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DeepArUco: Marker Detection and Classification in Challenging Lighting Conditions 11th

DeepArUco: Marker Detection and Classification in Challeng...

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

作者： Berral-Soler, Rafael Muñoz-Salinas, Rafael Medina-Carnicer, Rafael Marín-Jiménez, Manuel J. Department of Computing and Numerical Analysis University of Córdoba Córdoba Spain Córdoba Spain

ISBN: (纸本)9783031366154

Detection of fiducial markers in challenging lighting conditions can be useful in fields such as industry, medicine, or any other setting in which lighting cannot be controlled (e.g., outdoor environments or indoors with poor lighting). However, explicitly dealing with such conditions has not been done before. Hence, we propose DeepArUco, a deep learning-based framework that aims to detect ArUco markers in lighting conditions where the classical ArUco implementation fails. The system is built around Convolutional Neural Networks, performing the job of detecting and decoding ArUco markers. A method to generate synthetic data to train the networks is also proposed. Furthermore, a real-life dataset of ArUco markers in challenging lighting conditions is introduced and used to evaluate our system, which will be made publicly available alongside the implementation. Code available in GitHub: https://***/AVAuco/deeparuco/. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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An adaptive weight texture classification method based on Local Binary pattern Variance 22

An adaptive weight texture classification method based on Lo...

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3rd International Symposium on Artificial Intelligence for Medical Sciences, ISAIMS 2022

作者： Chen, Hao Tang, Wending Tao, Ran Department of Electrical and Computer Engineering Northeastern University United States Department of Engineering and Physics Tsinghua University China State Key Laboratory of Surface Physics Department of Physics Advanced Materials Laboratory Fudan University China

ISBN: (纸本)9781450398442

The extraction of local texture information using the traditional Local Binary Mode (LBP) is limited, and it ignores the representation of global texture information, which leads to an unsatisfactory outcome for the texture classification task. Local Binary Mode (LBP) has been widely used in texture classification. This paper utilizes LBPV to resolve this issue (Local Binary pattern Variance) and proposes a novel adaptive weight joint multi-scale LBPV2 texture picture classification algorithm. The typical variance weight is replaced by the square of covariance as the cumulative weight of the histogram in this method, and the multi-scale texture information is retrieved using an adaptive weight and multi-scale scheme. Thus, the texture classification performance is further improved. Simulation experiments on the commonly used Outex reference texture database show that the proposed adaptive weight combined with multi-scale LBPV2 can significantly improve the performance of texture classification. In the fields of computer vision and pattern recognition, texture analysis is a fundamental visual problem with a wide range of applications, including object detection, remote sensing, content-based image retrieval, and medical picture analysis. For various research questions, numerous academics have put forth various LBP versions in recent years. The dominant LBP [2] model was proposed by Liao et al. in 2009, and it was empirically chosen as the best model out of all the models. Guo et al. proposed LBPV [3], which expresses local contrast information into the straight square of texture images using a local variance confidence and global matching scheme. In order to increase classification performance, the author also proposed a Completed Local Binary pattern (CLBP) [4] in the same year. This pattern combines three complimentary groups - CLBPS, CLBPM, and CLBPC - using a combined probability distribution. To enhance the traditional local binary pattern's noise resistance and

关键词： Local binary pattern

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Synthetic Aperture Radar image target recognition based on hybrid attention mechanism 21

Synthetic Aperture Radar image target recognition based on h...

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3rd International conference on pattern recognition and Intelligent Systems, PRIS 2021

作者： Shi, Baodai Zhang, Qin Li, Yao Air Force Engineering University Shaanxi China

ISBN: (纸本)9781450390392

Deep learning algorithm has been more and more applied in the image field, but its application in the SAR image target recognition field is still faced with some problems, such as poor instantaneity and low precision. On this basis, this paper puts forward a convolutional neural network algorithm based on hybrid attention mechanism . The basic module of this model is composed of the trunk branch and the soft branch. The trunk branch composed of the residual shrinkage network and the improved channel attention mechanism is responsible for extracting the main characteristics. Soft branch composed of up sampling and down sampling is responsible for extracting the mixed attention weight, which can enhance the mapping capacity from input to output. The recognition rate of MSTAR dataset with this model is 99.6%. According to noise analysis, this model is of strong robustness for images with impulse noise added . © 2021 ACM.

关键词： Synthetic aperture radar

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Advanced Optimization of Discrete Orthogonal Hahn Moments for Precision image Reconstruction via the Firefly Algorithm

Advanced Optimization of Discrete Orthogonal Hahn Moments fo...

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Embedded Systems and Artificial Intelligence (ESAI), International conference on

作者： Abdelati Bourzik Belaid Bouikhalene Jaouad El-Mekkaoui Amal Hjouji LIMATI Laboratory Sultan Moulay Slimane University Beni Mellal Morocco LTI Laboratory Sidi Mohamed Ben Abdellah University Fez Morocco STIC Laboratory Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798331522056

ISBN: (纸本)9798331522063

In recent decades, discrete orthogonal moments have frequently been used to represent images in a variety of computer vision and pattern recognition applications. These moments commonly employ discrete orthogonal polynomials as their basis, some of which are parametric and characterized by localization parameters. Optimizing these parameters is crucial for enhancing the efficiency of orthogonal moments in image analysis. This paper takes a new approach for optimizing the discrete Hahn polynomial parameters ($\alpha, \beta$) using the Firefly optimization algorithm, focusing on minimizing the mean square reconstruction error. Where our method identifies the optimal $\alpha$ and $\beta$ values for constructing Hahn polynomials to achieve superior image moments. The results demonstrate that our Firefly algorithm-based method significantly improves image reconstruction quality, yielding lower reconstruction errors, particularly in low orders of Hahn moments. Furthermore, the proposed method outperforms both the artificial bee colony optimization method and the standard parameter selection method. The experiments clearly indicate the advantages of our proposed method, especially in the lower orders of moments.

关键词： Location awareness image analysis Noise Optimization methods Manuals Mean square error methods image representation Polynomials image reconstruction Standards

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Texture Unit pattern Approach for Fabric Classification 1

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3rd International conference on Machine Intelligence and Signal Processing, MISP 2021

作者： Murthy, Garapati S. N. Kumar, Pullela Svvsr Satya Kumari, Tadi Veerraju, T. Nagendra Kumar, Dirisala J. Murthy, Chakka Svvsn Aditya College of Engineering Surampalem India Vishnu Institute of Technology Bhimavaram India Aditya College of Engineering and Technology Surampalem India

ISBN: (数字)9789811908408

ISBN: (纸本)9789811908392

Fabric texture classification can be implemented automatically using fabric texture analysis using their images. Texture analysis using fabric images has lots of applications such as automatic recognition and classification of fabrics and to detect the defects on the fabrics and the quality of the fabrics in fabric industries. Conversely, for the existing manual systems, it is a tough task to estimate the correct fabric texture class group effectively. Manual inspection procedures are inefficient for classification due to lack of vigilance and boredom which deteriorates performance. To reduce the cost and time, an automated classification is required based on computer vision and image processing techniques. In this study, a well-organized fabric texture classification system is proposed. Based on the feature set values, the present paper proposes a user-defined approach to classify the fabric texture image into one of the familiar five pre-defined classes (silk, cotton, linen, wool and worsted). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Textures

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QAP Optimisation with Reinforcement Learning for Faster Graph Matching in Sequential Semantic image analysis 3rd

QAP Optimisation with Reinforcement Learning for Faster Gr...

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3rd International conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Chopin, Jérémy Fasquel, Jean-Baptiste Mouchère, Harold Dahyot, Rozenn Bloch, Isabelle LARIS Université d’Angers Angers France LS2N Universite de Nantes CNRS UMR Nantes6004 France Department of Computer Science Maynooth University Maynooth Ireland Sorbonne Université CNRS LIP6 Paris France

ISBN: (纸本)9783031090363

The paper addresses the fundamental task of semantic image analysis by exploiting structural information (spatial relationships between image regions). We propose to combine a deep neural network (CNN) with graph matching where graphs encode efficiently structural information related to regions segmented by the CNN. Our novel approach solves the quadratic assignment problem (QAP) sequentially for matching graphs. The optimal sequence for graph matching is conveniently defined using reinforcement-learning (RL) based on the region membership probabilities produced by the CNN and their structural relationships. Our RL based strategy for solving QAP sequentially allows us to significantly reduce the combinatorial complexity for graph matching. Preliminary experiments are performed on both a synthetic dataset and a public dataset dedicated to the semantic segmentation of face images. Results show that the proposed RL-based ordering significantly outperforms random ordering, and that our strategy is about 386 times faster than a global QAP-based approach, while preserving similar segmentation accuracy. © 2022, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Towards Depth Fusion into Object Detectors for Improved Benthic Species Classification 1

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26th International conference on pattern recognition, ICPR 2022

作者： Dawkins, Matthew Crall, Jon Leotta, Matt O’Hara, Tasha Siemann, Liese Kitware Inc. 1712 Route 9. Suite 300 Clifton ParkNY12065 United States Coonamessett Farm Foundation 277 Hatchville Rd E FalmouthMA02536 United States

ISBN: (数字)9783031377310

ISBN: (纸本)9783031377303

Coonamessett Farm Foundation (CFF) conducts one of the optical surveys of the sea scallop resource using a HabCam towed vehicle. The CFF HabCam v3 collects 5+ images per second, providing a continuous track of imagery as it is towed over scallop grounds. Annotations from HabCam images are translated into biomass estimates through a multi-step process that relies on adequate image subsampling and accurate scallop counts and measurements. Annotating images is the most time-consuming part of generating these reliable scallop biomass estimates. Reliably automating this process has the potential to increase annotation rates and improve biomass estimation, addressing questions about scallop patchiness and distribution over small-to-large scales on scallop grounds. Furthermore, optical survey images provide a wealth of raw data that is not consistently utilized. An additional high priority goal is to provide data for improving flounder stock assessments because the incidental bycatch of non-target species could have negative impacts on the long-term sustainability of the fishery. Kitware, Inc developed the Video and image Analytics for Marine Environments (VIAME) system for analysis of underwater imagery with support from the NOAA Automated image analysis Strategic Initiative. CFF collaborated with Kitware to develop improved automated detectors for scallop classes (live scallops, swimming scallops, and clappers) and flounder by incorporating depth disparity information obtained from stereo image pairs collected during surveys. The accuracy and precision of these new detectors, with depth information incorporated, was contrasted against that of single-image detectors when utilized upgraded deep learning networks. A few methods were investigated, including early and late fusion of the depth information. © 2023, Springer Nature Switzerland AG.

关键词： Biomass

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ResPho(SC)Net: A Zero-Shot Learning Framework for Norwegian Handwritten Word image recognition 11th

ResPho(SC)Net: A Zero-Shot Learning Framework for Norwegian...

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

作者： Gurav, Aniket Jensen, Joakim Krishnan, Narayanan C. Chanda, Sukalpa Department of Computer Science and Communication Østfold University College Halden1757 Norway Department of Data Science Indian Institute of Technology Palakkad Kerala India

ISBN: (纸本)9783031366154

Recent advances in deep Convolutional Neural Networks (CNNs) have established them as a premier technique for a wide range of classification tasks, including object recognition, object detection, image segmentation, face recognition, and medical image analysis. However, a significant drawback of utilizing CNNs is the requirement for a large amount of annotated data, which may not be feasible in the context of historical document analysis. In light of this, we present a novel CNN-based architecture ResPho(SC)Net, to recognize handwritten word images in a zero-shot learning framework. Our method proposes a modified version of the Phosc(Net) architecture with a much lesser number of trainable parameters. Experiments were conducted on word images from two languages (Norwegian and English) and encouraging results were obtained. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical character recognition

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Spine Curve Extraction based on Mask Segmentation 21

Spine Curve Extraction based on Mask Segmentation

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3rd International conference on Intelligent Medicine and image Processing (IMIP)

作者： Liu, Xiaomin Wang, Zhe Guo, Wei Beijing Univ Technol Sch Informat & Commun Engn Beijing 100124 Peoples R China Air Force Med Ctr Dept TCM Manipulat Orthoped Beijing 100142 Peoples R China

ISBN: (纸本)9781450390057

Cobb angle on X-ray image is a gold standard for the evaluation of scoliosis. When measuring the Cobb angle, there are some artificial measurement errors, which lead to the unreliable results. A spine curve extraction method based on mask segmentation is proposed in this paper. The central angle of the arc corresponding to the main C bending of the spine is calculated to evaluate the degree of scoliosis. Select 173 X-ray digital images of the patients with scoliosis randomly. Firstly, the two spine curves are extracted by using the new method proposed in this paper and the traditional cross method separately, and the central angles corresponding to the main C bending of the two different spine curves mentioned above are measured by three experienced surveyors respectively. After a week, the sets of data were measured repeatedly. SPSS 22.0 statistical analysis software is used to analyze the results statistically. Compared with the spine curve extracted by the cross method, the new method proposed in this paper has better reliability and stability in evaluating the degree of scoliosis.

关键词： scoliosis spine curve vertebra segmentation pattern recognition

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3rd International conference on Soft Computing and its Engineering Applications, icSoftComp 2021

3rd International Conference on Soft Computing and its Engin...

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3rd International conference on Soft Computing and its Engineering Applications, icSoftComp 2021

ISBN: (纸本)9783031057663

The proceedings contain 33 papers. The special focus in this conference is on Soft Computing and its Engineering Applications. The topics include: Sentiment analysis of Twitter Data Using Machine Learning Approaches;mining Spatio-Temporal Sequential patterns Using MapReduce Approach;a Split-Then-Join Lightweight Hybrid Majority Vote Classifier;identification of Barriers in Adoption of IoT: Commercial Complexes in India;a Dynamically Adapting Framework for Stock Price Prediction;evaluating Binary Classifiers with Word Embedding Techniques for Public Grievances;database Concentration Method for Efficient image Retrieval Using Clustering and image Tag Comparison;microstructure image Classification of Metals Using Texture Features and Machine Learning;segregation of Areca Nuts Using Three Band Photometry and Deep Neural Network;Early Diagnosis of Alzheimer’s Disease from MRI images Using Scattering Wavelet Transforms (SWT);constraint Pushing Multi-threshold Framework for High Utility Time Interval Sequential pattern Mining;KTSVidRec: A Knowledge-Based Topic Centric Semantically Compliant Approach for Video Recommendation on the Web;intelligent Facial Expression Evaluation to Assess Mental Health Through Deep Learning;intelligent Mobility: A Proposal for Modeling Traffic Lights Using Fuzzy Logic and IoT for Smart Cities;crop Disease Prediction Using Multiple Linear Regression Modelling;radial Basis Function Network Based Intelligent Scheme for Software Quality Prediction;generating the Base Map of Regions Using an Efficient Object Segmentation Technique in Satellite images;KCEPS: Knowledge Centric Entity Population Scheme for Research Document Recommendation;weighted Hybrid Recommendation System Using Singular Value Decomposition and Cosine Similarity;CT/MRI 3D Fusion for Cerebral System analysis;text analysis and Classification for Preprocessing Phase of Automatic Text Summarization Systems;arabic Cyberbullying Detection from Imbalanced Dataset Using Machine Learning;in

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