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

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

引用

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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Classification of Audio Signals Based on Deep Learning Methods for Google Net and Alex Net

Classification of Audio Signals Based on Deep Learning Metho...

引用

Advances in Engineering Science and Technology (AEST), international conference on

作者： Noor S. Ali Ehab Abdulrazaaq Hussein Laith Ali Abdul-Rahaim Department of Electrical Engineering College of Engineering University of Babylon Babylon Iraq

ISBN: (数字)9798331502690

ISBN: (纸本)9798331502706

Audio signals have a significant impact on every element of human life. Audio classification systems can be developed to solve the efficiency problems of conventional methods using the learning capabilities of cloud computing and deep learning architectures. This research proposes deep learning techniques to classify environmental audio signals based on the spectrograms generated for these sounds while using deep learning techniques. Spectrogram images of environmental audio signals train deep-learning networks such as Google Net and Alex Net on cloud computing. Both networks are trained on a large number of datasets. The achieved accuracy can reach 92.5% on Google Net, and 87.5% on Alex Net. This paper provides that the proposed technique for audio signal classification can be efficient for sound classification and recognition systems.

关键词： Deep learning Cloud computing Tensors Accuracy Convolution pattern classification Spectrogram

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Improved AOMDV Routing Protocol in Manet UAV Based on Virtual Hop 3

Improved AOMDV Routing Protocol in Manet UAV Based on Virtua...

引用

3rd international conference on Computer Communication and the Internet, ICCCI 2021

作者： Kiki, Mesmin J. Mbyamm Iddi, Ibrahim Yunusa, Haruna School of Electronic and Information Engineering Beihang University Beijing China School of Pattern Recognition and Intelligent Systems Beihang University Beijing China

ISBN: (纸本)9781728176185

In order to improve the load balancing capability of the AOMDV routing protocol of the ad hoc network, the network nodes are divided into two levels, Risk and No risk, based on the queue buffer ratio, and a different processing is done for different levels. Directly remove the risk level nodes and assign the corresponding virtual hops to the No Risk level nodes via the threshold comparison. The higher the queue cache rate, the higher the corresponding virtual hops. The RREP and Hello messages are expanded, and the node queue cache ratio is added to update and collect the latest path node status information. The improved protocol uses the minimum virtual hops containing node load information as the basis for optimal path selection, and uses OPNET to simulate at a fixed node rate under different network loads. The results show that the improved routing protocol plays a role in load balancing for nodes at the expense of limited routing overhead, relieves network congestion, increases the packet delivery rate, effectively reduces the end-to-end transmission delay, and improves self-organization. Network performance provides a relatively simple method. © 2021 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Analyze the User Behaviors via Network Traffic Data in Smart Home

Analyze the User Behaviors via Network Traffic Data in Smart...

引用

Automation, Robotics and Computer Engineering (ICARCE), international conference on

作者： Xintong Li Yulong Han Wenle Bai College of Information North China University of Technology Beijing China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

As a service medium for household units, smart homes can serve as energy management hubs by analyzing user behavior within the home to optimize energy allocation and usage, thereby avoiding unnecessary resource waste—for example, turning off the TV while the user is taking a shower. However, many users refuse to allow third-party analysis of their smart device data due to privacy concerns. To address this issue, we propose a technique for accurately extracting user behavior features from network traffic, enabling non-intrusive user behavior recognition. Recognizing user behavior poses significant challenges due to the uncertainty of user activities and the encrypted nature of network traffic. To overcome these challenges, we leverage a dynamic time warping (DTW) algorithm to transform encrypted traffic data into device event sequences using device event fingerprints, facilitating the matching of user activity patterns. Furthermore, we introduce a deterministic finite automaton (DFA) model to capture transition rules within activity sequences, enhancing recognition accuracy. We evaluated our approach on a self-constructed real-world dataset, and the experimental results demonstrate that our method achieves a behavior recognition accuracy of up to 87.3%.

关键词： Data privacy Privacy Accuracy Heuristic algorithms Smart homes Telecommunication traffic Cryptography Smart devices Protection pattern matching

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Application of Wave Cluster Algorithm Based on Kernel Optimization in pattern recognition of High-Frequency Financial Data

Application of Wave Cluster Algorithm Based on Kernel Optimi...

引用

Cloud Computing, Big Data Applications and Software Engineering (CBASE), international conference on

作者： Dazhi Yue Business School Xiamen Institute of Technology Xiamen China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

High-frequency financial data has the characteristics of high nonlinearity, non-stationarity and noise, which brings great challenges to traditional data mining algorithms. In this study, we propose a Wave Cluster algorithm based on kernel function optimization, which aims to improve its performance in pattern recognition of high-frequency financial data. The Wave Cluster algorithm has attracted much attention for its robustness to noise and multiresolution analysis capability, but it still has limitations when dealing with complex nonlinear patterns. In this paper, kernel-based functions are introduced to map the original high-dimensional financial data to a higher-dimensional feature space. This nonlinear transformation can effectively capture the potentially complex relationships in the data. Subsequently, the modified Wave Cluster algorithm is applied in the transformed feature space, and high-frequency trading data from the China stock market, including price, trading volume and order book information, are used in the empirical study. Experimental results show that compared with traditional methods, the proposed algorithm achieves significant improvement in the tasks of identifying market microstructure, detecting abnormal trading patterns and predicting short-term price movements. Especially during periods of high volatility, the algorithm shows greater stability and accuracy.

关键词： Accuracy Software algorithms Noise Time series analysis Clustering algorithms Prediction algorithms Stability analysis Kernel Stock markets Optimization

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An Enhanced Local Descriptor (ELD) for Face recognition 3

An Enhanced Local Descriptor (ELD) for Face Recognition

引用

3rd international conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Karanwal, Shekhar Graphic Era Deemed to Be University Cse Department Uttarakhand Dehradun India

ISBN: (纸本)9780738146270

The influence of light changes makes the task of feature extraction more difficult for the local descriptors. Most of local descriptors sacrifices their performance in harsh lightning variations. Some uses pre-processing approach some uses gradient based methods (with local descriptors) to improve accuracy. In this work, a novel Enhanced Local Descriptor (ELD) is introduced by taking advantages of two well behaved descriptors in harsh lightning changes. These 2 are Compound Local Binary pattern (CLBP) Median Robust Extended LBP based on Neighborhood Intensity (MRELBP-NI). CLBP is characterized by Sign Magnitude details, and MRELBP-NI is characterized by Median Mean statistics. Both of them are very essential in controlling harsh light variations. By merging features of both a discriminant descriptor ELD is gained. FLDA is taken further for size contraction SVMs is used for matching. ELD achieves stupendous outcomes on Extended Yale B (EYB) dataset. ELD wholly outstrip the singly implemented descriptors many methods from literature. ELD secure best accuracy of 93.42%. There is no pre-processing the gradient based methods are used. © 2021 IEEE.

关键词： Local binary pattern

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Motion Segmentation Based on Pixel Distribution Learning on Unseen Videos 3rd

Motion Segmentation Based on Pixel Distribution Learning on...

引用

3rd international conference on Smart Multimedia, ICSM 2022

作者： Lu, Zhiyuan Chen, Youwei Zhao, Chenqiu Department of Computing Science University of Alberta Edmonton ABT6G2R3 Canada

ISBN: (纸本)9783031220609

Motion segmentation plays an important role in many applications including autonomous driving, computer vision and robotics. Previous works mainly focus on segmenting objects from seen videos. In this paper, we present a novel approach based on pixel distribution learning for motion segmentation in unseen videos. In particular, optical flow is extracted from consecutive frames to describe motion information. We then randomly permute these modified motion features, which are used as input of a convolution neural network. The random permutation process forces the network to learn the pixels’ distributions rather than local pattern information. Consequently, the proposed approach has a favorable generalization capacity and can be applied for unseen videos. In contrast to previous approaches based on deep learning, the training videos and testing videos of our proposed approach are completely different. Experiments based on videos from the KITTI-MOD dataset demonstrates that the proposed approach achieves promising results and shows potential for better motion segmentation on unseen videos. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Achievement Degree Evaluation Method of Operating System Course Targets under the Background of Engineering Education Accreditation 3

Achievement Degree Evaluation Method of Operating System Cou...

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3rd IEEE international conference on Computer Science and Educational Informatization, CSEI 2021

作者： Zhang, Fan Zhang, Xinhong Henan University School of Computer and Information Engineering Kaifeng China Institute of Image Processing and Pattern Recognition Henan University Kaifeng China

ISBN: (纸本)9781728184739

Engineering education accreditation emphasizes the cultivation of students' ability to solve complex engineering problems, including the cultivation of students' ability to analyze, design and develop solutions, research, application of modern tools and other qualities related to non-technical factors to solve complex engineering problems. Operating system course is the core course of computer science and technology major in colleges and universities. The understanding and mastery of operating system course directly affects students' computer professional level. In view of the new requirements of new engineering construction and engineering education accreditation for major curriculum construction and personnel training, as well as the current characteristics of software engineering and personnel training, this paper, based on the core concept of OBE and complex engineering problems of engineering education accreditation, evaluates the achievement of operating system curriculum requirements under the background of engineering education accreditation. This paper analyzes the effectiveness and puts forward a new method of major curriculum construction and evaluation to meet the requirements of new engineering construction and engineering education accreditation. © 2021 IEEE.

关键词： Engineering education

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Enhancing Nucleotide pattern recognition: A Hybrid Encoding Approach with Bi-LSTM and GRU

Enhancing Nucleotide Pattern Recognition: A Hybrid Encoding ...

引用

3rd international conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Fatimah, Syeda Fizza Masood, Sarfaraz Rizvi, Danish Raza Department of Computer Engineering Jamia Millia Islamia University New Delhi110025 India

Gene sequence classification is based on the order of nucleotides present inside a DNA or RNA molecule. The nucleotide pattern recognition is challenging due to the complex structure and repetitive nature of gene sequences. Recent research on virus gene classification lacks a robust gene sequence classifier with parameter efficiency that can be evaluated on multiple datasets to produce efficient results. This work aims to utilize the computation power of RNN architectures Bi-LSTM and GRU to capture the sequential dependencies among nucleotides in viruses having variable length and complex pre-determined structure using a hybrid encoding approach by merging k-mer and label encoding techniques for data pre-processing. The results obtained for the RNN architectures were observed on 3 datasets with variable class sizes of 4, 6, and 14. The Bi-LSTM achieved accuracies of 96.11%, 98.82%, and 97.55% for datasets 1, 2, and 3, respectively. Similarly, the GRU architecture showed impressive results, with accuracies of 97.55%, 98.72%, and 97.54% across the same three datasets. The research outcome strongly suggests that, even though both GRU and LSTM produce nearly identical performance metrics values, the fine-tuned GRU network emerges as a better choice for classifying viruses' gene sequences due to 1,685,774 trainable parameters, which are significantly less when compared to Bi-LSTM which had 2,012,046 trainable parameters. This work contributes to the nucleotide pattern recognition using RNN architectures, primarily GRUs, that offer improved computational efficiency without compromised accuracy across datasets, therefore achieving a balance between accuracy and computational efficiency across several datasets. © 2025 The Author(s).

关键词： Recurrent neural networks

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