Periscope: A Content-based Image Retrieval Engine
•
0 likes•211 views
Periscope is a Content-Based Image Retrieval web application. The repository of the project can be found here: https://github.com/AndyFou/Periscope
Report
Share
Report
Share
Download to read offline
Recommended
GLOA:A New Job Scheduling Algorithm for Grid Computing
The paper review presentation of 'GLOA:A New Job Scheduling Algorithm for Grid Computing' published in International Journal of Artificial Intelligence and Interactive Multimedia, Vol. 2, Nº 1.
Graphical Objects and Scene Graphs
A scene graph is a general data structure commonly used by vector-based graphics editing applications and modern computer games, which arranges the logical and often (but not necessarily) spatial representation of a graphical scene. Examples of such programs include Acrobat 3D, Adobe Illustrator, AutoCAD, CorelDRAW, OpenSceneGraph, OpenSG, VRML97, X3D, Hoops and Open Inventor.
Proceedings for 2016 WV EPAN GIS Users Group Meeting
The annual Eastern WV Panhandle GIS Users Group Forum was held on September 14th, 2016 with 51 people attending. The one-day conference was organized by a committee from Jefferson County GIS and included sponsors from the WV Association of Geospatial Professionals and Eastern Panhandle Regional Planning and Development Council. Speakers presented on topics such as implementing parcel fabric in WV, using GIS for regional economic development, trends in GIS tools, integrating survey data into GIS, local contributions to state/federal datasets, evaluating riparian buffers, GIS for field mobility, and high accuracy data collection. Follow up contact information was provided for Jefferson County WV GIS.
Estimating Causal Effects from Observations
This document summarizes several methods for estimating causal effects from observational data:
1. The back-door criterion provides a method for identifying when causal effects are identifiable based on observable variables. It requires adjusting for a set of variables S that block back-door paths between the treatment X and outcome Y.
2. Estimation methods described include calculating average treatment effects, avoiding estimating high-dimensional marginal distributions using sampling, matching on propensity scores, and using instrumental variables.
3. Propensity score matching involves estimating propensity scores via logistic regression and then matching treated and control units based on their propensity to receive treatment.
4. Instrumental variables estimation uses an instrument I that is associated with treatment X
Τweetfix: Data Analytics on Match Fixing
Tweetfix is a visualization platform, developed for the Fix the Fixing european project, where users can explore the results of crowdsourced data analytics from Social Media on well-known Match Fixing cases.
Experimental Causal Inference
The document discusses experimental causal inference and key concepts in experimental design. It defines causal inference as trying to answer causal questions from data, and experimental causal inference as doing so using experiments rather than observations. The basic ideas of experimental design are outlined as maximizing useful variation, eliminating unhelpful variation, and randomizing what cannot be eliminated. Randomization is described as the key to ensuring treatment groups are statistically equivalent. Some open issues discussed include types of randomization, choice of treatment levels, and other challenges like multiple variables, blocking, and limitations of randomization.
Exploring Language Communities on Github
Creation of language graphs that are used by the github community and some sub-communities discriminated by location and network analysis.
Social Media Fraud Metrics
This document discusses metrics for detecting social media fraud. There are several forms of social media fraud, including identity theft, fake product promotions, and generating fake revenue. Detecting fake accounts and fraud groups is important for social media companies and users to prevent financial losses, damage to company image, and identity theft. Fraud metrics can analyze patterns in the social media network graph using social network analysis techniques. Graph metrics like density, centrality, and connected components can help identify potential fraud behaviors and focus investigations on pivotal nodes in the fraud network. Strongly and weakly connected components are useful for identifying other accounts connected to a known fraudulent user.
Recommended
GLOA:A New Job Scheduling Algorithm for Grid Computing
The paper review presentation of 'GLOA:A New Job Scheduling Algorithm for Grid Computing' published in International Journal of Artificial Intelligence and Interactive Multimedia, Vol. 2, Nº 1.
Graphical Objects and Scene Graphs
A scene graph is a general data structure commonly used by vector-based graphics editing applications and modern computer games, which arranges the logical and often (but not necessarily) spatial representation of a graphical scene. Examples of such programs include Acrobat 3D, Adobe Illustrator, AutoCAD, CorelDRAW, OpenSceneGraph, OpenSG, VRML97, X3D, Hoops and Open Inventor.
Proceedings for 2016 WV EPAN GIS Users Group Meeting
The annual Eastern WV Panhandle GIS Users Group Forum was held on September 14th, 2016 with 51 people attending. The one-day conference was organized by a committee from Jefferson County GIS and included sponsors from the WV Association of Geospatial Professionals and Eastern Panhandle Regional Planning and Development Council. Speakers presented on topics such as implementing parcel fabric in WV, using GIS for regional economic development, trends in GIS tools, integrating survey data into GIS, local contributions to state/federal datasets, evaluating riparian buffers, GIS for field mobility, and high accuracy data collection. Follow up contact information was provided for Jefferson County WV GIS.
Estimating Causal Effects from Observations
This document summarizes several methods for estimating causal effects from observational data:
1. The back-door criterion provides a method for identifying when causal effects are identifiable based on observable variables. It requires adjusting for a set of variables S that block back-door paths between the treatment X and outcome Y.
2. Estimation methods described include calculating average treatment effects, avoiding estimating high-dimensional marginal distributions using sampling, matching on propensity scores, and using instrumental variables.
3. Propensity score matching involves estimating propensity scores via logistic regression and then matching treated and control units based on their propensity to receive treatment.
4. Instrumental variables estimation uses an instrument I that is associated with treatment X
Τweetfix: Data Analytics on Match Fixing
Tweetfix is a visualization platform, developed for the Fix the Fixing european project, where users can explore the results of crowdsourced data analytics from Social Media on well-known Match Fixing cases.
Experimental Causal Inference
The document discusses experimental causal inference and key concepts in experimental design. It defines causal inference as trying to answer causal questions from data, and experimental causal inference as doing so using experiments rather than observations. The basic ideas of experimental design are outlined as maximizing useful variation, eliminating unhelpful variation, and randomizing what cannot be eliminated. Randomization is described as the key to ensuring treatment groups are statistically equivalent. Some open issues discussed include types of randomization, choice of treatment levels, and other challenges like multiple variables, blocking, and limitations of randomization.
Exploring Language Communities on Github
Creation of language graphs that are used by the github community and some sub-communities discriminated by location and network analysis.
Social Media Fraud Metrics
This document discusses metrics for detecting social media fraud. There are several forms of social media fraud, including identity theft, fake product promotions, and generating fake revenue. Detecting fake accounts and fraud groups is important for social media companies and users to prevent financial losses, damage to company image, and identity theft. Fraud metrics can analyze patterns in the social media network graph using social network analysis techniques. Graph metrics like density, centrality, and connected components can help identify potential fraud behaviors and focus investigations on pivotal nodes in the fraud network. Strongly and weakly connected components are useful for identifying other accounts connected to a known fraudulent user.
Transitivity of Trust
1) The document discusses several papers related to modeling trust in online networks and communities.
2) Key concepts discussed include TrustRank for identifying reputable web pages, propagation of both trust and distrust in social networks, the EigenTrust algorithm for reputation management in peer-to-peer networks, and attack-resistant trust metrics for public key certification.
3) The document also provides summaries and evaluations of experiments conducted using various real-world datasets to analyze different trust computation models.
Opinion mining
"Research on Opinion Mining and Sentiment Analysis" project, under the Machine Learning course of the Postgraduate Programme of Computer Science Department, AUTh.
A Hybrid Trademark Retrieval System Using Four-Gray-Level Zernike Moments & ...
A Hybrid Trademark Retrieval System Using Four-Gray-Level Zernike Moments & Image Compactness Indices
11.graph cut based local binary patterns for content based image retrieval
This document presents a new algorithm for content-based image retrieval (CBIR) based on graph cut theory and local binary patterns (LBP). The algorithm extracts nine LBP histograms from each image as features by comparing each pixel in a 3x3 pattern to the other pixels using graph cut theory. Two experiments on texture databases show the proposed Graph Cut Local Binary Patterns (GCLBP) algorithm achieves significantly better retrieval accuracy than LBP and other transform-based methods, as measured by average retrieval precision and rate.
Content based image retrieval for agriculture crops
Master these defend presentation delivered by Esraa. Title is Content-based Image Retrieval for Agricultural Crops
Features Analysis in CBIR Systems
Image retrieval is the major innovations in the development of images. Mining of images is used to mine latest information from
the general collection of images. CBIR is the latest method in which our target images is to be extracted on the basis of specific features of
the specified image. The image can be retrieved in fast if it is clustered in an accurate and structured manner. In this paper, we have the
combined the theories of CBIR and analysis of features of CBIR systems.
Amalgamation of contour, texture, color, edge, and spatial features for effic...
Abstract From the past few years, Content based image retrieval (CBIR) has been a progressive and curious research area. Image retrieval is a process of extraction of the set of images from the available image database resembling the query image. Many CBIR techniques have been proposed for relevant image recoveries. However most of them are based on a particular feature extraction like texture based recovery, color based retrieval system etc. Here in this paper we put forward a novel technique for image recovery based on the integration of contour, texture, color, edge, and spatial features. Contourlet decomposition is employed for the extraction of contour features such as energy and standard deviation. Directionality and anisotropy are the properties of contourlet transformation that makes it an efficient technique. After feature extraction of query and database images, similarity measurement techniques such as Squared Euclidian and Manhattan distance were used to obtain the top N image matches. The simulation results in Matlab show that the proposed technique offers a better image retrieval. Satisfactory precision-recall rate is also maintained in this method. Keywords: Contourlet Decomposition, Local Binary Pattern, Squared Euclidian Distance, Manhattan Distance
D010332630
This document summarizes a research paper on using discrete wavelet transform for medical image retrieval. It discusses extracting texture features like energy, entropy, contrast and correlation from images using DWT. Haar wavelet is used to analyze texture features. The texture features of images in a database are calculated and compared to an input image to retrieve similar images from the database. Local binary patterns are also extracted as features for classification and retrieval of medical images.
YFCC100M HybridNet fc6 Deep Features for Content-Based Image Retrieval
This document summarizes work on extracting deep features from the YFCC100M dataset using a HybridNet model for content-based image retrieval. The contributions include generating HybridNet fc6 features for the 100M images, developing CBIR systems called MI-File and Lucene Quantization, and establishing ground truth results for approximate k-NN search evaluation. Ongoing work involves image annotation, extracting additional deep features, and cross-media retrieval using text queries.
CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMP...
1. The document proposes a new fuzzy compact composite descriptor (BTDH) for content-based medical image indexing and retrieval.
2. BTDH uses both brightness and texture features in a compact 128-bin vector to describe images, with size under 48 bytes per image.
3. In experiments on 5000 images with 120 queries, BTDH achieved better retrieval accuracy than other descriptors, with an Average Normalized Modified Retrieval Rank of 0.272.
Scalable face image retrieval using attribute enhanced sparse codewords
This document proposes a new approach to content-based face image retrieval using both low-level features and high-level human attributes. It introduces two main modules: 1) Attribute-enhanced sparse coding which uses attributes to construct semantic codewords in the offline stage. 2) Attribute-embedded inverted indexing which embeds attribute information into the index structure and allows efficient retrieval online by considering the query image's local attributes. The proposed approach combines these two orthogonal methods to significantly improve face image retrieval by incorporating human attributes into the image representation and indexing.
Slides
The document discusses content-based image retrieval (CBIR) using different wavelet transforms for texture feature extraction and similarity measurement. It compares the performance of M-band wavelet transform, cosine-modulated wavelet transform, and Gabor wavelet transform in terms of retrieval accuracy and computational complexity. The M-band wavelet transform and cosine-modulated wavelet transform provide better retrieval accuracy than standard wavelet transform with much reduced computational complexity compared to Gabor wavelet transform.
Content Based Image Retrieval
This document summarizes a seminar presentation on Content Based Image Retrieval (CBIR). CBIR allows users to search for digital images in large databases based on the images' visual contents like color, shape, and texture, rather than keywords. The seminar covers the inspiration for CBIR, different types of image retrieval, how CBIR works by extracting features from images, applications like crime prevention and biomedicine, advantages like efficient searching, and limitations like accuracy issues. The goal of CBIR research is to develop algorithms that can characterize and understand images like human vision.
Region Of Interest Extraction
1. The document discusses different techniques for region of interest (ROI) extraction from images, including saliency maps, visual attention maps, the Itti-Koch model, and the Stentiford model.
2. It provides examples of the output from these techniques, such as highlighting the most salient points or areas of an image.
3. While the models may identify different ROIs, combining their common ROIs can provide a better output for applications like thumbnail cropping or image indexing.
Lbp based edge-texture features for object recoginition
IGeekS Technologies (Make Final Year Project)
No: 19, MN Complex, 2nd Cross,
Sampige Main Road, Malleswaram
Bangalore- 560003.
Phone No: 080-32487434 /9590544567 / 9739066172
Mail: nandu@igeekstechnologies.com , nanduigeeks2010@gmail.com
Land mark : Near to Mantri Mall, Malleswaram Bangalore
Local binary pattern
Divide the examined window into cells (e.g. 16x16 pixels for each cell).
2- For each pixel in a cell, compare the pixel to each of its 8 neighbors (on its left-top, leftmiddle,
left-bottom, right-top, etc.). Follow the pixels along a circle, i.e. clockwise or counterclockwise.
3- Where the center pixel's value is greater than the neighbor's value, write "1". Otherwise,
write "0". This gives an 8-digit binary number (which is usually converted to decimal for
convenience).
4- Compute the histogram, over the cell, of the frequency of each "number" occurring (i.e.,
each combination of which pixels are smaller and which are greater than the center).
Content Based Image Retrieval
Content-based image retrieval (CBIR) uses visual image content to search large image databases according to user needs. CBIR systems represent images by extracting features related to color, shape, texture, and spatial layout. Features are extracted from regions of the image and compared to features of images in the database to find the most similar matches. CBIR has applications in medical imaging, fingerprints, photo collections, and more. Techniques include representing images with histograms of color and texture features extracted through transforms.
Evolving a Medical Image Similarity Search
Slides for talk at Haystack Conference 2018. Covers evolution of an Image Similarity Search Proof of Concept built to identify similar medical images. Discusses various image vectorizing techniques that were considered in order to convert images into searchable entities, an evaluation strategy to rank these techniques, as well as various indexing strategies to allow searching for similar images at scale.
Encontra presentation
EnContRA is a framework for content-based retrieval approaches and applications. It provides modularity, ease of use, and fast development of new approaches. EnContRA supports indexing, feature extraction, search/retrieval algorithms, and extensible query processing for different multimedia types including images, drawings, 3D objects, and audio. Typical applications are built using EnContRA modules including descriptors, indexing structures, and search algorithms. The document provides examples of building simple and more complex image retrieval applications using EnContRA.
Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit,
The document discusses OpenCV, an open source computer vision and machine learning software library. It provides instructions for compiling OpenCV 3.2 on Windows 10 with Visual Studio 2015, an overview of OpenCV modules for tasks like image processing, video analysis, and machine learning, and examples of how to set up a basic OpenCV project in Visual Studio and write a simple program to read and display an image.
Object extraction from satellite imagery using deep learning
This document presents an overview of using deep learning for object extraction from satellite imagery. It discusses the needed data, training process, evaluation methods, appropriate tools, and literature review on the subject. Code samples applying techniques like VGGNet, Faster R-CNN, YOLO, and fully convolutional networks to datasets like SpaceNet and DSTL achieve preliminary results, with the YOLO model obtaining a maximum F1 score of 0.21 on test data.
I2DS Project.pdf
The project involves analyzing structural data related to different areas using Python. Students will be divided into groups to choose a dataset, import and process the data using pandas and matplotlib. The analysis will include an overview of the data, exploring its properties and columns, and visualizing information through graphs. Students are instructed to collaborate in Jupyter notebooks, citing sources and presenting their work which will be graded based on tasks like data manipulation, exploratory analysis, modeling and visualization.
More Related Content
Viewers also liked
Transitivity of Trust
1) The document discusses several papers related to modeling trust in online networks and communities.
2) Key concepts discussed include TrustRank for identifying reputable web pages, propagation of both trust and distrust in social networks, the EigenTrust algorithm for reputation management in peer-to-peer networks, and attack-resistant trust metrics for public key certification.
3) The document also provides summaries and evaluations of experiments conducted using various real-world datasets to analyze different trust computation models.
Opinion mining
"Research on Opinion Mining and Sentiment Analysis" project, under the Machine Learning course of the Postgraduate Programme of Computer Science Department, AUTh.
A Hybrid Trademark Retrieval System Using Four-Gray-Level Zernike Moments & ...
A Hybrid Trademark Retrieval System Using Four-Gray-Level Zernike Moments & Image Compactness Indices
11.graph cut based local binary patterns for content based image retrieval
This document presents a new algorithm for content-based image retrieval (CBIR) based on graph cut theory and local binary patterns (LBP). The algorithm extracts nine LBP histograms from each image as features by comparing each pixel in a 3x3 pattern to the other pixels using graph cut theory. Two experiments on texture databases show the proposed Graph Cut Local Binary Patterns (GCLBP) algorithm achieves significantly better retrieval accuracy than LBP and other transform-based methods, as measured by average retrieval precision and rate.
Content based image retrieval for agriculture crops
Master these defend presentation delivered by Esraa. Title is Content-based Image Retrieval for Agricultural Crops
Features Analysis in CBIR Systems
Image retrieval is the major innovations in the development of images. Mining of images is used to mine latest information from
the general collection of images. CBIR is the latest method in which our target images is to be extracted on the basis of specific features of
the specified image. The image can be retrieved in fast if it is clustered in an accurate and structured manner. In this paper, we have the
combined the theories of CBIR and analysis of features of CBIR systems.
Amalgamation of contour, texture, color, edge, and spatial features for effic...
Abstract From the past few years, Content based image retrieval (CBIR) has been a progressive and curious research area. Image retrieval is a process of extraction of the set of images from the available image database resembling the query image. Many CBIR techniques have been proposed for relevant image recoveries. However most of them are based on a particular feature extraction like texture based recovery, color based retrieval system etc. Here in this paper we put forward a novel technique for image recovery based on the integration of contour, texture, color, edge, and spatial features. Contourlet decomposition is employed for the extraction of contour features such as energy and standard deviation. Directionality and anisotropy are the properties of contourlet transformation that makes it an efficient technique. After feature extraction of query and database images, similarity measurement techniques such as Squared Euclidian and Manhattan distance were used to obtain the top N image matches. The simulation results in Matlab show that the proposed technique offers a better image retrieval. Satisfactory precision-recall rate is also maintained in this method. Keywords: Contourlet Decomposition, Local Binary Pattern, Squared Euclidian Distance, Manhattan Distance
D010332630
This document summarizes a research paper on using discrete wavelet transform for medical image retrieval. It discusses extracting texture features like energy, entropy, contrast and correlation from images using DWT. Haar wavelet is used to analyze texture features. The texture features of images in a database are calculated and compared to an input image to retrieve similar images from the database. Local binary patterns are also extracted as features for classification and retrieval of medical images.
YFCC100M HybridNet fc6 Deep Features for Content-Based Image Retrieval
This document summarizes work on extracting deep features from the YFCC100M dataset using a HybridNet model for content-based image retrieval. The contributions include generating HybridNet fc6 features for the 100M images, developing CBIR systems called MI-File and Lucene Quantization, and establishing ground truth results for approximate k-NN search evaluation. Ongoing work involves image annotation, extracting additional deep features, and cross-media retrieval using text queries.
CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMP...
1. The document proposes a new fuzzy compact composite descriptor (BTDH) for content-based medical image indexing and retrieval.
2. BTDH uses both brightness and texture features in a compact 128-bin vector to describe images, with size under 48 bytes per image.
3. In experiments on 5000 images with 120 queries, BTDH achieved better retrieval accuracy than other descriptors, with an Average Normalized Modified Retrieval Rank of 0.272.
Scalable face image retrieval using attribute enhanced sparse codewords
This document proposes a new approach to content-based face image retrieval using both low-level features and high-level human attributes. It introduces two main modules: 1) Attribute-enhanced sparse coding which uses attributes to construct semantic codewords in the offline stage. 2) Attribute-embedded inverted indexing which embeds attribute information into the index structure and allows efficient retrieval online by considering the query image's local attributes. The proposed approach combines these two orthogonal methods to significantly improve face image retrieval by incorporating human attributes into the image representation and indexing.
Slides
The document discusses content-based image retrieval (CBIR) using different wavelet transforms for texture feature extraction and similarity measurement. It compares the performance of M-band wavelet transform, cosine-modulated wavelet transform, and Gabor wavelet transform in terms of retrieval accuracy and computational complexity. The M-band wavelet transform and cosine-modulated wavelet transform provide better retrieval accuracy than standard wavelet transform with much reduced computational complexity compared to Gabor wavelet transform.
Content Based Image Retrieval
This document summarizes a seminar presentation on Content Based Image Retrieval (CBIR). CBIR allows users to search for digital images in large databases based on the images' visual contents like color, shape, and texture, rather than keywords. The seminar covers the inspiration for CBIR, different types of image retrieval, how CBIR works by extracting features from images, applications like crime prevention and biomedicine, advantages like efficient searching, and limitations like accuracy issues. The goal of CBIR research is to develop algorithms that can characterize and understand images like human vision.
Region Of Interest Extraction
1. The document discusses different techniques for region of interest (ROI) extraction from images, including saliency maps, visual attention maps, the Itti-Koch model, and the Stentiford model.
2. It provides examples of the output from these techniques, such as highlighting the most salient points or areas of an image.
3. While the models may identify different ROIs, combining their common ROIs can provide a better output for applications like thumbnail cropping or image indexing.
Lbp based edge-texture features for object recoginition
IGeekS Technologies (Make Final Year Project)
No: 19, MN Complex, 2nd Cross,
Sampige Main Road, Malleswaram
Bangalore- 560003.
Phone No: 080-32487434 /9590544567 / 9739066172
Mail: nandu@igeekstechnologies.com , nanduigeeks2010@gmail.com
Land mark : Near to Mantri Mall, Malleswaram Bangalore
Local binary pattern
Divide the examined window into cells (e.g. 16x16 pixels for each cell).
2- For each pixel in a cell, compare the pixel to each of its 8 neighbors (on its left-top, leftmiddle,
left-bottom, right-top, etc.). Follow the pixels along a circle, i.e. clockwise or counterclockwise.
3- Where the center pixel's value is greater than the neighbor's value, write "1". Otherwise,
write "0". This gives an 8-digit binary number (which is usually converted to decimal for
convenience).
4- Compute the histogram, over the cell, of the frequency of each "number" occurring (i.e.,
each combination of which pixels are smaller and which are greater than the center).
Content Based Image Retrieval
Content-based image retrieval (CBIR) uses visual image content to search large image databases according to user needs. CBIR systems represent images by extracting features related to color, shape, texture, and spatial layout. Features are extracted from regions of the image and compared to features of images in the database to find the most similar matches. CBIR has applications in medical imaging, fingerprints, photo collections, and more. Techniques include representing images with histograms of color and texture features extracted through transforms.
Viewers also liked (17)
A Hybrid Trademark Retrieval System Using Four-Gray-Level Zernike Moments & ...
A Hybrid Trademark Retrieval System Using Four-Gray-Level Zernike Moments & ...
11.graph cut based local binary patterns for content based image retrieval
11.graph cut based local binary patterns for content based image retrieval
Content based image retrieval for agriculture crops
Content based image retrieval for agriculture crops
Amalgamation of contour, texture, color, edge, and spatial features for effic...
Amalgamation of contour, texture, color, edge, and spatial features for effic...
YFCC100M HybridNet fc6 Deep Features for Content-Based Image Retrieval
YFCC100M HybridNet fc6 Deep Features for Content-Based Image Retrieval
CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMP...
CONTENT BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING A FUZZY COMPACT COMP...
Scalable face image retrieval using attribute enhanced sparse codewords
Scalable face image retrieval using attribute enhanced sparse codewords
Lbp based edge-texture features for object recoginition
Lbp based edge-texture features for object recoginition
Similar to Periscope: A Content-based Image Retrieval Engine
Evolving a Medical Image Similarity Search
Slides for talk at Haystack Conference 2018. Covers evolution of an Image Similarity Search Proof of Concept built to identify similar medical images. Discusses various image vectorizing techniques that were considered in order to convert images into searchable entities, an evaluation strategy to rank these techniques, as well as various indexing strategies to allow searching for similar images at scale.
Encontra presentation
EnContRA is a framework for content-based retrieval approaches and applications. It provides modularity, ease of use, and fast development of new approaches. EnContRA supports indexing, feature extraction, search/retrieval algorithms, and extensible query processing for different multimedia types including images, drawings, 3D objects, and audio. Typical applications are built using EnContRA modules including descriptors, indexing structures, and search algorithms. The document provides examples of building simple and more complex image retrieval applications using EnContRA.
Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit,
The document discusses OpenCV, an open source computer vision and machine learning software library. It provides instructions for compiling OpenCV 3.2 on Windows 10 with Visual Studio 2015, an overview of OpenCV modules for tasks like image processing, video analysis, and machine learning, and examples of how to set up a basic OpenCV project in Visual Studio and write a simple program to read and display an image.
Object extraction from satellite imagery using deep learning
This document presents an overview of using deep learning for object extraction from satellite imagery. It discusses the needed data, training process, evaluation methods, appropriate tools, and literature review on the subject. Code samples applying techniques like VGGNet, Faster R-CNN, YOLO, and fully convolutional networks to datasets like SpaceNet and DSTL achieve preliminary results, with the YOLO model obtaining a maximum F1 score of 0.21 on test data.
I2DS Project.pdf
The project involves analyzing structural data related to different areas using Python. Students will be divided into groups to choose a dataset, import and process the data using pandas and matplotlib. The analysis will include an overview of the data, exploring its properties and columns, and visualizing information through graphs. Students are instructed to collaborate in Jupyter notebooks, citing sources and presenting their work which will be graded based on tasks like data manipulation, exploratory analysis, modeling and visualization.
ICPC06.ppt
This document presents a novel technique for combining probabilistic ranking (SBP) and latent semantic indexing (LSI) to identify features in source code. SBP analyzes dynamic execution traces to probabilistically rank methods while LSI performs information retrieval on static source code. Their results are combined using an affine transformation. Two case studies on Mozilla demonstrate the hybrid technique achieves higher precision than SBP or LSI alone. Future work involves combining with other techniques and determining the best combination weighting.
Python ml
I am shubham sharma graduated from Acropolis Institute of technology in Computer Science and Engineering. I have spent around 2 years in field of Machine learning. I am currently working as Data Scientist in Reliance industries private limited Mumbai. Mainly focused on problems related to data handing, data analysis, modeling, forecasting, statistics and machine learning, Deep learning, Computer Vision, Natural language processing etc. Area of interests are Data Analytics, Machine Learning, Machine learning, Time Series Forecasting, web information retrieval, algorithms, Data structures, design patterns, OOAD.
課題ドリブン、フルスタックAI開発術 [MOBILITY:dev]
AIの精度向上において、学習データやモデルの出力の可視化や解析は非常に重要です。我々は、これまで手動で行っていたそれらのプロセスをマイクロサービスに置き換え自動化することで大幅な効率化を実現しました。本セッションでは、そうしたフルスタックなAI開発術についてお話しします。
Searching Images: Recent research at Southampton
Information Retrieval group seminar series. The University of Glasgow. 21st February 2011.
Southampton has a long history of research in the areas of multimedia information analysis. This talk will focus on some of the recent work we have been involved with in the area of image search. The talk will start by looking at how image content can be represented in ways analogous to textual information and how techniques developed for indexing text can be adapted to images. In particular, the talk will introduce ImageTerrier, a research platform for image retrieval that is built around Glasgow's Terrier software. The talk will also cover some of our recent work on image classification and image search result diversification.
opnet
The document provides an overview of OPNET Modeler, a network simulation tool. It describes OPNET Modeler's architecture, which includes tools for model specification, data collection and simulation, and analysis. It also discusses how to locate models and components using the model library and its organization. The goal is to help users understand what problems can be solved with OPNET Modeler and how to get started using it.
Python master class 3
Thirst part of the Python Master class including Image Processing using Python-OpenCV and Scikit-image
Build, Scale, and Deploy Deep Learning Pipelines with Ease Using Apache Spark
Deep Learning has shown a tremendous success, yet it often requires a lot of effort to leverage its power. Existing Deep Learning frameworks require writing a lot of code to work with a model, let alone in a distributed manner.
In this talk, we’ll survey the state of Deep Learning at scale, and where we introduce the Deep Learning Pipelines, a new open-source package for Apache Spark. This package simplifies Deep Learning in three major ways:
• It has a simple API that integrates well with enterprise Machine Learning pipelines.
• It automatically scales out common Deep Learning patterns, thanks to Spark.
• It enables exposing Deep Learning models through the familiar Spark APIs, such as MLlib and Spark SQL.
In this talk, we will look at a complex problem of image classification, using Deep Learning and Spark. Using Deep Learning Pipelines, we will show:
• how to build deep learning models in a few lines of code;
• how to scale common tasks like transfer learning and prediction; and
• how to publish models in Spark SQL.
UiPath Studio Web workshop series - Day 6
📣 Welcome to Day 6 of the UiPath Studio Web Workshop. In this session, we will dive into the essentials of PDF activities in UiPath Studio Web. Join us as we provide an overview of PDF automation, covering various activities related to data extraction and image extraction. Get ready for a hands-on experience with a live demonstration.
👉 Topics covered
📌 Task 1: PDF Automation Overview
Understanding PDF Automation in UiPath Studio Web
Consolidating PDF Activities
Live Demonstration
Speakers:
Vajrang Billlakurthi, Digital Transformation Leader, Vajrang IT Services Pvt Ltd. and UiPath MVP
Swathi Nelakurthi, Associate Automation Developer, Vajrang IT Services Pvt Ltd
Rahul Goyal, SR. Director, ERP Systems, Ellucian and UiPath MVP
👉 Visit the series page to register to all events.
Cubes – pluggable model explained
This document proposes a redesign of the Cubes analytical workspace to make it more pluggable and flexible. Key points of the redesign include:
1. Splitting backends into separate objects for browsers, stores, and model providers for more modular composition.
2. Allowing browsers and stores to work with different data sources and schemas within a single workspace.
3. Using an external workspace object to provide the appropriate browser and manage configuration, replacing the previous single backend concept.
A Survey about Object Retrieval
This document discusses unsupervised and supervised approaches to object retrieval.
It begins by covering unsupervised approaches, describing common local and global features used for object retrieval like SIFT, HOG, and deep features. It also discusses feature aggregation methods like bag-of-features and Fisher vectors.
The document then reviews state-of-the-art results, noting methods that achieved mean average precision scores over 0.8 on standard datasets using techniques like selective match kernels and sum-pooled convolutional features.
It concludes by proposing future attempts could explore improving features, distance metrics, and incorporating supervision, suggesting object retrieval may benefit from a dual supervised/unsupervised learning approach.
Searching Images: Recent research at Southampton
Intelligence, Agents, Multimedia Seminar series. University of Southampton. 7th March 2011.
Southampton has a long history of research in the areas of multimedia information analysis. This talk will focus on some of the recent work we have been involved with in the area of image search. The talk will
start by looking at how image content can be represented in ways analogous to textual information and how techniques developed for indexing text can be adapted to images. In particular, the talk will introduce ImageTerrier, a research platform for image retrieval that is built around the University of Glasgow's Terrier text retrieval software. The talk will also cover some of our recent work on image classification and image search result diversification.
Mini Project - DS Batch.pptx
This document outlines the steps for a mini project on image classification using machine learning. The steps include: gathering image data from sources like Kaggle or by scraping sites; preprocessing the data by resizing images and converting them to single dimension arrays; tuning hyperparameters and applying classification algorithms like KNN, SVM, decision trees, random forest, logistic regression or Naive Bayes using grid search cross validation; evaluating the model using metrics like confusion matrix and classification report; and predicting new image categories. Participants are to submit their project on GitHub including a Colab notebook, image data, and reference their submission by email.
Searching Images: Recent research at Southampton
Knowledge Media Institute seminar series. The Open University. 23rd March 2011.
Southampton has a long history of research in the areas of multimedia information analysis. This talk will focus on some of the recent work we have been involved with in the area of image search. The talk will start by looking at how image content can be represented in ways analogous to textual information and how techniques developed for indexing text can be adapted to images. In particular, the talk will introduce ImageTerrier, a research platform for image retrieval that is built around the University of Glasgow's Terrier text retrieval software. The talk will also cover some of our recent work on image classification and image search result diversification.
Hydrosphere.io for ODSC: Webinar on Kubeflow
Webinar video: https://www.youtube.com/watch?v=Y3_fcJBgpMw
Kubeflow and Beyond: Automation of Model Training, Deployment, Testing, Monitoring, and Retraining
Speakers:
Stepan Pushkarev, CTO, Hydrosphere.io and Ilnur Garifullin is an ML Engineer, Hydrosphere.io
Abstract: Very often a workflow of training models and delivering them to the production environment contains loads of manual work. Those could be either building a Docker image and deploying it to the Kubernetes cluster or packing the model to the Python package and installing it to your Python application. Or even changing your Java classes with the defined weights and re-compiling the whole project. Not to mention that all of this should be followed by testing your model's performance. It hardly could be named "continuous delivery" if you do it all manually. Imagine you could run the whole process of assembling/training/deploying/testing/running model via a single command in your terminal. In this webinar, we will present a way to build the whole workflow of data gathering/model training/model deployment/model testing into a single flow and run it with a single command.
Bachelor-Thesis
This document outlines an image classification workflow and application. It describes an architecture with three main components: 1) A Python image classification library for feature extraction, classifier creation and database storage, 2) A Flask server exposing an API for the library, and 3) An AngularJS web app as the user interface. It also details a case study on traffic sign classification using this system, achieving around 70% accuracy on 6 sign types with a SVM classifier and bag-of-features model. The conclusion discusses potential improvements like leveraging position/color and using cluster computing.
Similar to Periscope: A Content-based Image Retrieval Engine (20)
Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit,
Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit,
Object extraction from satellite imagery using deep learning
Object extraction from satellite imagery using deep learning
Build, Scale, and Deploy Deep Learning Pipelines with Ease Using Apache Spark
Build, Scale, and Deploy Deep Learning Pipelines with Ease Using Apache Spark
Recently uploaded
End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
The Ipsos - AI - Monitor 2024 Report.pdf
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
毕业原版【微信:176555708】【(UCSF毕业证书)旧金山分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
machine learning
apache nifi
apache pulsar
apache kafka
apache flink
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Intelligence supported media monitoring in veterinary medicine
Media monitoring in veterinary medicien
Learn SQL from basic queries to Advance queries
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
原版制作【微信:41543339】【利兹贝克特大学毕业证(LeedsBeckett毕业证书)】【微信:41543339】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Open Source Contributions to Postgres: The Basics POSETTE 2024
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
University of New South Wales degree offer diploma Transcript
澳洲UNSW毕业证书制作新南威尔士大学假文凭定制Q微168899991做UNSW留信网教留服认证海牙认证改UNSW成绩单GPA做UNSW假学位证假文凭高仿毕业证申请新南威尔士大学University of New South Wales degree offer diploma Transcript
Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
The Building Blocks of QuestDB, a Time Series Database
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
一比一原版(Unimelb毕业证书)墨尔本大学毕业证如何办理
原版制作【微信:41543339】【(Unimelb毕业证书)墨尔本大学毕业证】【微信:41543339】《成绩单、外壳、雅思、offer、留信学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同进口机器一比一制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Recently uploaded (20)
End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
Intelligence supported media monitoring in veterinary medicine
Intelligence supported media monitoring in veterinary medicine
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
Open Source Contributions to Postgres: The Basics POSETTE 2024
Open Source Contributions to Postgres: The Basics POSETTE 2024
A presentation that explain the Power BI Licensing
A presentation that explain the Power BI Licensing
University of New South Wales degree offer diploma Transcript
University of New South Wales degree offer diploma Transcript
The Building Blocks of QuestDB, a Time Series Database
The Building Blocks of QuestDB, a Time Series Database
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
Periscope: A Content-based Image Retrieval Engine
- 1. Periscope: A Content-Based Image Retrieval Engine By Antigoni M. Founta Student ID: 647
- 2. Goal: Search using an Image as a query
- 3. The Project
- 4. Components The essential components of the project! ● Python ● PostgreSQL ● OpenCV ● Scikit-Image ● Python Libraries: ScıPy, NumPy ● Flask ● Bootstrap
- 5. Algorithm 1. Create schema 2. Upload image and calculate histograms 3. Add image and calculated features to the database 4. Search for similarity in saved images 5. Return a subset with the most similar according to a distance measure
- 7. The Tasks Some required tasks for the project. 1. Extract Features 2. Add Image to Database 3. Search through Images and Compare Features 4. Return Similar Images
- 8. 1. Extract Features ● Color Vector: CalcHist() OpenCV method - HSV ● Texture Vector: LBP (Local Binary Patterns) Scıkıt-Image Lıbrary - GrayScale ● Shape Vector: Hu Moments OpenCV method - GrayScale Normalize all! 2. Add Image to DB ● Save image to local folder ● Get path of image ● Save path and features on Database →
- 9. 3. Search through Images ● Concatenate vectors ● Calculate ChiSquare (x2 ) distance ● Get results 4. Return Similar ● Sort Results ● Keep 8 best ● Present →
- 10. Add Many Images More Features Annotations Video Audio Ontology Crawling Future Work Text-Based Retrieval Image Segmentation
- 11. Resources ● PyImageSearch (http://www.pyimagesearch.com/) ● HanzraTech (http://hanzratech.in/2015/05/30/local-binary-patterns.html) ● Unsplash (https://unsplash.com/)