Analysis and discrimination of ten different sponges by multi-step infrared spectroscopy

In this study,a convenient method using multi-step infrared spectroscopy,including Fourier transform infrared spectroscopy（FT-IR）,second derivative infrared spectroscopy（SD-IR） and two-dimensional correlation infrared spectroscopy（2DCOS-IR）,was employed to analyze and discriminate ten marine sponges from two classes collected from the Xisha Islands in the South China Sea.Each sponge had an exclusive macroscopic fingerprint.From the IR spectra,it was noted that the main ingredient of calcareous sponges was calcium carbonate,but that of demosponges was proteins.For sponges from the same genus or having highly similar chemical profile（IR spectral profile）,SD-IR and 2DCOS-IR were applied to successfully reveal the tiny differences.It was demonstrated that the multi-step infrared spectroscopy was a feasible and objective approach for marine sponge identification.

Asymmetric discriminative correlation filters for visual tracking

Discriminative correlation filters(DCF)are efficient in visual tracking and have advanced the field significantly.However,the symmetry of correlation(or convolution)operator results in computational problems and does harm to the generalized translation equivariance.The former problem has been approached in many ways,whereas the latter one has not been well recognized.In this paper,we analyze the problems with the symmetry of circular convolution and propose an asymmetric one,which as a generalization of the former has a weak generalized translation equivariance property.With this operator,we propose a tracker called the asymmetric discriminative correlation filter(ADCF),which is more sensitive to translations of targets.Its asymmetry allows the filter and the samples to have different sizes.This flexibility makes the computational complexity of ADCF more controllable in the sense that the number of filter parameters will not grow with the sample size.Moreover,the normal matrix of ADCF is a block matrix with each block being a two-level block Toeplitz matrix.With this well-structured normal matrix,we design an algorithm for multiplying an N×N two-level block Toeplitz matrix by a vector with time complexity O(N log N)and space complexity O(N),instead of O(N^2).Unlike DCF-based trackers,introducing spatial or temporal regularization does not increase the essential computational complexity of ADCF.Comparative experiments are performed on a synthetic dataset and four benchmarks,including OTB-2013,OTB-2015,VOT-2016,and Temple-Color,and the results show that our method achieves state-of-the-art visual tracking performance.

Robust template feature matching method using motion-constrained DCF designed for visual navigation in asteroid landing

A robust and eficient feature matching method is necessary for visual navigation in asteroid-landing missions.Based on the visual navigation framework and motion characteristics of asteroids,a robust and efficient template feature matching method is proposed to adapt to feature distortion and scale change cases for visual navigation of asteroids.The proposed method is primarily based on a motion-constrained discriminative correlation filter(DCF).The prior information provided by the motion constraints between sequence images is used to provide a predicted search region for template feature matching.Additionally,some specific template feature samples are generated using the motion constraints for correlation filter learning,which is beneficial for training a scale and feature distortion adaptive correlation filter for accurate feature matching.Moreover,average peak-to-correlation energy(APCE)and jointly consistent measurements(JCMs)were used to eliminate false matching.Images captured by the Touch And Go Camera System(TAGCAMS)of the Bennu asteroid were used to evaluate the performance of the proposed method.In particular,both the robustness and accuracy of region matching and template center matching are evaluated.The qualitative and quantitative results illustrate the advancement of the proposed method in adapting to feature distortions and large-scale changes during spacecraft landing.

DFD-Net:lung cancer detection from denoised CT scan image using deep learning

The availability of pulmonary nodules in CT scan image of lung does not completely specify cancer.The noise in an image and morphology of nodules,like shape and size has an implicit and complex association with cancer,and thus,a careful analysis should be mandatory on every suspected nodules and the combination of information of every nodule.In this paper,we introduce a“denoising first”two-path convolutional neural network(DFD-Net)to address this complexity.The introduced model is composed of denoising and detection part in an end to end manner.First,a residual learning denoising model(DR-Net)is employed to remove noise during the preprocessing stage.Then,a two-path convolutional neural network which takes the denoised image by DR-Net as an input to detect lung cancer is employed.The two paths focus on the joint integration of local and global features.To this end,each path employs different receptive field size which aids to model local and global dependencies.To further polish our model performance,in different way from the conventional feature concatenation approaches which directly concatenate two sets of features from different CNN layers,we introduce discriminant correlation analysis to concatenate more representative features.Finally,we also propose a retraining technique that allows us to overcome difficulties associated to the image labels imbalance.We found that this type of model easily first reduce noise in an image,balances the receptive field size effect,affords more representative features,and easily adaptable to the inconsistency among nodule shape and size.Our intensive experimental results achieved competitive results.

KDLPCCA-Based Projection for Feature Extraction in SSVEP-Based Brain-Computer Interfaces

An electroencephalogram(EEG)signal projection using kernel discriminative locality preserving canonical correlation analysis(KDLPCCA)-based correlation with steady-state visual evoked potential(SSVEP)templates for frequency recognition is presented in this paper.With KDLPCCA,not only a non-linear correlation but also local properties and discriminative information of each class sample are considered to extract temporal and frequency features of SSVEP signals.The new projected EEG features are classified with classical machine learning algorithms,namely,K-nearest neighbors(KNNs),naive Bayes,and random forest classifiers.To demonstrate the effectiveness of the proposed method,16-channel SSVEP data corresponding to 4 frequencies collected from 5 subjects were used to evaluate the performance.Compared with the state of the art canonical correlation analysis(CCA),experimental results show significant improvements in classification accuracy and information transfer rate(ITR),achieving 100%and 240 bits/min with 0.5 s sample block.The superior performance demonstrates that this method holds the promising potential to achieve satisfactory performance for high-accuracy SSVEP-based brain-computer interfaces.