Automatic image annotation has been an active research topic in the last decade due to its potentially large impact on image retrieval, object recognition and image understanding. Many approaches have been proposed fo...
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Recently, asymmetric 3D-2D face recognition has been paid increasing attention. It enrolls in textured 3D faces and performs identification using only 2D facial images, therefore it generally achieves a better result ...
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Recently, asymmetric 3D-2D face recognition has been paid increasing attention. It enrolls in textured 3D faces and performs identification using only 2D facial images, therefore it generally achieves a better result than 2D algorithms do, and avoids inconvenience of data acquisition and computation of 3D methods as well. In this paper, a biological vision-based facial representation, namely Oriented Gradient Maps (OGMs), is introduced for such an application. It simulates the response of complex neurons to gradient information within a pre-defined neighborhood, and thus can describe local texture changes of 2D faces and local geometry variations of 3D faces at the same time. Due to its property of being highly distinctive, these OGMs improve accuracies of both matching steps of asymmetric face recognition, i.e. (1) 3D-2D matching using Canonical Correlation Analysis (CCA); (2) 2D-2D matching using LBP histogram based features and Sparse Representation Classifier (SRC). Some comparative experiments are carried out on the complete FRGC v2.0 database, and the achieved results clearly highlight the effectiveness of the biological vision-based facial description and its successful application to asymmetric face recognition.
The accuracy and repeatability of computer aided cervical vertebra landmarking (CACVL) were investigated in cephalogram.120 adolescents (60 boys,60 girls) aged from 9.1 to 17.2 years old were randomly ***-seven landma...
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The accuracy and repeatability of computer aided cervical vertebra landmarking (CACVL) were investigated in cephalogram.120 adolescents (60 boys,60 girls) aged from 9.1 to 17.2 years old were randomly ***-seven landmarks from the second to fifth cervical vertebrae on the lat-eral cephalogram were *** this study,the system of CACVL was developed and used to iden-tify and calculate the landmarks by fast marching method and parabolic curve *** accuracy and repeatability in CACVL group were compared with those in two manual landmarking groups [orthodon-tic experts (OE) group and orthodontic novices (ON) group].The results showed that,as for the accu-racy,there was no significant difference between CACVL group and OE group no matter in x-axis or y-axis (P>0.05),but there was significant difference between CACVL group and ON group,as well as OE group and ON group in both axes (P<0.05).As for the repeatability,CACVL group was more reli-able than OE group and ON group in both *** is concluded that CACVL has the same or higher ac-curacy,better repeatability and less workload than manual landmarking ***’s reliable for cervi-cal parameters identification on the lateral cephalogram and cervical vertebral maturation prediction in orthodontic practice and research.
Tissue P systems are distributed parallel and non-deterministic computing models in the framework of membrane computing, which are inspired by intercellular communication and cooperation between neurons. Recently, cel...
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Tissue P systems are distributed parallel and non-deterministic computing models in the framework of membrane computing, which are inspired by intercellular communication and cooperation between neurons. Recently, cell separation is introduced into tissue P systems, which enables systems to generate an exponential workspace in a polynomial time. In this work, the computational power of tissue P systems with cell separation is investigated. Specifically, a uniform family of tissue P systems with cell separation is constructed for effciently solving a well-known NP-complete problem, the partition problem.
Recently, Gutiérrez-Naranjo and Leporati considered performing basic arithmetic operations on a new class of bioinspired computing devices - spiking neural P systems (for short, SN P systems). However, the binary...
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The potential use of molecular computation in attacking the Data Encryption Standard (DES) is already known, but the used computing models are not autonomous and require many tedious laboratory steps to execute. In th...
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Common algorithmic problem is an optimization problem, which has the nice property that several other NPcomplete problems can be reduced to it in linear time. A tissue P system with cell division is a computing model ...
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Gender is an important demographic attribute of human beings, automatic face based gender classification has promising applications in various fields. Previous methods mainly deal with frontal face images, which in ma...
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ISBN:
(纸本)9781457713583
Gender is an important demographic attribute of human beings, automatic face based gender classification has promising applications in various fields. Previous methods mainly deal with frontal face images, which in many cases can not be easily obtained. In contrast, we concentrate on gender classification based on face profiles and ear images in this paper. Hierarchical and discriminative bag of features technique is proposed to extract powerful features which are classified by support vector classification (SVC) with histogram intersection kernel. With the output of SVC, fusion of multi-modalities is performed at the score level based on Bayesian analysis to improve the accuracy. Experiments are conducted using texture images of the UND biometrics data sets Collection F, and average classification accuracy of 97.65% is achieved, which is comparable to the state of the art. Our work can be used in cooperate with existing frontal face based methods for accurate multi- view gender classification.
This paper presents a novel method for synthesizing artificial visual light (VIS) face images from near-infrared (NIR) inputs. Active NIR imaging is now widely employed because it is unobtrusive, invariant of environm...
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This paper presents a novel method for synthesizing artificial visual light (VIS) face images from near-infrared (NIR) inputs. Active NIR imaging is now widely employed because it is unobtrusive, invariant of environmental illuminations, and can penetrate glasses and sweats. Unfortunately, NIR imaging exhibits discrepant photic properties compared with VIS imaging. Based on recent results of re search on compressive sensing, natural images can be compressed and recovered with an overcomplete dictionary by sparse representation coefficients. In our approach a pair wise dictionary is trained from randomly sampled coupled face patches, which contains sparse coded base functions to reconstruct representation coefficients via l 1 -minimization. We will demonstrate that this method is robust to moderate pose and expression variations, and is efficient in computing. Comparative experiments are conducted with state-of the-art algol 1 -minimization. We will demonstraterithms.
Although time of flight (TOF) cameras are becoming more popular, their low spatial resolution limits their usefulness. In this paper, we propose two new super-resolution methods to improve the resolution of depth maps...
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ISBN:
(纸本)9781612848334
Although time of flight (TOF) cameras are becoming more popular, their low spatial resolution limits their usefulness. In this paper, we propose two new super-resolution methods to improve the resolution of depth maps generated by TOF cameras. One is based on the Lucas Kanade (LK) optical flow algorithm, and another is based on the scale-invariant feature transform (SIFT) algorithm. The results show that the proposed LK optical flow based method is more accurate and efficient in improving the spatial and depth resolution of depth maps than the proposed SIFT based method. It is more useful for real-time processing of depth images.
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