检索结果-内蒙古大学图书馆

IEEE International conference on visual communications and image processing (VCIP)

作者： Ma, Tianfang Liu, Shuoyan Wang, Qian Tianjin Univ Tianjin Peoples R China Natl Univ Singapore Singapore Singapore Univ Durham Durham England

ISBN: (纸本)9781665475921

Crowd counting aims at automatically estimating the number of persons in still images. It has attracted much attention due to its potential usage in surveillance, intelligent transportation and many other scenarios. In the recent decade, most researchers have been focusing on the design of novel deep learning models for improved crowd counting performance. Such attempts include proposing advanced architectures of deep neural networks, using different training strategies and loss functions. Other than the capabilities of models, the crowd counting performance is also determined by the quantity and the quality of training data. Whilst the deep models are data-hungry and better performance can usually be expected with more training data, annotating images for training is time-consuming and expensive in real-world applications. In this work, we focus on the efficiency of data annotation for crowd counting. By varying the number of annotated images and the number of annotated points (one point is annotated per person head) for training, our experimental results demonstrate it is more efficient to annotate a small number of points per image across a large number of images for training. Based on this conclusion, we present a novel adaptive scaling mechanism for data augmentation to diversify the training images without extra annotation cost. The mechanism is proved effective via thorough experiments.

关键词： Crowd counting Data annotation efficiency Data augmentation Adaptive scaling

来源：评论

学校读者我要写书评

暂无评论

Tell Codec What Worth Compressing: Semantically Disentangled image Coding for Machine with LMMs

Tell Codec What Worth Compressing: Semantically Disentangled...

引用

2024 conference on visual communications and image processing

作者： Liu, Jinming Wei, Yuntao Lin, Junyan Zhao, Shengyang Sun, Heming Chen, Zhibo Zeng, Wenjun Jin, Xin Shanghai Jiao Tong Univ Shanghai Peoples R China Ningbo Inst Digital Twin Eastern Inst Technol Ningbo Peoples R China Univ Sci & Technol China Hefei Peoples R China Yokohama Natl Univ Yokohama Kanagawa Japan

ISBN: (纸本)9798331529543;9798331529550

We present a new image compression paradigm to achieve "intelligently coding for machine" by cleverly leveraging the common sense of Large Multimodal Models (LMMs). We are motivated by the evidence that large language/multimodal models are powerful general-purpose semantics predictors for understanding the real world. Different from traditional image compression typically optimized for human eyes, the image coding for machines (ICM) framework we focus on requires the compressed bitstream to more comply with different downstream intelligent analysis tasks. To this end, we employ LMM to tell codec what to compress: 1) first utilize the powerful semantic understanding capability of LMMs w.r.t object grounding, identification, and importance ranking via prompts, to disentangle image content before compression, 2) and then based on these semantic priors we accordingly encode and transmit objects of the image in order with a structured bitstream. In this way, diverse vision benchmarks including image classification, object detection, instance segmentation, etc., can be well supported with such a semantically structured bitstream. We dub our method "SDComp" for "Semantically Disentangled Compression", and compare it with state-of-the-art codecs on a wide variety of different vision tasks. SDComp codec leads to more flexible reconstruction results, promised decoded visual quality, and a more generic/satisfactory intelligent task-supporting ability.

关键词： image Coding for Machine Large Multimodal Model Semantically Structured Bitstream

来源：评论

学校读者我要写书评

暂无评论

Multi-information Aggregation Network for Fundus image Quality Assessment

Multi-information Aggregation Network for Fundus Image Quali...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Li, Yuan Yue, Guanghui Duan, Lvyin Wu, Honglv Wang, Tianfu Shenzhen Univ Hlth Sci Ctr Sch Biomed Engn Shenzhen 518060 Peoples R China

ISBN: (纸本)9781665475921

Fundus image quality assessment (IQA) is essential for controlling the quality of retinal imaging and guaranteeing the reliability of diagnoses by ophthalmologists. Existing fundus IQA methods mainly explore local information to consider local distortions from convolutional neural networks (CNNs), yet ignoring global distortions. In this paper, we propose a novel multi-information aggregation network, termed MA-Net, for fundus IQA by extracting both local and global information. Specifically, MA-Net adopts an asymmetric dual-branch structure. For an input image, it uses the ResNet50 and vision transformer (ViT) to obtain the local and global representations from the upper and lower branches, respectively. In addition, MA-Net separately feed different images into the two branches to rank their quality for supplementing the feature representations. Thanks to the exploration of intra- and inter-class information between images, our MA-Net is competent for the fundus IQA task. Experiment results on the EyeQ dataset show that our MA-Net outperforms the baselines (i.e., ResNet50 and ViT) by 3.06% and 7.61% in Acc, and is superior to the mainstream methods.

关键词： fundus image quality assessment multi-task visual transformer convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Enhancing Privacy-Utility Tradeoff with Few-Round Strategy in Heterogeneous Federated Learning

Enhancing Privacy-Utility Tradeoff with Few-Round Strategy i...

引用

2024 conference on visual communications and image processing

作者： Wei, Qingbin Zhang, Feilong Bai, Yuanchao Zhai, Deming Jiang, Junjun Liu, Xianming Harbin Inst Technol Fac Comp Harbin Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Federated learning inherently provides a certain level of privacy protection, which however is often inadequate in many real-world scenarios. Existing privacy-preserving methods frequently incur unbearable time overheads or result in non-negligible deterioration to model performance, thus suffering from the tradeoff between performance and privacy. In this work, we propose a novel Federated Privacy-Preserving Knowledge Transfer framework, namely FedPPKT, which employs data-free knowledge distillation in a meta-learning manner to rapidly generates pseudo data and performs privacy-preserving knowledge transfer. FedPPKT establishes a protective barrier between the original private data and the federated model, thereby ensuring user privacy. Furthermore, leveraging the few-round strategy of FedPPKT, it has the capability to reduce the number of communication rounds, further mitigating the risk of privacy exposure for user data. With the help of the meta generator, the problem of uneven local label distribution on clients is alleviated, mitigating data heterogeneity and improving model performance. Experiments show that FedPPKT outperforms the state-of-the-art privacy-preserving federated learning methods. Our code is publicly available at https://***/HIT-weiqb/FedPPKT.

关键词： Generative image Compression Learned image Compression

来源：评论

学校读者我要写书评

暂无评论

Optimization of Probability Distributions for Residual Coding of Screen Content

Optimization of Probability Distributions for Residual Codin...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Och, Hannah Strutz, Tilo Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg FAU Multimedia Commun & Signal Proc Cauerstr 7 D-91058 Erlangen Germany Leipzig Univ Telecommun Deutsch Telekom AG Inst Commun Engn Gustav Freytag Str 43-45 D-04277 Leipzig Germany

ISBN: (纸本)9781728185514

Probability distribution modeling is the basis for most competitive methods for lossless coding of screen content. One such state-of-the-art method is known as soft context formation (SCF). For each pixel to be encoded, a probability distribution is estimated based on the neighboring pattern and the occurrence of that pattern in the already encoded image. Using an arithmetic coder, the pixel color can thus be encoded very efficiently, provided that the current color has been observed before in association with a similar pattern. If this is not the case, the color is instead encoded using a color palette or, if it is still unknown, via residual coding. Both palette-based coding and residual coding have significantly worse compression efficiency than coding based on soft context formation. In this paper, the residual coding stage is improved by adaptively trimming the probability distributions for the residual error. Furthermore, an enhanced probability modeling for indicating a new color depending on the occurrence of new colors in the neighborhood is proposed. These modifications result in a bitrate reduction of up to 2.9 % on average. Compared to HEVC (HM-16.21 + SCM-8.8) and FLIF, the improved SCF method saves on average about 11 % and 18 % rate, respectively.

关键词： distribution modelling lossless coding screen content coding color image compression soft context formation

来源：评论

学校读者我要写书评

暂无评论

A STUDY ON MOUTH SHAPE-FEATURES SUITABLE FOR HMM SPEECH RECOGNITION USING FUSION OF visual AND AUDITORY INFORMATION

引用

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS communications AND COMPUTER SCIENCES 1995年第11期E78A卷 1548-1552页

作者： DOI, N SHINTANI, A HAYASHI, Y OGIHARA, A TAKAMATSU, S Univ of Osaka Prefecture Sakai-shi Japan

Recently, some speech recognition methods using fusion of visual and auditory information have been researched. In this paper, a study on the mouth shape image suitable for fusion of visual and auditory information has been described. Features of mouth shape which are extracted from gray level image and binary image are adopted, and speech recognition using linear combination method has been performed. From results of speech recognition, the studies on the mouth shape features which are effective in fusion of visual and auditory information have been performed. And the effectiveness of using two kinds of mouth shape features also has been confirmed.

关键词： SPEECH RECOGNITION FUSION OF visual AND AUDITORY INFORMATION FEATURE OF MOUTH SHAPE image processing

来源：评论

学校读者我要写书评

暂无评论

image Retrieval Method using visual Query Suggestion and Relevance Feedback

Image Retrieval Method using Visual Query Suggestion and Rel...

引用

4th International conference on Wireless communications and Signal processing (WCSP)

作者： Zhang, Jing Yang, Yuncong Zhuo, Li Diao, Mengmeng Beijing Univ Technol Signal & Informat Proc Lab Beijing Peoples R China

ISBN: (纸本)9781467358293;9781467358309

Query suggestion is an effective human-computer interaction (HCI) approach in information retrieval. According to human vision system, the image retrieval method using visual query suggestion (VQS) can provide a friendly query interface to solve the query ambiguity problem. In this paper, the contentbased image retrieval (CBIR) system is realized first. Human-computer interaction used VQS to obtain users' intention. In this step, user submitted a query keyword to the system. VQS is utilized to provide a list of suggestions, each containing a keyword and a collection of representative images. If the user selects one of the image suggestions, this image will be viewed as the key image. Then CBIR system will retrieve the image sets to return the similar images based on the similarity of image features. In relevance feedback, user scored each returned image by the slider to optimize retrieval results. A friendly query interface is designed to carry out HCI in our system and the experimental result shows the proposed method can improve the average recall and precision efficiently.

关键词： human-computer interaction visual query suggestion content-based image retrieval relevance feedback

来源：评论

学校读者我要写书评

暂无评论

Scene adaptive non-uniformity correction using TARID-Composite image prediction

Scene adaptive non-uniformity correction using TARID-Composi...

引用

conference on visual communications and image processing 2002

作者： Schuler, JM Howard, JG Warren, PR Scribner, DA USN Res Lab Div Opt Sci Washington DC 20375 USA

ISBN: (纸本)0819444111

imagery gathered by a Focal Plane Array (FPA) based sensor often suffers from the intrinsic non-uniform response of the individual detectors of the FPA. A digital Non-Uniformity Correction (NUC) can compensate for this distortion by implementing a functional transformation to the numerical output of each digitized FPA pixel. Such a NUC is often measured by exposing the sensor to one or more sources of uniform flux, and computed so that the post-NUC image of such uniform scenery has minimal spatial variation. Alternative NUC implementations adopt a scene-adaptive approach , using only the data in the gathered video sequence for which one wants to NUC. Several implementations, such as temporal high-pass filtering, neural-network, steepest-descent, or adaptive LMS, fundamentally depend on scene-predicted image necessary compute the appropriate functional correction. Such predicted images are invariably a spatial transformation of a single frame of video; it is because of the limited accuracy of such a single-image prediction that mandates algorithm compromises between slow convergence and pathological collapses, such as image scene burn-in or image washout. Previously reported research in image resolution enhancement mandates the construction of a Temporal Accumulation of Registered image Data (TARID) composite image as a pre-processing step. Such TARID composite images have significantly improved accuracy and robustness over any single-frame predicted image applied to scene adaptive NUC algorithms, resulting in markedly improved performance in both convergence and stability.

关键词： Nonuniformity corrections Video Digital filtering image filtering Staring arrays Linear filtering Sensors image enhancement image processing Analytical research

来源：评论

学校读者我要写书评

暂无评论

OBJECTIVE QUALITY ASSESSMENT FOR image RETARGETING BASED ON PERCEPTUAL DISTORTION AND INFORMATION LOSS

OBJECTIVE QUALITY ASSESSMENT FOR IMAGE RETARGETING BASED ON ...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Hsu, Chih-Chung Lin, Chia-Wen Fang, Yuming Lin, Weisi School of Computer Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781479902880

image retargeting techniques aim to obtain retargeted images with different sizes or aspect ratios for various display screens. Various content-aware image retargeting algorithms have been proposed recently. However, there is still no accurate objective metric for visual quality assessment of retargeted images. In this paper, we propose a novel objective metric for assessing visual quality of retargeted images based on perceptual geometric distortion and information loss. The proposed metric measures the geometric distortion of retargeted images by SIFT flow variation. Furthermore, a visual saliency map is derived to characterize human perception of the geometric distortion. On the other hand, the information loss in a retargeted image, which is calculated based on the saliency map, is integrated into the proposed metric. A user study is conducted to evaluate the performance of the proposed metric. Experimental results show the consistency between the objective assessments from the proposed metric and subjective assessments.

关键词： image Retargeting SIFT Flow Quality Assessment Quality Evaluation

来源：评论

学校读者我要写书评

暂无评论

An Efficient image Retrieval Model with Convolutional Neural Network based Text/image Identification for Copyright Violation Detection 30

An Efficient Image Retrieval Model with Convolutional Neural...

引用

30th IEEE Signal processing and communications Applications conference (SIU)

作者： Ozden, Harun Tavli, Bulent Demirci, M. Fatih Anadolu Ajansu Iletisim Teknol Direktorlugu Ankara Turkey TOBB Ekon Teknol Univ Elekt Elekt Muhendisligi Ankara Turkey Ankara Yildirim Beyazit Univ Bilgisayar Muhendishgi B Ankara Turkey

ISBN: (纸本)9781665450928

One of the most important problems faced by broadcasters is the unauthorized use of their images by third parties or organizations in a large-scale database, which contains hundreds of thousands of images. For this reason, it is important to perform an efficient and effective image retrieval, whose objective is to find the most similar images to a given test image. In addition, test images often contain text, and the presence of the text together with the visual part complicates the search process. In this paper, we present an image retrieval framework based on a bag of visual words, which has been shown to be effective in the literature. A convolutional neural network model is used to parse the text in the images. Experiments demonstrate the efficacy of this model in a large database.

关键词： Bag of visual Words SIFT Convolutional Neural Network image Retrieval

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：