Coding Images with Local Features
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Machine Generated Curvilinear Structures Extracted from Magnetic Data in the Fort Mcmurray Oil Sand Area Using 2D Structure Tensor Hassan H
Machine generated curvilinear structures extracted from magnetic data in the Fort McMurray oil sand area using 2D structure tensor Hassan H. Hassan Multiphysics Imaging Technology Summary Curvilinear structures are among the most important geological features that are detected by geophysical data, because they are often linked to subsurface geological structures such as faults, fractures, lithological contacts, and to some extent paleo-channels. Thus, accurate detection and extraction of curvilinear structures from geophysical data is crucial for oil and gas, as well as mineral exploration. There is a wide-range of curvilinear structures present in geophysical images, either as straight lines or curved lines, and across which image magnitude changes rapidly. In magnetic images, curvilinear structures are usually associated with geological features that exhibit significant magnetic susceptibility contrasts. Traditionally, curvilinear structures are manually detected and extracted from magnetic data by skilled professionals, using derivative or gradient based filters. However, the traditional approach is tedious, slow, labor-intensive, subjective, and most important they do not perform well with big geophysical data. Recent advances in digital imaging and data analytic techniques in the fields of artificial intelligence, machine and deep learnings, helped to develop new methods to automatically enhance, detect, and extract curvilinear structures from images of small and big data. Among these newly developed techniques, we adopted one that is based on 2D Hessian structure tensor. Structure tensor is a relatively modern imaging technique, and it reveals length and orientation of curvilinear structures along the two principal directions of the image. It is based on the image eigenvalues (λ1, λ2) and their corresponding eigenvectors (e1, e2), respectively. -
Edge Orientation Using Contour Stencils Pascal Getreuer
Edge Orientation Using Contour Stencils Pascal Getreuer To cite this version: Pascal Getreuer. Edge Orientation Using Contour Stencils. SAMPTA’09, May 2009, Marseille, France. Special session on sampling and (in)painting. hal-00452291 HAL Id: hal-00452291 https://hal.archives-ouvertes.fr/hal-00452291 Submitted on 1 Feb 2010 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Edge Orientation Using Contour Stencils Pascal Getreuer (1) (1) Department of Mathematics, University of California Los Angeles [email protected] Abstract: structure tensor J( u) = u u. The struc- ture tensor satisfies ∇J( u)∇ = J⊗( ∇u) and u is an eigenvector of J( u).−∇ The structure∇ tensor takes∇ into Many image processing applications require estimat- ∇ ing the orientation of the image edges. This estimation account the orientation but not the sign of the direc- is often done with a finite difference approximation tion, thus solving the antipodal cancellation problem. of the orthogonal gradient. As an alternative, we ap- As developed by Weickert [9], let ply contour stencils, a method for detecting contours from total variation along curves, and show it more Jρ( uσ) = Gρ J(Gσ u) (1) ∇ ∗ ∗ robustly estimates the edge orientations than several where Gσ and Gρ are Gaussians with standard devia- finite difference approximations. -
Plane-Wave Sobel Attribute for Discontinuity Enhancement in Seismic Imagesa
Plane-wave Sobel attribute for discontinuity enhancement in seismic imagesa aPublished in Geophysics, 82, WB63-WB69, (2017) Mason Phillips and Sergey Fomel ABSTRACT Discontinuity enhancement attributes are commonly used to facilitate the interpre- tation process by enhancing edges in seismic images and providing a quantitative measure of the significance of discontinuous features. These attributes require careful pre-processing to maintain geologic features and suppress acquisition and processing artifacts which may be artificially detected as a geologic edge. We propose the plane-wave Sobel attribute, a modification of the classic Sobel filter, by orienting the filter along seismic structures using plane-wave destruction and plane- wave shaping. The plane-wave Sobel attribute can be applied directly to a seismic image to efficiently and effectively enhance discontinuous features, or to a coherence image to create a sharper and more detailed image. Two field benchmark data examples with many faults and channel features from offshore New Zealand and offshore Nova Scotia demonstrate the effectiveness of this method compared to conventional coherence attributes. The results are reproducible using the Madagascar software package. INTRODUCTION One of the major challenges of interpreting seismic images is the delineation of reflection discontinuities that are related to distinct geologic features, such as faults, channels, salt boundaries, and unconformities. Visually prominent reflection features often overshadow these subtle discontinuous features which are critical to understanding the structural and depositional environment of the subsurface. For this reason, precise manual interpretation of these reflection discontinuities in seismic images can be tedious and time-consuming, es- pecially when data quality is poor. Discontinuity enhancement attributes are among the most widely used seismic attributes today. -
Context-Aware Features and Robust Image Representations 5 6 ∗ 7 P
CAKE_article_final.tex Click here to view linked References 1 2 3 4 Context-Aware Features and Robust Image Representations 5 6 ∗ 7 P. Martinsa, , P. Carvalhoa,C.Gattab 8 aCenter for Informatics and Systems, University of Coimbra, Coimbra, Portugal 9 bComputer Vision Center, Autonomous University of Barcelona, Barcelona, Spain 10 11 12 13 14 Abstract 15 16 Local image features are often used to efficiently represent image content. The limited number of types of 17 18 features that a local feature extractor responds to might be insufficient to provide a robust image repre- 19 20 sentation. To overcome this limitation, we propose a context-aware feature extraction formulated under an 21 information theoretic framework. The algorithm does not respond to a specific type of features; the idea is 22 23 to retrieve complementary features which are relevant within the image context. We empirically validate the 24 method by investigating the repeatability, the completeness, and the complementarity of context-aware fea- 25 26 tures on standard benchmarks. In a comparison with strictly local features, we show that our context-aware 27 28 features produce more robust image representations. Furthermore, we study the complementarity between 29 strictly local features and context-aware ones to produce an even more robust representation. 30 31 Keywords: Local features, Keypoint extraction, Image content descriptors, Image representation, Visual 32 saliency, Information theory. 33 34 35 1. Introduction While it is widely accepted that a good local 36 37 feature extractor should retrieve distinctive, accu- 38 Local feature detection (or extraction, if we want 39 rate, and repeatable features against a wide vari- to use a more semantically correct term [1]) is a 40 ety of photometric and geometric transformations, 41 central and extremely active research topic in the 42 it is equally valid to claim that these requirements fields of computer vision and image analysis. -
1 Introduction
1: Introduction Ahmad Humayun 1 Introduction z A pixel is not a square (or rectangular). It is simply a point sample. There are cases where the contributions to a pixel can be modeled, in a low order way, by a little square, but not ever the pixel itself. The sampling theorem tells us that we can reconstruct a continuous entity from such a discrete entity using an appropriate reconstruction filter - for example a truncated Gaussian. z Cameras approximated by pinhole cameras. When pinhole too big - many directions averaged to blur the image. When pinhole too small - diffraction effects blur the image. z A lens system is there in a camera to correct for the many optical aberrations - they are basically aberrations when light from one point of an object does not converge into a single point after passing through the lens system. Optical aberrations fall into 2 categories: monochromatic (caused by the geometry of the lens and occur both when light is reflected and when it is refracted - like pincushion etc.); and chromatic (Chromatic aberrations are caused by dispersion, the variation of a lens's refractive index with wavelength). Lenses are essentially help remove the shortfalls of a pinhole camera i.e. how to admit more light and increase the resolution of the imaging system at the same time. With a simple lens, much more light can be bought into sharp focus. Different problems with lens systems: 1. Spherical aberration - A perfect lens focuses all incoming rays to a point on the optic axis. A real lens with spherical surfaces suffers from spherical aberration: it focuses rays more tightly if they enter it far from the optic axis than if they enter closer to the axis. -
Pavement Crack Detection by Ridge Detection on Fractional Calculus and Dual-Thresholds
International Journal of Multimedia and Ubiquitous Engineering Vol.10, No.4 (2015), pp.19-30 http://dx.doi.org/10.14257/ijmue.2015.10.4.03 Pavement Crack Detection by Ridge Detection on Fractional Calculus and Dual-thresholds Song Hongxun, Wang Weixing, Wang Fengping, Wu Linchun and Wang Zhiwei Shaanxi Road Traffic Intelligent Detection and Equipment Engineering Technology Research Center, Chang’an University, Xi’an, China School of Information Engineering, Chang’an University, Xi’an, China songhongx @163.com Abstract In this paper, a new road surface crack detection algorithm is proposed; it is based on the ridge edge detection on fractional calculus and the dual-thresholds on a binary image. First, the multi-scale reduction of image data is used to shrink an original image to eliminate noise, which can not only smooth an image but also enhance cracks. Then, the main cracks are extracted by using the ridge edge detection on fractional calculus in a grey scale image. Subsequently, the resulted binary image is further processed by applying both short and long line thresholds to eliminate short curves and noise for getting rough crack segments. Finally the gaps in cracks are connected with a curve connection function which is an artificial intelligence routine. The experiments show that the algorithm for pavement crack images has the good performance of noise immunity, accurate positioning, and high accuracy. It can accurately locate and detect small and thin cracks that are difficult to identify by other traditional algorithms. Keywords: Crack; Multiscale; Image enhancement; Ridge edge; Threshold 1. Introduction In a time period, any road can be damaged gradually due to traffic loads and the other natural/artificial factors, and cracks are the most common road pavement surface defects that need to mend as early as possible. -
Feature Detection Florian Stimberg
Feature Detection Florian Stimberg Outline ● Introduction ● Types of Image Features ● Edges ● Corners ● Ridges ● Blobs ● SIFT ● Scale-space extrema detection ● keypoint localization ● orientation assignment ● keypoint descriptor ● Resources Introduction ● image features are distinct image parts ● feature detection often is first operation to define image parts to process later ● finding corresponding features in pictures necessary for object recognition ● Multi-Image-Panoramas need to be “stitched” together at according image features ● the description of a feature is as important as the extraction Types of image features Edges ● sharp changes in brightness ● most algorithms use the first derivative of the intensity ● different methods: (one or two thresholds etc.) Types of image features Corners ● point with two dominant and different edge directions in its neighbourhood ● Moravec Detector: similarity between patch around pixel and overlapping patches in the neighbourhood is measured ● low similarity in all directions indicates corner Types of image features Ridges ● curves which points are local maxima or minima in at least one dimension ● quality depends highly on the scale ● used to detect roads in aerial images or veins in 3D magnetic resonance images Types of image features Blobs ● points or regions brighter or darker than the surrounding ● SIFT uses a method for blob detection SIFT ● SIFT = Scale-invariant feature transform ● first published in 1999 by David Lowe ● Method to extract and describe distinctive image features ● feature -
Detection and Evaluation Methods for Local Image and Video Features Julian Stottinger¨
Technical Report CVL-TR-4 Detection and Evaluation Methods for Local Image and Video Features Julian Stottinger¨ Computer Vision Lab Institute of Computer Aided Automation Vienna University of Technology 2. Marz¨ 2011 Abstract In computer vision, local image descriptors computed in areas around salient interest points are the state-of-the-art in visual matching. This technical report aims at finding more stable and more informative interest points in the domain of images and videos. The research interest is the development of relevant evaluation methods for visual mat- ching approaches. The contribution of this work lies on one hand in the introduction of new features to the computer vision community. On the other hand, there is a strong demand for valid evaluation methods and approaches gaining new insights for general recognition tasks. This work presents research in the detection of local features both in the spatial (“2D” or image) domain as well for spatio-temporal (“3D” or video) features. For state-of-the-art classification the extraction of discriminative interest points has an impact on the final classification performance. It is crucial to find which interest points are of use in a specific task. One question is for example whether it is possible to reduce the number of interest points extracted while still obtaining state-of-the-art image retrieval or object recognition results. This would gain a significant reduction in processing time and would possibly allow for new applications e.g. in the domain of mobile computing. Therefore, the work investigates different corner detection approaches and evaluates their repeatability under varying alterations. -
Structure-Oriented Gaussian Filter for Seismic Detail Preserving Smoothing
STRUCTURE-ORIENTED GAUSSIAN FILTER FOR SEISMIC DETAIL PRESERVING SMOOTHING Wei Wang*, Jinghuai Gao*, Kang Li**, Ke Ma*, Xia Zhang* *School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China. **China Satellite Maritime Tracking and Control Department, Jiangyin, 214431, China. [email protected] ABSTRACT location. Among the different methods to achieve the denoising of 3D seismic data, a large number of approaches This paper presents a structure-oriented Gaussian (SOG) using nonlinear diffusion techniques have been proposed in filter for reducing noise in 3D reflection seismic data while the recent years ([6],[7],[8],[9],[10]). These techniques are preserving relevant details such as structural and based on the use of Partial Differential Equations (PDE). stratigraphic discontinuities and lateral heterogeneity. The Among these diffusion based seismic denoising Gaussian kernel is anisotropically constructed based on two methods, an excellent sample is the seismic fault preserving confidence measures, both of which take into account the diffusion (SFPD) proposed by Lavialle et al. [10]. The regularity of the local seismic structures. So that, the filter SFPD filter is driven by a diffusion tensor: shape is well adjusted according to different local wU divD JUV U U (1) geological features. Then, the anisotropic Gaussian is wt steered by local orientations of the geological features Anisotropy in the smoothing processes is addressed in terms (layers) provided by the Gradient Structure Tensor. The of eigenvalues and eigenvectors of the diffusion tensor. potential of our approach is presented through a Therein, the diffusion tensor is based on the analysis of the comparative experiment with seismic fault preserving Gradient Structure Tensor ([4],[11]): diffusion (SFPD) filter on synthetic blocks and an §·2 §·wwwwwUUUUUVVVVV application to real 3D seismic data. -
Rotational Features Extraction for Ridge Detection
1 Rotational Features Extraction for Ridge Detection German´ Gonzalez,´ Member, IEEE, Franc¸ois Fleuret, Member, IEEE, Franc¸ois Aguet, Fethallah Benmansour, Michael Unser Fellow, IEEE., and P. Fua, Senior Member, IEEE. Abstract State-of-the-art approaches to detecting ridge-like structures in images rely on filters designed to respond to locally linear intensity features. While these approaches may be optimal for ridges whose appearance is close to being ideal, their performance degrades quickly in the presence of structured noise that corrupts the image signal, potentially to the point where it truly does not conform to the ideal model anymore. In this paper, we address this issue by introducing a learning framework that relies on rich, local, rotationally invariant image descriptors and demonstrate that we can outperform state-of-the-art ridge detectors in many different kinds of imagery. More specifically, our framework yields superior performance for the detection of blood vessel in retinal scans, dendrites in bright-field and confocal microscopy image-stacks, and streets in satellite imagery. Index Terms Rotational features. Ridge Detection. Steerable filters. Data aggregation. Machine Learning. F 1 INTRODUCTION INEAR structures of significant width, such as roads in aerial images, blood vessels in retinal scans, or L dendrites in 3D-microscopy image stacks, are pervasive. State-of-the-art approaches to detecting them rely on ideal models of their appearance and of the noise. They are usually optimized to find ideal ribbon or tubular structures. However, ridge-like linear structures such as those of Fig. 1 often do not conform to these models, which can drastically impact performance. -
Lecture Foils
INF 5300 - 8.4.2015 Detecting good features for tracking Anne Schistad Solberg . Finding the correspondence between two images . What are good features to match? . Points? . Edges? . Lines? INF 5300 1 Curriculum .Chapter 4 in Szeliski, with a focus on 4.1 Point-based features .Recommended additional reading on SIFT features: . Distinctive Image Features from Scale-Invariant Keypoints by D. Lowe, International Journal of Computer Vision, 20,2,pp.91-110, 2004. 2 Goal of this lecture • Consider two images containing partly the the same objects but at different times or from different views. • What type of features are best for recognizing similar object parts in different images? • Features should work on different scales and rotations. • These features will later be used to find the match between the images. • This chapter is also linked to chapter 6 which we will cover in a later lecture. • This is useful for e.g. – Tracking an object in time – Mosaicking or stitching images – Constructing 3D models 3 Image matching • How do we compute the correspondence between these images? – Extract good features for matching (this lecture) – Estimating geometrical transforms for matching (later lecture) •by Diva Sian •by swashford What type of features are good? •Point-like features? •Region-based features? •Edge-based features? •Line-based features? 5 Point-based features • Point-based features should represent a set of special locations in an image, e.g. landmarks or keypoints. • Two main categories of methods: – Find points in an image that can be easily tracked, e.g. using correlation or least-squares matching. • Given one feature, track this feature in a local area in the next frame • Most useful when the motion is small – Find features in all images and match them based on local appearance. -
A Spatialized Model of Textures Perception Using Structure Tensor Formalism Grégory Faye, Pascal Chossat
A spatialized model of textures perception using structure tensor formalism Grégory Faye, Pascal Chossat To cite this version: Grégory Faye, Pascal Chossat. A spatialized model of textures perception using structure tensor formalism. Networks and Heterogeneous Media, AIMS-American Institute of Mathematical Sciences, 2013, pp.fay-cho2013. hal-00807371 HAL Id: hal-00807371 https://hal.archives-ouvertes.fr/hal-00807371 Submitted on 3 Apr 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Manuscript submitted to Website: http://AIMsciences.org AIMS’ Journals Volume X, Number 0X, XX 200X pp. X–XX A SPATIALIZED MODEL OF VISUAL TEXTURE PERCEPTION USING THE STRUCTURE TENSOR FORMALISM Gregory´ Faye School of Mathematics, University of Minnesota 206 Church Street S.E., Minneapolis, MN 55455, USA and Neuromathcomp Project Team, INRIA Sophia Antipolis 2004 Route des Lucioles, F-06902 Sophia Antipolis Pascal Chossat J-A Dieudonn´eLaboratory, CNRS and University of Nice Sophia-Antipolis Parc Valrose, 06108 Nice Cedex 02, France and Neuromathcomp Project Team, INRIA Sophia Antipolis (Communicated by the associate editor name) Abstract. The primary visual cortex (V1) can be partitioned into funda- mental domains or hypercolumns consisting of one set of orientation columns arranged around a singularity or “pinwheel” in the orientation preference map.