Results 1 - 10
of
11
Rectangle Detection based on a Windowed Hough Transform
- Proceedins of the XVII Brasilian Symposium on Computer Graphics and Image Processing (SIBGRAPI’04), 17~20
, 2004
"... The problem of detecting rectangular structures in images arises in many applications, from building extraction in aerial images to particle detection in cryo-electron microscopy. This paper proposes a new technique for rectangle detection using a windowed Hough Transform. Every pixel of the image i ..."
Abstract
-
Cited by 26 (0 self)
- Add to MetaCart
(Show Context)
The problem of detecting rectangular structures in images arises in many applications, from building extraction in aerial images to particle detection in cryo-electron microscopy. This paper proposes a new technique for rectangle detection using a windowed Hough Transform. Every pixel of the image is scanned, and a sliding window is used to compute the Hough Transform of small regions of the image. Peaks of the Hough image (which correspond to line segments) are then extracted, and a rectangle is detected when four extracted peaks satisfy certain geometric conditions. Experimental results indicate that the proposed technique produced promising results for both synthetic and natural images.
Detecting Circular and Rectangular Particles Based on Geometric Feature Detection . . .
- JOURNAL OF STRUCTURAL BIOLOGY
, 2004
"... Accurate and automatic particle detection from cryo-electron microscopy (cryo-EM images) is very important for high-resolution reconstruction of large macromolecular structures. In this paper, we present a method for particle picking based on shape feature detection. Two fundamental concepts of comp ..."
Abstract
-
Cited by 13 (1 self)
- Add to MetaCart
Accurate and automatic particle detection from cryo-electron microscopy (cryo-EM images) is very important for high-resolution reconstruction of large macromolecular structures. In this paper, we present a method for particle picking based on shape feature detection. Two fundamental concepts of computational geometry, namely, the distance transform and the Voronoi diagram, are used for detection of critical features as well as for accurate location of particles from the images or micrographs. Unlike the conventional template-matching methods, our approach detects the particles based on their boundary features instead of intensities. The geometric features derived from the boundaries provide an efficient way for locating particles quickly and accurately, which avoids a brute-force searching for the best position/orientation. Our approach is fully automatic and has been successfully applied to detect particles with approximately circular or rectangular shapes (e.g., KLH particles). Particle detection can be enhanced by multiple sets of parameters used in edge detection and/or by anisotropic filtering. We also discuss the extension of this approach to other types of particles with certain geometric features.
Detecting particles in cryo-em micrographs using learned features
- Journal of Structural Biology
, 2004
"... A new learning-based approach is presented for particle detection in cryo-electron micrographs using the Adaboost learning algorithm. The approach builds directly on the successful detectors developed for the domain of face detection. It is a discriminative algorithm which learns important features ..."
Abstract
-
Cited by 8 (1 self)
- Add to MetaCart
A new learning-based approach is presented for particle detection in cryo-electron micrographs using the Adaboost learning algorithm. The approach builds directly on the successful detectors developed for the domain of face detection. It is a discriminative algorithm which learns important features of the particle’s appearance using a set of training examples of the particles and a set of images that do not contain particles. The algorithm is fast (10 seconds on a 1.3 GHz Pentium M processor), is generic, and is not limited to any particular shape or size of the particle to be detected. The method has been evaluated on a publicly available dataset of 82 cryo-EM images of keyhole lympet hemocyanin (KLH). From 998 automatically extracted particle images, the 3-D structure of KLH has been reconstructed at a resolution of 23.2 ˚A which is the same resolution as obtained using particles manually selected by a trained user.
DETECTABILITY OF CONVEX-SHAPED OBJECTS IN DIGITAL IMAGES, ITS FUNDAMENTAL LIMIT AND MULTISCALE ANALYSIS
"... Abstract: Given a convex-shape inhomogeneous region embedded in a noisy image, we consider the conditions under which such an embedded region is detectable. The existence of low order-of-complexity detection algorithms is also studied. The main results are (1) an analytical threshold (of a statistic ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
Abstract: Given a convex-shape inhomogeneous region embedded in a noisy image, we consider the conditions under which such an embedded region is detectable. The existence of low order-of-complexity detection algorithms is also studied. The main results are (1) an analytical threshold (of a statistic) that specifies what is detectable, and (2) the existence of a multiscale detection algorithm whose order of complexity is roughly the optimal O(n 2 log 2 (n)). Our analysis has two main components. We first show that in a discrete image, the number of convex sets increases faster than any finite degree polynomial of the image size n. Hence the idea of generalized likelihood ratio test cannot be directly adopted to derive the asymptotic detectability bound. Secondly, we show that the maximally embedded hv-parallelogram is at least 2/9 of the convex region (in area). We then apply the results of hv-parallelograms in Arias-Castro, Donoho, and Huo (2005) on detecting convex sets. Numerical examples are provided. Our results have potential applications in several fields, which are described with corresponding references.
A Geometric Feature Detection Approach to Particle Picking in Electron
"... Accurate and automatic particle detection from cryo-electron microscopy (cryo-EM) images is very important for high-resolution reconstruction of large macromolecular structures. In this paper, we present a novel method, based on feature detection, for particle picking. Two fundamental concepts of co ..."
Abstract
- Add to MetaCart
(Show Context)
Accurate and automatic particle detection from cryo-electron microscopy (cryo-EM) images is very important for high-resolution reconstruction of large macromolecular structures. In this paper, we present a novel method, based on feature detection, for particle picking. Two fundamental concepts of computational geometry, namely, distance transform and Voronoi diagram, are used for detection of critical features as well as for accurate location of particles from the micrographs. Unlike the conventional template-matching methods, our approach detects the particles based on their boundary features instead of intensities. The geometric features derived from the boundaries provide an efficient way for locating particles quickly and accurately, which avoids a brute-force searching for the best position/orientation. Our approach is fully automatic and has been successfully applied to detect particles with roughly circular or rectangular shapes. Particle detection can be enhanced by multiple sets of parameters used in edge detection and/or by anisotropic filtering. We will also discuss the extension of this approach to other types of particles with certain geometric features. 1.
DOI 10.1007/s00138-006-0021-7 ORIGINAL PAPER Computer vision for nanoscale imaging
"... Abstract The main goal of Nanotechnology is to analyze and understand the properties of matter at the atomic and molecular level. Computer vision is rapidly expanding into this new and exciting field of application, and considerable research efforts are currently being spent on developing new image- ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract The main goal of Nanotechnology is to analyze and understand the properties of matter at the atomic and molecular level. Computer vision is rapidly expanding into this new and exciting field of application, and considerable research efforts are currently being spent on developing new image-based characterization techniques to analyze nanoscale images. Nanoscale characterization requires algorithms to perform image analysis under extremely challenging conditions such as low signal-to-noise ratio and low resolution. To achieve this, nanotechnology researchers require imaging tools that are able to enhance images, detect objects and features, reconstruct 3D geometry, and tracking. This paper reviews current advances in computer vision and related areas applied to imaging nanoscale objects. We categorize the algorithms, describe their representative methods, and conclude with several promising directions of future investigation. 1
Model-Based Particle Picking for Cryo-Electron Microscopy
"... We describe an algorithm for finding particle images in cryo-EM micrographs. The algorithm starts from a crude 3D map of the target particle, computed from a relatively small number of manually picked images, and then projects the map in many different directions to give synthetic 2D templates. The ..."
Abstract
- Add to MetaCart
We describe an algorithm for finding particle images in cryo-EM micrographs. The algorithm starts from a crude 3D map of the target particle, computed from a relatively small number of manually picked images, and then projects the map in many different directions to give synthetic 2D templates. The templates are clustered and averaged and then cross-correlated with the micrographs. A probabilistic model of the imaging process then scores cross-correlation peaks to produce the final picks. We give quantitative results on two quite different target particles: keyhole limpet hemocyanin and p97 AAA ATPase. On these particles our automatic particle picker shows human performance level, as measured by the Fourier shell correlations of 3D reconstructions. Keywords: 3D template, cross-correlation, maximum likelihood, p97 AAA ATPase. 1
Comparison of single-particle analysis and electron tomography approaches: an overview
, 2008
"... Three-dimensional structure of a wide range of biological specimens can be computed from images collected by transmission electron microscopy. This information integrated with structural data obtained with other techniques (e.g., X-ray crystallography) helps structural biologists to understand the ..."
Abstract
- Add to MetaCart
Three-dimensional structure of a wide range of biological specimens can be computed from images collected by transmission electron microscopy. This information integrated with structural data obtained with other techniques (e.g., X-ray crystallography) helps structural biologists to understand the function of macromolecular complexes and organelles within cells. In this paper, we compare two threedimensional transmission electron microscopy techniques that are becoming more and more related (at the image acquisition level as well as the image processing one): electron tomography and single-particle analysis. The first one is currently used to elucidate the three-dimensional structure of cellular components or smaller entire cells, whereas the second one has been traditionally applied to structural studies of macromolecules and macromolecular complexes. Also, we discuss possibilities for their integration with other structural biology techniques for an integrative study of living matter from proteins to whole cells.
Vision in the Small: Reconstructing the Structure of Protein Macromolecules
"... Single particle reconstruction using Cryo-Electron Microscopy (cryo-EM) is an emerging technique in structural biology for estimating the 3-D structure (density) of protein macromolecules. Unlike tomography where a large number of images of a specimen can be acquired, the number of images of an indi ..."
Abstract
- Add to MetaCart
(Show Context)
Single particle reconstruction using Cryo-Electron Microscopy (cryo-EM) is an emerging technique in structural biology for estimating the 3-D structure (density) of protein macromolecules. Unlike tomography where a large number of images of a specimen can be acquired, the number of images of an individual particle is limited because of radiation damage. Instead, the specimen consists of identical copies of the same protein macro-molecule embedded in vitreous ice at random and unknown 3-D orientations. Because the images are extremely noisy, thousands to hundreds-of-thousands of projections are needed to achieve the desired resolution of 5 ˚A. Along with differences of the imaging modality compared to photographs, single particle reconstruction provides a unique set of challenges to existing computer vision algorithms. Here, we introduce the challenge and opportunity of reconstruction from transmission electron micrographs, and briefly describe our contributions in areas of particle detection, contrast transfer function (CTF)
ACE: Automated CTF Estimation
"... In this paper we present a completely automated algorithm for estimating the parameters of the contrast transfer function (CTF) of a transmission electron microscope. The primary contribution of this paper is the determination of the astigmatism prior to the estimation of the CTF parameters. The CTF ..."
Abstract
- Add to MetaCart
(Show Context)
In this paper we present a completely automated algorithm for estimating the parameters of the contrast transfer function (CTF) of a transmission electron microscope. The primary contribution of this paper is the determination of the astigmatism prior to the estimation of the CTF parameters. The CTF parameter estimation is then reduced to a 1D problem using elliptical averaging. We have also implemented an automated method to calculate lower and upper cutoff frequencies to eliminate regions of the power spectrum which perturb the estimation of the CTF parameters. The algorithm is comprised of three optimization subproblems, two of which are proven to be convex. Results of the CTF estimation method are presented for images of carbon support films as well as for images of single particles embedded in ice and suspended over holes in the support film. A MATLAB implementation of the algorithm, called ACE, is freely available.
