Particle Image Velocimetry
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The document discusses particle image velocimetry (PIV), which is a non-intrusive method for measuring fluid flow velocities. PIV works by seeding the fluid with particles and using a laser sheet and camera to capture particle images. Software then tracks the particle movements between images to calculate velocity vectors across the flow field. PIV has applications in analyzing flows around objects like fish, helicopters, and prosthetic heart valves. Advanced PIV systems are being developed that can perform 3D motion tracking.
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This document discusses heat transfer in microchannels. It begins by defining microchannels as channels less than 1mm wide used for heat exchange. Microchannels allow for higher heat flux transfer due to their small size. They have various applications, especially in cooling electronics. The document outlines different channel designs, manufacturing methods, coolants used like nanofluids, and limitations of microchannels. It concludes that microchannels enable high heat flux transfer and have significant potential for further development.
PTV
Public media refers to nonprofit radio and television stations supported by individuals, corporations, and governments.
The document discusses Pakistan Television Network (PTV), the largest public broadcaster in Pakistan. PTV aims to provide refined education, accurate information, and healthy entertainment through its various channels.
It also seeks to modernize its programming while building on traditions and professional expertise. Although PTV remains the most watched channel in Pakistan, it faces competition from private broadcasters like Geo and Star Plus.
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MEDIAN BASED PARALLEL STEERING KERNEL REGRESSION FOR IMAGE RECONSTRUCTION
This document summarizes a research paper that proposes a modified version of Steering Kernel Regression called Median Based Parallel Steering Kernel Regression for improving image reconstruction. The key points are:
1. The proposed algorithm addresses two drawbacks of the original Steering Kernel Regression technique by implementing it in parallel on GPUs and multi-cores to improve computational efficiency, and using a median filter to suppress spurious edges in the output.
2. Experimental results show the proposed algorithm achieves a speedup of 21x using GPUs and 6x using multi-cores compared to serial implementation, while maintaining comparable reconstruction quality as measured by RMSE.
3. The algorithm is implemented iteratively, applying the median filter after each iteration
MEDIAN BASED PARALLEL STEERING KERNEL REGRESSION FOR IMAGE RECONSTRUCTION
Image reconstruction is a process of obtaining the original image from corrupted data.Applications of image reconstruction include Computer Tomography, radar imaging, weather forecasting etc. Recently steering kernel regression method has been applied for image reconstruction [1]. There are two major drawbacks in this technique. Firstly, it is computationally intensive. Secondly, output of the algorithm suffers form spurious edges(especially in case of denoising). We propose a modified version of Steering Kernel Regression called as Median Based Parallel Steering Kernel Regression Technique. In the proposed algorithm the first problem is overcome by implementing it in on GPUs and multi-cores. The second problem is addressed by a gradient based suppression in which median filter is used.Our algorithm gives better output than that of the Steering Kernel Regression. The results are compared using Root Mean Square Error(RMSE). Our algorithm has also shown a speedup of 21x using GPUs and shown speedup of 6x using multi-cores.
Median based parallel steering kernel regression for image reconstruction
Image reconstruction is a process of obtaining the original image from corrupted data.
Applications of image reconstruction include Computer Tomography, radar imaging, weather
forecasting etc. Recently steering kernel regression method has been applied for image
reconstruction [1]. There are two major drawbacks in this technique. Firstly, it is
computationally intensive. Secondly, output of the algorithm suffers form spurious edges
(especially in case of denoising). We propose a modified version of Steering Kernel Regression
called as Median Based Parallel Steering Kernel Regression Technique. In the proposed
algorithm the first problem is overcome by implementing it in on GPUs and multi-cores. The
second problem is addressed by a gradient based suppression in which median filter is used.
Our algorithm gives better output than that of the Steering Kernel Regression. The results are
compared using Root Mean Square Error(RMSE). Our algorithm has also shown a speedup of
21x using GPUs and shown speedup of 6x using multi-cores.
A Review on Deformation Measurement from Speckle Patterns using Digital Image...
This document reviews digital image correlation (DIC) for deformation measurement using speckle patterns. DIC is a non-contact optical method that uses digital images of a speckle pattern on a surface before and after deformation. By comparing the speckle patterns in the images, DIC can determine displacement and strain fields with high accuracy. The document discusses speckle pattern types, the DIC process, related works that have improved DIC methods, and applications of DIC such as for high-temperature testing. DIC provides full-field measurements and greater accuracy compared to conventional contact methods.
Hybrid Quantum Convolutional Neural Network for Tuberculosis Prediction Using...
This document presents a hybrid quantum-classical convolutional neural network called a quanvolutional neural network (QNN) for diagnosing tuberculosis from chest x-ray images. A new variant of QNN is proposed that uses Novel Enhanced Quantum Representation (NEQR) to encode pixel values from image patches into quantum states. The model showed 87% validation accuracy during training. The paper reviews existing work on quantum image representations and hybrid quantum-classical machine learning approaches. It then describes the proposed QNN model and its use of NEQR encoding for classifying chest x-rays to detect tuberculosis.
Image reconstruction through compressive sampling matching pursuit and curvel...
An interesting area of research is image reconstruction, which uses algorithms and techniques to transform a degraded image into a good one. The quality of the reconstructed image plays a vital role in the field of image processing. Compressive Sampling is an innovative and rapidly growing method for reconstructing signals. It is extensively used in image reconstruction. The literature uses a variety of matching pursuits for image reconstruction. In this paper, we propose a modified method named compressive sampling matching pursuit (CoSaMP) for image reconstruction that promises to sample sparse signals from far fewer observations than the signal’s dimension. The main advantage of CoSaMP is that it has an excellent theoretical guarantee for convergence. The proposed technique combines CoSaMP with curvelet transform for better reconstruction of image. Experiments are carried out to evaluate the proposed technique on different test images. The results indicate that qualitative and quantitative performance is better compared to existing methods.
Shift Invarient and Eigen Feature Based Image Fusion
Image fusion is a technique of fusing multiple images for better information and more accurate image
compared input images. Image fusion has applications in biomedical imaging, remote sensing, pattern
recognition, multi-focus image integration, and modern military. The proposed methodology uses benefits
of Stationary Wavelet Transform (SWT) and Principal Component Analysis (PCA) to fuse the two images.
The obtained results are compared with exiting methodologies and shows robustness in terms of entropy,
Peak Signal to Noise Ratio (PSNR) and standard deviation.
IRJET- Proposed System for Animal Recognition using Image Processing
The document proposes an animal recognition system using image processing. It would use PCA algorithms and eigenfaces methods to identify animals from images captured by existing cameras in nature reserves. This would allow authorities to be alerted of the presence of dangerous animals so people in the reserves can live without fear of attack. The system would analyze images to extract features, calculate covariances and eigenvectors to identify animals based on training data from five classes of animals. If implemented, it could automate animal detection compared to relying on human monitoring of video feeds.
Ijmsr 2016-10
The Journal of MC Square Scientific Research is published by MC Square Publication on the monthly basis. It aims to publish original research papers devoted to wide areas in various disciplines of science and engineering and their applications in industry. This journal is basically devoted to interdisciplinary research in Science, Engineering and Technology, which can improve the technology being used in industry. The real-life problems involve multi-disciplinary knowledge, and thus strong inter-disciplinary approach is the need of the research.
Object Distance Detection using a Joint Transform Correlator
1. The document proposes using an optical joint transform correlator to detect object distance from stereo images. It presents an experiment where distance measurements were taken from correlation peaks in stereo image pairs of a Post-It note. 2. Initial results found a relationship between correlation peak position and distance, though further work is needed to generalize the relationship for different setups. 3. Challenges include a "wrapping and flipping" effect where peaks wrap around the image at close distances, complicating the relationship.
Final Poster
1. The document describes using a deep neural network to detect changes between two SAR images by preclassifying the images, training the neural network on selected samples, and analyzing the results.
2. A similarity matrix and variance matrix are calculated during preclassification to identify and jointly label similar pixels, while different pixels are labeled separately. Good samples are selected to train the neural network.
3. The neural network is tested on images with different types and levels of noise and performs well at change detection, with performance increasing as noise decreases. Future work could focus on accelerating the training process.
A Comparative Case Study on Compression Algorithm for Remote Sensing Images
This document summarizes research on compression algorithms for remote sensing images. It begins with an abstract describing the challenges of transmitting large remote sensing images from sensors to networks. The document then reviews 18 different research papers on various compression algorithms for remote sensing images, including wavelet-based algorithms, fractal coding methods, and region-based approaches. It evaluates each algorithm's performance in compressing remote sensing images while maintaining quality. The document aims to perform a comparative case study of these different compression algorithms.
Segmentation and Classification of MRI Brain Tumor
This document presents a study comparing two techniques for detecting brain tumors in MRI images: level set segmentation and K-means segmentation. Features are extracted from the segmented tumors using discrete wavelet transform and gray level co-occurrence matrix. The features are then classified as benign or malignant using a support vector machine. The level set method and K-means method are evaluated based on accuracy, sensitivity, and specificity on a dataset of 41 MRI brain images. The level set method achieved slightly higher accuracy of 94.12% compared to the K-means method.
IRJET- Handwritten Decimal Image Compression using Deep Stacked Autoencoder
This document proposes using a deep stacked autoencoder neural network for compressing handwritten decimal image data. It involves training multiple autoencoders in sequence to form a deep network that can compress the high-dimensional input images into lower-dimensional encoded representations while minimizing information loss. The autoencoders are trained one layer at a time using scaled conjugate gradient descent. Testing on the MNIST handwritten digits dataset showed the deep stacked autoencoder achieved compression by encoding the 400-dimensional input images down to a 25-dimensional representation while maintaining good reconstruction accuracy, as measured by minimizing the mean squared error at each layer.
IRJET-Underwater Image Enhancement by Wavelet Decomposition using FPGA
This document describes a method for enhancing underwater images using wavelet decomposition and fusion on an FPGA (field programmable gate array). Underwater images often have low contrast and visibility due to light scattering in water. The proposed method performs color correction and contrast enhancement on an input underwater image. It then decomposes the color-corrected and contrast-enhanced images into low and high frequency components using wavelet transforms. Image fusion is performed on the wavelet coefficients to combine the detailed information from both images. The fused image is reconstructed via inverse wavelet transform. Experimental results show the proposed fusion-based approach improves underwater image visibility. Implementing the algorithm on an FPGA provides benefits over general processors for computationally intensive image processing.
Analog signal processing solution
1) The document proposes analog signal processing as a solution to reduce computation time for image alignment algorithms that have high computational loads.
2) It modifies the Normalized Cross Correlation (NCC) algorithm for image alignment by only using the diagonal elements of the template and reference image blocks to calculate correlation. This reduces computations compared to using all pixels.
3) A new imaging architecture is proposed that uses an analog processor to implement the modified NCC algorithm in parallel with digital image acquisition, providing faster computation.
COMPRESSION BASED FACE RECOGNITION USING DWT AND SVM
The biometric is used to identify a person effectively and employ in almost all applications of day to day
activities. In this paper, we propose compression based face recognition using Discrete Wavelet Transform
(DWT) and Support Vector Machine (SVM). The novel concept of converting many images of single person
into one image using averaging technique is introduced to reduce execution time and memory. The DWT is
applied on averaged face image to obtain approximation (LL) and detailed bands. The LL band coefficients
are given as input to SVM to obtain Support vectors (SV’s). The LL coefficients of DWT and SV’s are fused
based on arithmetic addition to extract final features. The Euclidean Distance (ED) is used to compare test
image features with database image features to compute performance parameters. It is observed that, the
proposed algorithm is better in terms of performance compared to existing algorithms.
IRJET- Image Segmentation using Classification of Radial Basis Function of Ne...
This document discusses using a radial basis function neural network for brain tumor detection through image segmentation. It begins by introducing the problem of brain tumor detection and importance of image segmentation. It then describes preprocessing steps including filtering and histogram equalization. Texture features are extracted from images using a gray level co-occurrence matrix. A radial basis function network is used for classification, which has three layers and faster training than a multilayer perceptron. Finally, image segmentation is performed to isolate the tumorous region.
International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Comparative Analysis of image Enhancement Techniques on Real Time images
This document presents a comparative analysis of image enhancement techniques, specifically discrete wavelet transform (DWT) and guided filtering. It proposes using guided filtering with both static and dynamic guidance to obtain approximation and detail layers of an image, which are then combined with weighting and optimization to produce the final enhanced image. The techniques are tested on real-time video from a Logitech webcam. Results show the proposed guided filtering method achieves better contrast improvement, edge enhancement, and color preservation compared to other methods like histogram equalization and DWT, as measured by quantitative metrics like PSNR and MSE. Future work could explore more adaptive local enhancement methods.
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MEDIAN BASED PARALLEL STEERING KERNEL REGRESSION FOR IMAGE RECONSTRUCTION
MEDIAN BASED PARALLEL STEERING KERNEL REGRESSION FOR IMAGE RECONSTRUCTION
MEDIAN BASED PARALLEL STEERING KERNEL REGRESSION FOR IMAGE RECONSTRUCTION
MEDIAN BASED PARALLEL STEERING KERNEL REGRESSION FOR IMAGE RECONSTRUCTION
Median based parallel steering kernel regression for image reconstruction
Median based parallel steering kernel regression for image reconstruction
A Review on Deformation Measurement from Speckle Patterns using Digital Image...
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Hybrid Quantum Convolutional Neural Network for Tuberculosis Prediction Using...
Hybrid Quantum Convolutional Neural Network for Tuberculosis Prediction Using...
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Image reconstruction through compressive sampling matching pursuit and curvel...
Shift Invarient and Eigen Feature Based Image Fusion
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IRJET- Proposed System for Animal Recognition using Image Processing
IRJET- Proposed System for Animal Recognition using Image Processing
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Object Distance Detection using a Joint Transform Correlator
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IRJET- Handwritten Decimal Image Compression using Deep Stacked Autoencoder
IRJET- Handwritten Decimal Image Compression using Deep Stacked Autoencoder
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IRJET-Underwater Image Enhancement by Wavelet Decomposition using FPGA
COMPRESSION BASED FACE RECOGNITION USING DWT AND SVM
COMPRESSION BASED FACE RECOGNITION USING DWT AND SVM
IRJET- Image Segmentation using Classification of Radial Basis Function of Ne...
IRJET- Image Segmentation using Classification of Radial Basis Function of Ne...
International Journal of Computational Engineering Research(IJCER)
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Comparative Analysis of image Enhancement Techniques on Real Time images
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Presented by Vladimir Iglovikov:
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Particle Image Velocimetry
- 1. Particle Image Velocimeter Mechanical Engineering Department ,GIET
- 2. Contents Introduction Principle 2 Components 3 Experiment & Results 4 Applications 5 1 Advanced PIV’s 6
- 4. Principle 1 2 3 4 5 6 Schematic diagram of principle behind PIV.
- 8. Head of displacement arrow at final position and tail of arrow at initial position of SEED. Magnified Results
- 9. 2 Motion Of Fishes in water. Aerodynamic Analysis Of Helicopters. Application 3 1 PROSTHETIC Heart Valves.
- 10. Future PIV’s are coming up with 3-D Motion tracing facilities. Advanced PIVs
- 11. OPEN FOR DISCUSSION References: [1] Markus Raffel, Christian E. Willert, and Jurgen Compenhans, Particle Image Velocimeter- A Practical Guide, Springer Publ. Ltd. [2] Operating Manual, PIV Data Processing, Mechanical Systems & Control Lab., JU, Kolkata, India. Dr.P.C.Mishra & Jadavpur University,kolkata Acknowledgements: By: SAMEER DUBEY
- 13. BACKUP2-Charged Coupled devices Charge coupled devices or CCD’s are arrays of semiconductor gates formed on a substrate of an integrated circuit or chip The charge collected and stored in each gate of the array represents a picture element or pixel of an image. IC (integrated circuit) and control circuits typically mounted on a printed wiring assembly for camera control. Add Your Title
- 14. BY: Sameer Dubey 4 th Year,Mechanical Engineer Gandhi Institute of Engineering and Technology (GIET) Gunupur,ORISSA And Dr.Purna Chandra Mishra Kalinga Institute of Industrial Technology (KIIT) Bhubaneshwar,ORISSA