrinsic dimensionality of image representations and develops DeepMDS for lower-dimensional mapping.
problem Estimating intrinsic dimensionality of image representations.
method Developed DeepMDS, a non-linear mapping from ambient to minimal intrinsic space.
result DeepMDS reduces intrinsic dimensionality while maintaining discriminative ability.
Study curvature of direct image bundles in deformations of maps.
problem Understanding curvature in deformations of maps with fixed targets.
method Analyzing curvature of direct image bundles related to deformation data.
result Proved seminegativity for a vector bundle of relative forms.
New method maps high-dimensional image spaces using MCMC to reveal patterns.
problem Characterizing complex probability densities in high-dimensional image spaces.
method Attraction-Diffusion (AD) MCMC tool to map metastable regions.
result AD efficiently maps highly non-convex probability densities.
Framework translates unlabeled images between domains.
problem Translating unlabeled images between domains with no supervision.
method Skip-connected encoder-generator structure trained with GAN, cycle, and semantic consistency losses.
result Framework can learn semantic mappings for face images without supervised one-to-one mapping.
These lecture notes explain the geometry and discuss some of the analytical questions underlying image registration within the framework of large deformation diffeomorphic metric mapping (LDDMM) used in computational anatomy.
KCS improves parametric maps from PET images by reducing noise and variance.
problem Improving the quality of parametric maps from PET images due to noise.
method Kinetic Compressive Sensing (KCS) method based on a hierarchical Bayesian model and novel reconstruction algorithm.
result KCS produces spatially coherent images and parametric maps with lower noise and better contrast.
LSOMs analyze images with a novel architecture.
problem Image analysis and feature extraction.
method Layered Self-Organizing Maps (LSOMs) using SOM and supervised-SOM learning.
result LSOMs provide an alternative to covnets for image analysis.
Finite image of mapping class group representations proved using graph embeddings.
problem Finiteness of images of mapping class group representations in twisted Dijkgraaf-Witten theory.
method Translation of problem into graph manipulation, using TVBW representations and spherical fusion categories.
result Finiteness of images of mapping class group representations in twisted Dijkgraaf-Witten theory is proven.
In this paper we provide a classification of all Moishezon twistor spaces on the connected sum of four complex projective planes. This is given by means of the anticanonical system of the twistor spaces. In particular, we show that the anticanonical map is birational, two to one over the image, or otherwise the image o…
We prove that either the images of the mapping class groups by quantum representations are not isomorphic to higher rank lattices or else the kernels have a large number of normal generators. Further we show that the images of the mapping class groups have nontrivial 2-cohomology, at least for small levels. For this pu…
Two JSMA variants improve speed and accuracy for image classification attacks.
problem Fooling deep neural networks with adversarial images.
method Maximizing pixel values to mislead classification models.
result Two JSMA variants perform well on digit and scene datasets.
A map from a circle to a graph splits if pre-image diameters are small.
problem Can a map from a circle to a graph split if pre-image diameters are small?
method Examines maps from a circle to a graph with small pre-image diameters.
result A map splits if pre-image diameters are small enough.
SmoothGrad improves visual clarity of deep network sensitivity maps.
problem Visualizing deep network decision-making processes.
method Introducing SmoothGrad, a method to enhance gradient-based sensitivity maps.
result SmoothGrad helps in creating clearer, more interpretable sensitivity maps.
Study on Gauss images of specific minimal surfaces with finite curvature.
problem Characterizing Gauss images of minimal surfaces with finite total curvature.
method Analyzing the number and weight of omitted and totally ramified values of Gauss maps.
result Construction of new minimal surfaces with specific Gauss map properties.
Transforms improve CNNs' invariance to image transformations.
problem Current CNN models lack robustness to spatial transformations.
method Randomly transform feature maps during training to learn invariant representations.
result Significant improvements on benchmark tasks, including image recognition and retrieval.
The paper tackles ambiguous image-to-image translation by modeling a distribution of possible outputs.
problem Ambiguity in image-to-image translation where a single input can have multiple possible outputs.
method The approach involves a conditional generative model that learns to map input images to a latent vector, which is then used to generate diverse outputs.
result The method produces more diverse and realistic outputs compared to other variants.
Generates synthetic laparoscopic images for training deep neural networks.
problem Lack of large labeled data sets for laparoscopic image processing.
method Unpaired image-to-image translation to generate realistic synthetic images.
result Synthetic data set improves liver segmentation performance without manual labeling.
Shy maps preserve path-connectedness in continuous functions.
problem Understanding properties of continuous functions in topological spaces.
method Defining shy maps as continuous functions with specific path-connected inverse images.
result Every shy map onto a semilocally simply connected space induces a surjection of fundamental groups.
ZegOT uses optimal transport to zero-shot segment images with text prompts.
problem Zero-shot semantic segmentation with limited image-text alignment knowledge.
method ZegOT uses optimal transport to match multiple text prompts with frozen image embeddings.
result ZegOT achieves state-of-the-art performance in zero-shot semantic segmentation.
TUNet improves protein classification in cell images.
problem Classifying specific proteins in human cells using microscopy images.
method TUNet model incorporating segmentation maps for improved classification.
result TUNet achieves competitive performance in protein classification.
AI generates retinal blood flow maps from standard OCT images.
problem Limited expert-generated labels in medical imaging.
method Training AI on OCTA images to infer perfusion from structural OCT.
result AI-generated maps have similar fidelity to OCTA and better than expert clinicians.