scholarly journals A New Feature Descriptor for Multimodal Image Registration Using Phase Congruency

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5105
Author(s):  
Guorong Yu ◽  
Shuangming Zhao
Keyword(s):  
Computational Time ◽  
Feature Descriptor ◽  
Phase Congruency ◽  
Multimodal Image Registration ◽  
Spectral Bands ◽  
Intensity Changes ◽  
New Feature ◽  
Image Pairs ◽  
Invariant Properties ◽  
Minimum Moment

Images captured by different sensors with different spectral bands cause non-linear intensity changes between image pairs. Classic feature descriptors cannot handle this problem and are prone to yielding unsatisfactory results. Inspired by the illumination and contrast invariant properties of phase congruency, here, we propose a new descriptor to tackle this problem. The proposed descriptor generation mainly involves three steps. (1) Images are convolved with a bank of log-Gabor filters with different scales and orientations. (2) A window of fixed size is selected and divided into several blocks for each keypoint, and an oriented magnitude histogram and the orientation of the minimum moment of a phase congruency-based histogram are calculated in each block. (3) These two histograms are normalized respectively and concatenated to form the proposed descriptor. Performance evaluation experiments on three datasets were carried out to validate the superiority of the proposed method. Experimental results indicated that the proposed descriptor outperformed most of the classic and state-of-art descriptors in terms of precision and recall within an acceptable computational time.

scholarly journals Random Binary Local Patch Clustering Transforms Based Image Matching for Nonlinear Intensity Changes

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Han Wang ◽  
Zhihuo Xu ◽  
Hanseok Ko
Keyword(s):  
Image Matching ◽  
Feature Descriptor ◽  
Coding Method ◽  
Intensity Changes ◽  
Patch Clustering ◽  
Local Patch ◽  
New Space ◽  
New Feature ◽  
Pattern Coding ◽  
Spatial Support

This paper presents a new feature descriptor that is suitable for image matching under nonlinear intensity changes. The proposed approach consists of the following three steps. First, a binary local patch clustering transform response is employed as the transform space. The value of the new space exhibits a high similarity after changes in intensity. Then, a random binary pattern coding method extracts raw feature histograms from the new space. Finally, the discrimination of the proposed feature descriptor is enhanced by using a multiple spatial support region-based binning method. Experimental results show that the proposed method is able to provide a more robust image matching performance under nonlinear intensity changes.

NSCT and focus measure optimization based multi-focus image fusion

2021 ◽  
pp. 1-13
Author(s):  
N. Aishwarya ◽  
C. BennilaThangammal ◽  
N.G. Praveena
Keyword(s):  
Simulation Analysis ◽  
Structural Information ◽  
Research Area ◽  
Processing Technique ◽  
The Novel ◽  
Phase Congruency ◽  
Focus Measure ◽  
Fused Image ◽  
Focus Image ◽  
Image Pairs

Getting a complete description of scene with all the relevant objects in focus is a hot research area in surveillance, medicine and machine vision applications. In this work, transform based fusion method called as NSCT-FMO, is introduced to integrate the image pairs having different focus features. The NSCT-FMO approach basically contains four steps. Initially, the NSCT is applied on the input images to acquire the approximation and detailed structural information. Then, the approximation sub band coefficients are merged by employing the novel Focus Measure Optimization (FMO) approach. Next, the detailed sub-images are combined using Phase Congruency (PC). Finally, an inverse NSCT operation is conducted on synthesized sub images to obtain the initial synthesized image. To optimize the initial fused image, an initial decision map is first constructed and morphological post-processing technique is applied to get the final map. With the help of resultant map, the final synthesized output is produced by the selection of focused pixels from input images. Simulation analysis show that the NSCT-FMO approach achieves fair results as compared to traditional MST based methods both in qualitative and quantitative assessments.

scholarly journals Assessment of the Single-Mixture Gas Assumption for the Correlated K-Distribution Fictitious Gas Method in H2O–CO2–CO Mixture at High Temperature

2008 ◽  
Vol 130 (10) ◽  
Author(s):  
C. Caliot ◽  
G. Flamant ◽  
M. El Hafi ◽  
Y. Le Maoult
Keyword(s):  
Remote Sensing ◽  
Path Length ◽  
Narrow Band ◽  
Specific Area ◽  
Sensitivity Study ◽  
Computational Time ◽  
Line Of Sight ◽  
Spectral Bands ◽  
Integrated Intensity ◽  
Atmospheric Path

This paper deals with the comparison of spectral narrow band models based on the correlated-K (CK) approach in the specific area of remote sensing of plume signatures. The CK models chosen may or may not include the fictitious gas (FG) idea and the single-mixture-gas assumption (SMG). The accuracy of the CK and the CK-SMG as well as the CKFG and CKFG-SMG models are compared, and the influence of the SMG assumption is inferred. The errors induced by each model are compared in a sensitivity study involving the plume thickness and the atmospheric path length as parameters. This study is conducted in two remote-sensing situations with different absolute pressures at sea level (105Pa) and at high altitude (16.6km, 104Pa). The comparisons are done on the basis of the error obtained for the integrated intensity while leaving a line of sight that is computed in three common spectral bands: 2000–2500cm−1, 3450–3850cm−1, and 3850–4150cm−1. In most situations, the SMG assumption induces negligible differences. Furthermore, compared to the CKFG model, the CKFG-SMG model results in a reduction of the computational time by a factor of 2.

A New Pedestrian Feature Description Method Named Neighborhood Descriptor of Oriented Gradients

2021 ◽  
Vol 16 (1) ◽  
pp. 23-55
Author(s):  
Qian Liu ◽  
Feng Yang ◽  
XiaoFen Tang
Keyword(s):  
Feature Vector ◽  
Pedestrian Detection ◽  
Weight Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Local Weight ◽  
Detection Approach ◽  
Training Samples ◽  
New Feature ◽  
Linear Svm

In view of the issue of the mechanism for enhancing the neighbourhood relationship of blocks of HOG, this paper proposes neighborhood descriptor of oriented gradients (NDOG), an improved feature descriptor based on HOG, for pedestrian detection. To obtain the NDOG feature vector, the algorithm calculates the local weight vector of the HOG feature descriptor, while integrating spatial correlation among blocks, concatenates this weight vector to the tail of the HOG feature descriptor, and uses the gradient norm to normalize this new feature vector. With the proposed NDOG feature vector along with a linear SVM classifier, this paper develops a complete pedestrian detection approach. Experimental results for the INRIA, Caltech-USA, and ETH pedestrian datasets show that the approach achieves a lower miss rate and a higher average precision compared with HOG and other advanced methods for pedestrian detection especially in the case of insufficient training samples.

scholarly journals Image Updating for Brain Shift Compensation During Resection

2017 ◽  
Vol 14 (4) ◽  
pp. 402-411 ◽  
Author(s):  
Xiaoyao Fan ◽  
David W Roberts ◽  
Jonathan D Olson ◽  
Songbai Ji ◽  
Timothy J Schaewe ◽  
...  
Keyword(s):  
Biomechanical Model ◽  
Partial Resection ◽  
Computational Time ◽  
Brain Shift ◽  
Registration Errors ◽  
Brain Deformation ◽  
Preoperative Magnetic Resonance ◽  
Surgical Field ◽  
Image Pairs ◽  
Dural Opening

Abstract BACKGROUND In open-cranial neurosurgery, preoperative magnetic resonance (pMR) images are typically coregistered for intraoperative guidance. Their accuracy can be significantly degraded by intraoperative brain deformation, especially when resection is involved. OBJECTIVE To produce model updated MR (uMR) images to compensate for brain shift that occurred during resection, and evaluate the performance of the image-updating process in terms of accuracy and computational efficiency. METHODS In 14 resection cases, intraoperative stereovision image pairs were acquired after dural opening and during resection to generate displacement maps of the surgical field. These data were assimilated by a biomechanical model to create uMR volumes of the evolving surgical field. A tracked stylus provided independent measurements of feature locations to quantify target registration errors (TREs) in the original coregistered pMR and uMR as surgery progressed. RESULTS Updated MR TREs were 1.66 ± 0.27 and 1.92 ± 0.49 mm in the 14 cases after dural opening and after partial resection, respectively, compared to 8.48 ± 3.74 and 8.77 ± 4.61 mm for pMR, respectively. The overall computational time for generating uMRs after partial resection was less than 10 min. CONCLUSION We have developed an image-updating system to compensate for brain deformation during resection using a computational model with data assimilation of displacements measured with intraoperative stereovision imaging that maintains TREs less than 2 mm on average.

Feature Extraction and Representation for Distributed Multi-View Human Action Recognition

2013 ◽  
Vol 3 (2) ◽  
pp. 145-154 ◽  
Author(s):  
Jiajia Luo ◽  
Wei Wang ◽  
Hairong Qi
Keyword(s):  
Action Recognition ◽  
Approximation Error ◽  
Human Action Recognition ◽  
Human Action ◽  
Base Station ◽  
Feature Representation ◽  
Superior Performance ◽  
Feature Descriptor ◽  
Testing Stage ◽  
New Feature

Multi-view human action recognition has gained a lot of attention in recent years for its superior performance as compared to single view recognition. In this paper, we propose a new framework for the real-time realization of human action recognition in distributed camera networks (DCNs). We first present a new feature descriptor (Mltp-hist) that is tolerant to illumination change, robust in homogeneous region and computationally efficient. Taking advantage of the proposed Mltp-hist, the noninformative 3-D patches generated from the background can be further removed automatically that effectively highlights the foreground patches. Next, a new feature representation method based on sparse coding is presented to generate the histogram representation of local videos to be transmitted to the base station for classification. Due to the sparse representation of extracted features, the approximation error is reduced. Finally, at the base station, a probability model is produced to fuse the information from various views and a class label is assigned accordingly. Compared to the existing algorithms, the proposed framework has three advantages while having less requirements on memory and bandwidth consumption: 1) no preprocessing is required; 2) communication among cameras is unnecessary; and 3) positions and orientations of cameras do not need to be fixed. We further evaluate the proposed framework on the most popular multi-view action dataset IXMAS. Experimental results indicate that our proposed framework repeatedly achieves state-of-the-art results when various numbers of views are tested. In addition, our approach is tolerant to the various combination of views and benefit from introducing more views at the testing stage. Especially, our results are still satisfactory even when large misalignment exists between the training and testing samples.

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