Image features using scale-space FAST corner detector and SURF descriptor

2014 ◽  
Vol 29 (4) ◽  
pp. 598-604
Author(s):  
王飞宇 WANG Fei-yu ◽  
邸男 DI Nan ◽  
贾平 JIA Ping
Keyword(s):  
Scale Space ◽  
Image Features

scholarly journals Stochastic analysis of image acquisition, interpolation and scale-space smoothing

1999 ◽  
Vol 31 (4) ◽  
pp. 855-894 ◽  
Author(s):  
Kalle Åström ◽  
Anders Heyden
Keyword(s):  
Stochastic Analysis ◽  
Traditional Approach ◽  
Image Acquisition ◽  
Scale Space ◽  
Image Features ◽  
Space Theory ◽  
Random Errors ◽  
Feature Detectors ◽  
Stochastic Properties ◽  
Discrete Scale

In the high-level operations of computer vision it is taken for granted that image features have been reliably detected. This paper addresses the problem of feature extraction by scale-space methods. There has been a strong development in scale-space theory and its applications to low-level vision in the last couple of years. Scale-space theory for continuous signals is on a firm theoretical basis. However, discrete scale-space theory is known to be quite tricky, particularly for low levels of scale-space smoothing. The paper is based on two key ideas: to investigate the stochastic properties of scale-space representations and to investigate the interplay between discrete and continuous images. These investigations are then used to predict the stochastic properties of sub-pixel feature detectors.The modeling of image acquisition, image interpolation and scale-space smoothing is discussed, with particular emphasis on the influence of random errors and the interplay between the discrete and continuous representations. In doing so, new results are given on the stochastic properties of discrete and continuous random fields. A new discrete scale-space theory is also developed. In practice this approach differs little from the traditional approach at coarser scales, but the new formulation is better suited for the stochastic analysis of sub-pixel feature detectors.The interpolated images can then be analysed independently of the position and spacing of the underlying discretisation grid. This leads to simpler analysis of sub-pixel feature detectors. The analysis is illustrated for edge detection and correlation. The stochastic model is validated both by simulations and by the analysis of real images.

scholarly journals Image Scale-Space Filtering Using Directional Local Variance Controlled Anisotropic Diffusion

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yong Chen ◽  
Taoshun He
Keyword(s):  
Anisotropic Diffusion ◽  
Scale Space ◽  
Image Features ◽  
Spatial Gradient ◽  
Trigger Time ◽  
Local Variance ◽  
Series Of Experiments ◽  
Edge Indicator ◽  
Anisotropic Diffusion Equation ◽  
Space Filter

The purpose of this paper is to develop an effective edge indicator and propose an image scale-space filter based on anisotropic diffusion equation for image denoising. We first develop an effective edge indicator named directional local variance (DLV) for detecting image features, which is anisotropic and robust and able to indicate the orientations of image features. We then combine two edge indicators (i.e., DLV and local spatial gradient) to formulate the desired image scale-space filter and incorporate the modulus of noise magnitude into the filter to trigger time-varying selective filtering. Moreover, we theoretically show that the proposed filter is robust to the outliers inherently. A series of experiments are conducted to demonstrate that the DLV metric is effective for detecting image features and the proposed filter yields promising results with higher quantitative indexes and better visual performance, which surpass those of some benchmark models.

scholarly journals Coherence Enhancement Based on Recursive Anisotropic Scale-Space with Adaptive Kernels

2020 ◽  
Vol 10 (15) ◽  
pp. 5079
Author(s):  
Numonov Sardorbek ◽  
Bong-Soo Sohn ◽  
Byung-Woo Hong
Keyword(s):  
Diffusion Process ◽  
Image Denoising ◽  
Geometrical Property ◽  
Scale Space ◽  
Image Features ◽  
Diffusion Kernel ◽  
Geometrical Properties ◽  
Curvilinear Structures ◽  
Lung Ct ◽  
Space Image

The reduction of unnecessary details is important in a variety of imaging tasks. Image denoising can be generally formulated as a diffusion process that iteratively suppresses undesirable image features with high variance. We propose a recursive diffusion process that simultaneously computes the local geometrical property of the image features and determines the size and shape of the diffusion kernel, leading to an anisotropic scale-space. In the construction of the proposed anisotropic scale-space, image features due to undesirable noise are suppressed while significant geometrical image features such as edges and corners are preserved across the scale-space. The diffusion kernels are iteratively determined based on the local geometrical properties of the image features. We demonstrate the effectiveness and robustness of the proposed algorithm in the detection of curvilinear features using simple yet effective synthetic and real images. The algorithm is quantitatively evaluated based on the identification of fissures in lung CT imagery. The experimental results indicate that the proposed algorithm can be used for the detection of linear or curvilinear structures in a variety of images ranging from satellite to medical images.

MULTISCALE MORPHOLOGICAL WATERSHED SEGMENTATION FOR GRAY LEVEL IMAGE

2006 ◽  
Vol 04 (04) ◽  
pp. 627-641 ◽  
Author(s):  
HAIHUA LIU ◽  
ZHOUHUI CHEN ◽  
CHANGSHENG XIE
Keyword(s):  
Scale Space ◽  
Image Features ◽  
Natural Scenes ◽  
Gray Level ◽  
Morphological Operators ◽  
Morphological Gradient ◽  
Wide Range ◽  
Gray Level Image ◽  
Contrast Information ◽  
Nonlinear Scale

Multiscale image analysis has been used successfully in a number of applications to segment image features according to their relative scale. In this paper, we present a new framework for the hierarchical segmentation of gray level image. The proposed scheme comprises a nonlinear scale-space and morphological gradient watersheds. Our aim is to produce a meaningful hierarchy among the objects in the image. The scale-space is based on morphological anisotropic diffusion that uses reconstruction morphological operators. Furthermore, we introduce the method to reconstruct morphological operators and the principle of the dynamics of contours in scale-space that combines scale and contrast information. The performance of the proposed segmentation scheme is presented via experimental results obtained with a wide range of image including natural scenes.

scholarly journals Stochastic analysis of image acquisition, interpolation and scale-space smoothing

1999 ◽  
Vol 31 (04) ◽  
pp. 855-894 ◽  
Author(s):  
Kalle Åström ◽  
Anders Heyden
Keyword(s):  
Stochastic Analysis ◽  
Traditional Approach ◽  
Image Acquisition ◽  
Scale Space ◽  
Image Features ◽  
Space Theory ◽  
Random Errors ◽  
Feature Detectors ◽  
Stochastic Properties ◽  
Discrete Scale

In the high-level operations of computer vision it is taken for granted that image features have been reliably detected. This paper addresses the problem of feature extraction by scale-space methods. There has been a strong development in scale-space theory and its applications to low-level vision in the last couple of years. Scale-space theory for continuous signals is on a firm theoretical basis. However, discrete scale-space theory is known to be quite tricky, particularly for low levels of scale-space smoothing. The paper is based on two key ideas: to investigate the stochastic properties of scale-space representations and to investigate the interplay between discrete and continuous images. These investigations are then used to predict the stochastic properties of sub-pixel feature detectors. The modeling of image acquisition, image interpolation and scale-space smoothing is discussed, with particular emphasis on the influence of random errors and the interplay between the discrete and continuous representations. In doing so, new results are given on the stochastic properties of discrete and continuous random fields. A new discrete scale-space theory is also developed. In practice this approach differs little from the traditional approach at coarser scales, but the new formulation is better suited for the stochastic analysis of sub-pixel feature detectors. The interpolated images can then be analysed independently of the position and spacing of the underlying discretisation grid. This leads to simpler analysis of sub-pixel feature detectors. The analysis is illustrated for edge detection and correlation. The stochastic model is validated both by simulations and by the analysis of real images.

scholarly journals Multi-Neighborhood Convolutional Networks

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Elnaz Barshan ◽  
Paul Fieguth ◽  
Alexander Wong
Keyword(s):  
Feature Learning ◽  
Scale Space ◽  
Image Features ◽  
Superior Performance ◽  
Convolutional Networks ◽  
Multi Scale ◽  
Image Characteristics ◽  
Single Scale ◽  
Nonlinear Scale

<p>We explore the role of scale for improved feature learning in convolutional<br />networks. We propose multi-neighborhood convolutional<br />networks, designed to learn image features at different levels of<br />detail. Utilizing nonlinear scale-space models, the proposed multineighborhood<br />model can effectively capture fine-scale image characteristics<br />(i.e., appearance) using a small-size neighborhood, while<br />coarse-scale image structures (i.e., shape) are detected through<br />a larger neighborhood. The experimental results demonstrate the<br />superior performance of the proposed multi-scale multi-neighborhood<br />models over their single-scale counterparts.</p>

7-beam lattice images of (110) oriented Ge

1978 ◽  
Vol 36 (1) ◽  
pp. 290-291
Author(s):  
J.R. Parsons ◽  
C.W. Hoelke
Keyword(s):  
Image Features ◽  
Objective Lens ◽  
Lattice Plane ◽  
Lattice Image ◽  
Crystalline Defects ◽  
Transmission Electron ◽  
Lattice Images ◽  
Electron Microscopes ◽  
Structural Aspects ◽  
Beam Lattice

The direct imaging of a crystal lattice has intrigued electron microscopists for many years. What is of interest, of course, is the way in which defects perturb their atomic regularity. There are problems, however, when one wishes to relate aperiodic image features to structural aspects of crystalline defects. If the defect is inclined to the foil plane and if, as is the case with present 100 kV transmission electron microscopes, the objective lens is not perfect, then terminating fringes and fringe bending seen in the image cannot be related in a simple way to lattice plane geometry in the specimen (1).The purpose of the present work was to devise an experimental test which could be used to confirm, or not, the existence of a one-to-one correspondence between lattice image and specimen structure over the desired range of specimen spacings. Through a study of computed images the following test emerged.

High-resolution electron microscopy of the atomic structure of some grain boundaries in Au

1986 ◽  
Vol 44 ◽  
pp. 414-415
Author(s):  
W. Krakow ◽  
D. A. Smith
Keyword(s):  
Atomic Structure ◽  
High Resolution Electron Microscopy ◽  
Image Features ◽  
Image Feature ◽  
Resolution Electron ◽  
Tilt Boundaries ◽  
Processing Techniques ◽  
Atom Position ◽  
Structure Configuration

The successful determination of the atomic structure of [110] tilt boundaries in Au stems from the investigation of microscope performance at intermediate accelerating voltages (200 and 400kV) as well as a detailed understanding of how grain boundary image features depend on dynamical diffraction processes variation with specimen and beam orientations. This success is also facilitated by improving image quality by digital image processing techniques to the point where a structure image is obtained and each atom position is represented by a resolved image feature. Figure 1 shows an example of a low angle (∼10°) Σ = 129/[110] tilt boundary in a ∼250Å Au film, taken under tilted beam brightfield imaging conditions, to illustrate the steps necessary to obtain the atomic structure configuration from the image. The original image of Fig. 1a shows the regular arrangement of strain-field images associated with the cores of ½ [10] primary dislocations which are separated by ∼15Å.

Imaging of polymer single crystals in low-voltage, high-resolution scanning electron microscopy

1990 ◽  
Vol 48 (4) ◽  
pp. 1106-1107
Author(s):  
W.W. Adams ◽  
G. Price ◽  
A. Krause
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
High Resolution ◽  
Single Crystals ◽  
Low Voltage ◽  
Polymer Surface ◽  
Beam Current ◽  
Image Features ◽  
First Results ◽  
Scanning Electron

It has been shown that there are numerous advantages in imaging both coated and uncoated polymers in scanning electron microscopy (SEM) at low voltages (LV) from 0.5 to 2.0 keV compared to imaging at conventional voltages of 10 to 20 keV. The disadvantages of LVSEM of degraded resolution and decreased beam current have been overcome with the new generation of field emission gun SEMs. In imaging metal coated polymers in LVSEM beam damage is reduced, contrast is improved, and charging from irregularly shaped features (which may be unevenly coated) is reduced or eliminated. Imaging uncoated polymers in LVSEM allows direct observation of the surface with little or no charging and with no alterations of surface features from the metal coating process required for higher voltage imaging. This is particularly important for high resolution (HR) studies of polymers where it is desired to image features 1 to 10 nm in size. Metal sputter coating techniques produce a 10 - 20 nm film that has its own texture which can obscure topographical features of the original polymer surface. In examining thin, uncoated insulating samples on a conducting substrate at low voltages the effect of sample-beam interactions on image formation and resolution will differ significantly from the effect at higher accelerating voltages. We discuss here sample-beam interactions in single crystals on conducting substrates at low voltages and also present the first results on HRSEM of single crystal morphologies which show some of these effects.

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