New approach for camera-specific color constancy using few-shot meta-learning.
problem Domain gaps and lack of generalization across different cameras.
method Formulates color constancy as few-shot meta-learning tasks, leveraging annotated samples across different cameras.
result Significant reduction in data collection time and improved generalization to new cameras.
Back-propagation learns camera sensor design for color images.
problem Designing efficient color camera sensors for deep learning.
method Jointly learns sensor design and image reconstruction networks.
result Significant accuracy improvements over traditional Bayer pattern.
(ABRIDGED) In previous work, two platforms have been developed for testing computer-vision algorithms for robotic planetary exploration (McGuire et al. 2004b,2005; Bartolo et al. 2007). The wearable-computer platform has been tested at geological and astrobiological field sites in Spain (Rivas Vaciamadrid and Riba de S…
Our research focuses on analysing human activities according to a known behaviorist scenario, in case of noisy and high dimensional collected data. The data come from the monitoring of patients with dementia diseases by wearable cameras. We define a structural model of video recordings based on a Hidden Markov Model. N…
Industrial vehicle uses LiDAR and camera to detect and avoid restricted areas.
problem Avoiding collisions in industrial settings with automated vehicles.
method Combining LiDAR and camera data, using deep learning for projection, and model-predictive control.
result Reduces false positives in LiDAR detection of reflective beacons.
Deep learning improves demosaicing but edge devices struggle.
problem Edge devices struggle with deep learning-based demosaicing.
method Exhaustive search of deep neural network architectures to find the best balance between performance and model complexity.
result Found architectures that outperform state-of-the-art demosaicing models on edge devices.
WoodScape dataset for fisheye cameras in autonomous driving.
problem Lack of public datasets for evaluating computer vision on fisheye images.
method Release WoodScape dataset with four surround view cameras and nine tasks.
result Encourages adaptation of computer vision models for fisheye cameras.
Study tackles open-set camera model identification, improving over state-of-the-art.
problem Identifying camera models from unknown ones in open-set scenarios.
method Feature extraction algorithms and classifiers for open-set recognition, evaluating different training protocols.
result A simple open-set training protocol yields the best results, improving over state-of-the-art solutions.
Deep learning system speeds up wildlife species identification from camera trap images.
problem Manual review of camera trap images is slow and resource-intensive.
method Combines machine and human intelligence for active learning.
result Matches state-of-the-art accuracy with minimal manual labels.
SoilingNet detects soiling on automotive cameras for better autonomous driving performance.
problem Soiling degrades the performance of automotive surround-view cameras, affecting autonomous driving.
method Created a new dataset and used a Convolutional Neural Network (CNN) architecture for soiling detection, combined with multi-task learning and data augmentation using GANs.
result Demonstrated high accuracy in soiling detection using CNN and multi-task learning.
Deep learning identifies precipitation clouds from all-sky camera data.
problem Automating cloud warning systems for observatories.
method Deep learning using EfficientNet network.
result Average accuracy of 99% in identifying rainfall potential and 96% in cloud coverage.
Single fisheye camera controls vehicle steering with neural networks.
problem Lateral control for autonomous vehicles using multiple cameras.
method Used a single fisheye camera for data generation and augmentation.
result 99% autonomy on urban road in simulator-based evaluation.
Paper proposes an edge detection method for robot navigation using low-SNR thermal cameras.
problem Efficient edge detection for robot navigation using low-SNR thermal camera.
method Raw image denoising, Canny edge detection, CSS method, edge ranking, edge linking.
result Enhanced edge detection method effectively detects smooth edges of the surrounding environment.
V-SysId identifies keypoints and 3D system from unlabeled videos.
problem Identifying keypoints and 3D system from unlabeled videos.
method Alternates between parameter estimation and extrinsic camera calibration, using motion equations as weak supervision.
result Utility of the approach demonstrated across various settings.
A method for camera calibration using heatmap regression for fisheye images.
problem Accurate and robust camera angle estimation from fisheye images in the Manhattan world.
method Heatmap regression to detect directions of labeled image coordinates, simultaneous rotation and fisheye distortion recovery.
result Our method outperforms conventional methods on large-scale datasets and with off-the-shelf cameras.
Y-GAN uses multi-camera data to estimate depth maps without expensive hardware.
problem Depth perception for autonomous systems requires accurate 3D spatial information.
method Proposes Y-GAN, a deep convolutional generative adversarial network.
result Y-GAN estimates depth maps from multi-camera stereo images without ground truth data.
Novel algorithm for separating moving camera video into static and dynamic components.
problem Foreground-background separation in noisy, moving camera video.
method Augmented robust PCA with total variation regularization, OptShrink low-rank matrix estimator.
result Panoramic low-rank component spanning entire field of view, automatically stitching corrupted data.
EvAn detects anomalies in event-based camera data with reduced complexity.
problem Anomaly detection in event-based camera data.
method Dual discriminator cGAN on event data with learned representation.
result Reduction in computational complexity compared to state-of-the-art methods.
Camera stickers can fool deep learning systems by manipulating the lens, achieving 49.6% misclassification rate.
problem The vulnerability of deep learning systems to physical adversarial attacks.
method Iterative procedure to update attack perturbation and threat model for physical realizability.
result Achieved 49.6% misclassification rate for targeted attacks on ImageNet classifiers.
New method for separating foreground from background in noisy, moving camera video.
problem Foreground-background separation in noisy, free-moving camera video.
method Registers frames, encodes perspective as missing data, uses OptShrink for low-rank estimation, and weighted total variation for smooth foreground.
result Panoramic background component that stitches together corrupted data from overlapping frames.
A drone-based MOT algorithm tracks vehicles using neural network detections and TPMBM filter.
problem Tracking multiple vehicles from drone-mounted cameras.
method Neural network for object detection, TPMBM filter for trajectory estimation, von-Mises Fisher distribution for DOA.
result TPMBM filter optimally estimates vehicle trajectories.
Paper uses VAEs and GANs to estimate cryo-EM image orientation and camera parameters.
problem Estimating orientation and camera parameters from noisy cryo-EM images.
method Combines VAEs and GANs to learn latent representation, then designs estimation method.
result Geometric approach for fast cryo-EM biomolecule reconstruction.
Edge filters reduce video data transmission to datacenters.
problem Strain on wide area network infrastructure due to video camera deployments.
method FilterForward system with lightweight edge filters and microclassifiers.
result Reduces bandwidth use by an order of magnitude.
New theory for curve-based 3D reconstruction and camera estimation.
problem Challenges in point feature extraction limit traditional methods.
method Differential geometry of general curves for image and space curves.
result Theoretical foundations for curve-based multiview reconstruction.
Self-supervised method estimates distances on fisheye cameras for autonomous driving.
problem Accurate Euclidean distance estimation on fisheye cameras for autonomous driving.
method Self-supervised scale-aware framework for monocular fisheye videos.
result State-of-the-art results on KITTI dataset, comparable to other methods.
A fast geometric regularizer improves event camera performance.
problem Event collapse in contrast maximization framework.
method Geometric regularizer to mitigate overfitting.
result State-of-the-art accuracy with reduced computational complexity.
Waymo Open Dataset provides a large, diverse, and synchronized LiDAR and camera dataset for autonomous driving research.
problem Limited diversity and scale in existing self-driving datasets hinder real-world problem alignment.
method Developed a new large-scale, high-quality, diverse dataset with synchronized LiDAR and camera data.
result The dataset is 15x more diverse than the largest existing dataset based on a proposed diversity metric.
Paper proposes a method to improve semantic segmentation for fisheye urban driving images.
problem Semantic segmentation for fisheye urban driving images is challenging due to distortion and lack of large datasets.
method A seven degrees of freedom augmentation method is proposed to transform rectilinear images into fisheye images.
result Training with seven-DoF augmentation improves model accuracy and robustness against distorted fisheye data.
DeepFall detects falls using autoencoders from non-invasive sensors.
problem Detecting rare falls from non-invasive data.
method Uses deep spatio-temporal convolutional autoencoders for anomaly detection.
result Superior fall detection compared to traditional methods.
New approach learns traffic behavior from unlabelled video.
problem Learning from demonstration in settings with unlabelled raw video data.
method Calibrates camera, detects objects, tracks them, and uses trajectories for LfD.
result Can learn traffic behavior from raw video without additional expert knowledge.
P3I learns holistic scene representations from a single image.
problem Inferring camera poses, object locations, and global scene structures from a single image.
method Combines search-based and gradient-based algorithms.
result P3I outperforms baselines on various image manipulation tasks.
A new CNN method for point cloud data.
problem Processing 3D point cloud data efficiently.
method Creating a mapping of nearest neighbors and applying weights to spatial relationships.
result Achieves a CNN-like architecture for point clouds without extensive feature engineering.
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.
Can machine learning predict pedestrian destinations from video data?
problem Determining pedestrian destinations from video data without direct observation.
method Used density heatmaps as input to train a Random Forest predictor.
result Motivated further analysis of the methodology.
Minimal coloring number found for Z-colorable links.
problem Finding the minimum number of colors needed for Z-colorings of Z-colorable links.
method Defined Z-coloring as a generalization of Fox coloring for links with zero determinants. Provided sufficient conditions for non-splittable Z-colorable links to have the least minimal coloring number.
result Sufficient conditions for non-splittable Z-colorable links to have the least minimal coloring number.
PIP-Net predicts pedestrian crossing intentions with up to 4-second lead.
problem Accurate pedestrian intention prediction for autonomous vehicles in real-world scenarios.
method Recurrent and temporal attention-based model using kinematic and spatial features.
result PIP-Net predicts pedestrian crossing intentions up to 4 seconds in advance.
We integrate camera pose correlations into deep models using Gaussian processes.
problem Lack of inter-frame reasoning in deep neural networks.
method Derive a principled framework combining camera pose information with deep models using a novel view kernel.
result Soft-prior knowledge aids pose-related vision tasks like novel view synthesis.
Person re-identification (re-id), an emerging problem in visual surveillance, deals with maintaining entities of individuals whilst they traverse various locations surveilled by a camera network. From a visual perspective re-id is challenging due to significant changes in visual appearance of individuals in cameras wit…
The paper explores minimal coloring numbers for Z-colorable links.
problem Finding the minimum number of colors needed for Z-colorings on minimal diagrams of Z-colorable links. method Investigates minimal diagrams and Z-colorings for Z-colorable links. result For any positive integer N, there exists a minimal diagram of a Z-colorable link with at least N colors in any Z-coloring. CNNs improve particle identification in ground-based gamma-ray astronomy.
problem Identifying particles in gamma-ray astronomy images.
method Used convolutional neural networks (CNNs) with PyTorch and TensorFlow.
result Improved accuracy in identifying gamma-rays and background particles.
The paper shows links can be colored with fewer colors than previously thought.
problem Coloring links using the symmetric group of degree three.
method Analyzing the number of colors for link colorings by S3. result 2-bridge links with 5 colors can be colored with only 4 colors.
Study finds minimal coloring numbers for torus links using rack colorings.
problem Determining the minimal number of colors for torus link diagrams.
method Using rack colorings on link diagrams to classify minimal colorings.
result Complete classifications of Z-colorings by four colors. Study on knots using 17 colors, finding specific color assignments.
problem Understanding the minimum number of colors needed for Fox colorings of knots.
method Investigated 17-colorable knots and their diagrams.
result Found that exactly 6 out of 17 colors are used in diagrams of 17-colorable knots.
Aicardi's invariant F(L) is extended to colored singular links using graphical calculus.
problem Constructing an invariant for colored classical and singular links.
method State-sum model using graphical calculus for oriented, colored, 4-valent planar graphs.
result Extends F(L) to colored singular links, showing it's stronger than HOMFLY-PT polynomial. This paper proves the minimal coloring number for a specific type of link is exactly 4.
problem Determining the minimal coloring number for a specific type of link.
method Investigated Z-colorable links and used their properties to prove the minimal coloring number is 4. result The minimal coloring number of any non-splittable Z-colorable link is exactly 4. The paper finds minimum Dehn colors for knots and defines useful graphs for coloring.
problem Finding the minimum number of colors for Dehn colorings of knots.
method Analyzes Dehn colorings for knots and defines R-palette graphs. result For Dehn p-colorable knots, the minimum number of colors is at least ⌊log2pfloor+2. Algorithm finds minimal colorings of tree structures.
problem Finding minimal unbounded factor complexity colorings of trees.
method Induction algorithm using colored balls.
result Characterization of Sturmian colorings.
This paper shows the minimal coloring number for certain Z-colorable links is four.
problem Determining the minimal number of colors for Z-colorings of links. method Analyzing diagrams of Z-colorable links and constructing specific diagrams to find the minimal coloring number. result The minimal coloring number for non-splittable Z-colorable links is four.