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18th international conference on pattern recognition (ICPR 2006)

作者： Dai, Fang Zheng, Nanning Xue, Jianru Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Sci Xian 710048 Peoples R China

ISBN: (纸本)0769525210

Primal sketch performs an important role in early vision. In this paper, we propose a novel method to obtain the primal sketch of natural images by integrating empirical mode decomposition (EMD) techniques and image gradient. 2D EMD approach can decompose the image into a finite number of intrinsic mode functions (IMF), and each one represents the original image in a different scale, with the 1st IMF representing the finest scale. To enhance the information represented by the IMF, we multiply the 1st IMF by the image gradient. This enhanced IMF highlights intensity changes in the image. By linking all the maximal points in the enhanced IMF, we obtain a primal sketch of the original image. Compared with the existed primal sketch extraction methods, our method is fully driven by the image data, and it needs neither to choose filters nor to learn the image bases. The experiment results show that our method is fast and effective.

关键词： image processing

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Some Scientific Results of the XXIV international conference DAMDID/RCDL-2022

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pattern recognition AND image ANALYSIS 2023年第2期33卷 81-85页

作者： Muromtsev, D. I. stupnikov, S. A. ITMO Univ St Petersburg 197101 Russia Russian Acad Sci Inst Informat Problems Fed Res Ctr Comp Sci & Control Moscow 119333 Russia

The paper discusses the main scientific results of the XXIV international conference "Data Analytics and Management in Data Intensive Domains" DAMDID/RCDL-2022, held on October 4-7, 2022, in st. Petersburg, Russia. A historical review of the development of the conference was carried out. Motivation, goals of the conference, main areas of the subject, related conferences, and held associated events are considered. The conference program of the current year is characterized, including the topics of invited and sectional reports. Conclusions are drawn on the basis of the results of the analysis of the scientific contribution of the conference. Attached is a list of papers selected by the conference program committee for a special issue of the journal. The papers relate to areas of data integration, data analysis in astronomy and biomedicine, machine learning applications, image analysis and processing, and information extraction from texts.

关键词： DAMDID/RCDL-2022 data analysis data management data intensive domains

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2018 1st international conference on Multimedia Analysis and pattern recognition, MAPR 2018 - Proceedings

2018 1st International Conference on Multimedia Analysis and...

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1st international conference on Multimedia Analysis and pattern recognition, MAPR 2018

ISBN: (纸本)9781538641804

The proceedings contain 15 papers. The topics discussed include: a vision based method for automatic evaluation of germination rate of rice seeds;towards real-time smile detection based on faster region convolutional neural network;Nissl stained rat brain cell image analysis post exposure to electromagnetic fields using image processing techniques;multivariate filter for saliency;measuring crowd collectiveness with trajectory smoothing;multi-task regression learning for prediction of response against a panel of anti-cancer drugs in personalized medicine;landmarks detection by applying deep networks;improving human action recognition with two-stream 3D convolutional neural network;heritage image classification by convolution neural networks;enhancing person re-identification based on recurrent feature aggregation network;document layout analysis: a maximum homogeneous region approach;discriminative deep feature learning for facial emotion recognition;blind deconvolution method using omnidirectional Gabor filter-based edge information;and algorithm for hiding high utility sensitive association rule based on intersection lattice.

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A machine learning Approach to Identify Potential Customer Based on Purchase Behavior 1

A Machine Learning Approach to Identify Potential Customer B...

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1st international conference on Robotics, Electrical and Signal processing Techniques (ICREst)

作者： Choudhury, Adil Mahmud Nur, Kamruddin AIUB Dept Comp Sci Dhaka Bangladesh

ISBN: (纸本)9781538680124

To lift the revenue boundary and stay ahead of the competitors it is important to understand customer's purchase behavior. Different business industries proposed different policies to explore the potentiality of a customer based on statistical analysis. In this paper, we rather propose a machine learning approach to identify potential customers for a retail superstore. The paper proposed an engineered approach to classify potential customer, based on previously recorded purchase behavior. Using this classification as ground truth, we then apply machine learning algorithms to find a pattern to predict potential customers with an accuracy of 99.4 %.

关键词： machine learning Purchase Behavior Analysis Customer Classification

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Research on communication performance indexes in the key area based on stream processing and LstM algorithm 24

Research on communication performance indexes in the key are...

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1st international conference on image processing machine learning and pattern recognition

作者： An, Xinzhou Sun, Yancheng Miao, Fengqing Wang, Zhe Kang, Zhiwen Zhang, Qingwei Gao, Zongbao China Mobile Commun Grp Design Inst Co Ltd Shandong Branch Jinan Peoples R China Xinjiang Univ Coll Math & Syst Sci Urumqi Peoples R China

ISBN: (纸本)9798400707032

In existing research, the analysis of network performance in the key area are is based on offline statistical data. This method has the disadvantages of slow speed in summarizing cell indexes, long time consumption for calculating and slow positioning of problematic cells. In addition, this method cannot predict the trend of index changes within a region and evaluate the optimization effect on communication cells. To solve the above problems, this study proposes using stream processing and LstM algorithm to analyze indexes of the key region. First, this study obtains the list information of all communication cells in the key area and downloads 15-minute granularity performance files required for six indexes calculations. The Java program completes parsing and sends them to Kafka. Next, Spark steaming reads data from Kafka, calculates the index values of communication cell, and writes them to Click-House database. If value of the index is abnormal, information about the communication cell in poor quality will be displayed as a report. If the number of exceptions for cell exceeds the set value, a text message reminder will be sent for network optimization. In addition, using Spring Boot programs to summarize the indexes in the key area into daily granularity. Based on the historical data of daily granularity, an LstM model is established to predict the trend of indexes in the key area. The optimization effect of the communication cell is evaluated based on this method, and if the prediction index exceeds a set value, an SMS alert will be triggered.

关键词： Index Key area stream processing LstM algorithm

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Multi-Scale Deep Pixel Distribution learning for Concrete Crack Detection 25

Multi-Scale Deep Pixel Distribution Learning for Concrete Cr...

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25th international conference on pattern recognition (ICPR)

作者： Wu, Xuanyi Ma, Jianfei Sun, Yu Zhao, Chenqiu Basu, Anup Univ Alberta Edmonton AB Canada

ISBN: (纸本)9781728188089

A number of methods including image processing technologies (IPTs) and deep learning methods, have been used to detect defects in civilian infrastructure. These methods have been introduced to extract features representing cracks in concrete surfaces. Inspired by recent advances of a pixel distribution learning method in background subtraction, we propose a novel multi-scale deep learning method (MS-DPDL) for concrete crack detection. The designed CNN network is trained on the dataset CRACK500 [1], [2] and tested on it for concrete segmentation. To show good transferability of our proposed model, it is later tested on the dataset Concrete Crack images for Classification [3]. Several existing deep learning methods are used to compare the performance of the proposed MS-DPDL method. Results show that our method has good performance and can effectively find concrete cracks in practical situations.

关键词： image processing technologies (IPTs) deep learning pixel distribution learning CNN concrete crack detection

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Some Open Questions on Morphological Operators and Representations in the Deep learning Era A Personal Vision 1st

Some Open Questions on Morphological Operators and Represent...

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1st IAPR international Joint conference on Discrete Geometry and Mathematical Morphology (DGMM)

作者： Angulo, Jesus PSL Res Univ CMM Ctr Morphol Math MINES ParisTech Fontainebleau France

ISBN: (纸本)9783030766566;9783030766573

"Work on deep learning or perish": folklore wisdom in 2021. During recent years, the renaissance of neural networks as the major machine learning paradigm and more specifically, the confirmation that deep learning techniques provide state-of-the-art results for most of computer vision tasks has been shaking up traditional research in image processing. The same can be said for research in communities working on applied harmonic analysis, information geometry, variational methods, etc. For many researchers, this is viewed as an existential threat. On the one hand, research funding agencies privilege mainstream approaches especially when these are unquestionably suitable for solving real problems and for making progress on artificial intelligence. On the other hand, successful publishing of research in our communities is becoming almost exclusively based on a quantitative improvement of the accuracy of any benchmark task. As most of my colleagues sharing this research field, I am confronted with the dilemma of continuing to invest my time and intellectual effort on mathematical morphology as my driving force for research, or simply focussing on how to use deep learning and contributing to it. The solution is not obvious to any of us since our research is not fundamental, it is just oriented to solve challenging problems, which can be more or less theoretical. Certainly, it would be foolish for anyone to claim that deep learning is insignificant or to think that one's favourite image processing domain is productive enough to ignore the state-of-the-art. I fully understand that the labs and leading people in image processing communities have been shifting their research to almost exclusively focus on deep learning techniques. My own position is different: I do think there is room for progress on mathematically grounded image processing branches, under the condition that these are rethought in a broader sense from the deep learning paradigm. Indeed, I firmly believe that

关键词： Mathematical morphology

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Human Activity recognition Using Hybrid Feature Extraction and machine learning 1

Human Activity Recognition Using Hybrid Feature Extraction a...

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1st IEEE international conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Jabade, Vaishali Mane, Suraj Khan, Zuben Vit Bibewadi Dept. of E&tc Engineering Pune India

ISBN: (纸本)9798350316469

Significant progress in Human Activity recognition has been accomplished in the field of image recognition using computer vision and machine learning. This study, which employs a CV-based technique, focuses on the accurate identification of behaviors such as walking, falling, and chitchatting. The dataset had 1305 photos that were evenly distributed throughout three activities, with 400-450 images for each activity. The feature extraction techniques VGG16, Histogram of Oriented Gradients (HOG), and Local Binary pattern (LBP) were utilized as well as many classifiers such as Random Forest, Artificial Neural Network (ANN), and Logistic Regression. The SVM and LR classifiers performed the best, with 97% accuracy. This research has several uses, including surveillance, assisted living, healthcare, human-robot interactions, and entertainment. © 2023 IEEE.

关键词： HAR HOG LBP LR machine learning SVM VGG16

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Malware Class recognition Using image processing Techniques 1

Malware Class Recognition Using Image Processing Techniques

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1st IEEE international conference on Data Management, Analytics and Innovation (ICDMAI)

作者： Makandar, Aziz Patrot, Anita Karnataka State Womens Univ Dept Comp Sci Vijayapura Karnataka India

ISBN: (纸本)9781509040834

Increasing suspicious instructions of various malware through a challenge to the malware analysts to identify and classify samples belongs to the malicious family. They have witnessed the very fast increase in both the number and complexity of malware set of instructions. Malware invest profoundly in technology and capability to reorganize the process of building and mutate existing malware set of instructions to avoid traditional protection. Classify malware variants by applying image processing techniques. The textures play an important role in many image processing applications. In this paper we proposed the Support Vector machine (SVM) multiclass malware image classification challenge from an image processing perspective. The multi-resolution and wavelets are used to build effective texture feature vector using Gabor Wavelet, GIst and Discrete wavelet Transform and other features. The proposed algorithm experimented on Malimg Dataset of malware total 12,470 samples are used. In that 1610 samples are trained and 1710 samples are tested on 8 malware family which is randomly selected from the dataset. We compare this approach to existing malware classification approaches previously published research work. This is an efficient and more accurate malware detection algorithm using Wavelet Transform with machine learning classifiers techniques to detect malware samples more capably compare to existing work.

关键词： Gabor Wavelet Discrete Wavelet Transform Malware Support Vector machine Texture Analysis

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Hand Gesture Controlled Drones: An Open Source Library 1

Hand Gesture Controlled Drones: An Open Source Library

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1st international conference on Data Intelligence and Security (ICDIS)

作者： Natarajan, Kathiravan Nguyen, Truong-Huy D. Mete, Mutlu Texas A&M Univ Commerce Dept Comp Sci Commerce TX 75429 USA Fordham Univ Dept Comp & Informat Sci Bronx NY 10458 USA Texas A&M Univ Commerce Grad Sch Commerce TX USA

ISBN: (纸本)9781538657621

Drones are conventionally controlled using joysticks, remote controllers, mobile applications, and embedded computers. A few significant issues with these approaches are that drone control is limited by the range of electromagnetic radiation and susceptible to interference noise. In this study we propose the use of hand gestures as a method to control drones. We investigate the use of computer vision methods to develop an intuitive way of agent-less communication between a drone and its operator. Computer vision-based methods rely on the ability of a drones camera to capture surrounding images and use pattern recognition to translate images to meaningful and/or actionable information. The proposed framework involves a few key parts toward an ultimate action to be taken. They are: image segregation from the video streams of front camera, creating a robust and reliable image recognition based on segregated images, and finally conversion of classified gestures into actionable drone movement, such as takeoff, landing, hovering and so forth. A set of five gestures are studied in this work. Haar feature-based AdaBoost classifier [1] is employed for gesture recognition. We also envisage safety of the operator and drone's action calculating the distance based on computer vision for this task. A series of experiments are conducted to measure gesture recognition accuracies considering the major scene variabilities, illumination, background, and distance. Classification accuracies show that well-lit, clear background, and within 3 ft gestures are recognized correctly over 90%. Limitations of current framework and feasible solutions for better gesture recognition are discussed, too. The software library we developed, and hand gesture datasets are open-sourced at project website.

关键词： Drone image processing machine learning machine vision

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