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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

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Pansharpening Methods Based on a Redundant Contourlet Transform

Pansharpening Methods Based on a Redundant Contourlet Transf...

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Earth Observation and Geo-Spatial Information (ICEOGI), international conference on

作者： Soumeya Ourabia Nadia Baaziz Youcef Smara Departement of Electronics University Center of Tipaza -Morsli Abdelah-Institut of Sciences Laboratory of Image Processing and Radiation -USTHB Tipaza Algeria Department of Computer Science and Engineering Research Laboratory in Multimedia Information University of Quebec in Outaouais - UQO Gatineau Québec Canada Laboratory of Image Processing and Radiation Faculty of electrical engineering University of Sciences and Technology Houari Boumediene -USTHB Algiers Algeria

The pansharpening of high-resolution panchromatic (Pan) image and low-resolution multispectral (MS) images represents an important task in the remote sensing field. It allows the joint exploitation of the information derived from both types of images. However, this may cause a distortion of spectral information or a lack in details and structures. The multiresolution analysis using wavelet transforms has proved its efficiency in the pansharpening domain, due to the representation of the image content over different resolution levels. In image fusion applications, we always need more detail information to be incorporated in the pansharpening procedure in order to produce enhanced results. However, the limitation of the wavelet transform to three types of oriented detail coefficients prevents this need from being met. To overcome this limitation, we propose to use the redundant contourlet transform (RCT) which extracts a richer multiscale directional information from the image. For this purpose, two RCT-based pansharpening methods are introduced and suitable data fusion procedures are described. We conducted several experiments on two different datasets acquired respectively by ALSAT-2A and IKONOS satellites. The visual results as well as quantitative results from evaluation metrics demonstrate the performance of the proposed methods.

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Expression of Concern for: Medical image Fusion Solid Works Supporting Multiple Techniques with Feature-Level Transforms

Expression of Concern for: Medical Image Fusion Solid Works ...

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Sustainable Computing and Data Communication Systems (ICSCDS), international conference on

作者： Rakhi Mutha Devendra Nath Pathak Vandana Ahuja Prabhakar Ramesh Bhandari Pankaj Rahi Udit Mamodiya Department of Computer Application Lovely Professional University Punjab India Department of Computer Science and Engineering Poornima College of Engineering Jaipur Rajasthan India Department of Computer Science and Engineering Swami Vivekanand Institute of Engineering and Technology Punjab INDIA Department of Microbiology Sevadal Mahila Mahavidyalaya Umred Road Nagpur Maharashtra India Department of Artificial Intelligence & Data Science Poornima Institute of Engineering and Technology Jaipur Rajasthan India Department of Electrical Engineering Coordinator at PIET AICTE IDEA LAB Poornima Institute of Engineering and Technology Jaipur Rajasthan India

ISBN: (数字)9781665491990

ISBN: (纸本)9781665492003

The use of group classifiers on similar features has resulted in computationally expensive and tedious, making them unsuitable for online applications. The clustering techniques are evaluated for both inter-subject and intra-subject variations, and it is discovered that intra-subject order execution is superior. Another approach to dealing with object recognition from medical images is considered. This is known as similarity matching, and it is a computationally simple process as compared to costly and complex characterization computations. The major challenges that occur in the image processing are high noise levels, limited image resolution, and geometric deformations throughout the image.

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DCSN: A Flexible and Efficient Lightweight Network for Dense Cell Segmentation

DCSN: A Flexible and Efficient Lightweight Network for Dense...

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Medical Artificial Intelligence (MedAI), ieee international conference on

作者： Futai Pan Fei Wang Shanghai Key Lab of Intelligent Information Processing School of Computer Science and Technology Fudan University Shanghai China

Cells serve as the fundamental building blocks of living organisms and necessitate the utilization of diverse microscopic imaging techniques for observation. Nevertheless, there exist numerous limitations within phase-contrast microscopy imaging technology, leading to indistinct imaging in a considerable number of acquired cell images. Furthermore, the wide range of cell shapes and sizes, often densely distributed, poses challenges for research in the field of cell image analysis. We propose Dense Cell Segmentation Network (DCSN), an anchor-based single-stage instance segmentation method, to address the challenge of dense detection and instance segmentation in high-density cell images. While methods based on transformers have emerged as prominent approaches in natural image detection and segmentation, they are limited in their applicability to cell image analysis due to the scarcity of cell image datasets. Consequently, we aim to introduce a simple yet effective instance segmentation method to further address the challenge of cell image segmentation. DCSN is a lightweight multi-task model, combining object detection and semantic segmentation to achieve accurate instance segmentation. We validate our approach on LIVECell, a publicly available annotated cell dataset, and demonstrate its efficacy. By combining the advantages of anchor-based methods and single-stage methods, DCSN achieves a favorable balance between speed and performance. In comparison to the anchor-based two-stage model Mask R-CNN, DCSN offers a streamlined process and faster inference speed. Compared to the anchor-free single-stage method, CenterMask, our model demonstrates superior segmentation performance and generalization capabilities. Moreover,DCSN is a general end-to-end model that can be applied to various domains beyond cell segmentation tasks.

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AG3D: Learning to Generate 3D Avatars from 2D image Collections

AG3D: Learning to Generate 3D Avatars from 2D Image Collecti...

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international conference on computer Vision (ICCV)

作者： Zijian Dong Xu Chen Jinlong Yang Michael J. Black Otmar Hilliges Andreas Geiger Department of Computer Science ETH Zürich University of Tübingen Max Planck Institute for Intelligent Systems Tübingen

While progress in 2D generative models of human appearance has been rapid, many applications require 3D avatars that can be animated and rendered. Unfortunately, most existing methods for learning generative models of 3D humans with diverse shape and appearance require 3D training data, which is limited and expensive to acquire. The key to progress is hence to learn generative models of 3D avatars from abundant unstructured 2D image collections. However, learning realistic and complete 3D appearance and geometry in this under-constrained setting remains challenging, especially in the presence of loose clothing such as dresses. In this paper, we propose a new adversarial generative model of realistic 3D people learned from 2D images. Our method captures shape and deformation of the body and loose clothing by adopting a holistic 3D generator and integrating an efficient, flexible, articulation module. To improve realism, we train our model using multiple discriminators while also integrating geometric cues in the form of predicted 2D normal maps. We experimentally find that our method outperforms previous 3D- and articulation-aware methods in terms of geometry and appearance. We validate the effectiveness of our model and the importance of each component via systematic ablation studies.

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Message Passing Neural Network based Light Field image Compression

Message Passing Neural Network based Light Field Image Compr...

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ieee conference on Multimedia Information processing and Retrieval (MIPR)

作者： Nguyen Gia Bach Chanh Minh Tran Tho Nguyen Duc Phan Xuan Tan Eiji Kamioka Graduate School of Engineering and Science Shibaura Institute of Technology Tokyo Japan

Light Field possesses the ability to capture comprehensive information about light distribution in space, making it an appealing technology for immersive media applications. However, due to the vast amount of data generated by the Light Field, there has been significant interest in developing methods for its compression. The use of a graph data structure that outlines the relationship between viewpoints within the Light Field has been found to be a suitable way to process this data. Additionally, the graph convolution network (GCN), which merges graph and neural network approaches, has shown great promise in effectively handling graph data. However, since GCN doesn't directly support edge features, it is unable to weigh the contribution of source key view features, aggregating at the target non-key view, in the graph representation of a Light Field data. In this work, a Light Field compression scheme based on Message Passing Neural Network (MPNN) is proposed to account for the edge features during the training and reconstruction of non-key views, which is found to perform well on a sparse down-sampling pattern of key views. Experimental results show a gain of about 1 dB in PSNR, compared with the previous GCN-based approach, and our proposal also outperformed other baselines at low bitrates, using an adaptive quantization parameters (QP) selection.

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A Probabilistic Framework for Pruning Transformers Via a Finite Admixture of Keys

A Probabilistic Framework for Pruning Transformers Via a Fin...

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

作者： Tan M. Nguyen Tam Nguyen Long Bui Hai Do Duy Khuong Nguyen Dung D. Le Hung Tran-The Nhat Ho Stan J. Osher Richard G. Baraniuk Department of Mathematics University of California Los Angeles USA Department of Electrical and Computer Engineering Rice University Houston USA FPT Software AI Center Ha Noi Vietnam College of Engineering and Computer Science Vin University Vietnam Applied Artificial Intelligence Institute Deakin University Geelong Australia Department of Statistics and Data Sciences The University of Texas Austin USA

Pairwise dot product-based self-attention is key to the success of transformers which achieve state-of-the-art performance across a variety of applications in language and vision, but are costly to compute. It has been shown that most attention scores and keys in transformers are redundant and can be removed without loss of accuracy. In this paper, we develop a novel probabilistic framework for pruning attention scores and keys in transformers. We first formulate an admixture model of attention keys whose input data to be clustered are attention queries. We show that attention scores in self-attention correspond to the posterior distribution of this model when attention keys admit a uniform prior distribution. We then relax this uniform prior constraint and let the model learn these priors from data, resulting in a new Finite Admixture of Keys (FiAK). The learned priors are used for pruning away redundant attention scores and keys in the baseline transformers, improving the diversity of attention patterns that the models capture. We corroborate the efficiency of transformers pruned with FiAK on the imageNet object classification and WikiText-103 language modeling tasks. Our experiments demonstrate that transformers pruned with FiAK yield similar or better accuracy than the baseline dense transformers while being much more efficient in terms of memory and computational cost.

关键词： Computational modeling Memory management Signal processing Transformers Probabilistic logic Data models Acoustics

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An Improved image Up-Scaling Technique using Optimize Filter and Iterative Gradient Method

An Improved Image Up-Scaling Technique using Optimize Filter...

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Mobile Networks and Wireless Communications (ICMNWC), ieee international conference on

作者： Ramesh Chandra Poonia Kamal Upreti Sheela Hundekari Priyanka Dadhich Khushboo Malik Anmol Kapoor Dept. of Computer Science CHRIST(Deemed to be University) Delhi NCR Ghaziabad India MIT ADT University Rajbaug Loni Kalbhor Pune India School of Computer Application Manav Rachna International Institute of Research and Studies School of Law CHRIST(Deemed to be University) Delhi NCR Ghaziabad India Dept. of Computer Science Maharaja Surajmal Institute of Technology Delhi India

In numerous realtime applications, image upscaling often relies on several polynomial techniques to reduce computational complexity. However, in high-resolution (HR) images, such polynomial interpolation can lead to blurring artifacts due to edge degradation. Similarly, various edge-directed and learning-based systems can cause similar blurring effects in high-frequency images. To mitigate these issues, directional filtering is employed post corner averaging interpolation, involving two passes to complete the corner average process. The initial step in low-resolution (LR) picture interpolation involves corner pixel refinement after averaging interpolation. A directional filter is then applied to preserve the edges of the interpolated image. This process yields two distinct outputs: the base image and the detail image. Furthermore, an additional cuckoo-optimized filter is implemented on the base image, focusing on texture features and boundary edges to recover neighboring boundary edges. Additionally, a Laplacian filter is utilized to enhance intra-region information within the detailed image. To minimize reconstruction errors, an iterative gradient approach combines the optimally filtered image with the sharpened detail image, generating an enhanced HR image. Empirical data supports the effectiveness of the proposed algorithm, indicating superior performance compared to state-of-the-art methods in terms of both visual appeal and measured parameters. The proposed method's superiority is demonstrated experimentally across multiple image datasets, with higher PSNR, SSIM, and FSIM values indicating better image degradation reduction, improved edge preservation, and superior restoration capabilities, particularly when upscaling High-Frequency regions of images.

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SEF-VSOD: A Static Ensemble Framework For Video Saliency Using Modified Doubly U-Net

SEF-VSOD: A Static Ensemble Framework For Video Saliency Usi...

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Innovations in High Speed Communication and Signal processing (IHCSP), international conference on

作者： Sandeep Chand Kumain Maheep Singh Lalit K. Awasthi Dept. of Computer Science and Engg. NIT Uttarakhand Srinagar (Garhwal) Uttarakhand India

In this digital age, video and images are commonly used to process information. Although an image or video contains a lot of information, not all of it is useful. One of the key areas of computer vision is salient object detection, whose major goal is to replicate the human visual system and recognize the scene's most prominent object. The salient object detection techniques have a significant impact on a wide range of applications, including video summarization, automated cropping, etc. Deep learning approaches have recently become more common in the saliency detection problem. However, it is highly challenging for a single model to perform well in all circumstances due to the different constraints in the model creation process (training, parameter adjustment). To address this issue, the author(s) present a static ensemble framework for estimating video saliency. This ensemble approach aided in model performance improvement. The proposed model's performance is evaluated using two well-known publicly available video datasets, namely ViSal and DAVSOD-Easy. Based on the various quantitative evaluation parameters the proposed model performance is compared with the state-of- the-art SOD models.

关键词： Training Deep learning Technological innovation Estimation Object detection Visual systems Signal processing

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Revealing Performance Issues in Server-Side WebAssembly Runtimes Via Differential Testing

Revealing Performance Issues in Server-Side WebAssembly Runt...

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ieee international conference on Automated Software Engineering (ASE)

作者： Shuyao Jiang Ruiying Zeng Zihao Rao Jiazhen Gu Yangfan Zhou Michael R. Lyu Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

WebAssembly (Wasm) is a bytecode format originally serving as a compilation target for Web applications. It has recently been used increasingly on the server side, e.g., providing a safer, faster, and more portable alternative to Linux containers. With the popularity of server-side Wasm applications, it is essential to study performance issues (i.e., abnormal latency) in Wasm runtimes, as they may cause a significant impact on server-side applications. However, there is still a lack of attention to performance issues in server-side Wasm runtimes. In this paper, we design a novel differential testing approach WarpDiff to identify performance issues in server-side Wasm runtimes. The key insight is that in normal cases, the execution time of the same test case on different Wasm runtimes should follow an oracle ratio. We identify abnormal cases where the execution time ratio significantly deviates from the oracle ratio and subsequently locate the Wasm runtimes that cause the performance issues. We apply WarpDiff to test five popular server-side Wasm runtimes using 123 test cases from the LLVM test suite and demonstrate the top 10 abnormal cases we identified. We further conduct an in-depth analysis of these abnormal cases and summarize seven performance issues, all of which have been confirmed by the developers. We hope our work can inspire future investigation on improving Wasm runtime implementation and thus promoting the development of server-side Wasm applications.

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One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer

One-Stage 3D Whole-Body Mesh Recovery with Component Aware T...

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conference on computer Vision and Pattern Recognition (CVPR)

作者： Jing Lin Ailing Zeng Haoqian Wang Lei Zhang Yu Li International Digital Economy Academy (IDEA) Shenzhen International Graduate School Tsinghua University

Whole-body mesh recovery aims to estimate the 3D human body, face, and hands parameters from a single image. It is challenging to perform this task with a single network due to resolution issues, i.e., the face and hands are usually located in extremely small regions. Existing works usually detect hands and faces, enlarge their resolution to feed in a specific network to predict the parameter, and finally fuse the results. While this copy-paste pipeline can capture the fine-grained details of the face and hands, the connections between different parts cannot be easily recovered in late fusion, leading to implausible 3D rotation and unnatural pose. In this work, we propose a one-stage pipeline for expressive whole-body mesh recovery, named OSX, without separate networks for each part. Specifically, we design a Component Aware Transformer (CAT) composed of a global body encoder and a local face/hand decoder. The encoder predicts the body parameters and provides a high-quality feature map for the decoder, which performs a feature-level upsample-crop scheme to extract highresolution part-specific features and adopt keypointguided deformable attention to estimate hand and face precisely. The whole pipeline is simple yet effective without any manual post-processing and naturally avoids implausible prediction. Comprehensive experiments demonstrate the effectiveness of OSX. Lastly, we build a large-scale Upper-Body dataset (UBody) with high-quality 2D and 3D whole-body annotations. It contains persons with partially visible bodies in diverse real-life scenarios to bridge the gap between the basic task and downstream applications.

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