Distance measuring Algrothim
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This document describes a distance measuring algorithm using a single camera. It has three parts: track the object using its aspect ratio, feed set points of known distances and areas into a table, and apply Lagrange's interpolation formula to the data to calculate distances based on measured areas. The algorithm tracks objects by finding those with an aspect ratio of around 1.3, and uses five sample points of distance and corresponding areas to determine distance for any given measured area value. A limitation is that the method does not work if the camera moves too close to the object.
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Localization does not mean that you stop marketing on a global scale, not at all. What it means is that you can market globally, but leverage localization strategies.
CV of Jianyu Hu (contact info not included)
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
ΣΕΒ για την Ελληνική Οικονομία - 061016
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LIVER, GALL BLADER, KIDNEY, UROLOGY INSTRUMENTS [SURGICOSE]
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How to increase website conversions by applying the laws of great product design
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Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
Comparison of Distance Transform Based Features
The distance transform based features are widely used in pattern recognition applications. A distance transform
assigns to each background pixel in a binary image a value equal to its distance to the nearest foreground pixel
according to a defined metric. Among these metrics the Chessboard, Euclidean, Chamfer and City-block are
popular. The role of a feature extraction method is quite important in pattern recognition applications. Before
applying a feature, it is essential to judge its performance on the given applications. In this research work, a
study on the performance of above mentioned distance transform based features is made. We have conducted
experiments with 500 hand-printed characters/class and the study has been performed on 43 classes. The
classifiers used are k-NN, MLP, SVM and PNN.
Purple and Black Simple Technology Keynote Presentation (1).pdf
The document discusses the Odd Rule method for calculating the distance an object moves between frames when acceleration is constant. It explains that the distance increases by multiples of odd numbers, such as 1, 3, 5, 7, depending on whether the object is speeding up or slowing down. Several examples are provided of using the Odd Rule to determine distances when the total distance, base distance, or first key distance are known for an object speeding up or slowing down over a number of frames. Finally, the document defines what a motion graph is and how it can be used to visualize an object's position over time in animation.
Introduction to Image Processing
The document provides an overview of key concepts in image processing, including definitions of digital images, image formats, data types, processing operations, and mathematical foundations. It defines what an image and digital image are, explains color models and image resolution. It also covers common image file formats, data types, the scope of image processing including low, mid, and high-level operations. Additionally, it introduces basic terms in image topology and the mathematical concepts of image formation, properties of the point spread function, and linear shift-invariance and convolution.
3 ijaems nov-2015-6-development of an advanced technique for historical docum...
3 ijaems nov-2015-6-development of an advanced technique for historical docum...INFOGAIN PUBLICATION
In this paper, technique used for historical document preservation is explored. In this paper a noise estimation technique is applied to know noise standard deviation. We first estimate or detect level of noise present in noisy images by selecting weak textured patches in image on the basis of gradient matrix and its statistical properties, then eliminate that noise through non local means(NLM) denoising technique that will use estimated noise level as filtering parameter for eliminating noise from the image. This technique is based on weighted average of the similar pixels in historical image. Non local means techniques removes noise from images without taking care of noise level ,it is mandatory to take care of noise level for best preserving Historical document images.Data Science Interview Questions | Data Science Interview Questions And Answe...
This video on Data science interview questions will take you through some of the most popular questions that you face in your Data science interviews. It’s simply impossible to ignore the importance of data and our capacity to analyze, consolidate, and contextualize it. Data scientists are relied upon to fill this need, but there is a serious dearth of qualified candidates worldwide. If you’re moving down the path to be a data scientist, you need to be prepared to impress prospective employers with your knowledge. In addition to explaining why data science is so important, you’ll need to show that you're technically proficient with Big Data concepts, frameworks, and applications. So, here we discuss the list of most popular questions you can expect in an interview and how to frame your answers.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. The data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data, you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to:
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
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Threshold Selection for Image segmentation
1. The document examines different image segmentation techniques and threshold selection methods. It analyzes thresholding applied to images of rice grains and spots.
2. Global and adaptive thresholding techniques are compared, with adaptive thresholding found to better handle non-uniform backgrounds. Histogram peak and valley methods for optimal threshold selection are described.
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MSE.pptx
The document discusses linear and non-linear support vector machine (SVM) algorithms. SVM finds the optimal hyperplane that separates classes with the maximum margin. For linear data, the hyperplane is a straight line, while for non-linear data it is a higher dimensional surface. The data points closest to the hyperplane that determine its position are called support vectors. Non-linear SVM handles non-linear separable data by projecting it into a higher dimension where it may become linearly separable.
A Novel Super Resolution Algorithm Using Interpolation and LWT Based Denoisin...
Image capturing technique has some limitations and due to that we often get low resolution(LR) images. Super Resolution(SR) is a process by which we can generate High Resolution(HR) image from one or more LR images. Here we have proposed one SR algorithm which take three shifted and noisy LR images and generate HR image using Lifting Wavelet Transform(LWT) based denoising method and Directional Filtering and Data Fusion based Edge-Guided Interpolation Algorithm.
Multiclass recognition with
This paper proposes a multiclass recognition scheme which uses multiple feature trees with an
extended scoring method evolved from TF-IDF. Feature trees consisting of different feature
descriptors such as SIFT and SURF are built by the hierarchical k-means algorithm. The
experimental results show that the proposed scoring method combing with the proposed
multiple feature trees yields high accuracy for multiclass recognition and achieves significant
improvement compared to methods using a single feature tree with original TF-IDF.
Multiclass Recognition with Multiple Feature Trees
This paper proposes a multiclass recognition scheme which uses multiple feature trees with an
extended scoring method evolved from TF-IDF. Feature trees consisting of different feature
descriptors such as SIFT and SURF are built by the hierarchical k-means algorithm. The
experimental results show that the proposed scoring method combing with the proposed
multiple feature trees yields high accuracy for multiclass recognition and achieves significant
improvement compared to methods using a single feature tree with original TF-IDF.
Bayes estimators for the shape parameter of pareto type i
This document discusses Bayesian estimators for the shape parameter of the Pareto Type I distribution under different loss functions. It begins by introducing the Pareto distribution and some classical estimators for the shape parameter, including the maximum likelihood estimator, uniformly minimum variance unbiased estimator, and minimum mean squared error estimator. It then derives the Bayesian estimators under a generalized square error loss function and quadratic loss function. Both informative priors (Exponential distribution) and non-informative priors (Jeffreys prior) are considered. The performance of the estimators is compared using Monte Carlo simulations and mean squared errors.
Bayes estimators for the shape parameter of pareto type i
This document discusses Bayesian estimators for the shape parameter of the Pareto Type I distribution under different loss functions. It begins by introducing the Pareto distribution and some classical estimators for the shape parameter, including the maximum likelihood estimator, uniformly minimum variance unbiased estimator, and minimum mean squared error estimator. It then derives the Bayesian estimators under a generalized square error loss function and quadratic loss function. Both informative priors (Exponential distribution) and non-informative priors (Jeffreys prior) are considered. The performance of the estimators is compared using Monte Carlo simulations and mean squared errors.
A modified pso based graph cut algorithm for the selection of optimal regular...
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3) The method is implemented in MATLAB and evaluated on various images. Evaluation metrics like Jaccard similarity, Dice coefficient, and accuracy show the proposed method achieves better performance than conventional PSO and graph cut approaches.
Wordoku Puzzle Solver - Image Processing Project
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Fuzzy Region Merging Using Fuzzy Similarity Measurement on Image Segmentation
Some image’s regions have unbalance information, such as blurred contour, shade, and uneven brightness. Those regions are called as ambiguous regions. Ambiguous region cause problem during region merging process in interactive image segmentation because that region has double information, both as object and background. We proposed a new region merging strategy using fuzzy similarity measurement for image segmentation. The proposed method has four steps; the first step is initial segmentation using mean-shift algorithm. The second step is giving markers manually to indicate the object and background region. The third step is determining the fuzzy region or ambiguous region in the images. The last step is fuzzy region merging using fuzzy similarity measurement. The experimental results demonstrated that the proposed method is able to segment natural images and dental panoramic images successfully with the average value of misclassification error (ME) 1.96% and 5.47%, respectively.
Blind Image Seperation Using Forward Difference Method (FDM)
In this paper, blind image separation is performed, exploiting the property of sparseness to represent images. A new sparse representation called forward difference method is proposed. It is known that most of the independent component analysis (ICA) basis functions, extracted from images are sparse and gives unreliable sparseness measure. In the proposed method, the image mixture is first transformed to sparse images. These images are divided into blocks and for each block the sparseness measure ε0 norm is applied. The block having the most sparseness is considered to determine the separation matrix. The efficiency of the proposed method is compared with other sparse representation functions.
2014-mo444-practical-assignment-04-paulo_faria
The document discusses applying machine learning techniques to identify compiler optimizations that impact program performance. It used classification trees to analyze a dataset containing runtime measurements for 19 programs compiled with different combinations of 45 LLVM optimizations. The trees identified optimizations like SROA and inlining that generally improved performance across programs. Analysis of individual programs found some variations, but also common optimizations like SROA and simplifying the control flow graph. Precision, accuracy, and AUC metrics were used to evaluate the trees' ability to classify optimizations for best runtime.
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Working with the data for Machine Learning
The document discusses various techniques for dimensionality reduction in machine learning. It explains that dimensionality reduction transforms high-dimensional data into a lower-dimensional representation while retaining important information. Techniques include feature selection, which selects a subset of relevant features, and feature extraction, which transforms existing features into a new set of features. Principal component analysis (PCA) is presented as a feature extraction method that finds new axes along which the data has maximum variance.
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Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...
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Bayes estimators for the shape parameter of pareto type i
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Bayes estimators for the shape parameter of pareto type i
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Distance measuring Algrothim
- 1. Methodology and theory behind Distance Measuring Algorithm Using One camera
- 2. In this document only theory and mathematics behind algorithm is described, details related programming is not discussed. Our Distance Measuring Algorithm (DMA) has three parts Track object Feed set points Measure distance Track object A picture is better than thousand words so first see these pictures.
- 6. From above images we notice that aspect ratio (width/height) is more or less 1.3 so for tracking particular object (sign board in our case) we find all objects in image and check aspect ratio of all objects found in image and discard all object whose aspect ratio is not equal to 1.3 so doing this we can easily track particular object. Feeding set points This method is not plug and play we have to feed some set points let us organize data obtained from above images in table. No Aspect ratio (width/height) no unit Distance (feet) Area (width x height) in pixels 1 1.37 0.5 280071 2 1.35 2 91520 3 1.313 3 46436
- 7. 4 1.33 4 26980 5 1.355 5 14664 Measuring Distance For measuring distance we are using Lagrange’s interpolation formula Formula for five point’s data (x- x1) (x- x2)(x- x3)(x- x4) (x- x0)(x- x1) (x- x2)(x- x3) f(x) = f0+ . . . + f4 (x0 - x1) (x0 - x2)(x0 - x3)(x0 - x4) (x4 - x0)(x4 - x1)(x4 - x2)(x4 - x3) Follow this link to understand Lagrange’s interpolation http://mat.iitm.ac.in/home/sryedida/public_html/caimna/interpolation/lagrange.html Applying langrage’s interpolation to above data to find distance at any point Distance (D) 0.5 2 3 4 5 Area (A) 280071 91520 46436 26980 14664 Our unknown quantity is distance we know area so we can easily find distance at any value of area. Limitations If camera move to close to object then this method does not works