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A Heterogeneous Face Recognition Approach for Matching Composite Sketch with Age Variation Digital images

A Heterogeneous Face Recognition Approach for Matching Compo...

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international conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Chethana H.T. Trisiladevi C. Nagavi Vidyavardhaka College of Engineering Mysore Karnataka India S.J.College of Engineering JSS Science & Technology University Mysuru Karnataka India

Law enforcement agencies use facial composite sketches as a forensic tool to identify the suspects involved in criminal activities. Criminal investigators use either hand drawn or software generated sketch for investigation. Matching facial composite sketch with its digital images across different age variations is an important challenge of heterogeneous face recognition. Current research works are mainly focusing on the automation of matching facial composite sketch to digital images. A framework for mapping of facial composite sketch to digital images across different age variations is proposed in this work. In the proposed framework, composite sketch is fed as input to Convolutional Neural Networks (CNN), face encoding techniques are applied to extract the important features from the face and best matching index is computed. Then bounding box is drawn for recognizing digital images of different age variations. To study the effects of age variations, experiments are conducted on the composite sketch with age variation dataset (CSA) which consist of composite sketches of 150 subjects with digital images of different age variations. The system performance is appraised through exhaustive testing and results analysis. Experimental result proves that proposed framework provides promising results of 86.6% compared with other existing sketch based algorithms.

关键词： Wireless communication image recognition Digital images Face recognition System performance signal processing algorithms Tools

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An Improved Algorithm for Intelligent Identification of Distribution Network Equipment

An Improved Algorithm for Intelligent Identification of Dist...

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IEEE international conference on signal and image processing (ICSIP)

作者： Shi Su Jun Yang Baofu Li Jinjiang Yang Electric Power Research Institute Yunnan Power Grid Co. Ltd Kunming China Yuanjiang Power Supply Bureau Yunnan Power Grid Co. Ltd Yuxi China

ISBN: (纸本)9781665446006

With the continuous development of the distribution network, the distribution network equipment is gradually diversifying and becoming more complex. The inability of humans to correctly identify the type and model of distribution network equipment has led to frequent safety accidents. Many studies have used deep learning algorithms to identify distribution network equipment in the field, which can effectively reduce equipment mishandling in field operation and maintenance. In the process of real-world photography of distribution network equipment in field operations, there are problems such as complex backgrounds, many co-occurring object objects, different shooting distances and variable angles. The existing methods are difficult to effectively and accurately identify the target equipment. In response to the above problems, this paper proposes an improved algorithm model for intelligent identification of distribution network equipment. The model is based on a VGG16 recognition model with initialized weights to extract image features, combined with data sets for data enhancement operations such as cropping and flipping. At the same time, we introduce an attention mechanism into the model to make it more focused on specific regions in the image. Based on this, the model performs scene discrimination and device prediction tasks. The experimental results show that the model has a high accuracy rate and is practical for the identification of distribution network equipment in complex backgrounds.

关键词： Training Deep learning image recognition Computational modeling Distribution networks Feature extraction Data models

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image Defect Detection and Segmentation Algorithm of Solar Cell Based on Convolutional Neural Network

Image Defect Detection and Segmentation Algorithm of Solar C...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Songsong Tian Weijun Li Shuang Li Guangyan Tian Linjun Sun Xin Ning Chinese Academy of Sciences Institute of Semiconductors Beijing China Institute of Power Source and Ecomaterials Science Hebei University of Technology Tianjin China

ISBN: (纸本)9781665447300

The use of infrared or electroluminescence(EL) images of solar cell modules for defect detection is a very important method in non-destructive testing. Traditionally, this work is done by skilled technicians, which is time-consuming and susceptible to subjective factors. The surface defect detection method of solar cells based on machine learning has become one of the main research directions because of its high efficiency and convenience. For this reason, this paper proposes an improved fusion model based on VGGNet and U-Net++, which is used for defect detection and segmentation of EL images of solar cells. In the defect detection stage, the input image is processed pertinently, and by modifying the convolutional layer and the fully connected layer of the network, while improving the performance of the algorithm, it accelerates the convergence and avoids the phenomenon of over-fitting. In the defect segmentation stage, the defect location is marked based on the public data set, which is used for the training of each segmentation model, and the effect of different segmentation networks is compared to select a reasonable model. The experimental results show that the defect detection accuracy of the improved VGG16 network on the elpv-dataset is 95.2%, and the U-Net++ defect segmentation model has an average MIoU value of 0.955, which is better than other existing methods.

关键词： Training image segmentation Convolution Photovoltaic cells signal processing algorithms Machine learning Data models

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Implementation of Real Time Static Hand Gestures Recognition for Sign Language

Implementation of Real Time Static Hand Gestures Recognition...

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international conference on advanced Computing and Communication Systems (ICACCS)

作者： S. Gobhinath S. Sophia Sri Krishna College of Engineering and Technology Coimbatore TamilNadu India

The paper propose that uses the computer vision of hand recognition. The camera records live video streams, where the image is taken with the help of the interface. The system is train for every type of hand gesture (one, two, three, four, and five) at least once. After that a test action is given and the system tries to detect it. A proposed system in which hand gestures are detected using image processing. The system detects the number of fingers. The system finds separate fingers above the palm. The system first detects skin color in an image using a filter. The image goes through various steps of image editing to give the right amount of fingers. The system detects the nearest point from the decision point [1]. The system deletes the image according to the centroid point. After that many steps are applied to adjust the image to the effective image so that the fingers are exposed. The system finally detects the number of fingers and displays the calculation to the user. Research has been done on many algorithms that can better distinguish hand gestures. It was found that the diagonal sum algorithm provided the highest accuracy measure. In the progression phase, an automated algorithm removes the background for each training action. The image is then converted into a binary image and taken statistics for all the separated elements of the image. This amount helps us to distinguish and distinguish different hand gestures.

关键词： Training image segmentation Neural networks Training data Gesture recognition Streaming media Skin

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Brain tumor area detection using Anisotropic Diffusion and Morphological Operations

Brain tumor area detection using Anisotropic Diffusion and M...

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international conference on Applied Electromagnetics, signal processing and Communication (AESPC)

作者： Sucharita Jena Mamata Panigrahy Jitendra Kumar Das School of Electronics Engineering KIIT University Bhubaneswar India

ISBN: (纸本)9781665443005

Analysis of brain tumor acts as a pivotal part in medical application. It delivers a large amount of structural and useful information, which removes the complexities in diagnosis and disease treatment planning. Timely detection of tumors improvises the treatment effects and thus ensures the patient’s survival. Physically assessing several magnetic resonance images is a herculean task and increases the risks of manual segmentation error. Thus, computer-aided detection is needed with enhanced accuracy for quick diagnosis of tumor. This paper describes the computer-aided (CAD) techniques such as noise reduction, segmentation and detection of tumor area of MR images by using anisotropic diffusion filtering, Otsu threshold segmentation and Morphological operations respectively. Furthermore, the simulation results shows that anisotropic diffusion filter outstands other filters with high PSNR value and the area of tumor is clearly detected.

关键词： image segmentation Visualization Anisotropic magnetoresistance PSNR Noise reduction Filtering algorithms Very large scale integration

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HOCA: Higher-Order Channel Attention for Single image Super-Resolution

HOCA: Higher-Order Channel Attention for Single Image Super-...

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IEEE international conference on Acoustics, Speech and signal processing

作者： Yalei Lv Tao Dai Bin Chen Jian Lu Shu-Tao Xia Jingchao Cao Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University China City University of Hong Kong Hong Kong

Convolutional neural networks (CNNs) have obtained great success in single image super-resolution (SR). More recent works (e.g., RCAN and SAN) have obtained remarkable performance with channel attention based on first- or second-order statistics of features. However, these methods neglect the rich feature statistics higher than second-order, thus hindering the representation ability of CNNs. To address this issue, we propose a higher-order channel attention (HOCA) module to enhance the representation ability of CNNs. In our HOCA module, to capture different types of semantic information, we first compute k-order of feature statistics, followed by channel attention to learn the feature interdependencies. Considering the diversity of input contents, we design a gate mechanism to adaptively select a specific k-order channel attention. Besides, our HOCA module serves as a plug-and-play module and can be easily plugged into existing state-of-art CNN-based SR methods. Extensive experiments on public benchmarks show that our HOCA module effectively improves the performance of various CNN-based SR methods.

关键词： Adaptation models conferences Superresolution Semantics signal processing Logic gates Benchmark testing

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Multifocus image Fusion using TE-CSR Technique

Multifocus Image Fusion using TE-CSR Technique

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Lakshmi Bharath Gogu S V Aswin Kumer Koneru Lakshmaiah Education Foundation Vaddeswaram A.P India

ISBN: (纸本)9781665438124

image fusion contains data from different heterogeneous sources to obtain more accurate information about an inherent scene. There are various methods are available to increase the quality of an image for better visualization of the input image. The last few years, the familiar technique is used which provides efficient image fusion at the output called Sparse Representation (SR) with number if effective algorithms being proposed. But there are two drawbacks of SR methods, they are high noise sensitivity and the preservation of image details for further processing. To get the better of these drawbacks, the Convolutional Sparse Representation with Gradient Penalties (CSRGP) technique is adopted, which is a signal decomposition model used to perform image fusion of the deviation components of the processing frame. The energy components of an image are also combined using the technique called averaging. To perform the reconstruction of an image after it is being fused, the inverse Taylor expansion is adopted. There is a gap between the image descriptions, if the existing decomposition algorithms are used. It can be suppressed by the proposed method by introducing the new method which can preserve the image details. The sparse patch coding plays an important role to limit the ability to preserve image details along with the SR based approaches. The efficiency and performance measure of the proposed method is demonstrated with the experimental results.

关键词： Smoothing methods Sensitivity image edge detection Process control Taylor series Robustness image fusion

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Action Recognition with Domain Invariant Features of Skeleton image

Action Recognition with Domain Invariant Features of Skeleto...

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IEEE conference on advanced Video and signal Based Surveillance (AVSS)

作者： Han Chen Yifan Jiang Hanseok Ko School of Electrical Engineering Korea University 145 Anam-ro Seongbuk-gu Seoul South Korea

ISBN: (纸本)9781665433976

Due to the fast processing-speed and robustness it can achieve, skeleton-based action recognition has recently received the attention of the computer vision community. The recent Convolutional Neural Network (CNN)-based methods have shown commendable performance in learning spatio-temporal representations for skeleton sequence, which use skeleton image as input to a CNN. Since the CNN-based methods mainly encoding the temporal and skeleton joints simply as rows and columns, respectively, the latent correlation related to all joints may be lost caused by the 2D convolution. To solve this problem, we propose a novel CNN-based method with adversarial training for action recognition. We introduce a two-level domain adversarial learning to align the features of skeleton images from different view angles or subjects, respectively, thus further improve the generalization. We evaluated our proposed method on NTU RGB+D. It achieves competitive results compared with state-of-the-art methods and 2.4%, 1.9%accuracy gain than the baseline for cross-subject and cross-view.

关键词： Training image recognition Surveillance Minimization Skeleton Adversarial machine learning Robustness

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Junction Monitoring System for Emergency Vehicles and Density control Using image processing

Junction Monitoring System for Emergency Vehicles and Densit...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： S Nagarathinam R Dhivyapriya C Pavithra M Harshapradha R Saralesh Kumar Kumaraguru College of Technology Tamil Nadu India

ISBN: (纸本)9781665428309

In today’s world, everyone is confronted with one of the most important challenges of traffic congestion, which affects almost all major cities. The main cause of increased traffic bottlenecks is a large number of vehicles on the road and insufficient infrastructure. With the help of current traffic lights, it is essential to successfully regulate traffic flow. Because most traffic signals have a predetermined green light sequence, the green light sequence is established without regard for the presence of emergency vehicles. As a result, emergency vehicles such as ambulances, police cars, fire engines, and other emergency vehicles become trapped in traffic and are delayed in arriving at their destination. The goal of this project is to regulate traffic by determining traffic density on each side of the road and enabling a controlling option; in the event that an emergency vehicle arrives, its direction is determined from its siren signal, and the signals are enabled. The image was shot by the traffic junction’s camera when it was free, and it serves as the reference image. The camera continuously captures real-time images of the traffic intersection. The microcontroller, on the other hand, generates the difference frame by comparing the real-time image to the reference image and calculating traffic density using Digital image processing algorithms. The density data is transferred to the server, and traffic lights are managed in accordance with it. When an emergency vehicle is recognized, traffic light sequences are automatically activated in such a way that a green signal is given in the direction where the vehicle is detected, and normal operation is assumed once it has passed through the signal.

关键词： Roads image processing Digital images Urban areas Traffic control Cameras Real-time systems

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Localization and Identification of Copy-Move Forgery using SIFR and FQRHFM

Localization and Identification of Copy-Move Forgery using S...

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international conference on ent Computing and Control Systems (ICICCS)

作者： Granty Regina Elwin J E.Ahila Devi E Anna Devi Ramesh Kumar R Sruthi Anand Sri Krishna College of Engineering and Technology Coimbatore India St.Josephs College of Engineering Chennai India Sathyabama Institute of Science and Technology Chennai India Sri Krishna College of Technology Coimbatore India

In an image, if a particular part or region is copied and further pasted in another part of the image, it is a type of image forgery known as region duplication or copy-move. This technique is commonly used to either conceal an object or to overemphasize on a particular object. There are a number of detection methodologies introduced over the years to identify copy-move images. But when the image is hidden in smooth background, it proves to be difficult to identify and localize. A novel method of localization of the forgery has been proposed with the help of FQRHFM (Fast Quanternion Radial Harmonic Fourier Moments) and SIFER (Scale-invariant feature detection with error resilience). The initial step involves splitting the image into segments that can be represented as uniform superpixel and non-overlapping blocks. Based on the extraction of key points, SIFER is used by incorporating color invariance and super pixel content. The next step involves using the extracted image key points to build an image with connected Delaunay triangle such that every triangle is formulated with gradient entropy and FQRHFMs. The last step would be to use the extracted features to match with the triangles and those that do not match will be removed as duplicated regions.

关键词： Location awareness image segmentation signal processing algorithms signal processing Feature extraction Harmonic analysis Forgery

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