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Enhancement of Degraded CCTV Footage for Forensic analysis 3rd

Enhancement of Degraded CCTV Footage for Forensic Analysis

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3rd International conference on Innovative Computing and Communication, ICICC 2020

作者： Vinay, A. Lokesh, Aditya Kamath, Vinayaka R. Murty, K.N.B. Natarajan, S. Center for Pattern Recognition and Machine Intelligence PES University Bengaluru India

ISBN: (纸本)9789811551123

Forensic analysis has proved to be one of the most utilitarian tool in investigating crime. Forensic analysis provides evidence/basic information of the said crime through analysis of physical evidence. In this paper, we present a scintillating technique to enhance the image quality of CCTV video to assist in the investigation of criminal cases. This paper, address the issue of lack of a budget surplus for a high-quality image/video capturing device. The problem of enhancing images is addressed by pure image processing methods and machine learning techniques. The paper analyzes both techniques and further concludes that the machine learning approach produces a more efficient result. The applications of this technique can range from simple image filtering/beautification to forensic image processing. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Crime

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Planning Marketing Strategies in Small-Scale Business Using Data analysis. 3

Planning Marketing Strategies in Small-Scale Business Using ...

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3rd International Informatics and Software Engineering conference, IISEC 2022

作者： Fernando, A.M.P. Adhikari, A.M.T.T. Wijesekara, W.H.A.T.K. Vithanage, T.V.T.I. Gamage, Anjalie Jayalath, Thilini Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

The proposed research work develops a system focused on business opportunities to enhance market returns and improve marketing strategies and new strategies by identifying how customers interact with products and their behavior. Existing research efforts attempt to identify and market consumer attraction to products and marketplace in the marketplace. Current research focuses on the challenges of identifying consumer buying patterns and how consumers interact with products, Existing research has not, however, integrated the essential elements into a single system. Consequently, the recommended study has been conducted on a number of significant issues, such as determining the high-value client base and the number of sectors, understanding the purchasing pattern of products that comprise the customers' basket, identifying customer lifetime value, and Customer Trajectory Determination for identifying customer attractive shelf. This system focuses on various machine learning algorithms. Customer segmentation and value analysis using K Mean, Agglomerative, Clustering algorithm, and Arima model. Association rules are generated using the Apriori algorithm for market basket analysis, which is built on the idea that a set of frequently purchased items is a subset of a set of frequently purchased items. Also using RFM analysis to create and prepare our data frame by using BG/NBD model and the Gamma-Gamma model to calculate the customer lifetime value standardization. Using image processing algorithms and retail video analysis algorithms, background reduction technology clearly identifies moving objects/tracks customer routes using different colors. Based on results from implementation and testing, it was determined that the suggested technique outperformed the use of CCTV to identify consumer behavior and satisfaction with the product in recognizing customer purchasing patterns. The proposed system can identify customers' buying patterns, how customers interact with products

关键词： Machine learning

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Improving UWB image Reconstruction for Breast Cancer Diagnosis by Doing an Iterative analysis of Radar Signals 3rd

Improving UWB Image Reconstruction for Breast Cancer Diagno...

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

作者： Torres-Quispe, Henrry Adrian Patino-Escarcina, Raquel Esperanza Universidad Católica San Pablo Arequipa Peru

ISBN: (纸本)9783031090363

UWB (Ultra-Wideband) radar technology is based on the general principle that an antenna transmits an electromagnetic signal and the echo of the reflected signal is detected. This technology is used in different applications, such as medical monitoring or imaging applications for a more precise diagnosis as breast cancer. The diagnostic process of it consists of detecting any lesion or abnormality in the breast tissue, for this, imaging techniques of the breast are used. This UWB technology has given good results compared to traditional methods that lack effectiveness, producing false positives. The images obtained by microwave signals have electrical properties according to the different tissues. Comparing healthy tissues with malignant tissues, a contrast of 8 % in permittivity and 10 % in conductivity was found in research on the dielectric properties of breast tissue. Previous research proposed reconstruction algorithms for the detection of breast tumors based on a maximum likelihood expectation maximization (MLEM) algorithm. This work proposes an iterative algorithm for imaging based on traditional delay-and-sum (DAS) and delay-multiply-and-sum (DMAS) methods. The main characteristic of this work is the elaboration of an algorithm based on MLEM for a bistatic radar system, to reconstruct an enhanced image where possible tumors with a diameter of up to 1 cm are better distinguished. In a bistatic system, signal processing and reconstruction algorithm differs from a monostatic system, because 2 antennas are considered (emitter and receiver). The MLEM algorithm reduces statistical noise over conventional back-projection algorithms. Experiments were performed with data taken in the laboratory with simulated tumors inside a breast phantom. The results show the images where the tumors are highlighted with 4 iterations. © 2022, Springer Nature Switzerland AG.

关键词： image enhancement

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Dealing with Incomplete Land-Cover Database Annotations Applied to Satellite image Time Series Semantic Segmentation 3rd

Dealing with Incomplete Land-Cover Database Annotations App...

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

作者： Colomines, Lucas Kurtz, Camille Puissant, Anne Vincent, Nicole Université Paris Cité LIPADE Paris France Université de Strasbourg LIVE – UMR Strasbourg7362 France

ISBN: (纸本)9783031090363

Machine learning is used in many supervised applications, nevertheless a learning process needs a sufficiently large amount of annotated data to build a model able to generalize. The lack of such annotated data is a bottleneck to the use of supervised methods in certain applications. Such data can be scarce or contain label errors. In this study, we are addressing both aspects in the context of a remote sensing application, for the task of segmentation. We aim to update the content of a database of vineyard plots, thanks to the analysis of satellite image time series (SITS). Such databases are far from being complete and constitute poor-quality training sets. After training a convolutional neural network for a categorization task, we then propose a segmentation-based methodology involving 2D spatio-temporal representations (STR), built from the content of the SITS and labeled thanks to a deep learning process. The semantic segmentation is achieved thanks to the retro projection of the STR in the geographical space. The experimental results highlight the efficiency of the method when focusing in searching for a specific crop, in this study vineyards, from high spatial resolution images. Starting from an initial labeling that fails to label 40% of the ground-truth, we are able to recall up to 96% of the searched vineyard pixels, increasing indirectly the quality of the initial annotations. © 2022, Springer Nature Switzerland AG.

关键词： Deep learning

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Multiple Input Branches Shift Graph Convolutional Network with DropEdge for Skeleton-Based Action recognition 1

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21st International conference on image analysis and Processing (ICIAP)

作者： Liu, Yan Deng, Yuelin Su, Jinping Wang, Ruonan Li, Chi Taikang Insurance Grp Co Ltd Taikang Innovat Ctr 21-1 Sci Pk Rd Beijing Peoples R China

ISBN: (数字)9783031064272

ISBN: (纸本)9783031064272;9783031064265

Graph Convolutional Networks (GCNs) achieve remarkable success in the skeleton-based action recognition tasks. However, the recent state-of-the-art (SOTA) methods for this task usually have a large model size and too heavy computational complexity. In this work, we propose an early fused model, Multiple Input Branches Shift Graph Convolutional Network with DropEdge (MIBSD-GCN). First, to reduce the complexity of the multi-stream model, we introduce a lightweight Shift Graph Convolutional Network (Shift-GCN) block. It is embedded into an early fused architecture, Multiple Input Branches (MIB), which can enrich input features and suppresses the model redundancy. Then, a novel spherical coordinate representation is added as one of the input branches to enhance the recognition effect. Finally, we design the Shift Graph Convolutional Network with DropEdge (SD-GCN) to prevent over-fitting and over-smoothing, while maintain the model accuracy. Extensive experiments on two large-scale datasets, NTU RGB+D 60 and NTU RGB+D 120, show that the proposed model outperforms previous SOTA methods. We achieve 96.6% accuracy on the Cross-view benchmark of the NTU RGB+D 60, while being 3.4-16.5 times fewer FLOPs than other SOTA models.

关键词： Skeleton-based action recognition Multiple Input Branches Shift Graph Convolutional Network DropEdge Spherical coordinate

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Study on Hyperthyroidism using Thermal image analysis

Study on Hyperthyroidism using Thermal Image Analysis

引用

Intelligent Technologies (CONIT), International conference on

作者： Dharshini Venkatesan Nirmala Krishnamoorthi Shriraam Mahadevan Department of Biomedical Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India Department of Endocrinology Sri Ramachandra Medical Centre Chennai Porur India

Hyperthyroidism is supposed to impact three major areas of the body: the eye, the neck, and the shin. The participants are examined, with a thermal camera, potentially non-invasive, less expensive technique to measure the level of temperature distributed in the eyes, neck, and shin region due to hyperthyroidism. The temperature distribution is seen through the color map of the images which indicates the minimum and maximum temperatures. The heat distribution in the eye, neck and shin region is studied and a qualitative and quantitative analysis is performed. The pre-processing techniques include cropping of the acquired image using the required threshold values. Then the cropped image are normalized and masked in the eyeball region, left and right eye respectively .The surface plot of the preprocessed image is obtained for visualization of the intensity distribution. The sum of extrema (SX) is computed from the masked image. On the other hand the gradient operator is applied to the normalized image and fractal dimensions (FD) are computed using Higuchi algorithm .The intensity levels are analyzed in the red, green and blue regions respectively. The temperature distribution pattern of the hyperthyroid participant and normal participants are determined using the FD and SX. The statistical analysis is also performed to study the complexities in each of the regions for healthy and hyperthyroid participants.

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Islanding Detection Using pattern recognition by HOG Features with Ensemble Classifier

Islanding Detection Using Pattern Recognition by HOG Feature...

引用

International conference on Electrical Power and Energy Systems (ICEPES)

作者： Kumaresh Pal Subhadip Goswami Anshuman Bhuyan Santanu Koley Soumya Roy Rishika Bhakat Department of Electrical Engineering National Institute of Technology Jamshedpur India Department of Electrical Engineering Gandhi Institute for Education & Technology Bhubaneswar India Department of Computer Science Engineering Haldia Institute of Technology Haldia India Department of Computational Sciences Brainware University West Bengal India Department of Management Cambridge Institute of Technology Ranchi India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

This study presents a novel approach for detecting islanding based upon image categorisation using Ensemble Classifier is proposed. images are used to derive the histogram of oriented gradient (HOG) characteristics so as to identify non-islanding as well as islanding events. The ever-increasing need for power encourages the growth of Distributed Generation (DG). Islanding is one of the key challenges associated with excessive penetration of DG sources and can cause harm to the clients and their equipment. The islanding needs to be detected in two seconds, according to the IEEE 1547 DG interconnection rules, and the DG must be turned off. In the suggestive technique, spectrogram images are acquired through the time-series signals that are derived from the point of common coupling (PCC). The features are extracted from these images using the histogram of oriented gradient (HOG). The resulting feature vector is then utilised as an input for both the training and testing processes of the ensemble classifier. For deriving the spectrogram image, the rate of change of voltage is utilised as a parameter. In order to evaluate the performance of the ensemble classifier, 5, 10, 20, and 25-fold cross-validations are performed. The findings from the classification analysis indicate that the utilisation of an ensemble classifier in conjunction with the histogram of oriented gradient feature for image classification yielded remarkable outcomes, including an accuracy of 97.45% and a validation error of 0.188.

关键词： Training Histograms Accuracy pattern classification Voltage Feature extraction Vectors

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Interactive Segmentation of Art images based on Point Cloud Pre-Training

Interactive Segmentation of Art Images based on Point Cloud ...

引用

Sentiment analysis and Deep Learning (ICSADL), International conference on

作者： Yuan Tian YiRan Tao Nanhang Jincheng College Nanjing City Jiangsu Province China

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

This study presents an innovative interactive segmentation algorithm for fine art images using point cloud pre-training principles. With increasing demands for precision and variety in image processing, conventional methods are proving inadequate. image segmentation, a central aspect of visual data manipulation, involves dividing an image into distinct regions based on static or dynamic features. This study reviews supervised and unsupervised segmentation strategies, emphasizing the importance of fuzzy clustering algorithms in practical applications. Despite advances in medical image segmentation, challenges remain due to the complex nature of image processing. A universally applicable segmentation method and standardized evaluation criteria are elusive. To address these challenges, the proposed algorithm combines Local Binary pattern (LBP) feature extraction with a point cloud pre-training model, utilizing the U-net architecture for accurate segmentation. Experiments conducted on an Ubuntu 14.04 64-bit OS using C++ and Python demonstrate the effectiveness of the algorithm. The visualized results show its potential for practical applications and lay the foundation for future advances in image segmentation.

关键词： Point cloud compression image segmentation Visualization Art Accuracy Reviews Heuristic algorithms

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Detecting pattern irregularities in astronomical images using deep learning

Detecting pattern irregularities in astronomical images usin...

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Artificial Intelligence For Internet of Things (AIIoT), International conference on

作者： Shankar T Bada Deekshitha Vempalli Rohini Venkataraman Muthiah Nakarajan Sivasankar P J Murali School of Electronics Engineering Vellore Institute of Technology Vellore India Department of ECE NITTTR Chennai India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Classification of astronomical images is a field of long standing interest. With modern technology, we have been able to obtain many techniques and consequently generated huge amounts and type of data. This amount of new and variety in astronomical data along with the complexities presents itself as a affair that a human analysis is not possible. In our project, we use deep learning techniques to explore the various challenges faced by astronomers when processing huge amounts of data, looking for information relevant to each target. We have implemented existing methods like InceptionV3 and CNN to detect and classify galaxies. Then we proposed to use VGG16, VGG19, and MobileNetV2 for classification and image segmentation to detect and classify galaxies. And we compared the performance of existing and proposed methods. Our proposal was approximately 20% more accurate than existing methods CNN and InceptionV3.

关键词： Deep learning image segmentation Accuracy image color analysis Classification algorithms Convolutional neural networks Reliability

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3rd International conference on Smart Innovations in Communications and Computational Sciences, ICSICCS 2020

3rd International Conference on Smart Innovations in Communi...

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3rd International conference on Smart Innovations in Communications and Computational Sciences, ICSICCS 2020

ISBN: (纸本)9789811553448

The proceedings contain 29 papers. The special focus in this conference is on Smart Innovations in Communications and Computational Sciences. The topics include: Multipath routing using improved grey wolf optimizer (igwo)-based ad hoc on-demand distance vector routing (aodv) algorithm on manet;analysis and prediction of fuel oil in a terminal;an android malware detection method based on native libraries;fire early warning system based on precision positioning technology;the construction of learning resource recommendation system based on recognition technology;review of ultra-low-power cmos amplifier for bio-electronic sensor interface;an enhancement of underwater images based on contrast restricted adaptive histogram equalization for image enhancement;hybridization of social spider optimization (sso) algorithm with differential evolution (de) using super-resolution reconstruction of video images;enhanced convolutional neural network (ecnn) for maize leaf diseases identification;facial expression recognition using improved local binary pattern and min-max similarity with nearest neighbor algorithm;visibility improvement of hazy image using fusion of multiple exposure images;comparative analysis of consensus algorithms and issues in integration of blockchain with iot;use of hadoop for sentiment analysis on twitter’s big data;enhanced mobility management model for mobile communications;multi-objective-based lion optimization algorithm for relay selection in wireless cooperative communication networks;embpred30: Assessing 30-days readmission for diabetic patients using categorical embeddings;prediction of different classes of skin disease using machine learning techniques;feature selection and classification based on swarm intelligence approach to detect malware in android platform;preface;trust-based fuzzy bat optimization algorithm for attack detection in manet;transfer learning: Survey and classification.

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