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Multiphase image segmentation model based on clustering algorithm 2

Multiphase image segmentation model based on clustering algo...

引用

2nd ieee Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2021

作者： Guo, Rui Zhang, Liang Yang, Ze Guangdong Polytechnic College Zhaoqing Guangdong526000 China Zhongdao Optoelectronic Equipment Co.Ltd Zhaoqing Guangdong526000 China

ISBN: (纸本)9781728190181

In the traditional Multiphase image segmentation algorithm, there are some problems, such as long operation time, high calculation cost and high error rate. In order to improve the above problem, originally proposed a multiphase image segmentation enhancement algorithm and model based on the combination of clustering algorithm. In this paper, for the problem of large amount of initial calculation for the segmentation model of multiple targets, a clustering algorithm is proposed to segment the image, and finally the model is established, the function data set is initialized, and the method of multiphase image segmentation is easier to segment idealized goal. This method can reduce the sensitivity of the clustering algorithm in the initialization, making the multiphase image segmentation model under the clustering algorithm easier to segment the ideal image. At the same time, the image segmentation model can quickly get the minimum value, reduce the amount of calculation, fully and effectively improve the efficiency. The M-level set implicit surface of multi-level set function is used to divide the image into m regions. The maximum value of level setting function is calculated to realize fast segmentation and reconstruction of constant value of multiphase segmentation. Experimental results show that the algorithm can greatly improve the contrast and clarity of images, and bring the best visual experience to people. Compared with the traditional clustering model, it has less iteration steps and faster segmentation speed. © 2021 ieee.

关键词： Clustering algorithms

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Self-supervised visual Transformers for Breast Cancer Diagnosis

Self-supervised Visual Transformers for Breast Cancer Diagno...

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Asia-Pacific-Signal-and-Information-processing-Association Annual Summit and conference (APSIPA ASC)

作者： Saidnassim, Nurbek Abdikenov, Beibit Kelesbekov, Rauan Akhtar, Muhammad Tahir Jamwal, Prashant Nazarbayev Univ Sch Engn & Digital Sci Nur Sultan Kazakhstan Rel Res LLP Astana Kazakhstan

ISBN: (纸本)9789881476890

Owing to the growing incidences, breast cancer is considered the world's most prevalent cancer both in terms of morbidity and mortality rates. While breast cancer treatment is quite effective, higher mortality is caused due to the delayed diagnosis. Mammogram image processing using artificial intelligence techniques can be promising in the early diagnosis and Convolutional Neural Networks (CNN) have shown some potential in image classification and semantic segmentation. However, working with unlabeled image data poses challenges and while the manual labeling is not efficient, pre-trained CNNs also perform poorly on the actual medical images. In this paper, we propose to use transformer-based networks in the field of computer vision. Previously published research also suggests a predominance of transformer-based vision models over the convolutional models. Bootstrap Your Own Latent (BYOL), which is Self-supervised learning (SSL) algorithm, is proposed in this paper to process mammograms images for diagnostic purposes. BYOL can handle unlabeled image data and improve the deep learning mechanism for data analytics. UNet networks with Xavier initializations have been considered for the baseline to compare with their performances when implemented with and without BYOL. It is shown that the overall network performance improves when they are trained using the proposed BYOL algorithm.

关键词： Mammogram image processing UNet-CNN style Bootstrap Your Own Latent (BYOL)

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A Relation-aware Cloth-Changing Person Re-identification Framework Based on Clothing Template

A Relation-aware Cloth-Changing Person Re-identification Fra...

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ieee/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and communications (GreenCom)

作者： Chenshuang Su Mingdong Zou Yujie Zhou Xiaoke Zhu Wenjuan Liang Caihong Yuan School of Computer and Information Engineering Henan University Henan Engineering Research Center of Intelligent Technology and Application Henan Province Spatial Information Processing Engineering Technology Research Center Kaifeng China

Cloth-Changing Person Re-identification (CC-ReID) aims to match pedestrian images captured from different camera views over long periods. The key challenge is to eliminate the disturbance of clothes and excavate the discriminative identity features. However, recent works mostly focus on body shape or contour sketching, the potential pedestrian features are not fully mining. Inadequate information mining may lead to suboptimal performance. In our framework, we propose a template-based clothes replacement method (TCR), which helps to obtain person images with similar but not exactly the same clothing. By doing so, the clothing interference might be largely eliminated while also retaining the identity information as much as possible. Additionally, we propose two relation learning modules based on Transformer to excavate discriminative relation features that exist within and between images, which are the inner-image relation mining module (INR), and inter-image relation mining module (ITR). INR first extracts body key-point features, and then explores the implicit correlation among key-points. Compared to isolated global and local features, the inner-image relation feature is more inclined to mine discriminative structural features. Based on the learned inner-image relation feature, ITR models the relation between pedestrian images. By exploring the relation between images, some distinct features which distinguish one from others could be learned. Finally, extensive experimental results on two publicly available datasets, PRCC, and LTCC demonstrate the effectiveness of our method.

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A parallel compression framework for fractal images using DCT block classification

A parallel compression framework for fractal images using DC...

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ieee International Symposium on Parallel and Distributed processing with Applications and ieee International conference on Ubiquitous Computing and communications (ISPA/IUCC)

作者： Jiahao Pang Jie He Hui Guo Minglang Chen Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China

Fractal image compression has higher space domain compression performance compared to the mainstream image compression algorithms using discrete cosine transform, but the search time for coding fractal image compression is too long and less practical. In this paper, we propose a fractal image block classification scheme based on discrete cosine transform, and use NVIDIA CUDA architecture to achieve parallel coding, which shows that this scheme can achieve 107 times faster speedup than the basic fractal scheme, and the coding time is significantly reduced, and the PSNR decreases only about 0.4, so this scheme has greater practicality.

关键词： image coding Graphics processing units Computer architecture Ubiquitous computing Fractals Classification algorithms Discrete cosine transforms

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MKD-YOLO: Multi-Scale and Knowledge-Distilling YOLO for Efficient PPE Compliance Detection

MKD-YOLO: Multi-Scale and Knowledge-Distilling YOLO for Effi...

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

作者： Juntao Zan Yang Fang Qilie Liu Uswah Khairuddin Yan Li Kaiwei Sun School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Malaysia-Japan International Institute of Technology University of Technology Malaysia Kuala Lumpur Malaysia Department of Electrical and Computer Engineering Inha University Incheon South Korea Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

YOLO-based models are widely used for personal protective equipment (PPE) compliance detection due to their excellent detection performance and efficiency. However, most YOLO models are not competent for detection tasks in complex industrial scenarios such as remote surveillance and extremely small targets. In addition, there is a lack of effective model lightweighting and knowledge transfer approaches for industrial deployment. To this end, this paper proposes a Multi-scale and Knowledge-Distilling YOLO (MKD-YOLO) based on YOLOv8n for efficient PPE compliance detection. Specifically, in backbone stage, we design an Efficient Multi-Scale Enhanced Convolution (C2f-EMSEC) module and Large Spatial Pyramid Pooling-Fast (LSPPF) module for multi-scale and global-contextual feature learning as well as reducing model complexity. Then, in neck stage, a refined Bidirectional feature Pyramid Network (BPNet) is designated to capture fine-grained details for extremely small object detection. Moreover, we apply channel-wise knowledge distillation to facilitate model lightweighting and domain-specific knowledge transfer learning. Experiments on our proposed dataset and public datasets show that the proposed MKD-YOLO achieves a new state-of-the-art (SOTA) detection performance and efficiency for practical PPE compliance detection tasks. Codes and the dataset are available at https://***/z1Zjt/MKD-YOLO.

关键词： Personal protective equipment YOLO Representation learning Convolution Surveillance Speech enhancement Feature extraction Neck Kernel Knowledge transfer

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Emotional Theory of Mind: Bridging Fast visual processing with Slow Linguistic Reasoning 12

Emotional Theory of Mind: Bridging Fast Visual Processing wi...

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12th International conference on Affective Computing and Intelligent Interaction, ACII 2024

作者： Etesam, Yasaman Yalçin, Özge Nilay Zhang, Chuxuan Lim, Angelica Simon Fraser University BC Canada

ISBN: (纸本)9798331516437

The emotional theory of mind problem requires facial expressions, body pose, contextual information and implicit commonsense knowledge to reason about the person's emotion and its causes, making it currently one of the most difficult problems in affective computing. In this work, we propose multiple methods to incorporate the emotional reasoning capabilities by constructing "narrative captions"relevant to emotion perception, that includes contextual and physical signal descriptors that focuses on "Who", "What", "Where"and "How"questions related to the image and emotions of the individual. We propose two distinct ways to construct these captions using zero-shot classifiers (CLIP) and fine-tuning visual-language models (LLaVA) over human generated descriptors. We further utilize these captions to guide the reasoning of language (GPT-4) and vision-language models (LLa Va, GPT-Vision). We evaluate the use of the resulting models in an image-to-language-to-emotion task. Our experiments showed that combining the "Fast"narrative descriptors and "Slow"reasoning of language models is a promising way to achieve emotional theory of mind. © 2024 ieee.

关键词： Computational grammars

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A3S: Adversarial Learning of Semantic Representations for Scene-Text Spotting

A3S: Adversarial Learning of Semantic Representations for Sc...

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

作者： Masato Fujitake Fast Accounting Co. Ltd

Scene-text spotting is a task that predicts a text area on natural scene images and recognizes its text characters simultaneously. It has attracted much attention in recent years due to its wide applications. Existing research has mainly focused on improving text region detection, not text recognition. Thus, while detection accuracy is improved, the end-to-end accuracy is insufficient. Texts in natural scene images tend to not be a random string of characters but a meaningful string of characters, a word. Therefore, we propose adversarial learning of semantic representations for scene text spotting (A3S) to improve end-to-end accuracy, including text recognition. A3S simultaneously predicts semantic features in the detected text area instead of only performing text recognition based on existing visual features. Experimental results on publicly available datasets show that the proposed method achieves better accuracy than other methods.

关键词： Training visualization image recognition Text recognition Semantics Signal processing Feature extraction

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EXPLOITING THE DUAL-TREE COMPLEX WAVELET TRANSFORM FOR SHIP WAKE DETECTION IN SAR imageRY

EXPLOITING THE DUAL-TREE COMPLEX WAVELET TRANSFORM FOR SHIP ...

引用

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

作者： Ma, Wanli Achim, Alin Karakus, Oktay Univ Bristol Visual Informat Lab Bristol BS1 5DD Avon England

ISBN: (纸本)9781728176055

In this paper, we analyse synthetic aperture radar (SAR) images of the sea surface using an inverse problem formulation whereby Radon domain information is enhanced in order to accurately detect ship wakes. This is achieved by promoting linear features in the images. For the inverse problem-solving stage, we propose a penalty function, which combines the dual-tree complex wavelet transform (DT-CWT) with the non-convex Cauchy penalty function. The solution to this inverse problem is based on the forward-backward (FB) splitting algorithm to obtain enhanced images in the Radon domain. The proposed method achieves the best results and leads to significant improvement in terms of various performance metrics, compared to state-of-the-art ship wake detection methods. The accuracy of detecting ship wakes in SAR images with different frequency bands and spatial resolution reaches more than 90%, which clearly demonstrates an accuracy gain of 7% compared to the second-best approach.

关键词： SAR image ship wake detection inverse problem DT-CWT FB splitting

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image Defogging Based on Joint Contrast Enhancement and Multi-scale Fusion

Image Defogging Based on Joint Contrast Enhancement and Mult...

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Power Electronics, Computer Applications (ICPECA), ieee International conference on

作者： Junjie Nie Haotian Wu Shuyang Yao Zhejiang University of Science and Technology Hangzhou Zhejiang China Southwest Jiaotong University Chengdu China Harbin Engineering University Harbin China

Although the single image dehazing techniques now in use work well in certain circumstances, real-world scenarios' complex and ever-changing nature makes it impossible for them to function well. This research offers a contrast enhancement and exposure fusion-based efficient paradigm for picture dehazing. Three basic preprocessing techniques are utilized to create intermediate pictures for contrast enhancement. The first processing method is gamma correction (GC), employed to improve an image's local quality. The second technique is the newly developed color-preserving histogram equalization, which combines each channel to enhance the overall contrast. The third kind of processing is called ACE, which uses adaptability to deal with a complex and shifting environment. This paper fuses the input from GC, CPAHE, and ACE during the fusion stage using the adaptive kernel size fusion technique based on rapid structural patch decomposition. According to the experimental findings, it outperforms other cutting-edge visual and quantitative analysis techniques. The average FADE scores for the daylight and night-time scenes have increased by at least 0.09 and 0.08, respectively. This paper also describes how this strategy may be used in various contexts.

关键词： visualization Histograms Statistical analysis Fuses Computer applications Power electronics Kernel

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One-Shot Medical Action Recognition With A Cross-Attention Mechanism And Dynamic Time Warping

One-Shot Medical Action Recognition With A Cross-Attention M...

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

作者： Leiyu Xie Yuxing Yang Zeyu Fu Syed Mohsen Naqvi Intelligent Sensing and Communications Research Group Newcastle University UK Department of Computer Science University of Exeter UK

In this paper, we address the classification of medical actions with only one single sample by developing a novel one-shot learning framework which contains both cross-attention and dynamic time warping (DTW) modules. To be concrete, we firstly transform the raw skeleton sequence into the signal-level image representation. We exploit a metric learning approach, which is the prototypical network for the proposed one-shot learning framework and choose the residual network (ResNet18) as the backbone which is widely used in recent years. Cross-attention is applied for guiding the network to focus on the more important joints from each specific action. The cross-attention mechanism that applies between the support and query set will be adapted for mining and matching the relationships with the human body. Furthermore, a DTW module is introduced to mitigate the temporal information mismatching issue between the actions from the support and query sets. The experimental results on the NTU RGB+D 120 dataset demonstrate the effectiveness of our proposed approach and the improved performance compared to the baseline approach. The code of this work is available at 1 .

关键词： Measurement Codes Transforms Medical services Signal processing image representation Skeleton

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