Feature Matching using SIFT algorithm; co-authored presentation on Photogrammetry studio by Sajid Pareeth, Gabriel Vincent Sanya, Sonam Tashi and Michael Mutale
The document describes using the Scale Invariant Feature Transform (SIFT) algorithm for sub-image matching. It discusses rejecting the chain code algorithm and instead using SIFT. It then explains the various steps of SIFT including creating scale-space and Difference of Gaussian pyramids, extrema detection, noise elimination, orientation assignment, descriptor computation, and keypoints matching.
The document describes the Histogram of Oriented Gradients (HOG) feature descriptor technique. HOG counts occurrences of gradient orientation in localized portions of an image to represent a distribution of intensity fluctuations along different orientations. It works by first calculating gradient images, then calculating histograms of gradients in 8x8 cells, followed by block normalization to account for lighting variations before forming the final HOG feature vector.
RANSAC is an algorithm for estimating model parameters from noisy data containing outliers. It works by:
1. Randomly selecting minimal samples needed to estimate a model
2. Calculating fit of model to all data to find inliers
3. Repeating for many iterations and selecting model with most inliers
The number of iterations needed depends on the expected outlier ratio and desired probability of finding the correct model. RANSAC is useful for problems like image alignment that involve fitting models to data containing outliers.
The document describes the Scale-invariant feature transform (SIFT) algorithm. It outlines the key steps: 1) constructing scale space by generating blurred images at different scales, 2) calculating difference of Gaussian images to find keypoints, 3) assigning orientations to keypoints, and 4) generating 128-element feature vectors for each keypoint to uniquely describe local image features in a way that is invariant to scale, rotation, and illumination changes. The SIFT algorithm allows for reliable object recognition and image stitching.
The document discusses two algorithms for object detection: HOG and SIFT.
HOG (Histogram of Oriented Gradients) focuses on the shape of an object by using the magnitude and direction of gradients to generate histograms and compute features. SIFT (Scale Invariant Feature Transform) describes local image areas by extracting invariant features to generate a set of key points for matching objects across different scales and rotations. Both algorithms can be used to detect objects by matching image features to trained models.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sept-2014-member-meeting-scottkrig
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Scott Krig, author of the book "Computer Vision Metrics: Survey, Taxonomy, and Analysis," delivers the presentation "Introduction to Feature Descriptors in Vision: From Haar to SIFT" at the September 2014 Embedded Vision Alliance Member Meeting.
1. The document discusses various image transforms including discrete cosine transform (DCT), discrete wavelet transform (DWT), and contourlet transform.
2. DCT transforms an image into frequency domain and organizes values based on human visual system importance. DWT analyzes images using wavelets of different scales and positions.
3. Contourlet transform is derived directly from discrete domain to capture smooth contours and edges at any orientation, decoupling multiscale and directional decompositions. It provides better efficiency than DWT for representing images.
The document discusses frequency filtering of images. It explains that the Fourier transform allows analyzing the frequency content of a signal. The discrete Fourier transform (DFT) is used to transform images into the frequency domain. Low pass filters suppress high frequencies to blur an image, while high pass filters remove low frequencies. The ideal low pass filter causes ringing artifacts due to the ripples in the sinc function impulse response. To avoid ringing, the Butterworth filter can be used as it has a flatter frequency response.
The document describes using the Scale Invariant Feature Transform (SIFT) algorithm for sub-image matching. It discusses rejecting the chain code algorithm and instead using SIFT. It then explains the various steps of SIFT including creating scale-space and Difference of Gaussian pyramids, extrema detection, noise elimination, orientation assignment, descriptor computation, and keypoints matching.
The document describes the Histogram of Oriented Gradients (HOG) feature descriptor technique. HOG counts occurrences of gradient orientation in localized portions of an image to represent a distribution of intensity fluctuations along different orientations. It works by first calculating gradient images, then calculating histograms of gradients in 8x8 cells, followed by block normalization to account for lighting variations before forming the final HOG feature vector.
RANSAC is an algorithm for estimating model parameters from noisy data containing outliers. It works by:
1. Randomly selecting minimal samples needed to estimate a model
2. Calculating fit of model to all data to find inliers
3. Repeating for many iterations and selecting model with most inliers
The number of iterations needed depends on the expected outlier ratio and desired probability of finding the correct model. RANSAC is useful for problems like image alignment that involve fitting models to data containing outliers.
The document describes the Scale-invariant feature transform (SIFT) algorithm. It outlines the key steps: 1) constructing scale space by generating blurred images at different scales, 2) calculating difference of Gaussian images to find keypoints, 3) assigning orientations to keypoints, and 4) generating 128-element feature vectors for each keypoint to uniquely describe local image features in a way that is invariant to scale, rotation, and illumination changes. The SIFT algorithm allows for reliable object recognition and image stitching.
The document discusses two algorithms for object detection: HOG and SIFT.
HOG (Histogram of Oriented Gradients) focuses on the shape of an object by using the magnitude and direction of gradients to generate histograms and compute features. SIFT (Scale Invariant Feature Transform) describes local image areas by extracting invariant features to generate a set of key points for matching objects across different scales and rotations. Both algorithms can be used to detect objects by matching image features to trained models.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sept-2014-member-meeting-scottkrig
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Scott Krig, author of the book "Computer Vision Metrics: Survey, Taxonomy, and Analysis," delivers the presentation "Introduction to Feature Descriptors in Vision: From Haar to SIFT" at the September 2014 Embedded Vision Alliance Member Meeting.
1. The document discusses various image transforms including discrete cosine transform (DCT), discrete wavelet transform (DWT), and contourlet transform.
2. DCT transforms an image into frequency domain and organizes values based on human visual system importance. DWT analyzes images using wavelets of different scales and positions.
3. Contourlet transform is derived directly from discrete domain to capture smooth contours and edges at any orientation, decoupling multiscale and directional decompositions. It provides better efficiency than DWT for representing images.
The document discusses frequency filtering of images. It explains that the Fourier transform allows analyzing the frequency content of a signal. The discrete Fourier transform (DFT) is used to transform images into the frequency domain. Low pass filters suppress high frequencies to blur an image, while high pass filters remove low frequencies. The ideal low pass filter causes ringing artifacts due to the ripples in the sinc function impulse response. To avoid ringing, the Butterworth filter can be used as it has a flatter frequency response.
This document discusses texture analysis in image processing. It defines texture as the spatial arrangement of color or intensities in an image that can help with image segmentation and classification. There are two main approaches to texture analysis: structural, which looks at regular patterns of texels, and statistical, which analyzes relationships between pixel intensities using methods like edge detection, co-occurrence matrices, and histograms. Statistical texture analysis captures the degrees of randomness and regularity in textures through metrics calculated from pixel intensity distributions and relationships.
Ray tracing is a technique for rendering images that simulates the physical behavior of light. It involves tracing the path of light as it interacts with virtual objects in a simulated scene. The key aspects of ray tracing include modeling reflection, refraction, color intensity and shadows produced by light sources. It produces highly realistic images but can be computationally expensive compared to other rendering methods.
Ray tracing is a technique for rendering 3D graphics by simulating the path of light in a scene. It works by casting rays from the viewpoint into the scene and recursively tracing the interactions of the rays with surfaces to determine what is visible. This allows for realistic lighting effects like reflections, refractions, and shadows. The core algorithm works by casting rays for each pixel to calculate the color based on ray intersections with objects, shadows, and simulating effects like reflection and refraction through recursive ray tracing.
This document discusses edge detection in images. It defines edges as areas of abrupt change in pixel intensity that often correspond to object boundaries. Several edge detection techniques are covered, including gradient-based methods using the Sobel and Prewitt operators to calculate the gradient magnitude and direction at each pixel and identify edges. The key steps of edge detection are described as smoothing, enhancement, thresholding and localization. Examples of edge detection code in C language using the Sobel operator are provided. Applications of edge detection include image enhancement, text detection and video surveillance.
The document discusses various techniques for image segmentation including discontinuity-based approaches, similarity-based approaches, thresholding methods, region-based segmentation using region growing and region splitting/merging. Key techniques covered include edge detection using gradient operators, the Hough transform for edge linking, optimal thresholding, and split-and-merge segmentation using quadtrees.
This document describes a method for pixel-level image fusion using principal component analysis (PCA). PCA is used to transform correlated image pixels into a set of uncorrelated principal components. The first principal component accounts for the most variance in the pixel values. To fuse images, the pixels of the input images are arranged into vectors and subtracted from their mean. PCA is applied to get the eigenvectors corresponding to the largest eigenvalues. The normalized eigenvectors are used to compute a fused image as a weighted sum of the input images. Performance is evaluated using metrics like standard deviation, entropy, cross-entropy, and fusion mutual information, with higher values of these metrics indicating better quality of the fused image.
Ray tracing is a technique for rendering realistic images by simulating the physical behavior of light, including reflection, refraction, color, and shadows. It works by tracing the path of light as rays emitted from the camera lens and calculating what objects they intersect. This allows for effects such as shadows, reflections off mirrors or transparent surfaces, and refraction through transparent objects. While it produces highly realistic images, ray tracing is also computationally intensive compared to other rendering techniques.
This document discusses various intensity transformation and spatial filtering techniques for digital image enhancement. It covers single pixel operations like negative image and contrast stretching. It also discusses neighborhood operations such as averaging and median filters. Finally, it discusses geometric spatial transformations like scaling, rotation and translation. The document provides details on basic intensity transformation functions including log, power law, and piecewise linear transformations. It also covers histogram processing techniques like histogram equalization, matching and local histogram processing. Spatial filtering and its mechanics are explained.
Anti-aliasing is a technique used to reduce jagged or stair-stepped edges in digital images by adding subtle color variations around edges. It works by averaging pixel color values across edges to make them appear smoother. There are several techniques for anti-aliasing including increasing image resolution, prefiltering by calculating pixel color based on object overlap within a pixel area, and postfiltering through supersampling at a higher resolution and then averaging down. Unweighted area sampling draws lines as rectangles and sets pixel intensity proportional to the amount of overlap with the rectangle rather than distance from the pixel center.
Image Enhancement: Introduction to Spatial Filters, Low Pass Filter and High Pass Filters. Here Discussed Image Smoothing and Image Sharping, Gaussian Filters
SIFT is a method to automatically detect distinctive keypoints in images that are invariant to scale, orientation, and illumination changes. It works by identifying locations and scales that remain consistent across different views of the same object using scale-space analysis and rejecting unstable points. It then assigns a consistent orientation and creates a keypoint descriptor for each point based on local gradient orientations. These keypoints can then be used to reliably match different views of an object or scene.
Presentation for the Berlin Computer Vision Group, December 2020 on deep learning methods for image segmentation: Instance segmentation, semantic segmentation, and panoptic segmentation.
The document discusses image alignment techniques in computer vision. It covers:
1) Computing transformations between images using matched points, by finding the transformation that minimizes error according to the least squares criterion. This can solve for translations, affine transformations, and homographies.
2) Solving the least squares problem results in a system of linear or linearized equations that can be solved efficiently.
3) Homographies are more complex than translations or affine transforms, as the equations are nonlinear. The problem can still be solved using least squares by taking the eigenvector of the smallest eigenvalue.
Edge detection is the name for a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities.
Deep learning based object detection basicsBrodmann17
The document discusses different approaches to object detection in images using deep learning. It begins with describing detection as classification, where an image is classified into categories for what objects are present. It then discusses approaches that involve separating detection into a classification head and localization head. The document also covers improvements like R-CNN which uses region proposals to first generate candidate object regions before running classification and bounding box regression on those regions using CNN features. This helps address issues with previous approaches like being too slow when running the CNN over the entire image at multiple locations and scales.
Video Stitching using Improved RANSAC and SIFTIRJET Journal
1. The document discusses techniques for stitching multiple video frames into a panoramic video using Scale-Invariant Feature Transform (SIFT) and an improved RANSAC algorithm.
2. Key points and feature descriptors are extracted from frames using SIFT to find correspondences between frames. The improved RANSAC algorithm is used to estimate homography matrices between frames and filter outlier matches.
3. Frames are blended together to compensate for exposure differences and misalignments before being mapped to a reference plane to create the panoramic video mosaic. The algorithm aims to produce a high quality panoramic video in real-time.
SHORT LISTING LIKELY IMAGES USING PROPOSED MODIFIED-SIFT TOGETHER WITH CONVEN...ijfcstjournal
The paper proposes the modified-SIFT algorithm which will be a modified form of the scale invariant feature transform. The modification consists of considering successive groups of 8 rows of pixel, along the height of the image. These are used to construct 8 bin histograms for magnitude as well as orientation individually. As a result the number of feature descriptors is significantly less (95%) than the standard SIFT approach. Fewer feature descriptor leads to reduced accuracy. This reduction in accuracy is quite drastic when searching for a single (RANK1) image match; however accuracy improves if a band of likely (say tolerance of 10%) images is to be returned. The paper therefore proposes a two-stage-approach where
First Modified-SIFT is used to obtain a shortlisted band of likely images subsequently SIFT is applied within this band to find a perfect match. It may appear that this process is tedious however it provides a significant reduction in search time as compared to applying SIFT on the entire database. The minor reduction in accuracy can be offset by the considerable time gained while searching a large database. The
modified-SIFT algorithm when used in conjunction with a face cropping algorithm can also be used to find a match against disguised images.
This document discusses texture analysis in image processing. It defines texture as the spatial arrangement of color or intensities in an image that can help with image segmentation and classification. There are two main approaches to texture analysis: structural, which looks at regular patterns of texels, and statistical, which analyzes relationships between pixel intensities using methods like edge detection, co-occurrence matrices, and histograms. Statistical texture analysis captures the degrees of randomness and regularity in textures through metrics calculated from pixel intensity distributions and relationships.
Ray tracing is a technique for rendering images that simulates the physical behavior of light. It involves tracing the path of light as it interacts with virtual objects in a simulated scene. The key aspects of ray tracing include modeling reflection, refraction, color intensity and shadows produced by light sources. It produces highly realistic images but can be computationally expensive compared to other rendering methods.
Ray tracing is a technique for rendering 3D graphics by simulating the path of light in a scene. It works by casting rays from the viewpoint into the scene and recursively tracing the interactions of the rays with surfaces to determine what is visible. This allows for realistic lighting effects like reflections, refractions, and shadows. The core algorithm works by casting rays for each pixel to calculate the color based on ray intersections with objects, shadows, and simulating effects like reflection and refraction through recursive ray tracing.
This document discusses edge detection in images. It defines edges as areas of abrupt change in pixel intensity that often correspond to object boundaries. Several edge detection techniques are covered, including gradient-based methods using the Sobel and Prewitt operators to calculate the gradient magnitude and direction at each pixel and identify edges. The key steps of edge detection are described as smoothing, enhancement, thresholding and localization. Examples of edge detection code in C language using the Sobel operator are provided. Applications of edge detection include image enhancement, text detection and video surveillance.
The document discusses various techniques for image segmentation including discontinuity-based approaches, similarity-based approaches, thresholding methods, region-based segmentation using region growing and region splitting/merging. Key techniques covered include edge detection using gradient operators, the Hough transform for edge linking, optimal thresholding, and split-and-merge segmentation using quadtrees.
This document describes a method for pixel-level image fusion using principal component analysis (PCA). PCA is used to transform correlated image pixels into a set of uncorrelated principal components. The first principal component accounts for the most variance in the pixel values. To fuse images, the pixels of the input images are arranged into vectors and subtracted from their mean. PCA is applied to get the eigenvectors corresponding to the largest eigenvalues. The normalized eigenvectors are used to compute a fused image as a weighted sum of the input images. Performance is evaluated using metrics like standard deviation, entropy, cross-entropy, and fusion mutual information, with higher values of these metrics indicating better quality of the fused image.
Ray tracing is a technique for rendering realistic images by simulating the physical behavior of light, including reflection, refraction, color, and shadows. It works by tracing the path of light as rays emitted from the camera lens and calculating what objects they intersect. This allows for effects such as shadows, reflections off mirrors or transparent surfaces, and refraction through transparent objects. While it produces highly realistic images, ray tracing is also computationally intensive compared to other rendering techniques.
This document discusses various intensity transformation and spatial filtering techniques for digital image enhancement. It covers single pixel operations like negative image and contrast stretching. It also discusses neighborhood operations such as averaging and median filters. Finally, it discusses geometric spatial transformations like scaling, rotation and translation. The document provides details on basic intensity transformation functions including log, power law, and piecewise linear transformations. It also covers histogram processing techniques like histogram equalization, matching and local histogram processing. Spatial filtering and its mechanics are explained.
Anti-aliasing is a technique used to reduce jagged or stair-stepped edges in digital images by adding subtle color variations around edges. It works by averaging pixel color values across edges to make them appear smoother. There are several techniques for anti-aliasing including increasing image resolution, prefiltering by calculating pixel color based on object overlap within a pixel area, and postfiltering through supersampling at a higher resolution and then averaging down. Unweighted area sampling draws lines as rectangles and sets pixel intensity proportional to the amount of overlap with the rectangle rather than distance from the pixel center.
Image Enhancement: Introduction to Spatial Filters, Low Pass Filter and High Pass Filters. Here Discussed Image Smoothing and Image Sharping, Gaussian Filters
SIFT is a method to automatically detect distinctive keypoints in images that are invariant to scale, orientation, and illumination changes. It works by identifying locations and scales that remain consistent across different views of the same object using scale-space analysis and rejecting unstable points. It then assigns a consistent orientation and creates a keypoint descriptor for each point based on local gradient orientations. These keypoints can then be used to reliably match different views of an object or scene.
Presentation for the Berlin Computer Vision Group, December 2020 on deep learning methods for image segmentation: Instance segmentation, semantic segmentation, and panoptic segmentation.
The document discusses image alignment techniques in computer vision. It covers:
1) Computing transformations between images using matched points, by finding the transformation that minimizes error according to the least squares criterion. This can solve for translations, affine transformations, and homographies.
2) Solving the least squares problem results in a system of linear or linearized equations that can be solved efficiently.
3) Homographies are more complex than translations or affine transforms, as the equations are nonlinear. The problem can still be solved using least squares by taking the eigenvector of the smallest eigenvalue.
Edge detection is the name for a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities.
Deep learning based object detection basicsBrodmann17
The document discusses different approaches to object detection in images using deep learning. It begins with describing detection as classification, where an image is classified into categories for what objects are present. It then discusses approaches that involve separating detection into a classification head and localization head. The document also covers improvements like R-CNN which uses region proposals to first generate candidate object regions before running classification and bounding box regression on those regions using CNN features. This helps address issues with previous approaches like being too slow when running the CNN over the entire image at multiple locations and scales.
Video Stitching using Improved RANSAC and SIFTIRJET Journal
1. The document discusses techniques for stitching multiple video frames into a panoramic video using Scale-Invariant Feature Transform (SIFT) and an improved RANSAC algorithm.
2. Key points and feature descriptors are extracted from frames using SIFT to find correspondences between frames. The improved RANSAC algorithm is used to estimate homography matrices between frames and filter outlier matches.
3. Frames are blended together to compensate for exposure differences and misalignments before being mapped to a reference plane to create the panoramic video mosaic. The algorithm aims to produce a high quality panoramic video in real-time.
SHORT LISTING LIKELY IMAGES USING PROPOSED MODIFIED-SIFT TOGETHER WITH CONVEN...ijfcstjournal
The paper proposes the modified-SIFT algorithm which will be a modified form of the scale invariant feature transform. The modification consists of considering successive groups of 8 rows of pixel, along the height of the image. These are used to construct 8 bin histograms for magnitude as well as orientation individually. As a result the number of feature descriptors is significantly less (95%) than the standard SIFT approach. Fewer feature descriptor leads to reduced accuracy. This reduction in accuracy is quite drastic when searching for a single (RANK1) image match; however accuracy improves if a band of likely (say tolerance of 10%) images is to be returned. The paper therefore proposes a two-stage-approach where
First Modified-SIFT is used to obtain a shortlisted band of likely images subsequently SIFT is applied within this band to find a perfect match. It may appear that this process is tedious however it provides a significant reduction in search time as compared to applying SIFT on the entire database. The minor reduction in accuracy can be offset by the considerable time gained while searching a large database. The
modified-SIFT algorithm when used in conjunction with a face cropping algorithm can also be used to find a match against disguised images.
PCA-SIFT: A More Distinctive Representation for Local Image Descriptorswolf
PCA-SIFT is a modification of SIFT that uses principal component analysis (PCA) to build more distinctive local image descriptors. It constructs a projection matrix from a large set of image patches, then projects each keypoint descriptor through this matrix to a compact vector of the top n principal components. This provides a more discriminative representation than SIFT while reducing descriptor dimensionality, leading to improved matching accuracy and efficiency. Evaluation on controlled transformation and graffiti datasets shows PCA-SIFT achieves higher recall rates at equivalent or lower false positive rates compared to SIFT.
This document discusses using SVD (singular value decomposition) as a filtering technique prior to clustering temporal usage data. It describes applying SVD to filter out noise and high dimensionality before performing k-means clustering. SVD is used to decompose the data matrix and filter out components associated with the smallest singular values. Then k-means clustering is applied to the correlation between observations and the remaining right eigenvectors. This approach provides a robust way to cluster high-dimensional temporal data and identify distinct customer usage patterns over time.
Realtime pothole detection system using improved CNN Modelsnithinsai2992
The document summarizes work on a real-time pothole detection system using improved CNN models. It discusses using the YOLOv5 model for pothole detection and training YOLOv5m6, YOLOv5s6, and YOLOv5n6 models on a dataset, achieving mAP scores of 80.8%, 82.2%, and 82.5% respectively. It also proposes further improving the system through techniques like better image processing during nighttime and enhancing detection of distant objects.
Key.Net is a keypoint detection network that combines handcrafted and learned CNN filters in a multi-scale pyramid architecture. It extracts features at different scale levels using a combination of handcrafted and learned filters. A novel multi-scale loss and operator are used for detecting and ranking stable keypoints across scales. Experimental results on ImageNet show that Key.Net outperforms state-of-the-art detectors in terms of repeatability, matching performance, and complexity.
The document discusses several projects and implementations done by Karishma Jain related to computer vision and deep learning. These include visual question answering using CNNs and RNNs, parallelizing an ADABOOST classifier on different platforms, designing a lane departure warning system using monocular camera, and implementing various CNN architectures for MNIST classification achieving up to 97.74% accuracy.
This document summarizes a technique for generating highly accurate 3D surface models from sparse sensor data using sparse surface adjustment. It proposes modeling the surface as small planar patches called surfels, optimizing the poses of the sensor and surfels jointly to minimize errors, and iteratively refining correspondences between surfels. Experiments on environmental and object datasets demonstrate improved consistency over standard SLAM techniques.
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Sift detector boosted by adaptive contrast threshold to improve matching robu...Venkat Projects
The document proposes an adaptive contrast threshold-SIFT (ACT-SIFT) method to improve the matching quality of the scale invariant feature transform (SIFT) detector for remote sensing panchromatic image matching. The ACT-SIFT method calculates separate contrast thresholds for target and reference images by minimizing the relative difference between the entropy of keypoints for each image. Keypoints are initially extracted using SIFT with an initial threshold, then thresholds are iteratively modified to minimize the entropy difference, and keypoints are re-extracted according to the optimized thresholds. The results from applying this adaptive thresholding approach promise improved matching pairs over standard SIFT.
An automatic algorithm for object recognition and detection based on asift ke...Kunal Kishor Nirala
This document presents an automatic algorithm for object recognition and detection based on ASIFT keypoints. The algorithm combines affine scale invariant feature transform (ASIFT) and a region merging algorithm. ASIFT is used to extract keypoints from a training image of the object. These keypoints are then used instead of user markers in a region merging algorithm to recognize and detect the object with full boundary in other images. Experimental results show the method is efficient and accurate at recognizing and detecting objects.
Lecture 01 frank dellaert - 3 d reconstruction and mapping: a factor graph ...mustafa sarac
Frank Dellaert presented an overview of visual SLAM, bundle adjustment, and factor graphs. Visual SLAM uses visual odometry to estimate camera poses incrementally from frame to frame. Bundle adjustment refines the camera pose estimates using non-linear optimization over all camera poses and 3D landmarks jointly. Factor graphs provide a graphical representation of the optimization problem in bundle adjustment.
This document summarizes a team's analysis of a flight delay prediction problem. The team analyzed a dataset with 29 features and 484,551 rows to understand missing values, duplicates, outliers, and categorical variables. They performed data visualization, feature engineering including encoding, scaling, and selection. Models tested include linear regression, Ridge regression, SVC, random forest, and neural network. Ridge regression and random forest performed best with 98-99% accuracy. Issues with linear regression like overfitting were addressed using regularization. SVC was unsuitable due to time and accuracy. Future work may include more data and relevant features.
Intelligent Auto Horn System Using Artificial IntelligenceIRJET Journal
The document proposes an intelligent auto horn system using artificial intelligence to reduce unnecessary noise pollution from vehicle horns. The system uses sensors and cameras to collect environmental data and an on-board computer uses AI techniques like SIFT, SURF and other computer vision algorithms to process the data. Based on factors like the distance between objects, road width, and object size, the AI will control the horn sound horizontally and vertically. The system aims to only sound the horn as loud as needed based on the situation to reduce noise pollution while maintaining safety. The system is described as not compromising safety and automatically adjusting the horn sound using fixed horn mechanisms based on AI analysis of the environment.
Using FME for Topographical Data Generalization at Natural Resources CanadaSafe Software
To meet increasing and diversified user needs for geographic information, Natural Resources Canada (NRCan) must produce and maintain geographic data at multiple scales. To automate the generalization process NRCan is using an approach based on FME and MetaAlgorithms.
This document describes implementing real-time non-linear regression models in a DeltaV controller to determine chromatography elution endpoints. Three models were used - smoothing, linear regression, and a non-linear extreme value function. The non-linear model fits peak data to an extreme value function using Newton-Raphson iterative numerical solving to determine peak height, width, and retention time. This allows calculating the endpoint as a percentage of peak maximum. The model was validated against laboratory data to within acceptable error ranges and allows robust, real-time endpoint determination for chromatography processes.
This document summarizes an experiment using SURF and SIFT algorithms to perform panoramic image stitching. It describes detecting keypoints in images using SURF and SIFT, extracting descriptors, matching features between images, and filtering matches using RANSAC to reject outliers and estimate a fundamental matrix. Homography is also estimated from correspondences to relate point positions between images related by pure rotation. Code examples are provided to detect features and match them using OpenCV.
Project Based on MATLAB For Final Year Research GuidanceMatlab Simulation
This document discusses potential topics for MATLAB-based final year projects, including 5 top research areas: rule-based classification and Taguchi optimization techniques for satellite images; stationary scene imaging and ground target indication for SAR systems; reconstruction algorithms for under sampled AFM imaging; cross-sectional optoacoustic tomography for real-time inversion; and high-resolution image segmentation using region-line constraints. It also lists key MATLAB algorithms for projects: compression, binarization, image analysis, edge detection, and classification algorithms. Popular segmentation algorithms mentioned are expectation maximization clustering, K-means, morphological operations, and fuzzy possibilistic C-means clustering.
Improved Characters Feature Extraction and Matching Algorithm Based on SIFTNooria Sukmaningtyas
The document describes an improved SIFT feature extraction and matching algorithm based on the MSER algorithm. It first uses MSER instead of DOG to detect maximally stable elliptical regions, increasing stability and reducing the number of features. It then divides each elliptical region into fan-shaped subregions instead of square subregions, and constructs a new SIFT descriptor using Gaussian-weighted gradient information. Experimental results showed the new algorithm has affine invariance while maintaining other properties of SIFT, making it faster and better suited for real-time image processing.
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PhD defence presentation, 12 July 2016 @ FU-BerlinSajid Pareeth
This document presents the key milestones and findings of a dissertation that developed a 30-year climate data record of daily lake surface water temperatures for five large lakes in Northern Italy using satellite remote sensing. The author resolved geometric issues with earlier satellite data, developed a homogenization method to produce consistent temperature time series from 1986-2015, and validated the results finding high accuracy compared to in-situ measurements. Statistical analysis revealed significant warming trends in the annual and summer mean lake surface temperatures over the 30-year period.
Monitoring and retrieving historical daily surface temperature of sub-alpine ...Sajid Pareeth
The document describes a study that aims to monitor and retrieve historical daily surface temperature data of sub-alpine lakes from satellite imagery over the past two decades. The study will leverage daily thermal imagery from multiple satellite sensors to develop daily homogenized lake surface water temperature time series for each lake. Statistical methods will be used to reconstruct temperature time series with gaps filled in. The reconstructed time series will then be analyzed to study long term warming trends and their links to climatic factors. Preliminary results show good agreement between reconstructed lake surface temperatures and in situ measurements.
Inter-sensor comparison of lake surface temperatures derived from MODIS, AVHR...Sajid Pareeth
This document discusses a study comparing lake surface water temperatures derived from thermal bands of MODIS, AVHRR, and AATSR sensors. The study aims to develop a daily homogenized lake surface water temperature dataset over the last two decades by leveraging thermal imagery from multiple satellite sensors. The methodology involves processing and calibrating thermal data from the different sensors, developing lake-specific algorithms to derive surface temperatures, and using statistical methods to reconstruct a continuous temperature time series accounting for gaps in the data. Validation is done using in-situ lake temperature measurements. The resulting long-term temperature dataset will be analyzed to study warming trends and links to climatic indices.
An open source framework for processing daily satellite images (AVHRR) over l...Sajid Pareeth
An open source framework was developed to process daily satellite images from Advanced Very High Resolution Radiometer (AVHRR) sensors over the last 28 years. The framework uses open source libraries like Pytroll, Orfeo Toolbox, and GRASS GIS to read, calibrate, correct geometrically, and analyze over 22,000 daily AVHRR images. The processed data will be used to study long term warming trends of sub-alpine lakes from derived land surface temperature.
This document summarizes the development of Spatial Data Infrastructure (SDI) in Europe and Germany. It discusses the establishment of early national SDI initiatives in countries like Australia and the US in the 1980s and 1990s. It also describes the establishment of the GSDI to foster global SDI development and data sharing. Regarding Germany specifically, it outlines how the country took a decentralized approach to SDI development led by its 16 states and key national organizations like the BKG and GDZ. It discusses Germany's development of common geospatial datasets and standards. Overall the document presents the evolution of SDI initiatives from national to global scales over the past few decades.
This document describes a methodology to develop a moderate resolution irrigated area map for South Asia using advanced remote sensing techniques. The methodology involves a two-level process of 1) segmenting high resolution imagery into homogeneous objects and classifying them using machine learning, and 2) analyzing MODIS time series data to identify irrigation intensity. The resulting map identifies irrigated areas in South Asian countries at a resolution of 250m, calculating total irrigated areas of 206.74 million hectares across the region. Automating parts of the process using open source tools could help speed up localized irrigated area mapping.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
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Dr. Sean Tan, Head of Data Science, Changi Airport Group
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Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
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Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
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Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
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Full-RAG: A modern architecture for hyper-personalization
Feature Matching using SIFT algorithm
1. DETECTING LEVELLING RODS
USING SIFT FEATURE MATCHING
GROUP 1
MSc Course 2006-08
25TH
June 2007
Sajid Pareeth
Sonam Tashi
Gabriel Vincent Sanya
Michael Mutale
PHOTOGRAMMETRY STUDIO
4. Background - SIFT
Published by David Lowe et al. 1999-2004
Algorithm to extract features that are invariant to
rotation,
scaling and
partially invariant to
changes in illumination and camera viewpoint
Resulting features are highly distinctive
Consists of (Major stages of computation):
Scale-space extrema detection-the Gaussian Widow
Keypoint localization- 4x4 samples per window in 8
directions
Orientation assignment
Keypoint descriptor-feature vector is modified to reduce the effects of illumination
change. The vector is normalized to unit length. large gradient magnitudes by thresholding the
values in the unit feature vector to each be no larger than 0.2, and then renormalizing to unit
length.
SIFT Algorithm
SIFT-Keypoints
Extraction
Keypoints
Matching
Work Flow
RANSAC
Advantages/
Limitations
Demo
Transformation
Examples
Introduction
5. A keypoint descriptor is created by first computing the
gradient magnitude and orientation at each image sample
point in a region around the keypoint location,
These are weighted by a Gaussian window, indicated by
the overlaid circle. These samples are then accumulated
into orientation histograms summarizing the contents over
4x4 subregions,
The length of each arrow corresponding to the sum of the
gradient magnitudes near that direction within
the region.
6. Scale Invariant Detection
Consider regions (e.g. circles) of different sizes around
a point
Regions of corresponding sizes will look the same in
both images SIFT-Keypoints
Extraction
Keypoints
Matching
Work Flow
RANSAC
Advantages/
Limitations
Demo
Transformation
Examples
SIFT Algorithm
Introduction
7. The problem: how do we choose
corresponding circles independently in each
image?
Scale Invariant Detection
SIFT Algorithm
SIFT-Keypoints
Extraction
Keypoints
Matching
Work Flow
RANSAC
Advantages/
Limitations
Demo
Transformation
Examples
SIFT Algorithm
Introduction
8. Solution:
Design a function on the region (circle), which is
“scale invariant” (the same for corresponding
regions, even if they are at different scales)
Example: average intensity. For corresponding regions
(even of different sizes) it will be the same.
For a point in one image, we can consider it as a
function of region size (circle radius)
scale = 1/2
f
region size
Image 1 f
region size
Image 2
Scale Invariant Detection
SIFT-Keypoints
Extraction
Keypoints
Matching
Work Flow
RANSAC
Advantages/
Limitations
Demo
Transformation
Examples
SIFT Algorithm
Introduction
9. Common approach
scale = 1/2
f
region size
Image 1 f
region size
Image 2
Take a local maximum of this function
Observation: region size, for which the maximum is achieved,
should be invariant to image scale.
s1 s2
Important: this scale invariant region size is found
in each image independently!
Scale Invariant Detection
SIFT-Keypoints
Extraction
Keypoints
Matching
Work Flow
RANSAC
Advantages/
Limitations
Demo
Transformation
Examples
SIFT Algorithm
Introduction
10. scale
x
y
← DoG →
←DoG→
SIFT(Lowe)
Maxima and minima of the difference-of-Gaussian
images detected by comparing a pixel (marked with X)
to its 26 neighbors in 3x3 regions at the current and
adjacent scales (marked with circles).
SIFT-Keypoints
Extraction
Keypoints
Matching
Work Flow
RANSAC
Advantages/
Limitations
Demo
Transformation
Examples
SIFT Algorithm
Introduction
12. Keypoint Descriptors
Feature Vectors
Thresholded image gradients sampled over:
16x16 array of locations in scale space
Histogram of 4x4 samples per window in 8 directions
Gaussian weighting around center
8 orientations x 4 x 4 histogram array = 128 dimensional
feature vector
4x4 Gradient window
SIFT Algorithm
Keypoints
Matching
Work Flow
RANSAC
Advantages/
Limitations
Demo
Transformation
Examples
SIFT-Keypoints
Extraction
Introduction
Gaussian Gradient Window
13. Criteria for Selection
Prominent
Distinguishable-from neighborhood
Invariant
Stable to disturbances
Rare (exceptional) - from other selected points
Meaningful - with respect to image interpretation
SIFT Algorithm
Keypoints
Matching
Work Flow
RANSAC
Advantages/
Limitations
Demo
Transformation
Examples
SIFT-Keypoints
Extraction
Introduction
14. SIFT keypoints
Detected Keypoints in reference and candidate image
Keypoints in both images will be matched
Candidate image with keypointsReference image
with keypoints
SIFT Algorithm
Keypoints
Matching
Work Flow
RANSAC
Advantages/
Limitations
Demo
Transformation
Examples
SIFT-Keypoints
Extraction
Introduction
15. Keypoint Matching
Fundamental aspect in computer vision.
Based on Euclidean distance of the feature vectors.
Nearest neighbor algorithms.
Product between descriptors is calculated.
Inverse cosine of the products gives the Euclidean distance
Matches with Ratio of vector angles from the nearest to second
nearest neighbor less than distRatio value are selected.
SIFT Algorithm
Keypoints
Matching
Work Flow
RANSAC
Advantages/
Limitations
Demo
Transformation
Examples
SIFT-Keypoints
Extraction
Introduction
16. 90% of the false matches are
removed.
False matches due to ambiguos
features or features arise from
background clutter.
Reliable object recognition with
few best Matches
Remove outliers
Matched points
SIFT Algorithm
SIFT-Keypoints
Extraction
Work Flow
RANSAC
Advantages/
Limitations
Demo
Transformation
Examples
Keypoints
Matching
Introduction
17. RANSAC: Algorithm
RANdom SAmple Consensus
Estimate parameters of a mathematical model from a set of
observed data which contains outliers.
A model is fitted using hypothetical inliers.
If other data fits to the model, added to the inliers.
Reestimated the model with the new set of inliers.
We used ransac-fit-homography which Robustly fits a
homography to a set of matched points.
SIFT Algorithm
SIFT-Keypoints
Extraction
Work Flow
Advantages/
Limitations
Demo
Transformation
Examples
Keypoints
Matching
RANSAC
Introduction
19. Make the shortest image the
same height as the other image.
Append Images
Transformation
tform = cp2tform(M1,M2,’affine')
SIFT Algorithm
SIFT-Keypoints
Extraction
Keypoints
Matching
Work Flow
Advantages/
Limitations
Demo
Transformation
Examples
RANSAC
Introduction
TStructure
20. 2
3
Rod vs. OpenShrubs
1
Matches: 9
2
Rod: 626 keypoints found
Image: 28958 keypoints found
Matches: 16
…Good Matches
SIFT Algorithm
SIFT-Keypoints
Extraction
Keypoints
Matching
Work Flow
RANSAC
Advantages/
Limitations
Demo
Examples
Transformation
Introduction
21. 2
Rod Vs Human Occlusion??
1
Rod: 626 keypoints found
Image: 2347 keypoints found
Matches: 155
Rod: 626 keypoints found
Image: 2347 keypoints found
Matches: 144
2
3
…Good Matches
SIFT Algorithm
SIFT-Keypoints
Extraction
Keypoints
Matching
Work Flow
RANSAC
Advantages/
Limitations
Demo
Transformation
Examples
Introduction
22. Limitations
1
Rod: 626 keypoints found
Image: 17927 keypoints found
Matches: 16
Matches: 4
2
Rod: 626 keypoints found
Image: 30505 keypoints found
3
SIFT Algorithm
SIFT-Keypoints
Extraction
Keypoints
Matching
Work Flow
RANSAC
Advantages/
Limitations
Demo
Transformation
Examples
Introduction
23. Advantages
Locality: features are local, so robust to occlusion and clutter
(no prior segmentation)
Distinctiveness: individual features can be matched to a large
database of objects
Quantity: many features can be generated for even small
objects
Efficiency: close to real-time performance
Extensibility: can easily be extended to wide range of differing
feature types, with each adding robustness
SIFT Algorithm
SIFT-Keypoints
Extraction
Keypoints
Matching
Work Flow
RANSAC
Advantages/
Limitations
Demo
Transformation
Examples
Introduction
24. Problems/Enhancement
Only invariant to affine transformations to a certain degree
Best performance on highly textured images
Use Principal components analysis PCA instead of Gaussian
weighting for gradients
Demo
SIFT Algorithm
SIFT-Keypoints
Extraction
Keypoints
Matching
Work Flow
RANSAC
Advantages/
Limitations
Transformation
Examples
Introduction