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A Review on Feature Extraction Techniques in Content Based image Retrieval

A Review on Feature Extraction Techniques in Content Based I...

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International conference on Wireless communications, Signal processing and Networking

作者： Jigisha M. Patel Nikunj C. Gamit Department of Computer Engineering C.G.P.I.T Uka Tarsadia University Bardoli India

ISBN: (纸本)9781467393393

The Content Based image retrieval (CBIR) is a very important for retrieving the most visual relevant images from the large image database. The various low level features are extracted based on their visual content which are color, shape, texture etc. The paper gives the overview of color and texture feature extraction techniques like color histogram, color correlogram, color co-occurrence matrix and tamura texture feature, steerable pyramid, wavelet transform, Gabor wavelet transform respectively and also the comparative analysis of this techniques is shown in the paper.

关键词： CBIR Feature extraction Color histogram Color correlogram DCD CCM Tamura feature Wavelet transform GWT

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Machine Vision Aided Adaptive Beamforming Decision for IRS-Assisted Wireless Networks

Machine Vision Aided Adaptive Beamforming Decision for IRS-A...

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European conference on Networks and communications (EuCNC)

作者： M. Munawar M. Guenach I. Moerman IMEC Leuven IDLab Ghent University

ISBN: (数字)9798350344998

ISBN: (纸本)9798350345001

This study leverages machine vision to assist communication in wireless networks, with a specific focus on intelligent reflecting surface (IRS)-assisted wireless networks. Instead of depending on traditional schemes such as alternating optimization or semidefinite relaxation to maximize signal strength in an IRS-assisted network, which are computationally expensive and impractical, we use visual data to make low-complexity beamforming decisions for users. Our approach involves employing a ceiling camera with a fish-eye view, covering a wide communication area. The user within the network is initially detected using the YOLOv2 object detection method. Subsequently, we propose closed-form analytical expressions to determine the distances between the access point (AP), user, and IRS. Accounting for the non-uniform nature of the fish-eye image, we introduce a novel method to determine non-uniform pixel weightages using trigonometric techniques. Based on the calculated distances, we make beamforming decisions depending on the user’s proximity to the AP or IRS. The proposed method significantly reduces computational complexity, making it nearly independent of the number of reflecting elements at the IRS. Simulation results indicate that the proposed approach exhibits extremely lower computational costs compared not only to conventional schemes such as alternating optimization and semidefinite relaxation-based convex solvers but also to low-complexity heuristic schemes.

关键词： Degradation visualization Array signal processing Wireless networks Machine vision Simulation Object detection

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Goal oriented edge detection

Goal oriented edge detection

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IEEE Signal processing and communications Applications (SIU)

作者： Binnur Kurt Muhittin Gokmen Bilgisayar Muhendisligi Bolumu Istanbul Teknik Universitesi Turkey

In many vision applications, there is a great demand for an edge detector which can produce edge maps with very different characteristics in nature, so that one of these edge maps may meet the requirements of the problem under consideration. Unfortunately it is not evident how to choose the desired or the optimum edge maps from these solutions that the edge detector offers. The proposed solutions are usually too general that cannot be easily adapted to the application needs by tuning edge detection parameters. One edge detector that we have studied in this study is generalized edge detector which is capable of producing edges with very different characteristics. Although the edge maps based on this representation are reasonable, no one set of scale parameters alone yields a solution close to the desired edges. In this study, we have developed powerful edge operators and have used them under a goal-based edge detection framework. Proposed framework is a two-stage process. First, user marks some pixels in the database as edge and non-edge pixels. Then feature vectors comprised of filter responses to G-filters at different scales are extracted at these marked pixels. Edge detection problem is imposed as two-class classification problem. Support vector machine (SVM) is used in the experiments. Classifier itself is not adequate to extract desired edges for the application under consideration. In the second stage continuous edges are treated as one contour. Then contours are matched with the contours in the training set. Only matched contours are kept and the other contours are eliminated. The purpose of the first stage is to keep only prominent edges and remove irrelevant edges with respect to the application. The classifier decides which discontinuity is prominent or irrelevant. Experimental studies on real license plate images show that the proposed edge detector can successfully detects edges only on license plate regions.

关键词： image edge detection Detectors Feature extraction Shape Pixel Support vector machines Signal processing

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An intricate spectrogram processing in speech enhancement using machine learning with cloud computing

An intricate spectrogram processing in speech enhancement us...

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International conference on image Information processing

作者： Yenna Geetha Reddy G Mahendran Ameer Al-Khaykan Omar A. Alkawak S. A. Kalaiselvan A. Priya Department of Computer Science and Engineering MAlla Reddy Engineering College for Women (UGC Autonomous)Kompally Secunderabad Telangana India Department of Mechancal Engineering R.M.K Engineering Collegekavaraipettai Chennai Tamil Nadu India Department of Electronic and Communications Engineering University of Kufa Najaf Iraq Department of Air Conditioning and Refrigeration Technical Engineering Al-Mustaqbal University College Hilla Babil Iraq Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Science (SIMATS) Chennai Tamil Nadu India School of Architecture Koneru Lakshmaiah Educational Foundation Guntur Andhra Pradesh India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

The spectrogram is defined as the visual representation of the signal strength with a particular interval of time regarding frequencies in the appropriate waveform. It is demonstrated using the Fourier transform. It is illustrated in a three-dimensional plot of signal composed with amplitude versus frequency and time. They are classified as long signal sections and short signal sections with filters. It demonstrates the consecutive spectacle of the sound signal at a particular interval of time. The time-frequency representation of audio signals is obtained through spectrograms. This helps in the understanding of the speech data. Machine learning plays an important role in the identification of the relevant features in the spectrogram. The intricate patterns in the time and frequency domains are represented through CNN and RNN. These models are trained and tested through a large dataset which includes both clean and noisy speech samples. This helps to learn complex interrelationships and patterns associated with varied environmental conditions and interference systems. The computational demands of these systems are handled through the cloud computing techniques. They provide a platform for effective solutions for performing real-time interference. These cloud services provide various collaborations to numerous models to achieve desired outcomes. The proposed system provides higher adaptability in varied environmental and noisy profiles. Thus the integration of cloud computing with machine learning provides real-time processing with a natural seamless user experience.

关键词： Cloud computing Time-frequency analysis visualization Computational modeling Machine learning Interference Speech enhancement

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Diabetic Retinopathy Detection Using CNN With Res-LSTMDN

Diabetic Retinopathy Detection Using CNN With Res-LSTMDN

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International conference on communications and Signal processing

作者： R. Gayathri S Karthikeyan T KausheekRaj S Keerthana Department of Electronics and Communication Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

For diabetic patients, early diagnosis and treatment of diabetic retinopathy (DR) are essential since they can avert irreversible visual loss. The ways we currently find DR aren't very accurate or fast, so we need better ways to do it automatically. CNNs, a type of machine learning algorithm, have been shown to work well for analyzing medical images and could be a good option for detecting DR. Among CNN architectures, ResNet-152 has gained reputation for its depth and effectiveness in image classification tasks. However, to further enhance its performance in DR detection, this study does a modification to the final layer of ResNet-152. Specifically, the final layer is replaced with Long-Short Term Memory (LSTM) and Deep Neural Network (DNN) components, resulting in an ensemble approach termed Res-LSTMDN. This novel architecture aims to improve the hierarchical feature learning of ResNet-152 while integrating the sequential memory capabilities of LSTM and the flexibility of DNN. Res-LSTMDN uses advanced technology to study eye images, specifically those from people with diabetes. By finding unique patterns in these images, this technique aims to improve the accuracy of detecting diabetes-related eye problems. This will allow for early intervention, potentially preventing vision loss. As part of the ongoing efforts to develop advanced computer tools for diabetes diagnosis, Res-LSTMDN has the potential to significantly impact global diabetes management strategies.

关键词： Representation learning Diabetic retinopathy visualization Machine learning algorithms Memory management Signal processing Convolutional neural networks

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Smart Sorting: Enhancing Almond Quality with DenseNet-201-Based image Classification

Smart Sorting: Enhancing Almond Quality with DenseNet-201-Ba...

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International conference on Information Science and communications Technologies (ICISCT)

作者： Arpanpreet Kaur Amit Kumar Mishra Bura Vijay Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computer Science and Artificial Intelligence SR University Warangal India

ISBN: (数字)9798331527921

ISBN: (纸本)9798331527938

Considered as two varieties: damaged and undamaged, almonds are a rather consumed agricultural product. Maintaining consumer satisfaction and industry standards depends on good detection of damaged almonds., which could be influenced by mechanical damage., mold, or pests. On the other hand, slow, labor-intensive hand inspection methods prone to mistake demand automated classification systems. This work meets that demand by automating the categorization process using DenseNet-121, a convolutional neural network (CNN), therefore lowering labor expenses and increasing accuracy. Particularly considering the tiny dataset of 1,000 photos split equally between damaged and undamaged almonds, DenseNet-121's architecture-with its dense connections and capacity to reuse features-is well-suited for the almond classification challenge. Pre-trained on imageNet and polished for this particular purpose, the model-trained across 60 epochs using a 32-batch size-achieved an accuracy of 95.27%. Random horizontal flips, resizing., and center cropping among other data augmentation techniques improved the model's generalizing capability. This work demonstrates the model's ability to substitute human methods in actual environments by showing how well it employs little visual information to classify almonds. Automating almond sorting speeds and provides higher consistency in quality control as well as efficiency of sorting. This work opens the road for further research of machine learning techniques in agricultural processing with possible applications in food quality assurance.

关键词： Accuracy Quality assurance Process control Quality control Machine learning Data augmentation Data models Convolutional neural networks Sorting image classification

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Expanding low dynamic range videos for high dynamic range applications 08

Expanding low dynamic range videos for high dynamic range ap...

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Proceedings of the 24th Spring conference on Computer Graphics

作者： Francesco Banterle Patrick Ledda Kurt Debattista Alan Chalmers University of Warwick

ISBN: (纸本)9781605589572

In this paper we introduce an algorithm and related methods that expand the contrast range of Low Dynamic Range (LDR) videos in order to regenerate missing High Dynamic Range (HDR) data. For content generated from single exposure LDR sequences, this is clearly an under constrained problem. We achieved the expansion by inverting established tone mapping operator, a process we term inverse tone mapping. This approach is augmented by a number of methods which help expand the luminance for the required pixels while avoiding artifacts. These methods may be used to convert the large libraries of available legacy LDR content for use, for instance, on new content-starved HDR devices. Moreover, these same methods may be used to provide animated emissive surfaces for image based lighting (IBL). We demonstrate results for all the above applications and validate the resultant HDR videos with original HDR references using the HDR visual Difference Predictor (HDR-VDP) image metric.

关键词： video processing high dynamic range imaging inverse tone mapping

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Classifications of Citrus Fruit Using image processing -GLCM Parameters

Classifications of Citrus Fruit Using Image Processing -GLCM...

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International conference on communications and Signal processing

作者： Chandan Kumar Siddharth Chauhan R. Narmadha Alla Harika Mounica Gurram National Institute of Standards and Technology Department of Physics Colorado State University Electrical Engineering Department University of Colorado

ISBN: (纸本)9781479980826

In recent years, automatic visual inspection system has become useful tool in industrial process. The citrus fruit sorting and grading process has now been completely automated with the exception of the final stage of classification concerned with visual inspection. Due to the growing demand of quality citrus fruit, an automated and reliable identification and classification mechanism in order to handle the bulk of data is implemented. Human vision sometimes leads to mismatch between the fruits. Using machine vision system, it will be simple to reduce human effort and achieve a better output. The objective of this project is to assess the fruit by color grading. In this paper we describe an integrated system developed for the sorting on citrus fruit and for the color grading of the defected fruits. The result suggests that the performance is adequate to provide the basis for a viable commercial visual inspection system. The following project aims at implementing such an automated system using image processing with images of citrus fruit as a basis of classification depending on its color. The goal of the inspection is not to give a statistical analysis of the production but to classify every citrus fruit into quality constant batches.

关键词： Quality inspection Machine vision image processing GLCM parameters Citrus fruits Sorting and grading

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A modular Controller Area Network vision system using programmable interface controller

A modular Controller Area Network vision system using progra...

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International conference on Electronic Design (ICED)

作者： Mohd Sharil b. Salleh Herdawatie bt.Abd. Kadir Mohd Helmy bin Abd Wahab Depertment of Mechatronic and Robotic Eng. Universiti Tun Hussein Batu pahat Malaysia Depertment of Computer Engineering Universiti Tun Hussein Batu pahat Malaysia

Controller area network (CAN) is a broadcast and differential serial bus standard. CAN provide a high decreasing in wiring complexity and, additionally, make it easy to connect with several devices using a single pair of wires, allowing the data exchange them at the same time. Nowadays using the serial communication technology, the vision system can be applied at the CAN bus system. It is still a new technique in devices communication but a lot of advantage can be absorbed by using the vision system that controlled by the CAN system. The CAN-based vision modular system offer a system that provides real time object tracking vision system using CAN bus communication protocol. This system will reduce the usage of wire and introduce a modular pick and plug components. The vision system using iV-CAM is used for on-board and real-time vision processing and PIC microprocessor is used to control the vision system and convert the output data in the appropriate format before it has been transmitted. When the master controller wants to used the data, the CAN transceiver will transmit the data to the CAN controller and by using SPI serial communication, the PIC microprocessor give the data back to the CAN Controller. The modular vision system prototype will be analyzed in term of physical communication and operation. This pick and plug modular system will be implement to other devices such as robot or vehicle to provides real time object tracking vision system that performs onboard real time image processing for objects and color recognition.

关键词： Control systems Machine vision Communication system control Real time systems Plugs Microprocessors Broadcasting Wiring Wires communications technology

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Performance Analysis of Different Equalization Algorithms in A Modified Iterative Scheme

Performance Analysis of Different Equalization Algorithms in...

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2008 the IET 2ND International conference on Wireless,Mobile & Multimedia Networks

作者： Shuangshuang Han Dongfeng Yuan Xiang Cheng School of Information Science and Engineering Shandong University Joint Research Institute for Signal and Image Processing School of EPSHeriot-Watt University

This paper proposes a modified iterative scheme(MI) for the data transmitted by more efficient way over inter-symbol interference(ISI) channels,then compares and analyzes the performance for three equalization algorithms in MI: improved MAX-Log-MAP(I-MLM) algorithm,maximum a posteriori(MAP) algorithm and linear minimum meansquared error(LMMSE) *** we analyze and compare these three algorithms in traditional turbo equalization(TE) ***,according to the good understanding of the aforementioned algorithms and the novel iterative(NI) scheme which combines parallel with serial concatenation turbo-like scheme,we propose the MI scheme and compare the performance of three algorithms in MI, named as MI-I-MLM,MI-MAP and MI-LMMSE *** analytical and simulation results demonstrate that the performance of MI-I-MLM is very close to MI-MAP and much better than MI-LMMSE while its computational complexity is much lower.

关键词： Linear minimum mean-squared error(LMMSE) maximum a posteriori(MAP) Max-Log-MAP algorithm turbo equalization(TE)

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