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Rendering Natural Camera Bokeh Effect with Deep Learning

Rendering Natural Camera Bokeh Effect with Deep Learning

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Andrey Ignatov Jagruti Patel Radu Timofte ETH Zurich Switzerland

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Bokeh is an important artistic effect used to highlight the main object of interest on the photo by blurring all out-of-focus areas. While DSLR and system camera lenses can render this effect naturally, mobile cameras are unable to produce shallow depth-of-field photos due to a very small aperture diameter of their optics. Unlike the current solutions simulating bokeh by applying Gaussian blur to image background, in this paper we propose to learn a realistic shallow focus technique directly from the photos produced by DSLR cameras. For this, we present a large-scale bokeh dataset consisting of 5K shallow / wide depth-of-field image pairs captured using the Canon 7D DSLR with 50mm f/1.8 lenses. We use these images to train a deep learning model to reproduce a natural bokeh effect based on a single narrow-aperture image. The experimental results show that the proposed approach is able to render a plausible non-uniform bokeh even in case of complex input data with multiple objects. The dataset, pre-trained models and codes used in this paper are available on the project website: https://***/ ihnatova/***.

关键词： Cameras Rendering (computer graphics) Lenses Apertures Training Machine learning Visualization

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Robust Real-Time Face Pose and Facial Expression Recovery

Robust Real-Time Face Pose and Facial Expression Recovery

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zhiwei Zhu Qiang Ji Department of ECSE Rensselaer Polytechnic Institute Troy NY USA Sarnoff Corporation USA

Face motion is the sum of rigid motion related with face pose and non-rigid motion related with facial expression. Both motions are coupled in the captured image so that they can not be easily recovered from the image directly. In this paper, a novel technique is proposed to recover 3D face pose and facial expression simultaneously from a monocular video sequence in real time. First, twenty-eight salient facial features are detected and tracked robustly under various face orientations and facial expressions. Second, after modelling the coupling between face pose and facial expression in the 2D image as a nonlinear function, a normalized SVD (N-SVD) decomposition technique is proposed to recover the pose and expression parameters analytically. A nonlinear technique is subsequently utilized to refine the solution obtained from the N-SVD technique by imposing the orthonormality constraint on the pose parameters. Compared to the original SVD technique proposed in [1], which is very sensitive to the image noise and numerically unstable in practice, the proposed method can recover the face pose and facial expression robustly and accurately. Finally, the performance of the proposed technique is evaluated in the experiments using both synthetic and real image sequences.

关键词： Robustness Facial animation Motion estimation Face detection Cameras Image analysis Head Human computer interaction Face recognition Video sequences

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Cross-Domain Synthetic-to-Real In-the-Wild Depth and Normal Estimation for 3D Scene Understanding

Cross-Domain Synthetic-to-Real In-the-Wild Depth and Normal ...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jay Bhanushali Manivannan Muniyandi Praneeth Chakravarthula Indian Institute of Technology Madras UNC Chapel Hill

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present a cross-domain inference technique that learns from synthetic data to estimate depth and normals for in-the-wild omnidirectional 3D scenes encountered in real-world uncontrolled settings. To this end, we introduce UBotNet, an architecture that combines UNet and Bottleneck Transformer elements to predict consistent scene normals and depth. We also introduce the OmniHorizon synthetic dataset containing 24,335 omnidirectional images that represent a wide variety of outdoor environments, including buildings, streets, and diverse vegetation. This dataset is generated from expansive, lifelike virtual spaces and encompasses dynamic scene elements, such as changing lighting conditions, different times of day, pedestrians, and vehicles. Our experiments show that UBotNet achieves significantly improved accuracy in depth estimation and normal estimation compared to existing models. Lastly, we validate cross-domain synthetic-to-real depth and normal estimation on real outdoor images using UBot-Net trained solely on our synthetic OmniHorizon dataset, demonstrating the potential of both the synthetic dataset and the proposed network for real-world scene understanding applications. The dataset and accompanying code are available at ***.

关键词： Space vehicles Three-dimensional displays Pedestrians Accuracy Estimation Vegetation mapping computer architecture

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SwinFuSR: an image fusion-inspired model for RGB-guided thermal image super-resolution

SwinFuSR: an image fusion-inspired model for RGB-guided ther...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Cyprien Arnold Philippe Jouvet Lama Seoud Polytechnique Montréal Montréal Canada CHU Sainte Justine Montréal Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Thermal imaging plays a crucial role in various applications, but the inherent low resolution of commonly available infrared (IR) cameras limits its effectiveness. Conventional super-resolution (SR) methods often struggle with thermal images due to their lack of high-frequency details. Guided SR leverages information from a high-resolution image, typically in the visible spectrum, to enhance the reconstruction of a high-res IR image from the low-res input. Inspired by SwinFusion, we propose SwinFuSR, a guided SR architecture based on Swin transformers. In real world scenarios, however, the guiding modality (e.g. RBG image) may be missing, so we propose a training method that improves the robustness of the model in this case. Our method has few parameters and outperforms state of the art models in terms of Peak Signal to Noise Ratio (PSNR) and Structural SIMilarity (SSIM). In Track 2 of the PBVS 2024 Thermal Image Super-Resolution Challenge, it achieves 3rd place in the PSNR metric. Our code and pretained weights are available at https://***/visionICLab/SwinFuSR.

关键词： Training Measurement PSNR conferences Superresolution computer architecture Transformers

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Hairy Ground Truth Enhancement for Semantic Segmentation

Hairy Ground Truth Enhancement for Semantic Segmentation

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sophie Fischer Irina Voiculescu Department of Computer Science Oxford University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Semantic segmentation is a key task within applications of machine learning for medical imaging, requiring large amounts of medical scans annotated by clinicians. The high cost of data annotation means that models need to make the most of all available ground truth masks; yet many models consider two false positive (or false negative) pixel predictions as ‘equally wrong’ regardless of the individual pixels’ relative position to the ground truth mask. These methods also have no sense of whether a pixel is solitary or belongs to a contiguous *** propose the Hairy transform, a novel method to enhance ground truths using 3D ‘hairs’ to represent each pixel’s position relative to objects in the ground truth. We illustrate its effectiveness using a mainstream model and loss function on a commonly used cardiac MRI dataset, as well as a set of synthetic data constructed to highlight the effect of the method during training. The overall improvement in segmentation results comes at the small cost of a one-off pre-processing step, and can easily be integrated into any standard machine learning model. Rather than looking to make minute improvements for mostly correct ‘standard’ masks we instead show how this method helps improve robustness against catastrophic failures for edge cases.

关键词： Training Costs Three-dimensional displays Semantic segmentation Transforms Predictive models Writing

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Augmentation of Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models

Augmentation of Atmospheric Turbulence Effects on Thermal Ad...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Engin Uzun Ahmet Anı l Dursun Erdem Akagü ndü z Dept. of Image Proc. &amp Computer Vis. Technologies Aselsan Inc. Turkey Graduate School of Informatics Middle East Technical University Turkey

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Atmospheric turbulence has a degrading effect on the image quality of long-range observation systems. As a result of various elements such as temperature, wind velocity, humidity, etc., turbulence is characterized by random fluctuations in the refractive index of the atmosphere. It is a phenomenon that may occur in various imaging spectra such as the visible or the infrared bands. In this paper, we analyze the effects of atmospheric turbulence on object detection performance in thermal imagery. We use a geometric turbulence model to simulate turbulence effects on a medium-scale thermal image set, namely "FLIR ADAS v2". We apply thermal domain adaptation to state-of-the-art object detectors and propose a data augmentation strategy to increase the performance of object detectors which utilizes turbulent images in different severity levels as training data. Our results show that the proposed data augmentation strategy yields an increase in performance for both turbulent and non-turbulent thermal test images.

关键词： Adaptation models Atmospheric modeling Wind speed Training data Refractive index Imaging Object detection

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Correspondence-free Associative Learning

Correspondence-free Associative Learning

引用

International conference on pattern recognition

作者： E. Jonsson M. Felsberg Computer Vision Laboratory Linköping University Sweden

We study the problem of learning a non-parametric mapping between two continuous spaces without having access to input-output pairs for training, but rather to groups of input-output pairs, where the correspondence structure within each group is unknown and where outliers may be present. This problem is solved by transforming each space using the channel representation, and finding a linear mapping on the transformed domain. The asymptotical behavior of the method for a large number of training samples is found to be very related to the case of known correspondences. The results are evaluated on simulated data

关键词： computer vision Laboratories Supervised learning Robustness Training data Delay pattern recognition Information representation Kernel Vectors

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Learning to Classify New Foods Incrementally Via Compressed Exemplars

Learning to Classify New Foods Incrementally Via Compressed ...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Justin Yang Zhihao Duan Jiangpeng He Fengqing Zhu Elmore School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Food image classification systems play a crucial role in health monitoring and diet tracking through image-based dietary assessment techniques. However, existing food recognition systems rely on static datasets characterized by a pre-defined fixed number of food classes. This contrasts drastically with the reality of food consumption, which features constantly changing data. Therefore, food image classification systems should adapt to and manage data that continuously evolves. This is where continual learning plays an important role. A challenge in continual learning is catastrophic forgetting, where ML models tend to discard old knowledge upon learning new information. While memory-replay algorithms have shown promise in mitigating this problem by storing old data as exemplars, they are hampered by the limited capacity of memory buffers, leading to an imbalance between new and previously learned data. To address this, our work explores the use of neural image compression to extend buffer size and enhance data diversity. We introduced the concept of continuously learning a neural compression model to adaptively improve the quality of compressed data and optimize the bitrates per pixel (bpp) to store more exemplars. Our extensive experiments, including evaluations on food-specific datasets including Food-101 and VFN-74, as well as the general dataset ImageNet-100, demonstrate improvements in classification accuracy. This progress is pivotal in advancing more realistic food recognition systems that are capable of adapting to continually evolving data. Moreover, the principles and methodologies we’ve developed hold promise for broader applications, extending their benefits to other domains of continual machine learning systems.

关键词： Continuing education Adaptation models computer vision Image coding conferences Machine learning Data models

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Real Time Localization and 3D Reconstruction

Real Time Localization and 3D Reconstruction

引用

conference on computer vision and pattern recognition (CVPR)

作者： E. Mouragnon M. Lhuillier M. Dhome F. Dekeyser P. Sayd Image and embedded computer Laboratory CEA/LIST/DTSI/SARC Gif-sur-Yvette France LASMEA UMR 6602 CNRS Université Blaise Pascal Aubiere France

In this paper we describe a method that estimates the motion of a calibrated camera (settled on an experimental vehicle) and the tridimensional geometry of the environment. The only data used is a video input. In fact, interest points are tracked and matched between frames at video rate. Robust estimates of the camera motion are computed in real-time, key-frames are selected and permit the features 3D reconstruction. The algorithm is particularly appropriate to the reconstruction of long images sequences thanks to the introduction of a fast and local bundle adjustment method that ensures both good accuracy and consistency of the estimated camera poses along the sequence. It also largely reduces computational complexity compared to a global bundle adjustment. Experiments on real data were carried out to evaluate speed and robustness of the method for a sequence of about one kilometer long. Results are also compared to the ground truth measured with a differential GPS.

关键词： Cameras Image reconstruction Robustness Robot vision systems Motion estimation Computational geometry Layout Optimization methods Simultaneous localization and mapping Iterative algorithms

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Few-Shot Supervised Prototype Alignment for Pedestrian Detection on Fisheye Images

Few-Shot Supervised Prototype Alignment for Pedestrian Detec...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Thaddä us Wiedemer Stefan Wolf Arne Schumann Kaisheng Ma Jü rgen Beyerer Tsinghua University Karlsruhe Institute of Technology Fraunhofer IOSB Fraunhofer Center for Machine Learning

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Complete and pre-trained models are readily available for download for object detection and can perform well on datasets containing everyday images. Domain adaptation is used to transfer models to more specific datasets with characteristics not present in pre-training. We propose the novel adaptation setting of pedestrian detection in fisheye images, where target samples are scarce but annotated. Our setting provides interesting new challenges for adaptation due to global perspective changes and geometric distortions not found in existing adaptation tasks. To this end, we introduce loss coupling for unsupervised adversarial adaptation and boost prototype-based adaptation with ground-truth information. We additionally propose a novel supervised adaptation head for features in the bounding box regressor. Our method leads to more stable adversarial training and outperforms supervised and unsupervised baselines. Our method requires half the amount of training samples for small datasets to achieve the same performance as supervised fine-tuning.

关键词： Training Couplings Adaptation models Schedules Surveillance Prototypes computer architecture

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