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共 4344 条记录，以下是131-140 订阅

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Multi-Direction Strategy with Honey Badger Algorithm and Convolutional Neural Network for Industrial Robot visual Servo image processing

Multi-Direction Strategy with Honey Badger Algorithm and Con...

引用

2024 International conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Chen, Hao Jilin Communications Polytechnic Deppartment of Engineering & Mechanics Changchun China

ISBN: (纸本)9798350360660

The Industrial robot visual servo image processing requires highly autonomous and intelligence robotic manipulators, with goal of performing manipulation tasks independently without human interventions. However, limit efficiency for large scale, sensitive to noise in input data which affect classification accuracy. This paper, proposed Multi-Direction Strategy with Honey Badger Algorithm (MHBA) and Convolutional Neural Network (CNN) for classification is effectively explore the hyper parameter space of CNN technique is help achieve classification accuracy. The MHBA ability to adapt and explore multiple directions in the parameter space makes CNN maximum efficient to variation in input data. The MHBA model is applied to large scale CNN model is efficiently narrowing down the search space and balances exploration and exploitation allowing for search space. The Pre-processing using linear transformation such as translation or rotation help in adjusting the image dimension while maintaining the essential content. The proposed MHBA-CNN technique for industrial robot is achieving better outcomes such as Mean Absolute Error (MAE) of 2.03, Mean Absolute Scaled Error (MASE) of 3.45, R2 score of 2.45 on raw dataset. The existing technique such as Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) are evaluated of proposed method. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimizing image Classification Using Bag of Features and Support Vector Machines 4

Optimizing Image Classification Using Bag of Features and Su...

引用

4th IEEE International conference on Mobile Networks and Wireless communications, ICMNWC 2024

作者： Mahantesh, K. Navyashree, K.S. Nairy, Devika S. Asha, R. Anshitha, B. Bengaluru India Sjb Institute of Technology Visvesvaraya Technological University Department of Ece Bengaluru India

ISBN: (纸本)9798350352931

image categorization is a fundamental task in computer vision, with applications in domains such as object recognition, medical imaging, and autonomous systems. Traditional approaches frequently fail to balance accuracy, computing efficiency, and scalability, particularly when dealing with big and complex datasets. This work presents a novel picture classification strategy that combines the Bag of Features (BoF) model with Support Vector Machines (SVM). The BoF model describes images by extracting local visual characteristics (such as SIFT, SURF, or ORB) from image patches and quantizing them into visual words to create a histogram representation. SVM, a powerful machine learning classifier, is used to classify these histograms, utilizing its capacity to handle high-dimensional, sparse data. Experiments using common image classification datasets show that the BoF-SVM system greatly outperforms previous methods, resulting in higher classification accuracy and lower processing costs. Furthermore, it has superior generalization to previously unseen data and is more resistant to noise and picture changes. The suggested BoF-SVM system produces promising results for boosting both accuracy and efficiency in image classification tasks, with room for further optimization in more complicated and diversified applications © 2024 IEEE.

关键词： image classification

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Low-complexity Transform Network Architecture for JPEG AI image Codec

Low-complexity Transform Network Architecture for JPEG AI Im...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Pan, Xiang Ding, Ding Wang, Liqiang Xu, Xiaozhong Liu, Shan Tencent Media Lab Shenzhen China Tencent Media Lab Palo AltoCA United States

ISBN: (纸本)9798350359855

Learning-based image coding schemes, exemplified by JPEG AI, have shown potential by greatly exceeding the conventional image compression standards in rate-distortion (RD) performance. However, their widespread applications are hindered by high decoding complexity, particularly from the upsampling and attention modules. Existing works sought to reduce this complexity, but their solutions are not fully effective, leaving considerable complexity unaddressed. In this paper, we present a simplified transform network architecture that employs an optimized attention module, a streamlined upsampling module, and a pared-down activation function to tackle this issue. Simulation results show that the simplified decoder sees its complexity (measured by kMACs/pixel) reduced by 80% (from 833 to 172), while the gain over Versatile Video Coding (VVC) increases slightly (from 27.3% to 27.5%). Partial methods in this paper have been integrated into the JPEG AI Verification Model (VM) software. © 2023 IEEE.

关键词： Signal distortion

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On Data Annotation Efficiency for image Based Crowd Counting

On Data Annotation Efficiency for Image Based Crowd Counting

引用

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

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Binocular Rivalry and Fusion Mechanisms Based No-Reference Stereoscopic image Quality Assessment Considering Feedback Guidance

Binocular Rivalry and Fusion Mechanisms Based No-Reference S...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Chang, Haoxiang Li, Sumei He, Dongyue Zhang, Chen Tianjin University Tianjin China

ISBN: (纸本)9798350359855

With the growing popularity of 3D content and virtual reality applications, effective no-reference stereoscopic image quality assessment (NR-SIQA) methods have become increasingly important. In this paper, we propose a convolutional neural network (CNN) SIQA model based on binocular rivalry and fusion mechanisms of the human visual system (HVS). In order to get a better representation of binocular information, we propose Two-Stage Enhanced Fusion Module (TSEFM) that consists of two stages for monocular features enhancement and binocular features fusion, respectively. Given the dynamical characteristics of binocular rivalry phenomenon, the proposed Content-Aware Binocular Rivalry Fusion Module (CABRFM) dynamically and adaptively adjusts its output based on the input content. Additionally, considering that feedback mechanism of HVS is indispensable and significant, we introduce feedback connections during feature aggregation to realize the guidance of high-level features to low-level features. Extensive experimental results demonstrate the superiority of our method over state-of-the-art metrics, showcasing its excellent performance. © 2023 IEEE.

关键词： Binoculars

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Teledermatologist: Automated Diagnosis of Skin Diseases with image Recognition

Teledermatologist: Automated Diagnosis of Skin Diseases with...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Ishai, Nadav Peretz, Dolev Vanetik, Natalia Litvak, Marina Shamoon College of Engineering Department on Software Engineering Beer Sheva Israel

ISBN: (纸本)9798350359855

Early diagnosis of skin diseases is crucial for effective treatment, preventing spread, minimizing long-Term damage, managing chronic conditions, detecting underlying health issues, and promoting psychological well-being. It is often necessary to do difficult tests in order to obtain a diagnosis, which demands a lot of resources from patients as well as medical personnel. Motivated by this shortage, we introduce a user-friendly and private system for the automatic detection of skin disease from a picture. Our system employs a deep neural model for the accurate classification of a given picture to one of the predefined classes of diseases. We created a large dataset of six skin lesion types and tested different neural models. The best-performing model, which achieved 92% accuracy, was integrated into our system. The application and its web demo can be accessed at https://***/. © 2023 IEEE.

关键词： Diagnosis

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The Complexity Optimization of Dependent Quantization in VVC by Delaying Starting Point

The Complexity Optimization of Dependent Quantization in VVC...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Feng, Shixuan Wang, Yifan Wang, Luge Yang, Fuzheng Wan, Shuai Xidian University School of Telecommunications Engineering Xi'an China Northwestern Polytechnical University School of Electronics and Information Xi'an China

ISBN: (纸本)9798350359855

Compared with the video compression standard High Efficiency Video Coding (HEVC), the latest standard Versatile Video Coding (VVC) has achieved great improvement in coding efficiency due to the application of more sophisticated coding tools, which also results in high coding complexity. In this paper, to reduce the computational complexity of dependent quantization in VVC, an optimization method based on theoretical analysis of rate-distortion performance and statistical low-complexity model establishment is proposed to delay the starting point of the quantized path. Experimental results demonstrate that the proposed method can postpone the starting point to a position closer to the last non-zero quantized coefficient, reducing 3.57% encoding time, with a 0.42% increase in Bjntegaard delta bit-rate (BD-BR) compared with VVenC 1.0.0 under medium preset. © 2023 IEEE.

关键词： image coding

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Multi-scale and Multi-view Feature Blending for Free-viewpoint Rendering of Dynamic Humans

Multi-scale and Multi-view Feature Blending for Free-viewpoi...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Park, Hyunwoo Kwon, Hyukmin Lim, Seong Yong Kim, Wonjun Konkuk University Dept. Electrical and Electronics Engineering Seoul Korea Republic of Electronics and Telecommunications Research Institute Media Research Division Daejeon Korea Republic of

ISBN: (纸本)9798350359855

With the great success in neural radiance fields (NeRF), human-specific NeRF has been actively introduced in recent years. However, such human rendering techniques often have difficulties to recover subtle details of textural surfaces such as wrinkles on clothes, which are mostly generated by complicated human motions. To solve this problem, we propose a new method for human-specific NeRF based on multi-scale and multi-view feature blending. Specifically, multi-scale image features, which are sampled from different views, are combined into the transformer architecture. This blended feature successfully guides the NeRF network to render photo-realistic results by enhancing local details of the human performer. Experimental results on the ZJU-MoCap dataset show that the proposed method outperforms previous methods both in qualitative and quantitative evaluations. © 2023 IEEE.

关键词： image enhancement

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Vectorized and Optimized Dependent Quantization for Practical VVC Encoding

Vectorized and Optimized Dependent Quantization for Practica...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Sainio, Joose Mercat, Alexandre Vanne, Jarno Tampere University Ultra Video Group Tampere Finland

ISBN: (纸本)9798350359855

Versatile Video Coding (VVC) introduces dependent quantization (DQ) as a key coding tool, but it also has a high computational complexity. Therefore, optimizing DQ is crucial for practical VVC encoder implementations. In this paper, we propose optimization techniques for reducing the complexity of the DQ process, including efficient structure initialization, vectorized state update, rate distortion (RD) cost calculation, and last nonzero (LNZ) coefficient identification. The proposed optimizations are incorporated into the uvg266 practical VVC encoder. According to our evaluations, these optimization techniques accelerate the DQ process by over 1.7× and the entire VVC intra encoder by 1.4×, without any coding efficiency degradation. The proposed optimizations achieve more than twice the speedup over the existing techniques. © 2023 IEEE.

关键词： image coding

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A Constrained Variable Bit Rate (CVBR) Algorithm for VVenC, an Open VVC Encoder Implementation

A Constrained Variable Bit Rate (CVBR) Algorithm for VVenC, ...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Helmrich, Christian R. Bartnik, Christian Brandenburg, Jens Wieckowski, Adam Bross, Benjamin Marpe, Detlev Video Communication and Applications Dept. Berlin10587 Germany

ISBN: (纸本)9798350359855

Rate control (RC) schemes allow audio and video encoders to produce bitstreams according to specific overall bitrate constraints. However, when no rate capping is enforced, the instantaneous bitrate may vary strongly and may exceed the target rate by an order of magnitude, potentially causing playback stutter especially in video streaming scenarios. This paper introduces a rate capping extension for the two RC modes in VVenC, an open Versatile Video Coding (VVC) compliant encoder implementation. After a revisit of VVenC's two-pass RC approach, the algorithmic details of the rate capping model are described. The paper concludes with an objective evaluation of the performance of the RC extension in a random-Access configuration. © 2023 IEEE.

关键词： image coding

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