A NOVEL METHOD USING VIDEOS FOR FINGERPRINT VERIFICATION

2012 ◽  
Vol 26 (01) ◽  
pp. 1256002 ◽  
Author(s):  
WEI QIN ◽  
YILONG YIN
Keyword(s):  
Experimental Results ◽  
Single Image ◽  
Fingerprint Verification ◽  
Static Information ◽  
Novel Method

Traditional fingerprint verifications use single image for matching. However, the verification accuracy cannot meet the need of some application domains. In this paper, we propose to use videos for fingerprint verification. To take full use of the information contained in fingerprint videos, we present a novel method to use the dynamic as well as the static information in fingerprint videos. After preprocessing and aligning processes, the Inclusion Ratio of two matching fingerprint videos is calculated and used to represent the similarity between these two videos. Experimental results show that video-based method can access better accuracy than the method based on single fingerprint.

Single image haze removal based on two steps

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740038
Author(s):  
Ruxi Xiang ◽  
Xifang Zhu ◽  
Feng Wu
Keyword(s):  
Input Image ◽  
Experimental Results ◽  
Filter Method ◽  
Ambient Light ◽  
Single Image ◽  
Atmospheric Scattering ◽  
Haze Removal ◽  
Novel Method ◽  
Realistic Scenes ◽  
Atmospheric Scattering Model

In this paper, a novel method named Haze Removal based on Two Steps (HRTS) for removing the haze has been proposed based on two steps, which obviously improves the image qualities such as color and visibility caused by haze. The proposed method mainly consists of two steps: the preprocessing step by decomposing the input image to reduce the influence of ambient light and the removed haze step for restoring the radiance. We first reduce the effect of the ambient light by decomposing the haze image, estimate the transmission map based on the result of the decomposition, and then use the modified guided filter method to refine it. Finally, the monochrome atmospheric scattering model is combined to restore the radiance image. Experimental results show that the proposed method could effectively remove the haze and obviously improve the color and visibility of the image in the realistic scenes by comparing other existing haze removal methods.

scholarly journals Obstacle Detection in Cluttered Traffic Environment Based on Candidate Generation and Classification

2006 ◽  
Vol 1 (4) ◽  
pp. 93 ◽  
Author(s):  
Qing Li ◽  
F.C. Sun
Keyword(s):  
Vehicle Detection ◽  
Obstacle Detection ◽  
Experimental Results ◽  
Geometrical Features ◽  
Traffic Environment ◽  
Novel Method ◽  
Shadow Cast

A novel method to detect vehicles is presented in the paper. Assumption of the vehicle is made using the geometrical features of the vehicle rear by the statistical histogram. Then hypothesis is verified using the property of the shadow cast by the car according to a prior acknowledgement of traffic scene. Finally, the vehicle detection is realized by hypothesis and verification of objects. The experimental results show the efficiency and feasibility of the method.

scholarly journals Single Image Dehazing Using Sparse Contextual Representation

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1266
Author(s):  
Jing Qin ◽  
Liang Chen ◽  
Jian Xu ◽  
Wenqi Ren
Keyword(s):  
Sparse Representation ◽  
State Of The Art ◽  
Input Image ◽  
Single Image ◽  
Image Dehazing ◽  
Dark Channel ◽  
Local Patch ◽  
Novel Method ◽  
Contextual Representation ◽  
Single Image Dehazing

In this paper, we propose a novel method to remove haze from a single hazy input image based on the sparse representation. In our method, the sparse representation is proposed to be used as a contextual regularization tool, which can reduce the block artifacts and halos produced by only using dark channel prior without soft matting as the transmission is not always constant in a local patch. A novel way to use dictionary is proposed to smooth an image and generate the sharp dehazed result. Experimental results demonstrate that our proposed method performs favorably against the state-of-the-art dehazing methods and produces high-quality dehazed and vivid color results.

Semi-supervised learning for raindrop removal on a single image

2021 ◽  
pp. 1-9
Author(s):  
Juan Zhang ◽  
Wenbin Guo
Keyword(s):  
Supervised Learning ◽  
Experimental Results ◽  
Real Image ◽  
Single Image ◽  
The Real ◽  
Rainy Weather

This article propose s a network that is mainly used to deal with a single image polluted by raindrops in rainy weather to get a clean image without raindrops. In the existing solutions, most of the methods rely on paired images, that is, the rain image and the real image without rain in the same scene. However, in many cases, the paired images are difficult to obtain, which makes it impossible to apply the raindrop removal network in many scenarios. Therefore this article proposes a semi-supervised rain-removing network apply to unpaired images. The model contains two parts: a supervised network and an unsupervised network. After the model is trained, the unsupervised network does not require paired images and it can get a clean image without raindrops. In particular, our network can perform training on paired and unpaired samples. The experimental results show that the best results are achieved not only on the supervised rain-removing network, but also on the unsupervised rain-removing network.

Novel Algorithmic Approach to Deciphering Rovash Inscriptions

2015 ◽  
pp. 7222-7233 ◽  
Author(s):  
Loránd Lehel Tóth ◽  
Raymond Pardede ◽  
Gábor Hosszú
Keyword(s):  
Experimental Results ◽  
The Novel ◽  
Statistical Probability ◽  
Algorithmic Approach ◽  
Topological Parameters ◽  
Novel Method ◽  
Novel Algorithm

The article presents a method to decipher Rovash inscriptions made by the Szekelys in the 15th-18th centuries. The difficulty of the deciphering work is that a large portion of the Rovash inscriptions contains incomplete words, calligraphic glyphs or grapheme errors. Based on the topological parameters of the undeciphered symbols registered in the database, the presented novel algorithm estimates the meaning of the inscriptions by the matching accuracies of the recognized graphemes and gives a statistical probability for deciphering. The developed algorithm was implemented in software, which also contains a built-in dictionary. Based on the dictionary, the novel method takes into account the context in identifying the meaning of the inscription. The proposed algorithm offers one or more words in a different random values as a result, from which users can select the relevant one. The article also presents experimental results, which demonstrate the efficiency of method.

scholarly journals Hierarchical Classification Based on Label Distribution Learning

2019 ◽  
Vol 33 ◽  
pp. 5533-5540 ◽  
Author(s):  
Changdong Xu ◽  
Xin Geng
Keyword(s):  
Hierarchical Classification ◽  
Experimental Results ◽  
Challenging Problem ◽  
Training Set ◽  
True Label ◽  
Major Bottleneck ◽  
Novel Method ◽  
Class Labels ◽  
Label Distribution ◽  
The Given

Hierarchical classification is a challenging problem where the class labels are organized in a predefined hierarchy. One primary challenge in hierarchical classification is the small training set issue of the local module. The local classifiers in the previous hierarchical classification approaches are prone to over-fitting, which becomes a major bottleneck of hierarchical classification. Fortunately, the labels in the local module are correlated, and the siblings of the true label can provide additional supervision information for the instance. This paper proposes a novel method to deal with the small training set issue. The key idea of the method is to represent the correlation among the labels by the label distribution. It generates a label distribution that contains the supervision information of each label for the given instance, and then learns a mapping from the instance to the label distribution. Experimental results on several hierarchical classification datasets show that our method significantly outperforms other state-of-theart hierarchical classification approaches.

scholarly journals Personalized Clothing Recommendation Based on User Emotional Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xueping Su ◽  
Meng Gao ◽  
Jie Ren ◽  
Yunhong Li ◽  
Matthias Rätsch
Keyword(s):  
Facial Expression ◽  
Recommendation System ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
Continuous Development ◽  
Expression Intensity ◽  
Novel Method ◽  
Multiclass Svm

With the continuous development of economy, consumers pay more attention to the demand for personalization clothing. However, the recommendation quality of the existing clothing recommendation system is not enough to meet the user’s needs. When browsing online clothing, facial expression is the salient information to understand the user’s preference. In this paper, we propose a novel method to automatically personalize clothing recommendation based on user emotional analysis. Firstly, the facial expression is classified by multiclass SVM. Next, the user’s multi-interest value is calculated using expression intensity that is obtained by hybrid RCNN. Finally, the multi-interest value is fused to carry out personalized recommendation. The experimental results show that the proposed method achieves a significant improvement over other algorithms.

Efficient Multi-Scale Registration of 3D Reconstructions Based on Camera Center Constraint

2014 ◽  
Vol 998-999 ◽  
pp. 1018-1023
Author(s):  
Rui Bin Guo ◽  
Tao Guan ◽  
Dong Xiang Zhou ◽  
Ke Ju Peng ◽  
Wei Hong Fan
Keyword(s):  
Experimental Results ◽  
Cayley Transform ◽  
3D Reconstructions ◽  
Multi Scale ◽  
The Matrix ◽  
Novel Method ◽  
3D Scene ◽  
Coordinates Transformation

Recent approaches for reconstructing 3D scenes from image collections only produce single scene models. To build a unified scene model that contains multiple subsets, we present a novel method for registration of 3D scene reconstructions in different scales. It first normalizes the scales of the models building on similarity reconstruction by the constraint of the 3D position of shared cameras. Then we use Cayley transform to fit the matrix of coordinates transformation for the models in normalization scales. The experimental results show the effectiveness and scalability of the proposed approach.

A novel method for detecting light source for digital images forensic

2011 ◽  
Vol 19 (2) ◽  
Author(s):  
A. Roy ◽  
S. Mitra ◽  
R. Agrawal
Keyword(s):  
Forensic Science ◽  
Light Source ◽  
Digital Image ◽  
Digital Images ◽  
Experimental Results ◽  
New Method ◽  
Light Sources ◽  
Image Forensic ◽  
Morality Politics ◽  
Novel Method

AbstractManipulation in image has been in practice since centuries. These manipulated images are intended to alter facts — facts of ethics, morality, politics, sex, celebrity or chaos. Image forensic science is used to detect these manipulations in a digital image. There are several standard ways to analyze an image for manipulation. Each one has some limitation. Also very rarely any method tried to capitalize on the way image was taken by the camera. We propose a new method that is based on light and its shade as light and shade are the fundamental input resources that may carry all the information of the image. The proposed method measures the direction of light source and uses the light based technique for identification of any intentional partial manipulation in the said digital image. The method is tested for known manipulated images to correctly identify the light sources. The light source of an image is measured in terms of angle. The experimental results show the robustness of the methodology.

scholarly journals Online Strategy Clustering Based on Action Sequences in RoboCupSoccer Small Size League

Robotics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 58
Author(s):  
Yusuke Adachi ◽  
Masahide Ito ◽  
Tadashi Naruse
Keyword(s):  
Experimental Results ◽  
Clustering Method ◽  
Learning Problem ◽  
Data Set ◽  
Geometric Data ◽  
Action Sequences ◽  
Novel Method ◽  
Online Strategy

This paper addresses a strategy learning problem in the RoboCupSoccer Small Size League (SSL). We propose a novel method based on action sequences to cluster an opponent’s strategies online. Our proposed method is composed of the following three steps: (1) extracting typical actions from geometric data to make action sequences, (2) calculating the dissimilarity of the sequences, and (3) clustering the sequences by using the dissimilarity. This method can reduce the amount of data used in the clustering process; handling action sequences instead of geometric data as data-set makes it easier to search actions. As a result, the proposed clustering method is online feasible and also is applicable to countering an opponent’s strategy. The effectiveness of the proposed method was validated by experimental results.

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