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A Lung Cancer Tumor image Segmentation Method of A CFC-MSPCNN based on PET/CT 3

A Lung Cancer Tumor Image Segmentation Method of A CFC-MSPCN...

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3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Lian, Jing Yang, Xuefeng Niu, Xiaojiao Cao, Yuzhu Lanzhou Jiaotong University School of Electronic and Information Engineering No. 88 West Anning Road Gansu Lanzhou China

ISBN: (纸本)9781510667563

To address the problems of low segmentation accuracy and machine complexness of ancient pulse coupled neural network (PCNN) in medical image process, a Converged-FCMSPCNN (CFC-MSPCNN) model is projected. Compared with earlier PCNN models, this model additionally optimizes and enhances the synaptic weight matrix, link strength and dynamic threshold, simplifies the parameter settings and reduces the quantity of iterations. In addition, we add a balance parameter Q to regulate the dynamic threshold to improve the model's control over neuronal image processing. Through relevant experiments, we demonstrate that our algorithm has higher results compared with alternative algorithms to accurately section carcinoma lots and considerably reduces the randomness and unpredictability of firing neurons. © 2023 SPIE.

关键词： image segmentation

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Py4MER: A CTC-Based Mathematical Expression recognition System 11th

Py4MER: A CTC-Based Mathematical Expression Recognition Syst...

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11th iberian conference on pattern recognition and image analysis, IbPRIA 2023

作者： Anitei, Dan Sánchez, Joan Andreu Benedí, José Miguel Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica València Valencia Spain Valencian Graduate School and Research Network of Artificial Intelligence Camí de Vera s/n Valencia46022 Spain

ISBN: (纸本)9783031366154

Mathematical expression recognition is a research field that aims to develop algorithms and systems capable of interpreting mathematical content. The recognition of MEs requires handling two-dimensional symbol relationships such as sub/superscripts, matrices and nested fractions, among others. The prevalent technology for addressing these challenges are based on encoder-decoder architectures with attention models. In this paper we propose the Py4MER system, based on Convolutional Recurrent Neural Network (CRNN) models, for the recognition and transcription of MEs into LaTeX mark-up sequences. This model is proposed as an alternative to encoder-decoder approaches, as CRNN models trained through Connectionist Temporal Classification (CTC) implicitly model the dependencies between symbols and do not suffer from under/over parsing of the input image, generating more consistent mark-up. The proposed model is evaluated on the Im2Latex-100k data set based on both textual and image-level metrics, showing a remarkable improvement from other CTC-based approaches. recognition results are analyzed for different ME lengths and ME structures. Furthermore, a study based on the edit distance is performed, showing a considerable improvement in precision when up to 5 edit operations are considered. Finally, we show that CTC-based CRNN models can adapt to non left-to-right ordering of ME elements, warranting more research for this approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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Comparison of Feature Histograms and Co-occurrence Matrix on analysis of the Light Spectrum Effect for Identification of Surface Roughness with Speckle images 3

Comparison of Feature Histograms and Co-occurrence Matrix on...

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3rd International Symposium on Physics and Applications 2023, ISPA 2023

作者： Bustomi, M.A. Utama, E.W. Surabaya60111 Indonesia

Speckle imaging is a technique that can be used for digital image analysis. Speckle imaging technology essentially uses a laser to create a pattern of dots through the laser's interference with an object. The advantage of speckle photography is that it is safe and does not require direct contact with the subject. In this study, speckle imaging techniques were used to analyze the surface roughness of objects. Speckle imaging techniques are used to investigate the influence of the choice of laser spectrum on the determination of the surface roughness of an object. For objects with rough surfaces, it comes in 80 sheets of coarse sandpaper and 2000 sheets of fine sandpaper. This study aims to compare the accuracy of identification results using Histogram Features and Co-occurrence Matrix Features when analyzing the influence of laser wavelength on object spot image identification. The research procedures included identifying rough objects, collecting point image data on rough surfaces using a series of experiments, pre-processing the point images, extracting features using histograms and co-occurrence matrices, and using a naive Bayes classifier. It includes determining surface roughness and performing comparisons between them. The resulting feature histogram and feature appearance matrix. This study shows that the use of Histogram Features and coexistence matrix features yields similar conclusions regarding the influence of the laser beam spectrum in determining the surface roughness of an object. The difference between Histogram Features and Co-occurrence Matrix Features lies in the pattern of detection results. Differences in the detection result patterns may be caused by differences in the characteristics of the Histogram Features and Co-occurrence Matrix Features of each speckle image used in the study. Although the detection result patterns of the Histogram Feature amount and the Co-occurrence Matrix feature amount are different, the accuracy of the detection resu

关键词： Surface roughness

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Wet and Wrinkled Finger recognition Using Voting Classification and GLCM Algorithm 3

Wet and Wrinkled Finger Recognition Using Voting Classificat...

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3rd International conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Kumar, Puneet Sharma, Suarabh Chandigarh University Department of Computer Science and Engineering Punjab Gharuan140301 India

ISBN: (纸本)9781665438117

The automated wrinkle fingerprint recognition techniques rely on some principles from the domain of pattern recognition. There are two prevalent techniques for wrinkle fingerprint recognition. The first method is based on the pixel intensity of the wrinkles, and the second relies upon the pattern of fingerprint orientation fields. In this research paper classification method is proposed for the wet and wrinkled image recognition. The proposed method is the combination of threshold segmentation, Grey Level C0-occurrence Matrix algorithm to extract the attributes and voting classification to recognize wet image. The proposed method is executed in python and the analysis of results is done with regard to accuracy, precision and recall. The accuracy of the proposed model is achieved up to 96 percent. © 2021 IEEE.

关键词： image segmentation

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analysis of image Segmentation Algorithms for Infrared images 3rd

Analysis of Image Segmentation Algorithms for Infrared Ima...

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3rd International conference on VLSI, Communication and Signal processing, VCAS 2020

作者： Isalkar, Akshay Manikandan, K. School of Computing Science and Engineering VIT Bhopal University Bhopal India

ISBN: (纸本)9789811627606

image segmentation plays an important role in pattern recognition, computer vision, and many applications nowadays. Visible image plays very significant role in object detection in day time but unsuccessful to detect object in dark. Infrared (IR) sensors have capability to shoot in day as well as night. Rapid expansion in the growth of IR sensor market and reducing price motivates IR researchers to expand IR object segmentation more promising. IR image segmentation grows in theoretical directions rather in practical directions. This paper proposed study of different IR image segmentation algorithms for human, animal, and object. The basic global Ostu, Max Entropy, Triangle, and Percentile methods are explored in study. IR input images are captured using SeekThermal sensor. The result shows comparative efficiency of these basic object segmentation algorithms. There is scope to develop robust and efficient algorithms for object segmentation in night. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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A Novel Speckle-Robust Feature Map and Research on Rotation Invariance of Feature Maps in the SAR image 3

A Novel Speckle-Robust Feature Map and Research on Rotation ...

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3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Li, Jiamu Wang, Yi Yu, Wenbo Wang, Zijian Yu, Zhongjun Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing101408 China School of Technology Beijing Forestry University Beijing100083 China Research Center for Biodiversity Intelligent Monitoring Beijing Forestry University Beijing100083 China

ISBN: (纸本)9781510667563

The feature map is often used for describing the characteristics of key points in image registration applications. Due to feature information being sensitive to rotation and noise, the values of calculation methods for various feature maps would change in different rotation or noise conditions. Consequently, it is unfavorable to obtain the stable descriptor for the same feature point in different images. Specifically, speckle noise damages the synthetic aperture radar (SAR) image seriously, which makes SAR image registration more difficult. In this paper, a novel feature map, which is robust to speckle noise, is proposed. A fully size-adaptive sliding window method is embedded into the ratio of exponentially weighted average method for its noise robustness improvement. Then, a series of Monte Carlo experiments compare the rotation and speckle stabilities among various feature map methods comprehensively. The experimental results reflect that the proposed feature map method is stable while speckle noise increases, and it has less mean orientation difference than others. Moreover, the data of experimental results can be used to support many other analyses on feature maps. © 2023 SPIE.

关键词： image registration

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Classification of Untranscribed Handwritten Notarial Documents by Textual Contents 10th

Classification of Untranscribed Handwritten Notarial Documen...

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10th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Jose Flores, Juan Ramon Prieto, Jose Garrido, David Alonso, Carlos Vidal, Enrique Univ Politecn Valencia PRHLT Res Ctr Valencia Spain Univ Cadiz HUM313 Res Grp Cadiz Spain TranSkriptorium IA Valencia Spain

ISBN: (纸本)9783031048814;9783031048807

Huge amounts of digital page images of important manuscripts are preserved in archives worldwide. The amounts are so large that it is generally unfeasible for archivists to adequately tag most of the documents with the required metadata so as to allow proper organization of the archives and effective exploration by scholars and the general public. The class or "typology" of a document is perhaps the most important tag to be included in the metadata. The technical problem is one of automatic classification of documents, each consisting of a set of untranscribed handwritten text images, by the textual contents of the images. The approach considered is based on "probabilistic indexing" , a relatively novel technology which allows to effectively represent the intrinsic word-level uncertainty exhibited by handwritten text images. We assess the performance of this approach on a large collection of complex notarial manuscripts from the Spanish Archivo Historico Provincial de Cadiz, with promising results.

关键词： Content-based image retrieval Document classification Historical manuscripts

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A Parallel Sea-Land Segmentation Method Based on Improved Unet Model 3

A Parallel Sea-Land Segmentation Method Based on Improved Un...

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3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Zhang, Xuebo Jiang, Min Li, Jinli School of Space Information Space Engineering University Beijing China 96944 troops Chinese People's Liberation Army Beijing China

ISBN: (纸本)9781510667563

Pixel-level sea-land segmentation on high-resolution remote sensing images is a basic task in remote sensing applications and is of great significance for coastline extraction and near-shore marine target detection. This paper proposes an improved Unet model for sea-land segmentation on high-resolution remote sensing images. This model outperforms UNet++ and other models in sea-land segmentation accuracy when applied to the HRSC2016-SL dataset. Based on this model, a parallel sea-land segmentation processing algorithm was developed, whose parallel efficiency reached 49.5% on 16K*14K remote sensing images. To complete this study, a parallel processing system for sea-land segmentation was developed, which achieved distributed and single-machine multi-core parallel sea-land segmentation tasks on high-resolution remote sensing images. © 2023 SPIE.

关键词： image segmentation

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Cross-Guided Feature Fusion with Intra-Modality Reweighting for Multi-Spectral Pedestrian Detection 26

Cross-Guided Feature Fusion with Intra-Modality Reweighting ...

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26th International conference on pattern recognition / 8th International Workshop on image Mining - Theory and Applications (IMTA)

作者： Deng, Jian Li, Mingyue Chen, Yongxin Shao, Zhenzhou Qu, Ying Guan, Yong Zhang, Jun Shi, Zhiping Capital Normal Univ Coll Informat Engn Beijing Peoples R China Beijing Key Lab Light Ind Robot & Safety Verifica Beijing Peoples R China Beijing Adv Innovat Ctr Imaging Technol Beijing Peoples R China Capital Normal Univ Sch Math Sci Beijing Peoples R China Univ Tennessee Engn Coll Knoxville TN USA Beijing Smartchip Microelect Technol Co Ltd Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Multi-spectral pedestrian detection has gained extensive attention over the past decade. To alleviate the problem of modality imbalance in the multi-spectral tasks, a novel cross-guided feature fusion network based on the auto-encoder framework is proposed using RGB-thermal image pairs as inputs. To obtain the complementary features, a cross-guided loss is designed, so that the output images are balanced with both modalities in an unsupervised manner. An intra-modality reweighting module is implemented to filter the redundant features before the fusion. Finally, YOLOv3 is chosen as the detector fed by the fused features. The proposed method is verified using the public KAIST and VOT-RGBT datasets. Experimental results demonstrate that the proposed method can outperform the state-of-the-art methods, the miss rate of pedestrian detection reaches 48.57% and 4.52% using KAIST and VOT-RGBT datasets, respectively.

关键词： Redundancy Detectors Feature extraction pattern recognition Task analysis

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Milky WAI: Unlocking the Secrets of Raw Cow Milk Through Speckle pattern and AI 3

Milky WAI: Unlocking the Secrets of Raw Cow Milk Through Spe...

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3rd IEEE International conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Bello, Valentina Bassi, Irene Fiocchi, Matteo Nuzzi, Cristina Bellassi, Paolo Cattaneo, Luca Pasinetti, Simone University of Pavia Diii Pavia Italy University of Brescia Dimi Brescia Italy Università Cattolica del Sacro Cuore DiSTAS Cremona Italy Universita Cattolica del Sacro Cuore Diana Piacenza Italy

ISBN: (纸本)9798350378009

This paper introduces a novel method for raw cow milk classification combining speckle pattern (SP) imaging and AI-based processing of statistical parameters. Raw cow milk has been classified considering (i) 6 statistical features extracted from raw SP images, and (ii) 4 features extracted from the Gray-Level Co-occurrence Matrix (GLCM) computed on each SP image. We conducted 4 experimental campaigns, resulting in 24, 000 SP images retrieved from 20 milks produced by 20 cows. We then considered two different datasets: (i) a Complete dataset (made of all 24,000 frames), and (ii) a Reduced dataset, built by excluding data from one acquisition campaign. The two datasets were then split into training and testing sub-datasets. We trained three Wide Neural Networks (WNN) using different strategies. WNN1 and WNN2 have been trained and tested using the Complete and the Reduced datasets respectively, while WNN3 has been trained on the Reduced tested on the Complete dataset. WNN3 only attained 72% accuracy (revealing sensitivity to environmental conditions), while WNN1 and WNN2 achieved a test accuracy higher than 90%, demonstrating effectiveness without relying on computationally expensive models such as CNNs. This approach could be considered as a promising initial step for cow milk classification using SP imaging. © 2024 IEEE.

关键词： Data analysis machine learning measurement science milk analysis speckle pattern

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