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Learning Joint Appearance and Shape Co-Representations for Co-Saliency Detection

Learning Joint Appearance and Shape Co-Representations for C...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Guanting Guo Shenglong Hu Huihui Song Kaihua Zhang Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Existing leading Co-saliency Detection (CoD) framework aims to segment the co-salient objects by learning the consensus visual representation of the foreground objects. However, despite different categories, some distractors may have similar appearance to the co-salient objects, such as Apples vs. Bananas have similar color and textures. This makes it challenging to distinguish the distractors only through learning the co-salient object appearance representations. To address this issue, we propose a joint appearance and shape co-representation learner for CoD, dubbed as ASCoD. The ASCoD is composed of a Co-Appearance learning Module (CoAM) and a Co-Shape learning Module (CoSM). The CoAM first learns a co-salient object appearance embedding that encodes the global cross-image and spatial context information. Then, this embedding is set as a co-appearance prototype, which guides the model to enhance the features to highlight the co-salient object regions. Afterwards, we design the CoSM that is a cross-attention module, among which the key and the value encode the shape information from a set of salient tokens dynamically selected by a Co-Shape Prototype generation Module (CSPM). Finally, through jointly optimizing the cascaded CoAM and CoSM, the optimal appearance and shape co-representations are achieved, marrying the merits of both appearance and shape co-representations that are not only robust to co-salient objects appearance variations, but also can well discriminate the co-salient objects from the distractors with similar appearance. Extensive evaluations on three challenging benchmarks including CoCA, CoSOD3k and CoSal2015, demonstrate superiority of the ASCoD to a variety of state-of-the-art CoD methods.

关键词： visualization Shape Prototypes Color Signal processing Benchmark testing Robustness Acoustics Speech processing

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Real-World Mobile image Denoising Dataset with Efficient Baselines

Real-World Mobile Image Denoising Dataset with Efficient Bas...

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conference on Computer Vision and Pattern Recognition (CVPR)

作者： Roman Flepp Andrey Ignatov Radu Timofte Luc Van Gool Computer Vision Laboratory ETH Zürich AI Witchlabs Ltd. Zollikerberg Computer Vision Laboratory University of Würzburg

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The recently increased role of mobile photography has raised the standards of on-device photo processing tremendously. Despite the latest advancements in camera hardware, the mobile camera sensor area cannot be increased significantly due to physical constraints, leading to a pixel size of 0.6-2.0μm, which results in strong image noise even in moderate lighting conditions. In the era of deep learning, one can train a CNN model to perform robust image denoising. However, there is still a lack of a substantially diverse dataset for this task. To address this problem, we introduce a novel Mobile image Denoising Dataset (MIDD) comprising over 400,000 noisy / noise-free image pairs captured under various conditions by 20 different mobile camera sensors. Additionally, we propose a new DPreview test set consisting of data from 294 different cameras for precise model evaluation. Furthermore, we present the efficient baseline model SplitterNet for the considered mobile image denoising task that achieves high numerical and visual results, while being able to process 8MP photos directly on smartphone GPUs in under one second. Thereby outperforming models with similar runtimes. This model is also compatible with recent mobile NPUs, demonstrating an even higher speed when deployed on them. The conducted experiments demonstrate high robustness of the proposed solution when applied to images from previously unseen sensors, showing its high generalizability. The datasets, code and models can be found on the official project website 1 1 https://***/-ihnatova/*** 2 2 https://***/rflepp/Efficient_Mobile_Denoising_Models.

关键词： Training visualization Runtime Cameras Robustness Real-time systems Numerical models

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A Study of Data processing for Object Recognition in Scene image using FRCNN: A Smart Grid Technology

A Study of Data Processing for Object Recognition in Scene I...

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Innovations in Energy management and Renewable Resources (IEMRE) conference

作者： Das, Kaushik Baruah, Arun Kumar Dibrugarh Univ Comp Sci & Engn Dibrugarh Assam India Dibrugarh Univ Dept Math Dibrugarh Assam India

ISBN: (纸本)9781665412599

This paper proposes a new learning about the significance of part elements of a scene image with the effort of data processing. A top down tree structure with every node representing an annotation or bounding box having labeled visual features of an object existing in the image is studied in the paper. The images and its object annotations are from a trained dataset and are parsed to obtain the proposed representation. The images from the datasets and their parsed tree representations will be trained using a network called LSTM (Long Short Term Memory) network. The object detection may not be agnostic to the entire content of the image due to being influenced by the image composition and the discovered parts. The attempt has been made to show the object detection as a representation of the objects and their locations, parts of these objects, and the accuracy of the object detection method has been noted to have an efficient record with the implementation of the baseline Fast Region Based Convolutional Neural Network(FR-CNN) method. The tested google open images datasets are used and found to have increased object detection record in notably respect to the use of high cost sensors in digital devices.

关键词： Computer Vision bounding box LSTM CNN FR-CNN YOLO

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Toward UAV-Enabled Stereoscopic UL Heavy NOMA: Joint Resource Allocation and 3D Trajectory Design

Toward UAV-Enabled Stereoscopic UL Heavy NOMA: Joint Resourc...

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ieee International conference on communications (ICC)

作者： Haiyong Zeng Rui Zhang Xu Zhu Yufei Jiang Zhongxiang Wei Fu-Chun Zheng Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China College of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

We study an unmanned aerial vehicle (UAV)-enabled uplink heavy non-orthogonal multiple access (NOMA) system in this paper, where the UL communication becomes extremely important in some hot spot areas such as live concerts or soccer stadiums, and investigate the joint optimization of bandwidth assignment (BA) and power allocation (PA) with UAV three-dimensional (3D) trajectory design to maximize the minimum average rate among all ground users, while meeting their heterogeneous rate requirements. More specifically, we propose a joint BA and PA algorithm by revealing that the inter-user interference in each NOMA group can be eliminated naturally while deriving the sum rate of users. The algorithm is proposed to get the optimal BA and PA solutions with the help of closed-form results and ellipsoid method. After that, in order to solve the UAV 3D trajectory design problem we introduce the elevation angle as a supplementary variable, and the successive convex approximation method is adopted to obtain the UAV 3D trajectory. The joint BA and PA algorithm and the UAV 3D trajectory design can be optimized alternatively, which leads to fast convergence and demonstrates a superior minimum average rate among users and user fairness performance over the previous methods.

关键词： NOMA Three-dimensional displays Stereo image processing Autonomous aerial vehicles Approximation algorithms Trajectory Resource management

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Efficient Large-Scale Pre-Trained Model Guided MR Imaging Super-Resolution

Efficient Large-Scale Pre-Trained Model Guided MR Imaging Su...

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ieee International conference on Bioinformatics and Biomedicine (BIBM)

作者： Fenghang Zhang Guang Feng Xizhan Gao Sijie Niu School of Information Science and Engineering University of Jinan Jinan China Shandong Xiangxiangli Information Technology Co. Ltd. Jinan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Super-resolution (SR) is a post-processing technique that can effectively improve the resolution of MR Imaging without upgrading hardware devices. Although existing single-contrast SR reconstruction algorithms and multi-contrast SR algorithms have achieved impressive results, the ill-posed nature of the SR task makes it challenging to achieve significant performance improvements by simply improving the model architecture. Recently, pre-trained models have shown great potential in low-level visual restoration tasks. In this study, we explore using the powerful representational capabilities of pre-trained models to improve the performance of MRI SR and propose a Pre-Training Guided MRI SR (PTG-SR) architecture. Firstly, an effective and simple baseline model is constructed that improves the local and global perceptual capabilities of the modules while maintaining low computational resources. Secondly, we design a Pre-Training Guided Dynamic Alignment Module (PTG-DAM) that transforms the features extracted by the pre-trained model into information related to image degradation, resulting in high-quality and fine-grained SR images. Furthermore, an improved routing attention module, termed DRAM, is proposed which captures the relationships between different paths from both local and global perspectives with fewer resources, further enhancing the representational capabilities of the model. Extensive experimental results on the IXI and BraTS2020 datasets demonstrate that PTG-SR achieves advanced performance and robustness, outperforming existing state-of-the-art (SOTA) SR approaches.

关键词： Computational modeling Biological system modeling Magnetic resonance imaging Superresolution Transforms Computer architecture Feature extraction Robustness image restoration Data mining

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SPCNet: A Panoramic image depth estimation method based on spherical convolution

SPCNet: A Panoramic image depth estimation method based on s...

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ieee visual communications and image processing (VCIP)

作者： Si He Yu Liu Yumei Wang School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Peng Cheng Laboratory Research Center of Networks and Communications Shenzhen China

ISBN: (纸本)9781728173221

As an emerging media format, virtual reality (VR) has attracted the attention of researchers. 6-DoF VR can reconstruct the surrounding environment with the help of the depth information of the scene, so as to provide users with immersive experience. However, due to the lack of depth information in panoramic image, it is still a challenge to convert panorama to 6-DOF VR. In this paper, we propose a new depth estimation method SPCNet based on spherical convolution to solve the problem of depth information restoration of panoramic image. Particularly, spherical convolution is introduced to improve depth estimation accuracy by reducing distortion, which is attributed to Equi-Rectangular Projection (ERP). The experimental results show that many indicators of SPCNet are better than other advanced networks. For example, RMSE is 0.419 lower than UResNet. Moreover, the threshold accuracy of depth estimation has also been improved.

关键词： Solid modeling Convolution visual communication Estimation Immersive experience Media Feature extraction

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Towards Robust VSLAM in Dynamic Environments: A Light Field Approach

Towards Robust VSLAM in Dynamic Environments: A Light Field ...

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ieee International conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

作者： Kaveti, Pushyami Nir, Jagatpreet Singh Singh, Hanumant Northeastern Univ Khoury Coll Comp Sci Boston MA 02115 USA Northeastern Univ Dept Elect Engn Boston MA 02115 USA

ISBN: (纸本)9781665445214

There is a general expectation that robots should operate in urban environments often consisting of potentially dynamic entities like people, automobiles etc. Dynamic objects pose challenges to visual SLAM algorithms by introducing errors into the front-end. This paper presents a Light Field powered SLAM front-end which is robust to dynamic environments. A Light Field captures a bundle of light rays emerging from a single point in space, allowing us to see through dynamic objects occluding the static background via Synthetic Aperture Imaging (SAI). We detect apriori dynamic objects using semantic segmentation and perform semantic guided SAI on the Light Field acquired from a linear camera array. The combined use of SAI with semantic segmentation results in a significant reduction of the dynamic content and also adds valuable observations from the occluded static parts of the scene. The GPU implementation of our algorithm facilitates running at a speed of similar to 6 fps. We demonstrate considerable improvement in robustness and accuracy of pose estimation in dynamic environments by comparing it with state of the art VSLAM algorithms.

关键词： image segmentation visualization Simultaneous localization and mapping Heuristic algorithms Semantics Urban areas Graphics processing units

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Two-Directional Two-Dimensional PCA: An Efficient Face Recognition Method for Thermal Infrared images 5

Two-Directional Two-Dimensional PCA: An Efficient Face Recog...

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5th International conference on Information Communication and Signal processing, ICICSP 2022

作者： Gao, Chi Zhang, Xinming Wang, Hui Song, Liyao Hu, Bingliang Wang, Quan Xi'an Institute of Optics and PrecisionMechanics of the Chinese Academy of Sciences Key Laboratory of Spectral Imaging Technology Key Laboratory of Biomedical Spectroscopy of Xi'an Xi'an China University of Chinese Academy of Sciences Beijing China The School of Information and Communications Engineeringm Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9781665485890

Compared with face recognition in the environment of visible light, thermal infrared face recognition has the advantages of being independent of light, working around the clock, and capable of detecting hidden targets easily. In this paper, we propose a thermal infrared face recognition method based on the two-directional two-dimensional PCA (2D2DPCA) and random forest classifier. We compared this with two deep learning networks: Alexnet, Three-dimensional Convolutional Neural Networks (3DCNN), and applied these with two databases: the Terravic Facial IR database (with different facial angles) and the NVIE database (with various emotional expressions). Among these methods, the accuracy of face recognition with the 2D2DPCA method achieves the best recognition effect, it reached 99.92% and 99.97% in both databases, respectively. We statistically verified that our method could not only accurately and robustly recognize thermal infrared faces with large variations in angle and expression, but also greatly reduce computational complexity and data dimension, improving the speed of face recognition. With the two sample sets tested, our work has demonstrated that 2D2DPCA has excellent potential for facial image compression and may broaden thermal face recognition applications. © 2022 ieee.

关键词： Face recognition

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Leavening control system based on machine learning techniques 7

Leavening control system based on machine learning technique...

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7th ieee World Forum on Internet of Things, WF-IoT 2021

作者： Toska, Desilda Pulla, Alfredo Robustelli, Stefano Fiamma, Gianmarco 20068 Italy 20068 Italy

ISBN: (纸本)9781665444316

Our research work describes how deep learning techniques can be applied with success to create a non-invasive method to control the dynamic density of dough rising during the fermentation process. This paper explains in detail the steps performed to train and apply on the field a Convolutional Neural Network (CNN) to monitor the leavening of a traditional Christmas Italian type of sweet bread made in Milan, called Panettone, that usually needs an accurate and supervised leavening process of around three days. One of our main goals was to prove how these CNNs and their learned inner representations could easily become the foundation for developing a remote-supervision framework capable to monitor the leavening process. Since the duration of this crucial phase is not exactly predictable, as it depends on many external factors, and usually takes place during the night, it makes sense to adopt a not supervised approach that is able to autonomously detect and notify to the bakery personnel (sending sms, email, whatsapp, etc.) when the density of dough is considered optimal, appropriate and ready to start the baking *** demonstrated that a CNN based paradigm is more effective and more accurate than the current used empirical methods. The model converged and the average loss value was near to zero, even if the set of images and examples adopted to train and test the classifier was limited. Though applied in the bakery products context, the designed approach can be easily adapted to other monitoring tasks or industry domains, and its independence from User expert knowledge and specific artisanal skills can be considered one of the major advantages. © 2021 ieee.

关键词： image recognition

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Highly Efficient Speckle Noise Removal in Medical images Using GSO Optimization 7

Highly Efficient Speckle Noise Removal in Medical Images Usi...

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7th International conference on Advanced Computing and Communication Systems, ICACCS 2021

作者： Shabana Sulthana, S L Sucharitha Noorul Islam University Electronics and Communication Engineering Tamilnadu Kanyakumari India Electronics and Communication Engineering Telangana Maisammaguda India

ISBN: (纸本)9781665405201

Biomedical imaging assumes a significant function in different applications, such as early diagnosis, surgery, and classification. In general, the captured image is affected by noise, voltage, temperature fluctuations, and environmental conditions. Before image processing, the image must be pre-processed and unwanted pixel deviation should be removed to obtain a more accurate result. In image classification, the accuracy is directly proportional to the quality of the input image. This paper suggests a new way to remove speckle noise from input images using Glowworm Swarm optimization (GSO) and Wavelet Packet Transformation (WPT). The proposed technique is tested with several sample images and results were obtained with improved quality metrics. Various quality metrics such as Mean Square Error (MSE), Structural Similarity Index Metrics (SSIM), and Mean Absolute Error (MAE) are used to evaluate performance. From the analysis of the results, the proposed technique accomplishes better visual quality than the traditional methods. © 2021 ieee.

关键词： image denoising

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