Application of Matrices in real life. Presentation on application of matrices
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Application of matrices in real life. how matrices dominate our real life? how to relate matrices in real life problem and solve those problems . matrices on engineering sector. some interesting examples are included. this is the presentation slide. department of Electrical and Electronic Engineering , University of Chittagong.
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Link: https://machine-learning-made-simple.medium.com/learnings-from-simclr-a-framework-contrastive-learning-for-visual-representations-6c145a5d8e99
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This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels.
Comments: ICML'2020. Code and pretrained models at this https URL
Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
Cite as: arXiv:2002.05709 [cs.LG]
(or arXiv:2002.05709v3 [cs.LG] for this version)
Submission history
From: Ting Chen [view email]
[v1] Thu, 13 Feb 2020 18:50:45 UTC (5,093 KB)
[v2] Mon, 30 Mar 2020 15:32:51 UTC (5,047 KB)
[v3] Wed, 1 Jul 2020 00:09:08 UTC (5,829 KB)
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본 논문은 single depth map으로부터의 정확한 3D hand pose estimation을 목표로 한다. 3D hand pose estimation은 HCI, AR등의 기술을 구현함에 있어서 매우 중요한 기술이다. 이를 위해 많은 연구자들이 정확도를 높이기 위해 여러 방법을 제시하였지만, 여전히 손가락들의 비슷한 생김새, 가려짐, 다양한 손가락의 움직임으로 인한 복잡성 때문에 정확도를 올리는데 한계가 있었다. 본 논문은 기존 방법들의 한계를 극복하기 위해 기존 방법들이 사용하는 입력 형태와 출력 형태를 바꾸었다. 2d depth image를 입력으로 받아 hand joint의 3D coordinate를 직접 regress하는 대부분의 기존 방법들과는 달리, 제안하는 모델은 3D voxelized depth map을 입력으로 받아 3D heatmap을 출력한다. 이를 위해 encoder-decoder 형식의 3D CNN을 사용하였고, 달라진 입력과 출력 형태로 인해 제안하는 모델은 널리 사용되는 3개의 3d hand pose estimation dataset, 1개의 3d human pose estimation dataset에서 가장 높은 성능을 내었다. 또한 ICCV 2017에서 주최된 HANDS 2017 challenge에서 우승 하였다.
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large key space and efficient permutation-substitution operation, and therefore it is suitable for practical image and video encryption.
COLOR IMAGE ENCRYPTION BASED ON MULTIPLE CHAOTIC SYSTEMS
This document proposes a novel color image encryption scheme based on multiple chaotic systems. The scheme utilizes the ergodic properties of chaotic systems to perform pixel permutation and applies a substitution operation to achieve diffusion. In the permutation stage, two generalized Arnold maps are used to generate hybrid chaotic sequences to permute pixel positions. In the diffusion stage, four pseudo-random gray value sequences generated by another generalized Arnold map are used to diffuse the permuted image via bitwise XOR operations. Security analysis shows the scheme has a large key space and is highly secure against statistical attacks, differential attacks, and chosen/known plaintext attacks.
International Journal of Engineering Research and Development (IJERD)
1) The document discusses wavelet transforms as a recent algorithm for image compression. Wavelet transforms can capture variations at different scales in an image, making them well-suited for reducing spatial redundancy.
2) A typical lossy image compression system uses four main components - source encoding, thresholding, quantization, and entropy encoding - to achieve compression by removing different types of redundancy in images.
3) Experimental results on the Lena test image showed that soft thresholding followed by quantization achieved higher peak signal-to-noise ratios than hard thresholding and quantization, demonstrating the effectiveness of wavelet transforms for image compression.
Comparison of image segmentation
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Translation Invariance (TI) based Novel Approach for better De-noising of Dig...
1. The document discusses a novel Translation Invariance (TI) approach for improving the performance of various digital image processing filters for image denoising.
2. It describes applying filters like convolution, wiener, gaussian etc. both without TI (directly on noisy image) and with TI (by shifting the image and averaging results) to denoise images.
3. The results found that using the TI approach, where the filters are applied after shifting the image and averaging the outputs, produced better performance and noise removal compared to directly applying the filters without translation invariance. This was also verified using edge detection tests.
On image intensities, eigenfaces and LDA
The document reports on the results of three image processing projects. The first project implemented Lloyd-Max quantization to reduce image file sizes and Retinex theory to compensate for uneven illumination. The second project used principal component analysis to compute eigenfaces for face recognition. The third project performed linear discriminant analysis and tensor-based linear discriminant analysis for binary classification and visual object recognition. Illumination compensation subtracted an estimated illumination plane from image intensities to reduce shadows. Eigenfaces were the principal components of a training set of face images. Tensor-based linear discriminant analysis treated images as higher-order tensors to outperform conventional LDA.
SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Micr...
We can find the simultaneous monitoring of thousands of genes in parallel Microarray technology. As per these measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, Intensity extraction, Enhancement and Segmentation are important steps in microarray image analysis. This paper gives simple linear iterative clustering (SLIC) based self organizing maps (SOM) algorithm for segmentation of microarray image. The clusters of pixels which share similar features are called Superpixels, thus they can be used as mid-level units to decrease the computational cost in many vision applications. The proposed algorithm utilizes superpixels as clustering objects instead of pixels. The qualitative and quantitative analysis shows that the proposed method produces better segmentation quality than k-means, fuzzy cmeans and self organizing maps clustering methods.
Reed_Solomon_Implementation
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A simple framework for contrastive learning of visual representations
Link: https://machine-learning-made-simple.medium.com/learnings-from-simclr-a-framework-contrastive-learning-for-visual-representations-6c145a5d8e99
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This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels.
Comments: ICML'2020. Code and pretrained models at this https URL
Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
Cite as: arXiv:2002.05709 [cs.LG]
(or arXiv:2002.05709v3 [cs.LG] for this version)
Submission history
From: Ting Chen [view email]
[v1] Thu, 13 Feb 2020 18:50:45 UTC (5,093 KB)
[v2] Mon, 30 Mar 2020 15:32:51 UTC (5,047 KB)
[v3] Wed, 1 Jul 2020 00:09:08 UTC (5,829 KB)
Performance Study of BCH Error Correcting Codes Using the Bit Error Rate Term...
The quality of a digital transmission is mainly dependent on the amount of errors introduced into the transmission channel. The codes BCH (Bose-Chaudhuri-Hocquenghem) are widely used in communication systems and storage systems. In this paper a Performance study of BCH error correcting codes is proposed. This paper presents a comparative study of performance between the Bose-Chaudhuri-Hocquenghem codes BCH (15, 7, 2) and BCH (255, 231, 3) using the bit error rate term (BER). The channel and the modulation type are respectively AWGN and PSK where the order of modulation is equal to 2. First, we generated and simulated the error correcting codes BCH (15, 7, 2) and BCH (255, 231, 3) using Math lab simulator. Second, we compare the two codes using the bit error rate term (BER), finally we conclude the coding gain for a BER = 10-4.
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.
Introducing New Parameters to Compare the Accuracy and Reliability of Mean-Sh...
Mean shift algorithms are among the most functional tracking methods which are accurate and have almost simple computation. Different versions of this algorithm are developed which are differ in template updating and their window sizes. To measure the reliability and accuracy of these methods one should normally rely on visual results or number of iteration. In this paper we introduce two new parameters which can be used to compare the algorithms especially when their results are close to each other.
Detection of leaf diseases and classification using digital image processing
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Thank you
V2 v posenet
본 논문은 single depth map으로부터의 정확한 3D hand pose estimation을 목표로 한다. 3D hand pose estimation은 HCI, AR등의 기술을 구현함에 있어서 매우 중요한 기술이다. 이를 위해 많은 연구자들이 정확도를 높이기 위해 여러 방법을 제시하였지만, 여전히 손가락들의 비슷한 생김새, 가려짐, 다양한 손가락의 움직임으로 인한 복잡성 때문에 정확도를 올리는데 한계가 있었다. 본 논문은 기존 방법들의 한계를 극복하기 위해 기존 방법들이 사용하는 입력 형태와 출력 형태를 바꾸었다. 2d depth image를 입력으로 받아 hand joint의 3D coordinate를 직접 regress하는 대부분의 기존 방법들과는 달리, 제안하는 모델은 3D voxelized depth map을 입력으로 받아 3D heatmap을 출력한다. 이를 위해 encoder-decoder 형식의 3D CNN을 사용하였고, 달라진 입력과 출력 형태로 인해 제안하는 모델은 널리 사용되는 3개의 3d hand pose estimation dataset, 1개의 3d human pose estimation dataset에서 가장 높은 성능을 내었다. 또한 ICCV 2017에서 주최된 HANDS 2017 challenge에서 우승 하였다.
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Application of Matrices in real life. Presentation on application of matrices
- 1. Presentation On “Application Of Matrices” Presented By MD RASED KHAN ID: 22702031 SESSION:2021-22 Under The Guidance Of Mohammad Jahed Hasan Lecturer, dept. of EEE Department of Electrical and Electronics Engineering University Of Chittagong
- 2. Some basic methods of Matrix Addition Subtraction Multiplication Inverse Transpose
- 3. Eigen vectors: Vectors that are only scaled by matrices are called Eigen vectors of that corresponding matrix. Eigen value: How much the vector is scaled by is called the eigenvalue.
- 4. 2x + y + 2z = 0 2x – y + z = 10 X + 3y – z = 5 Linear Equation solving: 2 1 2 3 -1 1 1 3 -1 X Y z 0 10 5 𝐴𝑥 = 𝐵
- 6. Humans 150 Covid positive 150 20% 10% Virus spread prediction: Humans: .8(150) + .1(150) = 135 Covid positive : .2(150) + .9(150) = 165
- 7. Humans 135 Covid positive 165 20% 10% Virus spread prediction: Humans: .8(135) + .1(165) = 125 Covid positive : .2(135) + .9(165) = 175
- 8. Covid positive If we represent those percentages as matrices then we get a linear equation: h and z are initial values of humans and covid positive population And h and z are population after an hour. 𝑓 𝑓
- 9. Google page rank Algorithm: PageRank (PR) is an algorithm used by Google Search to rank web pages in their search engine results. It is named after both the term "web page” and co-founder Larry Page. The spectrum of eigenvalues of the Google matrix of University of Cambridge from Fig.1 at α = 1, blue points show eigenvalues of isolated subspaces, red points show eigenvalues of core component. Fig.1
- 10. Image processing: Blurred image Box Blur
- 11. If we take the standard arithmetic mean of the component matrices R, G and B from a color image A ,we will get a gray scale version of the image (non-integer values are rounded to the nearest integer)
- 12. Before After Box Blur Edge detection
- 13. Matrices in Cryptography: 1.Encode a message using matrix multiplication. 2.Decode a coded message using the matrix inverse and matrix multiplication
- 14. Some common applications of matrices include: Computer Graphics Statistics and Data Analysis Economics and Finance Machine Learning and Artificial Intelligence Network Analysis Optimization Neaural networks
- 15. Thank You