A Tracking Window Adaptive Compressive Tracking Algorithm

In the original compression tracking algorithm, the size of the tracking box is fixed. There should be better tracking results for scale-invariant objects, but worse tracking results for scale-variant objects. To overcome this defect, a scale-adaptive compressive tracking (CT) algorithm is proposed. First of all, the imbalance of the gray and texture features in the original CT algorithm is balanced by the multi-feature method, which makes the algorithm more robust. Then, searching different candidate regions by using the method of multi-scale search along with feature normalization makes the features extracted from different scales comparable. Finally, the candidate region with the maximum discriminate degree is selected as the object region. Thus, the tracking-box size is adaptive. The experimental results show that when the object scale changes, the improving CT algorithm has higher accuracy and robustness than the original CT algorithm.

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Variable Scale Compressive Tracking Based on Structural Constraint Sample

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.488-489.1074 ◽

2014 ◽

Vol 488-489 ◽

pp. 1074-1078

Author(s):

Lu Ping Zhang ◽

Meng Cai ◽

Biao Li ◽

Lu Ping Wang

Keyword(s):

State Of The Art ◽

Tracking Algorithm ◽

Experimental Results ◽

Structural Constraint ◽

Scale Problem ◽

Scale Selection ◽

Compressive Tracking ◽

Image Patches ◽

Basic Attitude ◽

Selection Of

A variable scale compressive tracking algorithm based on structural constraint sample is presented to solve the variable scale problem in this paper. A number of scanning windows with different scales and positions are obtained by structural constraint sampling.Some sparse random sensing matrices with different scales that can be computed offline easily are adopted to extract the features of different foreground target and background sample image patches with relevant scales online, the sample patch having a maximal score is regarded as the new tracking result by classifying the compressive features via a naive bayesian classifier,meanwhile,to update the location and scale. Experimental results show the proposed algorithm performs favorably against state-of-the-art algorithms on challenging sequences in terms of the basic attitude and scale change, which is robust and does not depend on the scale selection of the initial tracking area.

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An Improved Tracking Algorithm Combing Color and LBP Texture Features Based on Particle Filter

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.385-386.1484 ◽

2013 ◽

Vol 385-386 ◽

pp. 1484-1487

Author(s):

En Zeng Dong ◽

Li Ya Su ◽

Yan Hong Fu

Keyword(s):

Particle Filter ◽

Object Tracking ◽

Reference Model ◽

Texture Features ◽

Tracking Algorithm ◽

Color Histogram ◽

Experimental Results ◽

Single Feature ◽

Highly Effective

In this paper, an tracking algorithm combing color and LBP texture features based on particle filter is proposed to overcome the disadvantages of existing particle filter object tracking methods. A color histogram and a texture histogram were combined to build the objects reference model, effectively improving the accuracy of object tracking. Experimental results demonstrate that, compared with the method based on single feature, the proposed method is highly effective, valid and is practicable.

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Multi-scale characterizations of colon polyps via computed tomographic colonography

Visual Computing for Industry Biomedicine and Art ◽

10.1186/s42492-019-0032-7 ◽

2019 ◽

Vol 2 (1) ◽

Author(s):

Weiguo Cao ◽

Marc J. Pomeroy ◽

Yongfeng Gao ◽

Matthew A. Barish ◽

Almas F. Abbasi ◽

...

Keyword(s):

Area Under The Curve ◽

Texture Features ◽

Learning Model ◽

Colon Polyps ◽

Texture Descriptor ◽

Operating Characteristics ◽

Computed Tomographic ◽

Multi Scale ◽

Definition Of ◽

Occurrence Matrix

AbstractTexture features have played an essential role in the field of medical imaging for computer-aided diagnosis. The gray-level co-occurrence matrix (GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications. This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor. In this study, we first introduce a new parameter - stride, to explore the definition of GLCM. Then we propose three multi-scaling GLCM models according to its three parameters, (1) learning model by multiple displacements, (2) learning model by multiple strides (LMS), and (3) learning model by multiple angles. These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model. To further analyze the three parameters, we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas. Finally, the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model. LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.

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A parametric prediction of the Young’s modulus of polymers enhanced with ΜWCNTs

MATEC Web of Conferences ◽

10.1051/matecconf/201823300025 ◽

2018 ◽

Vol 233 ◽

pp. 00025

Author(s):

P.V. Polydoropoulou ◽

K.I. Tserpes ◽

Sp.G. Pantelakis ◽

Ch.V. Katsiropoulos

Keyword(s):

Young’S Modulus ◽

Elastic Properties ◽

Young's Modulus ◽

Experimental Results ◽

Scale Model ◽

Fe Model ◽

Multi Scale ◽

Unit Cells ◽

Random Dispersion

In this work a multi-scale model simulating the effect of the dispersion, the waviness as well as the agglomerations of MWCNTs on the Young’s modulus of a polymer enhanced with 0.4% MWCNTs (v/v) has been developed. Representative Unit Cells (RUCs) have been employed for the determination of the homogenized elastic properties of the MWCNT/polymer. The elastic properties computed by the RUCs were assigned to the Finite Element (FE) model of a tension specimen which was used to predict the Young’s modulus of the enhanced material. Furthermore, a comparison with experimental results obtained by tensile testing according to ASTM 638 has been made. The results show a remarkable decrease of the Young’s modulus for the polymer enhanced with aligned MWCNTs due to the increase of the CNT agglomerations. On the other hand, slight differences on the Young’s modulus have been observed for the material enhanced with randomly-oriented MWCNTs by the increase of the MWCNTs agglomerations, which might be attributed to the low concentration of the MWCNTs into the polymer. Moreover, the increase of the MWCNTs waviness led to a significant decrease of the Young’s modulus of the polymer enhanced with aligned MWCNTs. The experimental results in terms of the Young’s modulus are predicted well by assuming a random dispersion of MWCNTs into the polymer.

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A Multi-Resolution Approach to GAN-Based Speech Enhancement

Applied Sciences ◽

10.3390/app11020721 ◽

2021 ◽

Vol 11 (2) ◽

pp. 721

Author(s):

Hyung Yong Kim ◽

Ji Won Yoon ◽

Sung Jun Cheon ◽

Woo Hyun Kang ◽

Nam Soo Kim

Keyword(s):

Speech Enhancement ◽

Optimal Solution ◽

Experimental Results ◽

Generative Adversarial Networks ◽

The Real ◽

Multi Scale ◽

Adversarial Networks ◽

Speech Characteristics ◽

Conventional Methods ◽

Convex Property

Recently, generative adversarial networks (GANs) have been successfully applied to speech enhancement. However, there still remain two issues that need to be addressed: (1) GAN-based training is typically unstable due to its non-convex property, and (2) most of the conventional methods do not fully take advantage of the speech characteristics, which could result in a sub-optimal solution. In order to deal with these problems, we propose a progressive generator that can handle the speech in a multi-resolution fashion. Additionally, we propose a multi-scale discriminator that discriminates the real and generated speech at various sampling rates to stabilize GAN training. The proposed structure was compared with the conventional GAN-based speech enhancement algorithms using the VoiceBank-DEMAND dataset. Experimental results showed that the proposed approach can make the training faster and more stable, which improves the performance on various metrics for speech enhancement.

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A multi-scale image and dynamic candidate region-based automatic detection of foreign targets intruding the railway perimeter

Measurement ◽

10.1016/j.measurement.2021.109853 ◽

2021 ◽

pp. 109853

Author(s):

Chuan Li ◽

Zhengyu Xie ◽

Yong Qin ◽

Limin Jia ◽

Qi Chen

Keyword(s):

Automatic Detection ◽

Candidate Region ◽

Multi Scale

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A simple tracking algorithm using multi-scale gradient feature

2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing(ICCWAMTIP) ◽

10.1109/iccwamtip.2014.7073353 ◽

2014 ◽

Author(s):

Chao Cheng ◽

Zhen-Hua Guo ◽

Xue-Dan Zhang ◽

You-Bin Chen

Keyword(s):

Tracking Algorithm ◽

Multi Scale ◽

Gradient Feature

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Object Tracking Algorithm based on Multi-channel Extraction of AHLBP Texture Features

2018 International Conference on Advanced Mechatronic Systems (ICAMechS) ◽

10.1109/icamechs.2018.8506735 ◽

2018 ◽

Cited By ~ 2

Author(s):

Yitao Liang ◽

Yafei Li ◽

Kuibin Zhao ◽

Lei Meng

Keyword(s):

Object Tracking ◽

Texture Features ◽

Tracking Algorithm

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Tracking Algorithm of Multiple Pedestrians Based on Particle Filters in Video Sequences

Computational Intelligence and Neuroscience ◽

10.1155/2016/8163878 ◽

2016 ◽

Vol 2016 ◽

pp. 1-17 ◽

Cited By ~ 2

Author(s):

Hui Li ◽

Yun Liu ◽

Chuanxu Wang ◽

Shujun Zhang ◽

Xuehong Cui

Keyword(s):

Particle Filters ◽

A Priori ◽

Texture Features ◽

Tracking Algorithm ◽

Video Sequences ◽

Particle State ◽

Pedestrian Tracking ◽

Estimation Problems ◽

Non Gaussian ◽

Gaussian Estimation

Pedestrian tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in pedestrian tracking for nonlinear and non-Gaussian estimation problems. However, pedestrian tracking in complex environment is still facing many problems due to changes of pedestrian postures and scale, moving background, mutual occlusion, and presence of pedestrian. To surmount these difficulties, this paper presents tracking algorithm of multiple pedestrians based on particle filters in video sequences. The algorithm acquires confidence value of the object and the background through extracting a priori knowledge thus to achieve multipedestrian detection; it adopts color and texture features into particle filter to get better observation results and then automatically adjusts weight value of each feature according to current tracking environment. During the process of tracking, the algorithm processes severe occlusion condition to prevent drift and loss phenomena caused by object occlusion and associates detection results with particle state to propose discriminated method for object disappearance and emergence thus to achieve robust tracking of multiple pedestrians. Experimental verification and analysis in video sequences demonstrate that proposed algorithm improves the tracking performance and has better tracking results.

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