Scale Invariant feature transform
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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.
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Thesis report and full details: https://imatge.upc.edu/web/publications/contextless-object-recognition-shape-enriched-sift-and-bags-features
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Advisors: Xavier Giró-i-Nieto (UPC) and Matthias Zeppelzauer (TU Wien)
Degree: Telecommunications Engineering (5 years) at Telecom BCN-ETSETB (UPC)
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Face recogntion Using PCA Algorithm
This document discusses face recognition and the PCA algorithm for face recognition. It begins with an introduction to face recognition and its uses. It then explains the PCA algorithm for face recognition in 6 steps: 1) converting images to vectors, 2) normalizing the vectors, 3) calculating eigenvectors from the normalized vectors, 4) selecting important eigenvectors, 5) representing faces as combinations of eigenvectors, and 6) recognizing faces. It discusses the strengths and weaknesses of face recognition and lists several applications such as access control, law enforcement, and banking.
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This used dual FIS Optimization technique to find the high frequency or the edges in the images and neglect the lower frequencies.
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This document discusses feature invariance in computer vision. It covers several key points:
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This legal document provides several notices and disclaimers regarding the information presented. Specifically:
- The presentation is for informational purposes only and Intel makes no warranties regarding the information or summaries of the information.
- Any performance claims depend on system configuration and hardware/software/service activation. Performance varies depending on system configuration.
- The sample source code is released under the Intel Sample Source Code License Agreement.
- Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and other countries. Other names may belong to other owners.
- Copyright of the content is held by Intel Corporation and all rights are reserved.
Computer Vision descriptors
This document discusses feature descriptors and matching in computer vision. It covers three main components: 1) detecting interest points in images, 2) extracting feature descriptors around each interest point, and 3) determining correspondences between descriptors to match features across images. The document focuses on SIFT (Scale Invariant Feature Transform) descriptors, which are histograms of gradient orientations computed over localized patches that provide robust matching across changes in scale, rotation and illumination. SIFT descriptors have been widely and successfully used for applications like image stitching, 3D reconstruction, object recognition and augmented reality.
MLIP - Chapter 6 - Generation, Super-Resolution, Style transfer
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The document discusses image segmentation techniques including thresholding. Thresholding divides an image into foreground and background regions based on pixel intensity values. Global thresholding uses a single threshold value for the entire image, while adaptive or local thresholding uses variable thresholds that change across the image. Multilevel thresholding can extract objects within a specific intensity range using multiple threshold values. The Hough transform is also presented as a way to connect disjointed edge points and detect shapes like lines in an image.
Elementary Landscape Decomposition of Combinatorial Optimization Problems
This document discusses elementary landscape decomposition for analyzing combinatorial optimization problems. It begins with definitions of landscapes, elementary landscapes, and landscape decomposition. Elementary landscapes have specific properties, like local maxima and minima. Any landscape can be decomposed into a set of elementary components. This decomposition provides insights into problem structure and can be used to design selection strategies and predict search performance. The document concludes that landscape decomposition is useful for understanding problems but methodology is still needed to decompose general landscapes.
image segmentation by ppres.pptx
This document provides an overview of image segmentation techniques. It begins with an introduction to image analysis and segmentation. It then covers various discontinuity-based techniques like point, line, and edge detection. Next it discusses edge linking and boundary detection methods. Thresholding techniques such as global, optimal, and adaptive thresholding are also covered. Finally, the document discusses region-based segmentation methods including region growing and region splitting/merging. The overall goal of the document is to introduce and explain the main categories and techniques used for image segmentation.
image segmentation image segmentation.pptx
The document discusses image segmentation techniques. It describes image segmentation as dividing an image into regions that are similar within and different between adjacent regions. There are two main approaches: discontinuity, which looks for sudden changes or edges, and similarity, which groups similar pixels. Common techniques include thresholding, region growing, boundary detection using Hough transforms, and adaptive segmentation methods that account for non-uniform lighting.
Computer vision series
Computer Vision Fundamentals
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Cognition
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Accelerometers to Augmented Reality
1. Mobile devices can be used to interact with the world through sensors like accelerometers, gyroscopes, GPS, and cameras.
2. Core Motion provides a high-level interface to motion sensor data from the accelerometer and gyroscope. OpenCV can be used for computer vision tasks like face detection using Haar classifiers.
3. Augmented reality is possible by tracking markers or locations and overlaying virtual objects on live video. Commercial augmented reality SDKs are available but some open source options also exist.
Image segmentation
This document discusses various techniques for image segmentation. It describes two main approaches to segmentation: discontinuity-based methods that detect edges or boundaries, and region-based methods that partition an image into uniform regions. Specific techniques discussed include thresholding, gradient operators, edge detection, the Hough transform, region growing, region splitting and merging, and morphological watershed transforms. Motion can also be used for segmentation by analyzing differences between frames in a video.
Kintinuous review
Real-time large scale dense RGB-D SLAM with volumetric fusion extends KinectFusion to larger scales. It represents the volumetric reconstruction as a rolling buffer that translates as the camera moves. It estimates camera pose through combined geometric and photometric constraints. It closes loops by non-rigidly deforming the map with constraints from loop closures and jointly optimizes the camera poses and map. Evaluation shows it produces large, globally consistent, real-time dense reconstructions.
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Cahall Final Intern Presentation
The document proposes improving object detection and recognition capabilities. It discusses challenges with current methods like different object sizes and color variations. The objectives are to build a module that can learn and detect objects without a sliding box or datastore. A high-level design approach is outlined using techniques like contouring, BING, sliding box, and feature selection methods. The design considers optimal feature selection, dimensionality reduction, and classification algorithms to function in real-time.
Chapter10_Segmentation.ppt
This document discusses various techniques for image segmentation including discontinuity-based and similarity-based approaches. Discontinuity-based approaches partition images based on sharp changes in intensity, such as edges, and detect points, lines and edges using gradient and Laplacian operators. Similarity-based approaches partition images into regions with similar predefined characteristics. Specific techniques covered include gradient masks, Canny edge detection using zero-crossings of the Laplacian of Gaussian, thresholding methods including global, adaptive and multilevel thresholding, and region-based segmentation using region growing.
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Scale Invariant feature transform
- 1. 1
- 2. Team Members : Chinmay Samant Rajdeep Mandrekar Shanker Naik Scale Invariant Feature Transform Laxman Pednekar Guide : Prof. Rachael Dhanraj
- 3. Sub-Image Matching • Sub-Image Matching – the main part of our project. • Rejection of the Chain code Algorithm. • Using Scale invariant Feature Transform (or SIFT) Algorithm. 3
- 4. Sub-Image Matching Scale-invariant feature transform Algorithm • Creating Scale-space and Difference of Gaussian pyramid • Extrema detection • Noise Elimination • Orientation assignment • Descriptor Computation • Keypoints matching 4
- 5. Creating Scale-space and Difference of Gaussian pyramid • In scale Space we take the image and generate progressively blurred out images, then resize the original image to half and generate blurred images. • Images that are of same size but different scale are called octaves. 5
- 6. How Blurring is performed? • Mathematically blurring is defined as convolution of Gaussian operator and image. • where G= Gaussian Blur operator 6
- 8. Extrema detection In the image X is current pixel, while green circles are its neighbors, X is marked as Keypoint if it is greatest or least of all 26 neighboring pixels. First and last scale are not checked for keypoints as there are not enough neighbors to compare. 8
- 9. Noise Elimination 1. Removing Low Contrast features - If magnitude of intensity at current pixel is less than certain value then it is rejected. 2. Removing edges – For poorly defined peaks in the DoG function, the principal curvature across the edge would be much larger than the principal curvature along it – To determine edges Hessian matrix is used. 9
- 10. Tr (H) = Dxx + Dyy Det(H) = DxxDyy - (Dxy )2 R=Tr(H)^2/Det(H) If the value of R is greater for a candidate keypoint, then that keypoint is poorly localized and hence rejected. 10
- 11. Orientation assignment • The gradient magnitude, m(x, y), and orientation, θ(x, y), is precomputed using pixel differences: 11
- 12. Orientation assignment 12
- 13. Descriptor Computation 13
- 14. Keypoints matching • Each keypoint in the original image is compared to every keypoints in the transformed image using the descriptors. • The descriptors of the two respective, keypoints must be closest. Then match is found. 14
- 15. Thank You 15