Haar-Like Multi-Granularity Texture Features for Pedestrian Detection

2017 ◽  
Vol 17 (04) ◽  
pp. 1750023 ◽  
Author(s):  
Ruzhong Cheng ◽  
Yongjun Zhang ◽  
Guoping Wang ◽  
Yong Zhao ◽  
Rahmatulloev Khusravsho
Keyword(s):  
Human Body ◽  
Pedestrian Detection ◽  
Texture Feature ◽  
Texture Features ◽  
Surveillance Systems ◽  
Robust Detection ◽  
Driver Support Systems ◽  
Haar Feature ◽  
Feature Dimension ◽  
Self Driving Cars

Pedestrian detection has been a significant problem for decades and remains a hot topic in computer vision. Pedestrian detection is one of the key algorithms for self-driving cars and some other functions in robotics, including driver support systems, road surveillance systems. In this paper, based on the characteristics of the human body and the Haar feature, the Haar-like multi-granularity local texture feature, i.e., multi-granularity Haar-like LBP (mgh-LBP), is proposed for pedestrian detection. The mgh-LBP feature combines four characteristics of the human body and their backgrounds to construct the Haar-like features, which can better describe human body texture and edge information. Compared with other texture features, including the rotation-invariant LBP feature, uniform LBP feature and basic-LBP feature, the proposed method greatly reduces the feature dimension and computational complexity, and obtains a higher pedestrian detection rate and robust detection performance.

scholarly journals Research on college gymnastics teaching model based on multimedia image and image texture feature analysis

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Ying Wu ◽  
Jikun Liu
Keyword(s):  
Human Body ◽  
Rapid Development ◽  
Texture Feature ◽  
Texture Features ◽  
Dynamics Simulation ◽  
Image Texture ◽  
Whole Body ◽  
Database Modeling ◽  
Rigid Model ◽  
The Subject

AbstractWith the rapid development of gymnastics technology, novel movements are also emerging. Due to the emergence of various complicated new movements, higher requirements are put forward for college gymnastics teaching. Therefore, it is necessary to combine the multimedia simulation technology to construct the human body rigid model and combine the image texture features to display the simulation image in texture form. In the study, GeBOD morphological database modeling was used to provide the data needed for the modeling of the whole-body human body of the joint and used for dynamics simulation. Simultaneously, in order to analyze and summarize the technical essentials of the innovative action, this experiment compared and analyzed the hem stage of the cross-headstand movement of the subject and the hem stage of the 180° movement. Research shows that the method proposed in this paper has certain practical effects.

MULTI-RESOLUTION CROWD DENSITY ESTIMATION BASED ON TEXTURE ANALYSIS AND LEARNING FROM DEMONSTRATION

2007 ◽  
Vol 04 (01) ◽  
pp. 1-14 ◽  
Author(s):  
GUOYUAN LIANG ◽  
KA KEUNG LEE ◽  
YANGSHENG XU
Keyword(s):  
Texture Analysis ◽  
Density Estimation ◽  
Texture Feature ◽  
Texture Features ◽  
Input Image ◽  
Support Vector ◽  
Surveillance Systems ◽  
Regression Problem ◽  
Crowd Density Estimation ◽  
Crowd Density

Crowd density estimation is very important for intelligent surveillance systems in public places. This paper presents an automatic method of estimating crowd density using texture analysis and machine learning. First the crowd scene is modeled as a series of multi-resolution image cells based on perspective projection. The cell size is normalized to obtain a uniform representation of texture features. Then the feature vectors of textures are extracted from each input image cell and the support vector machine (SVM) method is utilized to solve the regression problem for calculating the crowd density. In order to diminish the instability of texture feature measurements, a technique of searching the extrema in the Harris–Laplacian space is applied. Finally, the SVM method is used again to detect some abnormal situations caused by the changes in density distribution. Experiments on real crowd videos show the effectiveness of the proposed system.

scholarly journals Dimension Reduction of Hand and Face Feature Level Fusion in Multimodal Biometric Authentication

2019 ◽  
Vol 8 (9) ◽  
pp. 2930-2937
Keyword(s):  
Dimension Reduction ◽  
Feature Vector ◽  
Texture Feature ◽  
Texture Features ◽  
Image Texture ◽  
Computational Time ◽  
Biometric Authentication ◽  
Person Identification ◽  
Face Features ◽  
Feature Dimension

The proposed work is a multimodal biometric authentication approach with image texture feature dimension reduction of trained feature vector which leads reduction in memory size and in turn reduces the computational time. In this paper hand and face features are used for person identification. The texture features of hand image are extracted using Haar and several Daubechie’s of 2D-DWT followed by 2D- edge detector gives better identification with reduction in feature vector and face features are extracted by neighborhood common characterization with block based segmentation approach to estimate the disparity in face. The neighborhood common characterization based structure recognition with a person representative per sample is more effective. The neighborhood features are constructed by extracting the similar blocks in the image, the intra pixel disparity feature is obtained by exploiting external common images to estimate the feasible facial disparities. Neighborhood common characterization reduces the overall residual of the given features over the local feature, common disparity dictionary, and shape based residual of a block. Neighbourhood common characterization representation, of face recognize with one representative per person more effectively. The system uses either of the biometric traits for person identification with 99.98% of authentication rate.

scholarly journals MRI texture feature repeatability and image acquisition factor robustness, a phantom study and in silico study

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Joshua Shur ◽  
Matthew Blackledge ◽  
James D’Arcy ◽  
David J. Collins ◽  
Maria Bali ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Coefficient Of Variation ◽  
In Silico ◽  
Image Acquisition ◽  
Texture Feature ◽  
Texture Features ◽  
Concordance Correlation Coefficient ◽  
Concordance Correlation ◽  
Simulation Data ◽  
Mri Features

Abstract Purpose To evaluate robustness and repeatability of magnetic resonance imaging (MRI) texture features in water and tissue phantom test-retest study. Materials and methods Separate water and tissue phantoms were imaged twice with the same protocol in a test-retest experiment using a 1.5-T scanner. Protocols were acquired to favour signal-to-noise ratio and resolution. Forty-six features including first order statistics and second-order texture features were extracted, and repeatability was assessed by calculating the concordance correlation coefficient. Separately, base image noise and resolution were manipulated in an in silico experiment, and robustness of features was calculated by assessing percentage coefficient of variation and linear correlation of features with noise and resolution. These simulation data were compared with the acquired data. Features were classified by their degree (high, intermediate, or low) of robustness and repeatability. Results Eighty percent of the MRI features were repeatable (concordance correlation coefficient > 0.9) in the phantom test-retest experiment. The majority (approximately 90%) demonstrated a strong or intermediate correlation with image acquisition parameter, and 19/46 (41%) and 13/46 (28%) of features were highly robust to noise and resolution, respectively (coefficient of variation < 5%). Agreement between the acquired and simulation data varied, with the range of agreement within feature classes between 11 and 92%. Conclusion Most MRI features were repeatable in a phantom test-retest study. This phantom data may serve as a lower limit of feature MRI repeatability. Robustness of features varies with acquisition parameter, and appropriate features can be selected for clinical validation studies.

scholarly journals Upgraded Spatial Gray Level Dependence Matrices for Textural Analysis in Colon Cancer Tissues

2018 ◽  
Vol 7 (2.20) ◽  
pp. 291 ◽  
Author(s):  
B Saroja ◽  
A Selwin Mich Priyadharson
Keyword(s):  
Colon Cancer ◽  
Texture Feature ◽  
Image Data ◽  
Texture Features ◽  
Gray Level ◽  
Colon Tissue ◽  
Data Set ◽  
Histopathological Image ◽  
Cancer Biopsy ◽  
Cancer Tissues

Colon or Bowel or Colorectal Cancer (CRC) is commonly determined by diagnosing a sample of colon tissue and further analysed by medical imaging. The colon tissue classification method count on specific changes between texture features extracted from benign and malignant regions. The variations in the image acquisition methods effects the colon tissue analysis. In this paper, an Upgraded Spatial Gray Level Dependence Matrices (U-SGLDM) is emphasized to extract textural features. The licensed image set of all applicable types of tissues within colon cancer are used for experimentation. Several texture feature sets are extracted to show the significant differences among the eight colon cancer biopsy images in the image data set. The fractal dimension-Hurst Coefficient is added to U-SGLDM for long range assessment. The Prominence of the analysis evoked in the representation of histopathological image structure over longer periods.  

Magnetic Resonance Imaging Breast Scan Classification based on Texture Features and Long Short-Term Memory Model

2021 ◽  
Vol 19 (7) ◽  
pp. 41-47
Author(s):  
Suha Raheem Hilal ◽  
Hussain S. Hasan ◽  
Ali M. Hasan
Keyword(s):  
Mirror Symmetry ◽  
Short Term Memory ◽  
Texture Feature ◽  
Breast Mri ◽  
Texture Features ◽  
Gray Level ◽  
Short Term ◽  
Term Memory ◽  
Mri Scans ◽  
Long Short Term Memory

The aim of study is building new program for processing MRI images using MATLAB and to investigate different breast MRI detection algorithms that inform normal and abnormal scans of MRI. In this research an algorithm is proposed to extract texture feature and inform normal and abnormal scans of MRI. First, the MRI scans are pre- processed by image enhancement, intensity normalization, background segmentation and detection of mirror symmetry of breast. Second, the proposed gray level co- occurrence matrix (GLCM) and gray level run length matrix (GLRLM) methods are used to extract texture features from MRI T2-weighted and STIR images. Finally, these features are classified into normal and abnormal by using long short term memory (LSTM) model. The research will be validated using 326 datasets that downloaded from cancer imaging archive (TCIA). The achieved classification accuracy was 98.80%.

NIMG-56. USING RADIOMIC FEATURES FROM DAILY MAGNETIC RESONANCE IMAGING TO PREDICT RESPONSE TO RADIATION THERAPY IN GLIOBLASTOMA PATIENTS: A PILOT STUDY

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi142-vi142
Author(s):  
Kaylie Cullison ◽  
Garrett Simpson ◽  
Danilo Maziero ◽  
Kolton Jones ◽  
Radka Stoyanova ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Tumor Growth ◽  
Texture Feature ◽  
Texture Features ◽  
Gray Level ◽  
Difference Matrix ◽  
Patient Response ◽  
Significant Difference ◽  
Occurrence Matrix ◽  
Surrounding Areas

Abstract A dilemma in treating glioblastoma is that MRI after chemotherapy and radiation therapy (chemoRT) shows areas of presumed tumor growth in up to 50% of patients. These areas can represent true progression (TP), tumor growth with tumors non-responsive to treatment, or pseudoprogression (PP), edema and tumor necrosis with favorable treatment response. On imaging, TP and PP are usually not discernable. Patients in this study undergo six weeks of chemoRT on a combination MRI/RT device, receiving daily MRIs. The goal of this study is to explore the correlation of radiomics features with progression. The tumor lesion and surrounding areas of growth/edema were manually outlined as regions of interest (ROIs) for each daily T2-weighted MRI scan. The ROIs were used to calculate texture features: statistical features based on the gray-level co-occurrence matrix (GLCM), the gray-level zone size matrix (GLZSM), the gray-level run length matrix (GLRLM), and the neighborhood gray-tone difference matrix (NGTDM). Each of these matrix classes describe the probability of spatial relationships of gray levels occurring within the ROI. Daily texture features were averaged per week of treatment for each patient. Patient response was retrospectively defined as no progression (NP), TP, or PP. A Kruskal-Wallis test was performed to identify texture features that correlated most strongly with patient response. Forty texture features were calculated for 12 patients (19 treated, 7 excluded due to no T2 lesion or progression status unknown, 6 NP, 3 TP, 3 PP). There was a trend of more texture features correlating significantly with response in weeks 4-6 of treatment, compared to weeks 1-3. A particular texture feature, GLSZM Small Zone Low Gray-Level Emphasis, showed increasing difference between PP and TP over time, with significant difference during week 6 of treatment (p=0.0495). Future directions include correlating early outcomes with greater numbers of patients and daily multiparametric MRI.

scholarly journals Copy Move Forgery Detection Through Differential Excitation Component-Based Texture Features

2020 ◽  
Vol 12 (3) ◽  
pp. 27-44
Author(s):  
Gulivindala Suresh ◽  
Chanamallu Srinivasa Rao
Keyword(s):  
Feature Vector ◽  
Texture Feature ◽  
Texture Features ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Copy Move Forgery Detection ◽  
Occurrence Matrix ◽  
Differential Excitation

Copy-move forgery (CMF) is an established process to copy an image segment and pastes it within the same image to hide or duplicate a portion of the image. Several CMF detection techniques are available; however, better detection accuracy with low feature vector is always substantial. For this, differential excitation component (DEC) of Weber Law descriptor in combination with the gray level co-occurrence matrix (GLCM) approach of texture feature extraction for CMFD is proposed. GLCM Texture features are computed in four directions on DEC and this acts as a feature vector for support vector machine classifier. These texture features are more distinguishable and it is validated through other two proposed methods based on discrete wavelet transform-GLCM (DWT-GLCM) and GLCM. Experimentation is carried out on CoMoFoD and CASIA databases to validate the efficacy of proposed methods. Proposed methods exhibit resilience against many post-processing attacks. Comparative analysis with existing methods shows the superiority of the proposed method (DEC-GLCM) with regard to detection accuracy.

The Influence and Application of Textile Texture Features to Interior Soft Decoration

2014 ◽  
Vol 510 ◽  
pp. 293-296
Author(s):  
Xiao Dong Liu ◽  
Bo Han Yang ◽  
Geng Huang
Keyword(s):  
Rapid Development ◽  
Texture Feature ◽  
Texture Features ◽  
Development Trend ◽  
Domestic Economy ◽  
Human History ◽  
Interior Decoration ◽  
The People ◽  
Main Development

Textile, as a kind of universal significant material in the human history, becomes an increasing number of popular among the people. Besides, it has the double needs in the material in the interior decoration. With the rapid development of our domestic economy, people, in China, put forward to more higher standard for the demands of their indoor living, The soft decoration dominated by interior textiles has become the mainstream and main development trend of design field in our society. This article intends to analyze from the level of the texture feature, and from the four aspects to analyze the influence of textile texture in the indoor decoration: textile texture, the color of textile, pattern and cultural of textile, and the emotional of textile, meanwhile, around the texture features of the textile and extend the wide inspiration and thinking to the interior adornment.

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