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A Multi-Level Supervision Model: A novel approach for Thermal Image Super Resolution

A Multi-Level Supervision Model: A novel approach for Therma...

引用

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

作者： Priya Kansal Sabari Nathan Couger Inc Shibuya Japan

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

This paper proposes a novel architecture for thermal image super-resolution. A very large dataset is provided by PBVS 2020 in their super-resolution challenge. This dataset contains the images with three different resolution scales(low, medium, high) [1]. This dataset is used to train the proposed architecture to generate the super-resolution images in x2, x3, x4 scales. The proposed architecture is based on the residual blocks as the base units of the network. Along with this, the coordinate convolution layer and the convolutional block attention Module (CBAM) are also used in the architecture. Further, the multi-level supervision is implemented to supervise the output image resolution similarity with the real image at each block during training. To test the robustness of the proposed model, we evaluated our model on the Thermal-6 dataset [13]. The results show that our model is efficient to achieve the state of art results on the PBVS'2020 dataset. Further the results on the Thermal-6 dataset show that the model has a decent generalization capacity.

关键词： Spatial resolution Training Task analysis computer architecture Image edge detection Convolution

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Vehicle Re-Identification Based on Complementary Features

Vehicle Re-Identification Based on Complementary Features

引用

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

作者： Cunyuan Gao Yi Hu Yi Zhang Rui Yao Yong Zhou Jiaqi Zhao School of Computer Science and Technology China University of Mining and Technology China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

In this work, we present our solution to the vehicle reidentification (vehicle Re-ID) track in AI City Challenge 2020 (AIC2020). The purpose of vehicle Re-ID is to retrieve the same vehicle appeared across multiple cameras, and it could make a great contribution to the Intelligent Traffic System(ITS) and smart city. Due to the vehicle's orientation, lighting and inter-class similarity, it is difficult to achieve robust and discriminative representation feature. For the vehicle Re-ID track in AIC2020, our method is to fuse features extracted from different networks in order to take advantages of these networks and achieve complementary features. For each single model, several methods such as multi-loss, filter grafting, semi-supervised are used to increase the representation ability as better as possible. Top performance in City-Scale Multi-Camera Vehicle ReIdentification demonstrated the advantage of our methods, we got 5-th place in the vehicle Re-ID track of AIC2020.

关键词： Feature extraction Training Task analysis Encoding Testing Information filters

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Multi-object Graph-based Segmentation with Non-overlapping Surfaces

Multi-object Graph-based Segmentation with Non-overlapping S...

引用

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

作者： Patrick M. Jensen Anders B. Dahl Vedrana A. Dahl Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs Lyngby Denmark

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

For 3D images, segmentation via fitting surface meshes to object boundaries provides an efficient way to handle large images and enforce geometric prior knowledge. Furthermore, fitting such meshes with graph cuts has proven to be a versatile and robust framework. However, when segmenting multiple distinct objects in one image, current methods do not allow the natural constraint that objects should not overlap. In this paper, we present an extension to graph cut based methods which can provide a globally optimal segmentation of thousands of objects while guaranteeing no overlap. Our method works by separating objects with planes whose positions are determined as part of the graph cut. To demonstrate the general applicability of our method, we apply it to several 3D microscopy data sets from both biology and materials science. Our results show both quantitative and qualitative improvements.

关键词： Image segmentation Three-dimensional displays Microscopy Optical microscopy Shape Image edge detection Optimization

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Image Denoising with Shrinkage and Redundant Representations

Image Denoising with Shrinkage and Redundant Representations

引用

conference on computer vision and pattern recognition (CVPR)

作者： M. Elad B. Matalon M. Zibulevsky Department of Computer Science Technion-Israel Institute of Technology Haifa Israel Department of Electrical Engineering Israel

Shrinkage is a well known and appealing denoising technique. The use of shrinkage is known to be optimal for Gaussian white noise, provided that the sparsity on the signal’s representation is enforced using a unitary transform. Still, shrinkage is also practiced successfully with nonunitary, and even redundant representations. In this paper we shed some light on this behavior. We show that simple shrinkage could be interpreted as the first iteration of an algorithm that solves the basis pursuit denoising (BPDN) problem. Thus, this work leads to a novel iterative shrinkage algorithm that can be considered as an effective pursuit method. We demonstrate this algorithm, both on synthetic data, and for the image denoising problem, where we learn the image prior parameters directly from the given image. The results in both cases are superior to several popular alternatives.

关键词： Image denoising Wavelet transforms Noise reduction Iterative algorithms Table lookup White noise Pursuit algorithms computer science Iterative methods Maximum a posteriori estimation

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Seamless Image Stitching of Scenes with Large Motions and Exposure Differences

Seamless Image Stitching of Scenes with Large Motions and Ex...

引用

conference on computer vision and pattern recognition (CVPR)

作者： A. Eden M. Uyttendaele R. Szeliski Computer Science Division University of California Berkeley USA Interactive Visual Media Group Microsoft Research Limited USA

This paper presents a technique to automatically stitch multiple images at varying orientations and exposures to create a composite panorama that preserves the angular extent and dynamic range of the inputs. The main contribution of our method is that it allows for large exposure differences, large scene motion or other misregistrations between frames and requires no extra camera hardware. To do this, we introduce a two-step graph cut approach. The purpose of the first step is to fix the positions of moving objects in the scene. In the second step, we fill in the entire available dynamic range. We introduce data costs that encourage consistency and higher signal-to-noise ratios, and seam costs that encourage smooth transitions. Our method is simple to implement and effective. We demonstrate the effectiveness of our approach on several input sets with varying exposures and camera orientations.

关键词： Layout Cameras Dynamic range Pixel Hardware Costs Radiometry computer science Signal to noise ratio Calibration

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Dynamic Attention-based Visual Odometry

Dynamic Attention-based Visual Odometry

引用

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

作者： Xin-Yu Kuo Chien Liu Kai-Chen Lin Chun-Yi Lee Elsa Lab Department of Computer Science National Tsing Hua University Hsinchu Taiwan

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

This paper proposes a dynamic attention-based visual odometry framework (DAVO), a learning-based VO method, for estimating the ego-motion of a monocular camera. DAVO dynamically adjusts the attention weights on different semantic categories for different motion scenarios based on optical flow maps. These weighted semantic categories can then be used to generate attention maps that highlight the relative importance of different semantic regions in input frames for pose estimation. In order to examine the proposed DAVO, we perform a number of experiments on the KITTI Visual Odometry and SLAM benchmark suite to quantitatively and qualitatively inspect the impacts of the dynamically adjusted weights on the accuracy of the evaluated trajectories. Moreover, we design a set of ablation analyses to justify each of our design choices, and validate the effectiveness as well as the advantages of DAVO. Our experiments on the KITTI dataset shows that the proposed DAVO framework does provide satisfactory performance in ego-motion estimation, and is able deliver competitive performance when compared to the contemporary VO methods.

关键词： Semantics Cameras Trajectory Pose estimation Visual odometry Dynamics

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StegaNeRV: Video Steganography using Implicit Neural Representation

StegaNeRV: Video Steganography using Implicit Neural Represe...

引用

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

作者： Monsij Biswal Tong Shao Kenneth Rose Peng Yin Sean McCarthy Department of Electrical and Computer Engineering University of California Santa Barbara Santa Barbara CA USA Dolby Laboratories Inc Sunnyvale CA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Numerous studies have recently advanced the state-of-the art for representing videos through an implicit neural network (INR). As these models become increasingly ubiquitous, there is a growing demand for concealing data within INR reconstructed videos such as for storing content metadata and sensitive licensing information. In this paper, we explore a new space in video steganography, hiding a distinct image within each RGB frame output by an INR. We propose a joint training strategy of a U-Net based steganographic decoder with an INR model for video. Experimental results show that hidden images can be embedded and subsequently reconstructed with high fidelity while preserving the quality of the cover frames. Furthermore we demonstrate that by introducing an attention module which emphasizes hiding within the edges and rich texture patches in the cover frame, secret images can be reconstructed with superior quality and can also be concealed at greater resolutions.

关键词： Training Steganography Image resolution Neural networks Metadata Space exploration Decoding

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CSPNet: A New Backbone that can Enhance Learning Capability of CNN

CSPNet: A New Backbone that can Enhance Learning Capability ...

引用

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

作者： Chien-Yao Wang Hong-Yuan Mark Liao Yueh-Hua Wu Ping-Yang Chen Jun-Wei Hsieh I-Hau Yeh Institute of Information Science Academia Sinica Taiwan Department of Computer Science National Chiao Tung University Taiwan College of Artificial Intelligence and Green Energy National Chiao Tung University Taiwan Elan Microelectronics Corporation Taiwan

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, such success greatly relies on costly computation resources, which hinders people with cheap devices from appreciating the advanced technology. In this paper, we propose Cross Stage Partial Network (CSPNet) to mitigate the problem that previous works require heavy inference computations from the network architecture perspective. We attribute the problem to the duplicate gradient information within network optimization. The proposed networks respect the variability of the gradients by integrating feature maps from the beginning and the end of a network stage, which, in our experiments, reduces computations by 20% with equivalent or even superior accuracy on the ImageNet dataset, and significantly outperforms state-of-the-art approaches in terms of AP 50 on the MS COCO object detection dataset. The CSPNet is easy to implement and general enough to cope with architectures based on ResNet, ResNeXt, and DenseNet.

关键词： Object detection computer architecture Detectors Mathematical model Computational modeling computer vision computer science

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ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier Transformer

ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fou...

引用

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

作者： Wei Dong Han Zhou Yuqiong Tian Jingke Sun Xiaohong Liu Guangtao Zhai Jun Chen McMaster University Shanghai Jiao Tong University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Shadow-affected images often exhibit pronounced spatial discrepancies in color and illumination, consequently degrading various vision applications including object detection and segmentation systems. To effectively eliminate shadows in real-world images while preserving intricate details and producing visually compelling outcomes, we introduce a mask-free Shadow Removal and Refinement network (ShadowRefiner) via Fast Fourier Transformer. Specifically, the Shadow Removal module in our method aims to establish effective mappings between shadow-affected and shadow-free images via spatial and frequency representation learning. To mitigate the pixel misalignment and further improve the image quality, we propose a novel Fast-Fourier Attention based Transformer (FFAT) architecture, where an innovative attention mechanism is designed for meticulous refinement. Our method wins the championship in the Perceptual Track and achieves the second best performance in the Fidelity Track of NTIRE 2024 Image Shadow Removal Challenge. Besides, comprehensive experiment result also demonstrate the compelling effectiveness of our proposed method. The code is publicly available: https://***/movingforward100/Shadow_R.

关键词： Representation learning Image quality Image segmentation computer vision Image color analysis conferences Lighting

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Acceleration Strategies for Gaussian Mean-Shift Image Segmentation

Acceleration Strategies for Gaussian Mean-Shift Image Segmen...

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conference on computer vision and pattern recognition (CVPR)

作者： M.A. Carreira-Perpinan Department of Computer Science & Electrical Engineering OGI Oregon Health & Science University USA

Gaussian mean-shift (GMS) is a clustering algorithm that has been shown to produce good image segmentations (where each pixel is represented as a feature vector with spatial and range components). GMS operates by defining a Gaussian kernel density estimate for the data and clustering together points that converge to the same mode under a fixed-point iterative scheme. However, the algorithm is slow, since its complexity is O(kN2), where N is the number of pixels and k the average number of iterations per pixel. We study four acceleration strategies for GMS based on the spatial structure of images and on the fact that GMS is an expectation-maximisation (EM) algorithm: spatial discretisation, spatial neighbourhood, sparse EM and EM-Newton algorithm. We show that the spatial discretisation strategy can accelerate GMS by one to two orders of magnitude while achieving essentially the same segmentation; and that the other strategies attain speedups of less than an order of magnitude.

关键词： Acceleration Image segmentation Kernel Clustering algorithms Pixel Image converters Iterative algorithms Bandwidth computer science Equations

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