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Works419 from public data
- 6,164
- Robust wide-baseline stereo from maximally stable extremal regions5,266
The high utility of MSERs, multiple measurement regions and the robust metric is demonstrated in wide-baseline experiments on image pairs from both indoor and outdoor scenes.
- A Comparison of Affine Region Detectors3,526
A snapshot of the state of the art in affine covariant region detectors, and compares their performance on a set of test images under varying imaging conditions to establish a reference test set of images and performance software so that future detectors can be evaluated in the same framework.
- 3,285
- DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks1,732
DeblurGAN achieves state-of-the art performance both in the structural similarity measure and visual appearance and is 5 times faster than the closest competitor - Deep-Deblur.
- 1,608
- XM2VTSDB: The Extended M2VTS Database1,581
This poster presents a poster presenting a probabilistic procedure for estimating the response of the immune system to laser-spot assisted surgery to treat central giant cell granuloma.
- 1,366
- 1,181
- 1,124
- Locally Optimized RANSAC1,024
The locally optimized ransac makes no new assumptions about the data, on the contrary – it makes the above-mentioned assumption valid by applying local optimization to the solution estimated from the random sample.
- 914
- The Visual Object Tracking VOT2015 Challenge Results902
The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and presents a new VOT 2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute.
- 886
- Discriminative Correlation Filter Tracker with Channel and Spatial Reliability841
This work introduces the channel and spatial reliability concepts to DCF tracking and provides a learning algorithm for its efficient and seamless integration in the filter update and the tracking process.
- Working hard to know your neighbor's margins: Local descriptor learning loss818
A novel loss for learning local feature descriptors which is inspired by the Lowe's matching criterion for SIFT is introduced, and it is shown that the proposed loss that maximizes the distance between the closest positive and closest negative patch in the batch is better than complex regularization methods.
- The Sixth Visual Object Tracking VOT2018 Challenge Results804
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
- The Visual Object Tracking VOT2016 Challenge Results776
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
- 685
- A Novel Performance Evaluation Methodology for Single-Target Trackers667
The requirements are the basis of a new evaluation methodology that aims at a simple and easily interpretable tracker comparison and a fully-annotated dataset with per-frame annotations with several visual attributes, which is the largest benchmark to date.
- Robust Detection of Lines Using the Progressive Probabilistic Hough Transform654
The progressive probabilistic Hough transform minimizes the amount of computation needed to detect lines by exploiting the difference in the fraction of votes needed to reliably detect lines with different numbers of supporting points.
- T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-Less Objects614
Initial evaluation results indicate that the state of the art in 6D object pose estimation has ample room for improvement, especially in difficult cases with significant occlusion.
- A Method for Text Localization and Recognition in Real-World Images593
The paper is first to report both text detection and recognition results on the standard and rather challenging ICDAR 2003 dataset, and the text localization works for number of alphabets and the method is easily adapted to recognition of other scripts, e.g. cyrillics.
- The Visual Object Tracking VOT2014 Challenge Results588
The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and introduces a new dataset with full annotation of targets by rotated bounding boxes and per-frame attribute.
- BOP: Benchmark for 6D Object Pose Estimation571
A benchmark for 6D pose estimation of a rigid object from a single RGB-D input image shows that methods based on point-pair features currently perform best, outperforming template matching methods, learning-based methods and methodsbased on 3D local features.
- The BANCA Database and Evaluation Protocol516
A protocol for evaluating verification algorithms on the BANCA database, a new large, realistic and challenging multi-modal database intended for training and testing multi- modal verification systems, is described.
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- 496
- Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features472
In the experiments, it is shown that these features outperform non-invariant and earlier version of rotation invariant LBP and the MR8 descriptor in texture classification, material categorization and face recognition tests.
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- Graph-Cut RANSAC403
GC-RANSAC is shown experimentally, both on synthesized tests and real image pairs, to be more geometrically accurate than state-of-the-art methods on a range of problems, e.g. line fitting, homography, affine transformation, fundamental and essential matrix estimation.
- MAGSAC++, a Fast, Reliable and Accurate Robust Estimator385
MAGSAC++ and Progressive NAPSAC sampler is proposed and it is shown that the progressive spatial sampling in P-NAPSAC can be integrated with PROSAC sampling, which is applied to the first, location-defining, point.
- 385
- Randomized RANSAC with T, test372
A new randomized (hypothesis evaluation) version of the ransac algorithm, r-ransac, is introduced and a mathematically tractable class of statistical preverification test of samples is introduced that derives an approximate relation for the optimal setting of its single parameter.
- MAGSAC: Marginalizing Sample Consensus358
Applying sigma-consensus, MAGSAC is proposed with no need for a user-defined sigma and improving the accuracy of robust estimation significantly, and is superior to the state-of-the-art in terms of geometric accuracy on publicly available real-world datasets for epipolar geometry and homography estimation.
- The Eighth Visual Object Tracking VOT2020 Challenge Results350
A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in theVDT challenges.
- BOP Challenge 2020 on 6D Object Localization345
Although the top-performing methods rely on RGB-D image channels, strong results were achieved when only RGB channels were used at both training and test time, and the photorealism of PBR images was demonstrated effective despite the augmentation.
- Visual Heart Rate Estimation with Convolutional Neural Network.333
A novel two-step convolutional neural network is proposed to estimate a heart rate from a sequence of facial images to test the robustness of heart rate estimation methods to illumination changes and subject’s motion.
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- Systematic evaluation of convolution neural network advances on the Imagenet308
It is shown that the use of 128x128 pixel images is sufficient to make qualitative conclusions about optimal network structure that hold for the full size Caffe and VGG nets, and an order of magnitude faster than with the standard 224 pixel images.
- 308
- ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition — RRC-MLT-2019301
The dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge are presented, which has 4 tasks covering various aspects of multi-lingual scene text.
- D3S – A Discriminative Single Shot Segmentation Tracker299
Without per-dataset finetuning and trained only for segmentation as the primary output, D3S outperforms all trackers on VOT2016, VOT2018 and GOT-10k benchmarks and performs close to the state-of-the-artTrackers on the TrackingNet.
- EPOS: Estimating 6D Pose of Objects With Symmetries291
A new method for estimating the 6D pose of rigid objects with available 3D models from a single RGB input image using a robust and efficient variant of the PnP-RANSAC algorithm, which outperforms all RGB and most RGB-D and D methods on the T-LESS and LM-O datasets.
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- Robust scale-adaptive mean-shift for tracking277
This paper addresses the problem of scale adaptation of the Hellinger distance based Mean-Shift tracker with a simple and scalable implementation.
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- Computer Vision – ECCV 2016272
This work proposes to fine-tune CNN for image retrieval from a large collection of unordered images in a fully automated manner and shows that both hard positive and hard negative examples enhance the final performance in particular object retrieval with compact codes.
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- Object Recognition using Local Affine Frames on Distinguished Regions234
A novel approach to appearance based object recognition based on matching of local image features, reliably recognises objects under very different viewing conditions that is invariant to piecewise-affine image deformations, but still remains very discriminative.
- Repeatability Is Not Enough: Learning Affine Regions via Discriminability233
It is shown that maximizing geometric repeatability does not lead to local regions, a.k.a features, that are reliably matched and this necessitates descriptor-based learning, and a novel hard negative-constant loss function is proposed for learning of affine regions.
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- Progressive probabilistic Hough transform for line detection231
The algorithm is ideally suited for real-time applications with a fixed amount of available processing time, since voting and line detection is interleaved and the most salient features are likely to be detected first.
- Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook224
This survey presents a systematic and thorough review of more than 90 DCFs and Siamese trackers, based on results in nine tracking benchmarks, and distinguishes and comprehensively review the shared as well as specific open research challenges in both these tracking paradigms.
- Fixing the Locally Optimized RANSAC222
Improvements of the LO-RANSAC procedure are proposed: a use of truncated quadratic cost function, an introduction of a limit on the number of inliers used for the least squares computation and several implementation issues are addressed.
- On Evaluation of 6D Object Pose Estimation206
The paper defines 6D object pose estimation problems, proposes an evaluation methodology and introduces three new pose error functions that deal with pose ambiguity, compared with functions commonly used in the literature and shown to remove certain types of non-intuitive outcomes.
- Support vector machines for face authentication203
This study supports the hypothesis that the SVM approach is able to extract the relevant discriminatory information from the training data, and believes that this is the main reason for its superior performance over benchmark methods.
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- Guiding Monocular Depth Estimation Using Depth-Attention Volume181
This paper proposes guiding depth estimation to favor planar structures that are ubiquitous especially in indoor environments by incorporating a non-local coplanarity constraint to the network with a novel attention mechanism called depth-attention volume (DAV).
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- MODS: Fast and robust method for two-view matching177
An improved method for tentative correspondence selection, applicable both with and without view synthesis, and a modification of the standard first to second nearest distance rule increases the number of correct matches by 5–20% at no additional computational cost are introduced.
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- Learning a Fine Vocabulary159
It is shown experimentally that the novel similarity function achieves mean average precision that is superior to any result published in the literature on a number of standard datasets.
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- The Ninth Visual Object Tracking VOT2021 Challenge Results127
The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative; results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years.
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- BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects117
Out of 49 pose estimation methods evaluated since 2019, the top 18 are from 2022, and Methods based on point pair features, which were introduced in 2010 and achieved competitive results even in 2020, are now clearly outperformed by deep learning methods.
- Combining evidence in personal identity verification systems112
A methodology for fusing multiple instances of biometric data to improve the performance of a personal identity verification system is developed and it is shown that the fusion based on rank order statistic, i.e., the median, is robust to outliers.
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- Progressive Probabilistic Hough Transform108
Experiments show PPHT has, in many circumstances, advantages over the Standard Hough Transform, and is ideally suited for real-time applications with a fixed amount of available processing time.
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- BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects102
The best 2023 method for 6D localization of unseen objects (GenFlow) notably reached the accuracy of the best 2020 method for seen objects (CosyPose), although being noticeably slower than the best 2020 method (CosyPose).
- Weighted Sampling for Large-Scale Boosting102
A new general sampling strategy ”quasi-random weighted sampling + trimming” (QWS+) that includes well established strategies as special cases that minimizes the variance of hypothesis error estimate and leads to significant improvement in performance compared to standard sampling techniques.
- E2E-MLT - An Unconstrained End-to-End Method for Multi-language Scene Text101
The experiments show that obtaining accurate multi-language multi-script annotations is a challenging problem and the proposed end-to-end method achieves state-of-the-art performance for both joint localization and script identification in natural images and in cropped word script identification.
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- FEAR: Fast, Efficient, Accurate and Robust Visual Tracker98
A novel and efficient way to benefit from dual-template representation for object model adaption, which incorporates temporal information with only a single learnable parameter is presented, and it is shown that energy consumption is a limiting factor for trackers on mobile devices.
- Object Tracking by Reconstruction With View-Specific Discriminative Correlation Filters98
The proposed long-term RGB-D tracker called OTR – Object Tracking by Reconstruction performs online 3D target reconstruction to facilitate robust learning of a set of view-specific discriminative correlation filters (DCFs).
- Graph-Cut RANSAC: Local Optimization on Spatially Coherent Structures97
Graph-Cut RANSAC, equipped with “the bells and whistles” of USAC and MAGSAC++, was tested on a range of problems using a number of publicly available datasets and is more geometrically accurate than state-of-the-art robust estimators, fails less often and runs faster or with speed similar to less accurate alternatives.
- Sub-linear Indexing for Large Scale Object Recognition97
A method capable of recognising one of N objects in log(N) time, which preserves all the strengths of local affine region methods – robustness to background clutter, occlusion, and large changes of viewpoints.
- On Matching Scores for LDA-based Face Verification95
In extensive experimental studies on the publicly available XM2VTS database, it is shown that the proposed metric is consistently superior to both the Euclidean distance and normalised correlation matching scores.
- Efficient Scene text localization and recognition with local character refinement94
An unconstrained end-to-end text localization and recognition method that detects initial text hypothesis in a single pass by an efficient region-based method and refines the text hypothesis using a more robust local text model, which deviates from the common assumption of region- based methods that all characters are detected as connected components.
- On Finding Gray Pixels85
The grayness index, GI in short, is derived using the Dichromatic Reflection Model and is learning-free, and outperforms state-of-the-art statistical methods and many recent deep methods.
- Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm84
The Progressive-X algorithm, Prog-X in short, is proposed for geometric multi-model fitting and is superior to the state-of-the-art in terms of accuracy in both synthetic experiments and on publicly available real-world datasets for homography, two-view motion, and motion segmentation.
- CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmark83
The CDTB dataset is the largest and most diverse dataset in RGB-D tracking, with an order of magnitude larger number of frames than related datasets, indicating a large gap between the two fields, which has not been previously detected by the prior benchmarks.
- Learning Vocabularies over a Fine Quantization83
It is demonstrated that the performance of specific object retrieval increases with the size of the vocabulary and that the large vocabularies increase the speed of the tf-idf scoring step.
- MFT: Long-Term Tracking of Every Pixel81
It is shown that MFT achieves competitive performance on the TAP-Vid benchmark, outperforming baselines by a significant margin, and tracking densely orders of magnitude faster than the state-of-the-art point-tracking methods.
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- FuCoLoT – A Fully-Correlational Long-Term Tracker77
The proposed FuCoLoT exploits the novel DCF constrained filter learning method to design a detector that is able to re-detect the target in the whole image efficiently and has an order of magnitude smaller memory footprint than its best-performing competitors and runs at 15 fps in a single CPU thread.
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- WxBS: Wide Baseline Stereo Generalizations76
The WxBS-M matcher dominantes the state-of-the-art methods both on both the new and existing datasets and simple adaptive thresholding improves Hessian-Affine, DoG, MSER, and possibly other detectors and allows to use them on infrared and low contrast images.
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- Recall@k Surrogate Loss with Large Batches and Similarity Mixup75
This work focuses on learning deep visual representation models for retrieval by exploring the interplay between a new loss function, the batch size, and a new regularization approach by proposing a differentiable surrogate loss for the recall.
- Combining evidence in multimodal personal identity recognition systems73
A sensitivity analysis of the various schemes to estimation errors is carried out to show that the integration scheme developed under the most restrictive assumptions — the sum rule — and its derivatives are least affected by estimation errors.
- Danish Fungi 2020 – Not Just Another Image Recognition Dataset69
A novel fine-grained dataset and bench-mark, the Danish Fungi 2020 (DF20), which is unique in its taxonomy-accurate class labels, small number of errors, highly unbalanced long-tailed class distribution, rich observation metadata, and well-defined class hierarchy.
- Fine-grained recognition of plants from images68
The proposed texture analysis and deep learning methods outperform the state-of-the-art in leaf and bark classification and achieve very competitive results in plant recognition “in the wild”, suggesting that recognition of segmented leaves is practically a solved problem, when high volumes of training data are available.
- Gradient based progressive probabilistic Hough transform67
It is shown that using the angle information in controlling the voting process and in assigning pixels to a line, the PPHT performance can be significantly improved, with an overall improvement in output quality greater than that found for the standard Hough transform.
- Fast face localisation and verification67
The optimised robust correlation outperformed two standard techniques based on the dynamic link architecture and this Monte Carlo technique speeds the evaluation of correlation approximately twenty five times and makes the optimisation process near-real time.
- The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results66
A significant general improvement of results has been observed, which partly compensate for the more difficult sequences introduced into the dataset in VOT-TIR2016.
- How to Make an RGBD Tracker?65
A generic framework for converting an arbitrary short-term RGB tracker into an RGBD tracker that is particularly powerful in occlusion scenarios where it provides 13.5% average improvement and 26% for the best tracker (CSR-DCF).
- Colour-based object recognition64
The CAG is shown to be preserving and combining the best features of these two approaches while avoiding their drawbacks, and is tested on a range of diicult object recognition and localisation problems involving complex imagery of non rigid 3D objects under varied viewing conditions with excellent results.
- Marginalizing Sample Consensus62
It is shown that MAGSAC++ is significantly less sensitive to the setting of the threshold upper bound than the other state-of-the-art algorithms to the inlier-outlier threshold, making it easier to be applied to unseen problems and scenes without acquiring information by hand about theSetting of theInlier- outlier threshold.
- Automatic Fungi Recognition: Deep Learning Meets Mycology61
An AI-based fungi species recognition system for a citizen-science community, based on a Vision Transformer architecture, that achieves a recognition error that is 46.75% lower than the current system and creates a virtuous cycle helping both communities.
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- DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image59
The diver-sity of DAD-3DHeads in camera angles, facial expressions, and occlusions enables a benchmark to study in-the-wild generalization and robustness to distribution shifts.
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- Spatial and feature space clustering: Applications in image analysis59
The proposed feature and spatial domain clustering method is devised to group pixel data by taking into account simultaneously both their feature space similarity and spatial coherence, and can resolve image features even if their distributions significantly overlap in the feature space.
- Plant recognition by AI: Deep neural nets, transformers, and kNN in deep embeddings58
A novel retrieval-based method for recognition by nearest neighbor classification in a deep embedding space, based on a model trained via the Recall@k surrogate loss, is proposed and achieved superior performance in all measured scenarios.
- The Enhanced Flock of Trackers57
The enhancements of the Flock of Trackers are new reliability predictors for the local trackers - the Neighbourhood consistency predictor and the Markov predictor, new rules for combining the predictions and introduction of a RANSAC-based estimator of object motion.
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- Estimation of curvature and tangent direction by median filtered differencing53
A new method, median filtered differencing, is presented, for estimation of tangent direction and curvature of digitised curves, which performs successfully on both straight and curved segments even in the neighbourhood of discontinuities.
- DeFMO: Deblurring and Shape Recovery of Fast Moving Objects52
This work proposes a method that, given a single image with its estimated background, outputs the object’s appearance and position in a series of sub-frames as if captured by a high-speed camera (i.e. temporal super-resolution).
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- Face verification using error correcting output codes51
This work shows how to apply the ECOC concept to automatic face verification, which is inherently a two-class problem, and proposes two different combining strategies as the matching score for face verification.
- Generalized Differentiable RANSAC50
The proposed ▽-RANSAC is a generalized differentiable RANSAC that allows learning the entire randomized robust estimation pipeline, and directly maximizes the probability of drawing a good hypothesis, allowing us to learn better sampling distributions.
- Multi-class Model Fitting by Energy Minimization and Mode-Seeking49
Considering that a group of outliers may form spatially coherent structures in the data, a cross-validation-based technique removing statistically insignificant instances is proposed, which achieves fast and robust optimization.
- Colour Image Retrieval and Object Recognition Using the Multimodal Neighbourhood Signature49
The multimodal neighbourhood signature method is shown to operate successfully under changing illumination, viewpoint and object pose, as well as non-rigid object deformation, partial occlusion and the presence of background clutter dominating the scene.
- The World of Fast Moving Objects48
It is shown that it is possible to recover the appearance of the object and its axis of rotation, despite its blurred appearance, and two applications of localization, temporal superresolution and highlighting, are presented.
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- Fast Detection of Multiple Textureless 3-D Objects45
A fast edge-based approach for detection and approximate pose estimation of multiple textureless objects in a single image that outperforms the state-of-the-art approach by Damen et al. (2012).
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- Image Retrieval Using Local Compact DCT-Based Representation44
An image retrieval system based on local affine frames is introduced that provides highly discriminative retrieval of rigid objects under a very wide range of viewing and illumination conditions, and is robust to occlusion and background clutter.
- Evaluating Colour-Based Object Recognition Algorithms Using the SOIL-47 Database44
Multimodal Neighbourhood Signature performance was fairly stable, outperforming ARG for most viewing angles, and using only a single model image of each of the 47 objects, MNS performed well even for extreme test views close to degrees.
- Learning Efficient Linear Predictors for Motion Estimation42
A novel object representation for tracking is proposed, represented as a constellation of spatially localised linear predictors which are learned on a single training image, achieving frame rate generally higher than 30 frames per second despite the Matlab implementation.
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- Statistical chromaticity models for lip tracking with B-splines42
A method for lip tracking intended to support personal verification that exploits grey-level gradient projections as well as chromaticity models to find the lips in an automatically segmented region corresponding to the face area.
- A New Class of Learnable Detectors for Categorisation41
A new class of image-level detectors that can be adapted by machine learning techniques to detect parts of objects from a given category is proposed, and high detection rates were obtained for text detection, face segmentation and leopard skin detection.
- Face verification via error correcting output codes41
A novel approach to face verification based on the Error Correcting Output Coding (ECOC) classifier design concept, which proposes the average first order Minkowski distance between the probe and gallery images.
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- Continual Occlusion and Optical Flow Estimation40
It is shown that providing occlusion masks as an additional input to flow estimation improves the standard performance metric by more than 25% on both KITTI and Sintel, and a novel method for incorporating information from past frames into flow estimation is introduced.
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- Online adaptive hidden Markov model for multi-tracker fusion38
A novel method for fusion of multiple trackers based on Hidden Markov Models, which outperforms the state-of-the-art, often significantly, on all data-sets in almost all criteria.
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- Planar Affine Rectification from Change of Scale35
The proposed method is simple to implement and without parameters; linear and thus supporting (algebraic) least squares solutions; and general, without restrictions on either the shape of the corresponding features or their mutual position.
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- State of the Journal32
In this issue, the editor-in-chief of TC shares with you some of the developments that TC is undertaking and welcomes your ideas and suggestions on ways in which the journal can improve.
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- The VOT2013 challenge: overview and additional results21
An overview of the VOT2013 challenge is provided, its main results are pointed out, and the additional previously unpublished experiments and results are documented.
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- Cumulative attribute space regression for head pose estimation and color constancy14
It is shown how the original CA space can be generalized to multiple output by the Cartesian product (CartCA), but for target spaces with more than two outputs the CartCA becomes computationally infeasible and therefore an approximate solution - multi-view CA (MvCA) - where CartCA is applied to output pairs is proposed.
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- Guest Editorial: Special Issue on “Computer Vision for All Seasons: Adverse Weather and Lighting Conditions”3
The last decade has witnessed tremendous progress in many sub-fields of computer vision, due to the increasing availability of large annotated datasets such as PASCAL VOC, but the performance is mainly benchmarked under good weather and favorable lighting conditions.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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