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Quaternion-driven CNN for Object Pose Tracking

Quaternion-driven CNN for Object Pose Tracking

引用

IEEE visual communications and image processing (VCIP)

作者： Mateusz Majcher Bogdan Kwolek AGH University of Science and Technology Kraków Poland

ISBN: (纸本)9781728173221

In this work we propose a new mixed-input neural network for instance level monocular object tracking. The proposed Y-like architecture has two inputs and one output. An object id with a quaternion representing the object rotation in the previous frame are fed to the first input, whereas the object sub-window is fed to the second input. We demonstrate that on the basis of quaternions the neural network learns attention blobs representing the object rotation in the previous frame. A single neural network has been trained for six objects to estimate their fiducial points in sequences of RGB images. A tracking by optimization approach has been leveraged in the experiments. The algorithm has been evaluated on the OPT benchmark dataset for 6DoF object pose tracking as well as a custom dataset including image sequences with both real and rendered objects.

关键词： visual communication Quaternions Neural networks Pose estimation Optimized production technology Benchmark testing image sequences

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Aerial image Classification with Label Splitting and Optimized Triplet Loss Learning

Aerial Image Classification with Label Splitting and Optimiz...

引用

IEEE visual communications and image processing (VCIP)

作者： Rijun Liao Zhu Li Shuvra S. Bhattacharyya George York University of Missouri-Kansas City MO USA University of Maryland College Park MD USA US Air Force Academy UAS Research Center Colorado Springs CO USA

ISBN: (纸本)9781728173221

With the development of airplane platforms, aerial image classification plays an important role in a wide range of remote sensing applications. The number of most of aerial image dataset is very limited compared with other computer vision datasets. Unlike many works that use data augmentation to solve this problem, we adopt a novel strategy, called, label splitting, to deal with limited samples. Specifically, each sample has its original semantic label, we assign a new appearance label via unsupervised clustering for each sample by label splitting. Then an optimized triplet loss learning is applied to distill domain specific knowledge. This is achieved through a binary tree forest partitioning and triplets selection and optimization scheme that controls the triplet quality. Simulation results on NWPU, UCM and AID datasets demonstrate that proposed solution achieves the state-of-the-art performance in the aerial image classification.

关键词： Training Histograms visual communication Simulation image processing Semantics Forestry

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Improving Transferability Reversible Adversarial Examples Based on Flipping Transformation 9th

Improving Transferability Reversible Adversarial Examples Ba...

引用

9th International conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Fang, Youqing Jia, Jingwen Yang, Yuhai Lyu, Wanli Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (纸本)9789819959679

Adding subtle perturbations to an image can cause the classification model to misclassify, and such images are called adversarial examples. Adversarial examples threaten the safe use of deep neural networks, but when combined with reversible data hiding (RDH) technology, they can protect images from being correctly identified by unauthorized models and recover the image lossless under authorized models. Based on this, the reversible adversarial example (RAE) is rising. However, existing RAE technology focuses on feasibility, attack success rate and image quality, but ignores transferability and time complexity. In this paper, we optimize the data hiding structure and combine data augmentation technology, which flips the input image in probability to avoid overfitting phenomenon on the dataset. On the premise of maintaining a high success rate of white-box attacks and the image’s visual quality, the proposed method improves the transferability of reversible adversarial examples by approximately 16% and reduces the computational cost by approximately 43% compared to the state-of-the-art method. In addition, the appropriate flip probability can be selected for different application scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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SDE-DualENet: A Novel Dual Efficient Convolutional Neural Network for Robust Stereo Depth Estimation

SDE-DualENet: A Novel Dual Efficient Convolutional Neural Ne...

引用

IEEE visual communications and image processing (VCIP)

作者： Rithvik Anil Mansi Sharma Rohit Choudhary Mechanical Engineering IIT Madras Electrical Engineering IIT Madras

ISBN: (纸本)9781728173221

Stereo depth estimation is dependent on optimal correspondence matching between pixels of stereo-pair image to infer depth. In this paper, we attempt to revisit the stereo depth estimation problem in a simple dual convolutional neural network (CNN) based on EfficientNet that avoids the construction of a cost volume in stereo matching. This has been performed by considering different weights in otherwise identical towers of the CNN. The proposed algorithm is dubbed as SDE-DualENet. The architecture of SDE-DualENet eliminates the construction of cost-volume by learning to match correspondence between pixels with a different set of weights in the dual towers. The results are demonstrated on complex scenes with high details and large depth variations. The SDE-DualENet depth prediction network outperforms state-of-the-art monocular and stereo depth estimation methods, both qualitatively and quantitatively on challenging scene flow dataset. The code and pre-trained models will be made publicly available.

关键词： Costs Codes visual communication image processing Poles and towers Estimation Prediction algorithms

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Adaptive Threshold-based Sparse Representation Network for image Compressive Sensing Reconstruction

Adaptive Threshold-based Sparse Representation Network for I...

引用

IEEE visual communications and image processing (VCIP)

作者： Yunyi Xuan Chunling Yang Xin Yang School of Electronic and Information Engineering South China University of Technology

ISBN: (纸本)9781728173221

Recently, network-based image Compressive Sensing (ICS) algorithms show superior performance in reconstruction quality and speed, yet non-interpretable. Herein, we propose an Adaptive Threshold-based Sparse Representation Reconstruction Network (ATSR-Net), composed of the Convolutional Sparse Representation subnet (CSR-subnet) and the truly Adaptive Threshold Generation subnet (ATG-subnet). The traditional iterations are unfolded into several CSR-subnets, which can fully exploit the local and nonlocal similarities. The ATG-subnet automatically determines a threshold map based on the image intrinsic characterization for flexible feature selection. Moreover, we present a three-level consistency loss based on pixel-level, measurement-level, and feature-level, to accelerate the network convergence. Extensive experiment results demonstrate the superiority of the proposed network to the existing state-of-the-art methods by large margins, both quantitatively and qualitatively.

关键词： Integrated circuits Adaptive systems image coding visual communication Machine learning Loss measurement Sensors

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Cross-Component Sample Offset for image and Video Coding

Cross-Component Sample Offset for Image and Video Coding

引用

IEEE visual communications and image processing (VCIP)

作者： Yixin Du Xin Zhao Shan Liu Tencent America Palo AltoCAUnited States94306

ISBN: (纸本)9781728173221

Existing cross-component video coding technologies have shown great potential on improving coding efficiency. The fundamental insight of cross-component coding technology is respecting the statistical correlations among different color components. In this paper, a Cross-Component Sample Offset (CCSO) approach for image and video coding is proposed inspired by the observation that, luma component tends to contain more texture, while chroma component is relatively smoother. The key component of CCSO is a non-linear offset mapping mechanism implemented as a look-up-table (LUT). The input of the mapping is the co-located reconstructed samples of luma component, and the output is offset values applied on chroma component. The proposed method has been implemented on top of a recent version of libaom. Experimental results show that the proposed approach brings 1.16% Random Access (RA) BD-rate saving on top of AV1 with marginal encoding/decoding time increase.

关键词： Video coding image coding Correlation visual communication image color analysis Filtering Simulation

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Human Action Recognition on Raw Depth Maps

Human Action Recognition on Raw Depth Maps

引用

IEEE visual communications and image processing (VCIP)

作者： Jacek Trelinski Bogdan Kwolek AGH University of Science and Technology Kraków Poland

ISBN: (纸本)9781728173221

We propose an effective framework for human action recognition on raw depth maps. We leverage a convolutional autoencoder to extract on sequences of deep maps the frame-features that are then fed to a 1D-CNN responsible for embedding action features. A Siamese neural network trained on repre-sentative single depth map for each sequence extracts features, which are then processed by shapelets algorithm to extract action features. These features are then concatenated with features extracted by a BiLSTM with TimeDistributed wrapper. Given the learned individual models on such features we perform a selection of a subset of models. We demonstrate experimentally that on SYSU 3DHOI dataset the proposed algorithm outperforms considerably all recent algorithms including skeleton-based ones.

关键词： Solid modeling Three-dimensional displays image recognition visual communication Neural networks Benchmark testing Feature extraction

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A High Accuracy Camera Calibration Method for Sport Videos

A High Accuracy Camera Calibration Method for Sport Videos

引用

IEEE visual communications and image processing (VCIP)

作者： Neng Zhang Ebroul Izquierdo Multimedia and Vision Research Group School of Electronic Engineering and Computer Science Queen Mary University of London London UK

ISBN: (纸本)9781728173221

Camera calibration for sport videos enables precise and natural delivery of graphics on video footage and several other special effects. This in turns substantially improves the visual experience in the audience and facilitates sports analysis within or after the live show. In this paper, we propose a high accuracy camera calibration method for sport videos. First, we generate a homography database by uniformly sampling camera parameters. This database includes more than 91 thousand different homography matrices. Then, we use the conditional generative adversarial network (cGAN) to achieve semantic segmentation splitting the broadcast frames into four classes. In a subsequent processing step, we build an effective feature extraction network to extract the feature of semantic segmented images. After that, we search for the feature in the database to find the best matching homography. Finally, we refine the homography by image alignment. In a comprehensive evaluation using the 2014 World Cup dataset, our method outperforms other state-of-the-art techniques.

关键词： image segmentation visualization Databases visual communication Semantics Feature extraction Cameras

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Learning-Based Complexity Reduction Scheme for VVC Intra-Frame Prediction

Learning-Based Complexity Reduction Scheme for VVC Intra-Fra...

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IEEE visual communications and image processing (VCIP)

作者： Mário Saldanha Gustavo Sanchez César Marcon Luciano Agostini Graduate Program in Computing (PPGC) Federal University of Pelotas Pelotas Brazil IF Farroupilha Alegrete Brazil Pontificial Catholic University of Rio Grande do Sul Porto Alegre Brazil

ISBN: (纸本)9781728173221

This paper presents a learning-based complexity reduction scheme for Versatile Video Coding (VVC) intra-frame prediction. VVC introduces several novel coding tools to improve the coding efficiency of the intra-frame prediction at the cost of a high computational effort. Thus, we developed an efficient complexity reduction scheme composed of three solutions based on machine learning and statistical analysis to reduce the number of intra prediction modes evaluated in the costly Rate-Distortion Optimization (RDO) process. Experimental results demonstrated that the proposed solution provides 18.32% encoding timesaving with a negligible impact on the coding efficiency.

关键词： Video coding image coding visual communication Statistical analysis Current measurement Rate-distortion Machine learning

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Generative DNA: Representation Learning for DNA-based Approximate image Storage

Generative DNA: Representation Learning for DNA-based Approx...

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IEEE visual communications and image processing (VCIP)

作者： Giulio Franzese Yiqing Yan Giuseppe Serra Ivan D'Onofrio Raja Appuswamy Pietro Michiardi EURECOM Biot France

ISBN: (纸本)9781728173221

Synthetic DNA has received much attention recently as a long-term archival medium alternative due to its high density and durability characteristics. However, most current work has primarily focused on using DNA as a precise storage medium. In this work, we take an alternate view of DNA. Using neural-network-based compression techniques, we transform images into a latent-space representation, which we then store on DNA. By doing so, we transform DNA into an approximate image storage medium, as images generated back from DNA are only approximate representations of the original images. Using several datasets, we investigate the storage benefits of approximation, and study the impact of DNA storage errors (substitutions, indels, bias) on the quality of approximation. In doing so, we demonstrate the feasibility and potential of viewing DNA as an approximate storage medium.

关键词： Representation learning Sequential analysis image coding Costs visual communication Roads DNA

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