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Learning Hybrid Representations of Semantics and Distortion for Blind image Quality Assessment

Learning Hybrid Representations of Semantics and Distortion ...

引用

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

作者： Xiaoqi Wang Jian Xiong Bo Li Jinli Suo Hao Gao School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China School of Computer and Software Engineering Xihua University Chengdu China Department of Automation Tsinghua University Beijing China

Recently, some studies have shown that semantic and distortion representations both benefit the evaluation of image quality. However, the images of existing synthetic distortion databases are annotated with subjective quality scores and distortion types, lacking labels with semantic objects. Therefore, it is virtually infeasible to learn the representations of image semantics and distortion by co-guiding with semantic and distortion labels. To address this issue, we propose a dual-perception network (DPNet) via an end-to-end multi-task learning method, where knowledge distillation is lever-aged as a semantic label-free strategy. Specifically, semantic representation derived from pre-trained ResNet152 is applied to supervise the output of DPNet, while the output is utilized to construct a distortion recognition task. In this way, image semantics and distortion can be hybridly represented in an identical feature map. Finally, image quality is regressed based on the hybrid representations. Experimental results conducted on five benchmark databases validate that the proposed method can achieve state-of-the-art performance.

关键词： image quality Learning systems Knowledge engineering Acoustic distortion image recognition Databases Semantics

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Hierarchical Histogram Threshold Segmentation - Auto-terminating High-detail Oversegmentation

Hierarchical Histogram Threshold Segmentation - Auto-termina...

引用

conference on Computer Vision and Pattern Recognition (CVPR)

作者： Thomas V. Chang Simon Seibt Bartosz von Rymon Lipinski Game Tech Lab Faculty of Computer Science Nuremberg Institute of Technology Germany

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Superpixels play a crucial role in image processing by partitioning an image into clusters of pixels with similar visual attributes. This facilitates subsequent image processing tasks, offering computational advantages over the manipulation of individual pixels. While numerous oversegmentation techniques have emerged in recent years, many rely on predefined initialization and termination criteria. In this paper, a novel top-down superpixel segmentation algorithm called Hierarchical Histogram Threshold Segmentation (HHTS) is introduced. It eliminates the need for initialization and implements auto-termination, outperforming state-of-the-art methods w.r.t. boundary recall. This is achieved by iteratively partitioning individual pixel segments into foreground and background and applying intensity thresholding across multiple color channels. The underlying iterative process constructs a superpixel hierarchy that adapts to local detail distributions until color information exhaustion. Experimental results demonstrate the superiority of the proposed approach in terms of boundary adherence, while maintaining competitive runtime performance on the BSDS500 and NYUV2 datasets. Further-more, an application of HHTS in refining machine learning-based semantic segmentation masks produced by the Segment Anything Foundation Model (SAM) is presented.

关键词： Histograms visualization Runtime Thresholding (Imaging) image color analysis Semantic segmentation Refining

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Wildlife image Generation from Scene Graphs

Wildlife Image Generation from Scene Graphs

引用

ieee Computer Society conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Yoshio Rubio Marco A. Contreras-Cruz Samsung Research America Mountain View United States Samsung Research Tijuana Tijuana Mexico

image generation from natural language descriptions is an exciting and challenging task in computer vision and natural language processing. In this work, we propose a novel method to generate synthetic images from scene graphs in the context of wildlife scenarios. Given a scene graph, our method uses a graph convolutional network to predict semantic layouts, and a semi-parametric approach based on a cascade refinement network to synthesize the final image. We test our approach on a subset of COCO dataset, which we call COCO-Wildlife. Our results outperform the baselines, both quantitatively and qualitatively, and the visual results show the ability of our approach to generate stunning images with natural interaction between the different objects. Our findings show the potential to expand the use case of the proposed method to other contexts where scale and realism is fundamental.

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High-Performance image visual Feature Detection and Matching

High-Performance Image Visual Feature Detection and Matching

引用

International conference on communications, Signal processing and VLSI, IC2SV 2019

作者： Sambamurthy, N. Kamaraju, M. Department of ECE Gudlavalleru Engineering College Gudlavalleru India

ISBN: (纸本)9789813340572

The fundamental task in high-speed and high-accuracy CCTV video surveillance system is attention-based image visual point detection and matching. The image viewpoints are different at different instants of time, so that the image recognition and matching systems are having large computational complexity. The designed work is to present an ROI-based SIFT image detection and BRIEF matching algorithms with a processing speed of 44 ns, and it meets the real-time requirement. The performance of the designed system is improved with a hybrid sorting-based median filter. These algorithms are able to establish accurate feature extraction and matching at 30fps for 640 Pixel video. This can be implemented using MATLAB and Xilinx tools. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Median filters

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Research on Improved image Reconstruction Based on Residual Network

Research on Improved Image Reconstruction Based on Residual ...

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ieee International conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Yuyan Hui Xianjin Li Northwest Normal University Lanzhou China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

This paper addresses the limitations of image super-resolution reconstruction techniques, where traditional methods often struggle to maintain both sharp detail and natural texture when increasing image resolution, particularly when dealing with complex textures and edge information, leading to blur. We propose an improved Enhanced Deep Super-Resolution (EDSR) model that employs a deep residual network structure. This model stacks multiple residual blocks to build a deep network, effectively utilizing hierarchical information within images. Additionally, it incorporates sub-pixel convolution layers for upsampling, as well as multi-scale features and a global residual learning mechanism. During training, we utilize Adam optimizer, dynamic learning rate scheduling, and batch normalization techniques to ensure robust convergence and efficient learning in complex data distributions. Based on this foundation, we evaluate the improved EDSR model against the original EDSR and Super Resolution Generative Adversaria Network SRGAN models using various performance metrics. Experimental results demonstrate that the improved EDSR model achieves outstanding performance on the Flickr 1024 dataset at a scale factor of 4. Specifically, the unimproved EDSR (PSNR of 32.91dB, SSIM of 0.9581), SRGAN (despite potentially superior visual results, PSNR of 30.42dB, SSIM of 0.9727), and improved EDSR model (PSNR of 34.98dB, SSIM of 0.9828) not only surpass existing models in evaluation metrics but also strike an ideal balance between visual quality and computational efficiency. The enhanced EDSR algorithm improves the quality of image super-resolution reconstruction while optimizing computational efficiency. It provides a more efficient and accurate image upscaling solution for various applications including high-definition displays, medical imaging analysis, remote sensing image processing, and beyond.

关键词： Measurement visualization Convolution Computational modeling Surveillance Superresolution Dynamic scheduling Security image reconstruction Residual neural networks

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Revelio: A Real-World Screen-Camera Communication System with visually Imperceptible Data Embedding

Revelio: A Real-World Screen-Camera Communication System wit...

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

作者： Abbaas Alif Mohamed Nishar Shrinivas Kudekar Bernard Kintzing Ashwin Ashok Georgia State University Atlanta Georgia USA Revelio Communications Inc. Atlanta Georgia USA Bernard Kintzing LLC Helena Montana USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We present ‘Revelio’, a real-world screen-camera communication system leveraging temporal flicker fusion in the OKLAB color space. Using spatially-adaptive flickering and encoding information in pixel region shapes, Revelio achieves visually imperceptible data embedding while remaining robust against noise, asynchronicity, and distortions in screen-camera channels, ensuring reliable decoding by standard smartphone cameras. The decoder, driven by a two-stage neural network, uses a weighted differential accumulator for precise frame detection and symbol recognition. Initial experiments demonstrate Revelio’s effectiveness in interactive television, offering an unobtrusive method for meta-information transmission.

关键词： visualization Shape image color analysis Communication systems Neural networks Symbols Encoding Decoding Payloads Videos

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Enhancing Social Media Accessibility: Automatic Alternative Text Generation in X by image Captioning

Enhancing Social Media Accessibility: Automatic Alternative ...

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ieee International conference on System Engineering and Technology

作者： Jessica Lynn Wibowo Gabriel Seemore Gunawan Ivan Sebastian Edbert Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504526

ISBN: (纸本)9798331504533

In the contemporary digital landscape, social media’s reliance on visual content poses significant accessibility barriers for individuals with visual impairments, sidelining a substantial user base. This study applies Artificial Intelligence to improve accessibility on social media by automating alternative text generation for images on platform X (formerly Twitter). The Generative image-to-text Transformer (GIT) model is analyzed and then integrated with the Twitter API. Using the Flickr30k dataset, further fine-tuning and evaluation processes are performed on the model. Concurrently, the model demonstrates higher accuracy of 106% over the baseline VGG model as quantified by ROUGE metrics. The results confirm our model’s capability to produce descriptive and contextually relevant captions that significantly enhance accessibility for visually impaired users. Furthermore, the integration of this AI-driven solution into social media platforms showcases the practical application of advanced machine learning techniques in addressing real-world accessibility challenges. This research highlights the potential of AI to transform digital communication, making it more inclusive and ensuring that visually impaired users can fully participate in the social media experience.

关键词： Measurement visualization Social networking (online) Blogs visual impairment Neural networks Transforms Transformers Systems engineering and theory Natural language processing

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Edge-Assisted Camera Selection in Vehicular Networks

Edge-Assisted Camera Selection in Vehicular Networks

引用

ieee Annual Joint conference: INFOCOM, ieee Computer and communications Societies

作者： Ruiqi Wang Guohong Cao Department of Computer Science and Engineering The Pennsylvania State University

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Camera sensors have been widely used to perceive the vehicle surrounding environments, understand the traffic condition, and then help avoid traffic accidents. Since most sensors are limited by line of sight, the perception data collected through individual vehicle can be uploaded and shared through the edge server. To reduce the bandwidth, storage and processing cost, we propose an edge-assisted camera selection system that only selects the necessary camera images to upload to the server. The selection is based on the camera metadata which describes the coverage of the cameras represented with GPS locations, orientations, and field of views. Different from existing work, our metadata based approach can detect and locate camera occlusions by leveraging LiDAR sensors, and then precisely and quickly calculate the real camera coverage and identify the coverage overlap. Based on the camera metadata, we study two camera selection problems, the Max-Coverage problem and the Min-Selection problem, and solve them with efficient algorithms. Moreover, we propose similarity based redundancy suppression techniques to further reduce the bandwidth consumption which becomes significant due to vehicle movements. Extensive evaluations demonstrate that the proposed algorithms can effectively select cameras to maximize coverage or minimize bandwidth consumption based on the application requirements.

关键词： Laser radar Costs image edge detection Redundancy Bandwidth Metadata Cameras

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A New Model for Prototype-based Continual Learning in Hyperspherical Space

A New Model for Prototype-based Continual Learning in Hypers...

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

作者： Yixin Ren Yewei Xia Li Ke Longtao Huang Hui Xue Shuigeng Zhou School of Computer Science Fudan University Shanghai China Alibaba Group Hangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The continuous emergence of new objects in the visual world poses a serious challenge to deep object recognition methods, which sparks the increasing study on continual or incremental learning. However, learning new tasks faces the tough catastrophic forgetting problem, i.e., dramatic performance degradation on old tasks. A good continual learning model should be robustly adapted to the upcoming tasks while effectively handling catastrophic forgetting. In this paper, we focus on the class-incremental learning (CIL) task, and propose a novel prototype-based continual learning model C-HPN that projects the visual features into a hypersphere geometric space, where continual learning is conducted. C-HPN features two-fold contributions. On the one hand, instead of using the popular cross-entropy loss, we develop an instance-prototype compact loss to obtain well-clustered hyperspherical embeddings and a prototype-prototype separability loss to boost the model’s generalization by introducing large angle distance inductive bias between prototypes in the hyperspherical space. On the other hand, prototype construction and adaptation strategies are designed for effectively adapting new classes, and an instance-prototype relationship preservation distillation mechanism is introduced to overcome catastrophic forgetting. Extensive experiments on several image datasets validate the effectiveness of the proposed method.

关键词： Continuing education Hands Adaptation models visualization Incremental learning Prototypes Signal processing Sparks Object recognition Speech processing

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Evcap: Retrieval-Augmented image Captioning with External visual-Name Memory for Open-World Comprehension

Evcap: Retrieval-Augmented Image Captioning with External Vi...

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conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jiaxuan Li Duc Minh Vo Akihiro Sugimoto Hideki Nakayama The University of Tokyo Japan National Institute of Informatics Japan

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Large language models (LLMs)-based image captioning has the capability of describing objects not explicitly observed in training data; yet novel objects occur frequently, necessitating the requirement of sustaining up-to-date object knowledge for open-world comprehension. Instead of relying on large amounts of data and/or scaling up network parameters, we introduce a highly effective retrieval-augmented image captioning method that prompts LLMs with object names retrieved from External visual-name memory (EVCAP). We build ever-changing object knowledge memory using objects' visuals and names, enabling us to (i) update the memory at a minimal cost and (ii) effort-lessly augment LLMs with retrieved object names by uti-lizing a lightweight and fast-to-train model. Our model, which was trained only on the COCO dataset, can adapt to out-of-domain without requiring additional fine-tuning or retraining. Our experiments conducted on benchmarks and synthetic commonsense-violating data show that EV-CAP, with only 3.97M trainable parameters, exhibits superior performance compared to other methods based on frozen pretrained LLMs. Its performance is also competitive to specialist SOTAs that require extensive training.

关键词： Training visualization Adaptation models Computer vision Costs Large language models Memory management

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