SIFT Method Research Articles

Separating Sheep from Goats

AbstractDrawing on the theology of individual differences, Jungian psychological type theory and the SIFT method of biblical hermeneutics and liturgical preaching, this article outlines a method for constructing preaching workshops and documents the responses of 25 participants (11 Anglican clergywomen and 14 Anglican clergymen). The data support the view that naive groups selected to separate out and to cluster together dominant sensing types, dominant intuitive types, dominant feeling types and dominant thinking types approach the passage in characteristically distinctive ways. The implication of these findings are discussed for developing the capacity of preachers to extend their preaching voice in light of insights offered by the SIFT approach.

What happened to the fig tree? An empirical study in psychological type and biblical hermeneutics

The SIFT method of biblical hermeneutics and liturgical preaching has its roots in Jungian psychological type theory and maintains that the reading and interpretation of text is shaped by individual preferences within the perceiving process (sensing and intuition) and within the evaluating process (thinking and feeling). The present study tests the empirical foundation for this method by examining the way in which three groups of participants familiar with handling scripture (N = 31, 14, and 47) interpret the Marcan narrative concerning the cleansing of the temple and the cursing of the fig tree. The data provide further support for the psychological principles underpinning the SIFT method.

Biblical literalism among Anglican clergy: what is the role of psychological type?

The SIFT method of preaching argues that preachers should attend to the different learning styles implied by psychological type theory when preparing and delivering sermons. The evidence to date that supports the theory behind the method has mainly been based on offering readers of known psychological type a range of interpretations specifically created to appeal to particular type preferences. This paper extends these studies by looking at how a more general interpretative strategy (literalism) is related to psychological type preferences. A sample of 1039 recently ordained Anglican clergy in the UK completed the Francis Psychological Type Scales and a 10-item Biblical Literalism Scale. There was a positive association between a preference for sensing and biblical literalism, after controlling for general biblical conservatism and church tradition. The implications for preachers are discussed.

Multi-scale local features based on anisotropic heat diffusion and global eigen-structure

Multi-scale local feature detection enables downstream registration and recognition tasks in medical image analysis. This paper articulates a novel robust method for multi-scale local feature extraction on volumetric data. The central idea is the elegant unification of local/global eigen-structures within the powerful framework of anisotropic heat diffusion. First, the local vector field is constructed by way of Hessian matrix and its eigenvectors/eigenvalues. Second, anisotropic heat kernels are computed using the vector field’s global graph Laplacian. Robust local features are manifested as extrema across multiple time scales, serving as volumetric heat kernel signature. To tackle the computational challenge for massive volumetric data, we propose a multiresolution strategy for hierarchical feature extraction based on our feature-preserving down-sampling approach. As a result, heat kernels and local feature identification can be approximated at a coarser level first, and then are pinpointed in a localized region at a finer resolution. Another novelty of this work lies at the initial heat design directly using local eigenvalue for anisotropic heat diffusion across the volumetric domain. We conduct experiments on various medical datasets, and draw comparisons with 3D SIFT method. The diffusion property of our local features, which can be interpreted as random walks in statistics, makes our method robust to noise, and gives rise to intrinsic multi-scale characteristics.

드 브루인 수열을 이용한 효과적인 위치 인식 마커 구성

컴퓨터비전에서 안정적으로 대응점을 획득하는 것은 매우 중요한 일이다. 그러나 이들은 스케일, 조명, 시점 등이 변하는 환경에서 정확한 대응점을 찾는 과정은 쉽지 않다. SIFT 알고리즘은 객체의 모서리나 꼭지점으로부터 추출한 특징벡터를 사용하므로 스케일링, 회전, 조명변화를 가지는 영상에서도 뛰어난 매칭을 수행한다. 그러나 SIFT는 엣지에 의해 특징점을 추출하므로 엣지가 존재하지 않는 영역에서는 원하는 대응점을 찾을 수 없다. 본 연구는 SIFT에 의한 대응 특징점 추출과 매칭 성능을 향상시키기 위한 마커 모양 및 배치 방법을 제안한다. 제안 방법에서 사용한 마커의 모양은 부착 방향에 따라 SIFT 알고리즘에 의해 한 방향으로 우세한 벡터를 검출할 수 있는 반원형(SemiCircle)으로 구성한다. 그리고 대응점 매칭의 성능을 향상시키기 위하여 마커의 방향 배치는 드 브루인 수열(De Bruijn Sequence)을 이용한다. 실험을 통해 제안한 방법이 기존의 방법보다 더 정확한 특징점 검출과 매칭에 효과적임을 증명하였다. In computer vision, it is very important to obtain reliable corresponding feature points. However, we know it is not easy to find the corresponding feature points exactly considering by scaling, lighting, viewpoints, etc. Lots of SIFT methods applies the invariant to image scale and rotation and change in illumination, which is due to the feature vector extracted from corners or edges of object. However, SIFT could not find feature points, if edges do not exist in the area when we extract feature points along edges. In this paper, we present a new placement method of marker to improve the performance of SIFT feature detection and matching between different view of an object or scene. The shape of the markers used in the proposed method is formed in a semicircle to detect dominant direction vector by SIFT algorithm depending on direction placement of marker. We applied De Bruijn sequence for the markers direction placement to improve the matching performance. The experimental results show that the proposed method is more accurate and effective comparing to the current method.

HoGG: Gabor and HoG-based human detection for surveillance in non-controlled environments

A new method (HoGG) for human detection based on Gabor filters and Histograms of Oriented Gradients is presented in this paper. The effect of Gabor preprocessing is analyzed in detail, in particular the improvement experienced by the image's information and the influence exerted over the extracted feature. To compare the performance of the proposed method, several alternative algorithms for human detection have been considered. In order to evaluate these techniques in non-controlled environments, a collection of standard databases, well known in the surveillance research community, has been used: PETS 2006, PETS 2007, PETS 2009 and CAVIAR. An exhaustive test design has been built based on two complementary evaluations: an evaluation oriented to counting people and a novel evaluation oriented to identification. Moreover, with the purpose of studying the performance of the Gabor-based preprocessing, a test adding Gabor filters to other local feature extraction methods, such as Steerable filters and the SIFT method, has been implemented. The HoGG method has achieved a good performance regardless of the difficulty of the images (occlusions, overlapping, carrying baggage, etc.). The proposed method has surpassed the alternative techniques in most of the analyzed situations. When the Gabor preprocessing is introduced into other local feature extraction methods, they achieve a better detection of the relevant information by enhancing the human shape. The results show that using Gabor preprocessing in techniques based on features like orientation or magnitude of gradient improve their performance. Given the excellent results obtained by HoGG at the identification-oriented evaluation, the method presented in this paper should be taken into account in the future design of intelligent surveillance systems.

Slope safety factor search strategy for multiple sample points for reliability analysis

A linked list-based sifting method is proposed to calculate the slope safety factors of multiple sample points, which requires less time to calculate the failure probability of a slope. The proposed method combines the advantages of the sequential search and bisection search of the strength reduction method (SRM). In addition, the proposed method can sift out the failure sample points without the need to calculate the safety factors of all sample points, which is helpful for the Monte Carlo method to determine the failure probability of a slope with much less time. The proposed method can obtain the safety factors of sample points near the limit-state surface, which is helpful for the response surface method (RSM) to get the more precise result of reliability index with much less time.

Interpreting and responding to the Johannine feeding narrative: An empirical study in the SIFT hermeneutical method amongst Anglican ministry training candidates

Drawing on Jungian psychological type theory, the SIFT method of biblical hermeneutics and liturgical preaching maintains that different psychological type preferences are associated with distinctive readings of scripture. In the present study this theory was tested amongst two groups of ministry training candidates (a total of 26 participants) who were located within working groups according to their psychological type preferences, and invited to reflect on the Johannine feeding narrative (Jn 6:4−22), and to document their discussion. Analysis of these data provided empirical support for the theory underpinning the SIFT method.

Study and Realization of Large Rotation Angle Matching Images Algorithm Based on Charateristic Matching

Combining the advantages of SIFT charateristic matching and pyramid image matching, this paper aims to solve the matching problem of automatic rotation with large rotation angle in aerial photographing. To start with, match the images at the highest pyramid level with SIFT characteristic matching method. Then use the RANSAC algorithm to combine the results from SIFT method. Before matching, predict the initial location of the matching points by using the estimated rotation angle and then make rotation compensation of the matching window images. Last, validate the method by carrying out an aerial digital imagery with large rotation of a certain area.

Mining concise and distinctive affine-stable features for object detection in large corpus

Invariant features extraction is important for object detection. Affine-SIFT (ASIFT) [J.M. Morel and G. Yu, ASIFT: A new framework for fully affine invariant image comparison, SIAM J. Imaging Sci. 2(2) (2009)] has been proved to be fully affine-invariant. However, the high cost of memory and query time hampers its application in large-scale object detection tasks. In this paper, we present a novel algorithm for mining concise and distinctive invariant features called affine-stable characteristics (ASC). Two new notions, global stability and local stability, are introduced to calculate the robustness of each feature from two mutually complementary aspects. Furthermore, to make these stable characteristics more distinctive, spatial information taken from several representative scales is encoded in a concise method. Experiments show that the robustness of our ASC is comparable with ASIFT, while the cost of memory can be reduced significantly to only 5%. Moreover, compared with the traditional SIFT method [D. Lowe, Distinctive image features from scale invariant keypoints, Int. J. Comput. Vis. 60(2) (2004), pp. 91–110], the accuracy of object detection can be improved 38.6% by our ASC using similar amount of features.

Random interest regions for object recognition based on texture descriptors and bag of features

In this work we propose a novel method for object recognition based on a random selection of interest regions, texture features (local binary/ternary patterns and local phase quantization) for describing each region, a bag-of-features approach for describing each object, and classification using support vector machines (SVMs). In our approach, a set of features is extracted from each subwindow of the object image. These sets are quantified, and the resulting global descriptor vector is used as a characterization of the image (e.g., as a feature vector for learning an image classification rule based on a SVM classifier). The standard texture descriptor is not widely utilized in region description. One of the first texture descriptors explored in region description is the CS-LBP descriptor, where a local binary pattern (LBP) feature is used as the local feature in the SIFT method, the most well-known object recognition algorithm. Our approach based on texture descriptors is much simpler than the SIFT algorithm, yet it performs comparably well. Furthermore, we show that the fusion between our approach and SIFT obtains a very high AUC in the well-known PASCAL VOC2006 dataset.

Fail-safe detection of threshold crossings of linear integrate-and-fire neuron models in time-driven simulations

The characteristics of time-driven simulation are a fixed-size simulation step and a fixed-size communication interval [1]. The former defines update-and-check points, which are the discrete points in time when all neurons update their state variables and check for a super-threshold membrane potential. The latter defines the discrete points in time when all neurons communicate their spikes. The communication interval is a multiple of the simulation step size and limited only by the minimum synaptic transmission delay in the network. The time-driven environment of the simulator NEST [2] provides an 'on-grid' and an 'off-grid' framework that handle spikes differently. In the on-grid framework, spikes are incorporated, detected and emitted only at the pre-defined update-and-check points. In the off-grid framework, spikes can be incorporated and emitted at any point in time [3]. For each neuron the arrival times of incoming spikes introduce additional update-and-check points. Hence, the simulation step can be increased up to the size of the communication interval. The detection of a threshold crossing can only take place at a check point, but the timing of the referring spike is estimated with precision limited only by the limits of double representation. In general, a time-driven simulator that supports the off-grid framework performs neural network simulations with the same precision and faster than an event-driven simulator [4]. However, time-driven simulation still bears the risk of missing a threshold crossing as a very brief excursion of the membrane potential above threshold may not be detected at the next check point. In the off-grid framework, this problem is more pronounced in networks with low connectivity and strong coupling as well as in the case of low firing rates. The on-grid framework is even more affected due to fewer check points and the synchronized arrival of spikes. Here, we present algorithms which are guaranteed to detect all threshold crossings by supplementing the standard test for a super-threshold membrane potential at each check point and that exploit the information about the neuronal state at nearby check points. These additional tests need to be invoked whenever the membrane potential is sub-threshold, which means at virtually all check points. We develop sub-tests of increasing complexity and specificity, starting with simple sifting methods and ending up with a complex expression that faithfully indicates the existence of a threshold crossing between the last and the current check point. An analysis of the test specificities and computational costs results in a cascade of tests which locates all threshold crossings at a low computational cost.

Comparing Phylogeny and the Predicted Pathogenicity of Protein Variations Reveals Equal Purifying Selection across the Global Human mtDNA Diversity

We used detailed phylogenetic trees for human mtDNA, combined with pathogenicity predictions for each amino acid change, to evaluate selection on mtDNA-encoded protein variants. Protein variants with high pathogenicity scores were significantly rarer in the older branches of the tree. Variants that have formed and survived multiple times in the human phylogenetics tree had significantly lower pathogenicity scores than those that only appear once in the tree. We compared the distribution of pathogenicity scores observed on the human phylogenetic tree to the distribution of all possible protein variations to define a measure of the effect of selection on these protein variations. The measured effect of selection increased exponentially with increasing pathogenicity score. We found no measurable difference in this measure of purifying selection in mtDNA across the global population, represented by the macrohaplogroups L, M, and N. We provide a list of all possible single amino acid variations for the human mtDNA-encoded proteins with their predicted pathogenicity scores and our measured selection effect as a tool for assessing novel protein variations that are often reported in patients with mitochondrial disease of unknown origin or for assessing somatic mutations acquired through aging or detected in tumors.

ASIFT: An Algorithm for Fully Affine Invariant Comparison

If a physical object has a smooth or piecewise smooth boundary, its images obtained by cameras in varying positions undergo smooth apparent deformations. These deformations are locally well approximated by affine transforms of the image plane. In consequencethe solid object recognition problem has often been led back to the computation of affine invariant image local features. The similarity invariance (invariance to translation, rotation, and zoom) is dealt with rigorously by the SIFT method The method illustrated and demonstrated in this work, AffineSIFT (ASIFT), simulates a set of sample views of the initial images, obtainable by varying the two camera axis orientation parameters, namely the latitude and the longitude angles, which are not treated by the SIFT method. Then it applies the SIFT method itself to all images thus generated. Thus, ASIFT covers effectively all six parameters of the affine transform. Source Code The source code (ANSI C), its documentation, and the online demo are accessible at the IPOL web page of this article 1 .

Moving Path Estimation from Video Sequences

The aim of the study is estimating a robot's path from its camera sequences. Firstly, Scale Invariant Feature Transform - SIFT method was used to detect the identical points of the successive frames. The histograms of the coordinate differences of the matched points are used for estimating the path. The respective pathway was represented in two dimensional spaces by directional combining of the matched (identical) points. The goal of the experiment is to be able to achieve a comparison of the experimental path and theoretical path of a certain object that holding a camera. The experimental results show, the path estimation by using successive images is applicable in static environments, but in the dynamic environments, the generated paths are very noisily.

Reading and Proclaiming the Resurrection: An Empirical Study in Psychological Type Theory among Trainee and Experienced Preachers Employing Mark 16 and Matthew 28

The SIFT method of biblical hermeneutics and liturgical preaching has its roots in a theological perspective grounded in the doctrine of creation, in a hermeneutical method grounded in reader perspective, and in a psychological model of individual differences concerning perceiving and evaluating rooted in Jungian psychological type theory. The present study set out to test the empirical bases of the SIFT method among one group of trainee preachers (26 ministry training candidates) and one group of experienced preachers (21 Anglican clergy and readers) who explored the resurrection narratives presented in Mark 16:1-8 and Matthew 28:1-15 within working groups constructed according to psychological type preferences. These data support the psychological principles underpinning the SIFT method of biblical hermeneutics and liturgical preaching.

Efficient spike tests for linear integrate-and-fire neuron models in time-driven simulations

Event Abstract Back to Event Efficient spike tests for linear integrate-and-fire neuron models in time-driven simulations Susanne Kunkel1, 2*, Moritz Helias3, Markus Diesmann3, 4, 5, 6 and Abigail Morrison1, 2 1 Albert-Ludwig University of Freiburg, Faculty of Biology, Germany 2 Albert-Ludwig University of Freiburg, Bernstein Center Freiburg, Germany 3 Research Center Jülich, Computational and Systems Neuroscience, Germany 4 RIKEN Computational Science Research Program, Japan 5 RIKEN Brain Science Institute, Japan 6 RWTH Aachen University, Medical Faculty, Germany The characteristics of time-driven simulation are a fixed-size simulation step and a fixed-size communication interval [1]. The former defines update-and-check points, which are the discrete points in time when all neurons update their state variables and check for a super-threshold membrane potential. The latter defines the discrete points in time when all neurons communicate their spikes. The communication interval is a multiple of the simulation step size and limited only by the minimum synaptic transmission delay in the network. Traditionally, spikes are incorporated, detected and emitted only at the pre-defined update-and-check points. However, the time-driven environment of the simulator NEST [2] provides an 'off-grid' framework that enables spikes to be be incorporated and emitted at any point in time [3,4]. For each neuron the arrival times of incoming spikes introduce additional update-and-check points. As the detection of a threshold crossing can only take place at the next check point, time-driven simulation still bears the risk of missing a threshold crossing: a very brief excursion of the membrane potential above threshold may not be detected. This problem is more pronounced in networks with low connectivity and strong coupling as well as in the case of low firing rates. Here, we investigate spike tests of increasing complexity and specificity that can supplement the standard test for a super-threshold membrane potential at each check point and that guarantee the detection of all threshold crossings. Firstly, we determine the specificities of simple sifting methods for a range of input scenarios. Secondly, we compare the performances of complex spike tests which faithfully indicate the existence of a threshold crossing between the last and the current check point. This stepwise analysis enables us to identify a cascade of tests which locates all threshold crossings at a low computational cost. Acknowledgements Partially funded by BMBF Grant 01GQ0420 to BCCN Freiburg, EU Grant 15879 (FACETS), EU Grant 269921 (BrainScaleS), the Helmholtz Alliance on Systems Biology (Germany), the Next-Generation Supercomputer Project of MEXT (Japan), Neurex, and the Junior Professor Program of Baden-Württemberg.

Saliency and Gist Features for Target Detection in Satellite Images

Reliably detecting objects in broad-area overhead or satellite images has become an increasingly pressing need, as the capabilities for image acquisition are growing rapidly. The problem is particularly difficult in the presence of large intraclass variability, e.g., finding "boats" or "buildings," where model-based approaches tend to fail because no good model or template can be defined for the highly variable targets. This paper explores an automatic approach to detect and classify targets in high-resolution broad-area satellite images, which relies on detecting statistical signatures of targets, in terms of a set of biologically-inspired low-level visual features. Broad-area images are cut into small image chips, analyzed in two complementary ways: "attention/saliency" analysis exploits local features and their interactions across space, while "gist" analysis focuses on global nonspatial features and their statistics. Both feature sets are used to classify each chip as containing target(s) or not, using a support vector machine. Four experiments were performed to find "boats" (Experiments 1 and 2), "buildings" (Experiment 3) and "airplanes" (Experiment 4). In experiment 1, 14 416 image chips were randomly divided into training (300 boat, 300 nonboat) and test sets (13 816), and classification was performed on the test set (ROC area: 0.977 ± 0.003). In experiment 2, classification was performed on another test set of 11 385 chips from another broad-area image, keeping the same training set as in experiment 1 (ROC area: 0.952 ± 0.006). In experiment 3, 600 training chips (300 for each type) were randomly selected from 108 885 chips, and classification was conducted (ROC area: 0.922 ± 0.005). In experiment 4, 20 training chips (10 for each type) were randomly selected to classify the remaining 2581 chips (ROC area: 0.976 ± 0.003). The proposed algorithm outperformed the state-of-the-art SIFT, HMAX, and hidden-scale salient structure methods, and previous gist-only features in all four experiments. This study shows that the proposed target search method can reliably and effectively detect highly variable target objects in large image datasets.

Large scale egomotion and error analysis with visual features

Several works deal with 3D data in SLAM problem but many of them are focused on short scale maps. In this paper, we propose a method that can be used for computing the 6DoF trajectory performed by a robot from the stereo images captured during a large scale trajectory. The method transforms robust 2D features extracted from the reference stereo images to the 3D space. These 3D features are then used for obtaining the correct robot movement. Both Sift and Surf methods for feature extraction have been used. Also, a comparison between our method and the results of the ICP algorithm have been performed. We have also made a study about errors in stereo cameras.

Automated inference of molecular mechanisms of disease from amino acid substitutions

Advances in high-throughput genotyping and next generation sequencing have generated a vast amount of human genetic variation data. Single nucleotide substitutions within protein coding regions are of particular importance owing to their potential to give rise to amino acid substitutions that affect protein structure and function which may ultimately lead to a disease state. Over the last decade, a number of computational methods have been developed to predict whether such amino acid substitutions result in an altered phenotype. Although these methods are useful in practice, and accurate for their intended purpose, they are not well suited for providing probabilistic estimates of the underlying disease mechanism. We have developed a new computational model, MutPred, that is based upon protein sequence, and which models changes of structural features and functional sites between wild-type and mutant sequences. These changes, expressed as probabilities of gain or loss of structure and function, can provide insight into the specific molecular mechanism responsible for the disease state. MutPred also builds on the established SIFT method but offers improved classification accuracy with respect to human disease mutations. Given conservative thresholds on the predicted disruption of molecular function, we propose that MutPred can generate accurate and reliable hypotheses on the molecular basis of disease for approximately 11% of known inherited disease-causing mutations. We also note that the proportion of changes of functionally relevant residues in the sets of cancer-associated somatic mutations is higher than for the inherited lesions in the Human Gene Mutation Database which are instead predicted to be characterized by disruptions of protein structure. http://mutdb.org/mutpred predrag@indiana.edu; smooney@buckinstitute.org.