به نام خدا
Title: Using the idea of the sparse representation to perform coarse- to-fine face recognition
Authors: Yong Xu , Qi Zhu a , Zizhu Fan , David Zhang d , Jianxun Mi a , Zhihui Lai
Abstract: In this paper, we propose a coarse-to-fine face recognition method. This method consists of two stages and works in a similar way as the well-known sparse representation method. The first stage determines a linear combination of all the training samples that is approximately equal to the test sample. This stage exploits the determined linear combination to coarsely determine candidate class labels of the test sample. The second stage again deter- mines a weighted sum of all the training samples from the candidate classes that is approximately equal to the test sample and uses the weighted sum to perform classification. The rationale of the proposed method is as follows: the first stage identifies the classes that are far from the test sample and removes them from the set of the training samples. Then the method will assign the test sample into one of the remaining classes and the classification problem becomes a simpler one with fewer classes. The proposed method not only has a high accuracy but also can be clearly interpreted.
Publish Year: 2013
Published in: Information Sciences - Science Direct
موضوع: شناسایی چهره (Face Detection)
ایران سای – مرجع علمی فنی مهندسی
حامی دانش بومی ایرانیان
به نام خدا
Title: Application of the Mean Field Methods to MRF Optimization in Computer Vision
Authors: Masaki Saito Takayuki Okatani Koichiro Deguchi
Abstract: The mean field (MF) methods are an energy optimization method for Markov random fields (MRFs). These methods, which have their root in solid state physics, estimate the marginal density of each site of an MRF graph by iterative computation, similarly to loopy belief propagation (LBP).It appears that, being shadowed by LBP, the MF methods have not been seriously considered in the computer vision community. This study investigates whether these methods are useful for practical problems, particularly MPM (Maxi-mum Posterior Marginal) inference, in computer vision. To be specific, we apply the naive MF equations and the TAP (Thou less-Anderson-Palmer) equations to interactive segmentation and stereo matching. In this paper, firstly, we show implementation of these methods for computer vision problems. Next, we discuss advantages of the MF methods to LBP. Finally, we present experimental results that the MFmethods are well comparable to LBP in terms of accuracy and global convergence; furthermore, the 3rd-order TAP equation often outperforms LBP in terms of accuracy.
Publish Year: 2012
Published in: CVPR - IEEE
موضوع: بینایی ماشین (Computer Vision)
ایران سای – مرجع علمی فنی مهندسی
حامی دانش بومی ایرانیان
به نام خدا
Title: Threading Machine Generated Email
Authors: Nir Ailon, Zohar S Karnin, Edo Liberty, Yoelle Maarek
Abstract: Viewing email messages as parts of a sequence or a thread isa convenient way to quickly understand their context. Cur-rent threading techniques rely on purely syntactic methods,matching sender information, subject line, and reply/forwardpreï¬xes. As such, they are mostly limited to personal con-versations. In contrast, machine-generated email, whichamount, as per our experiments, to more than 60% of theoverall email traffic, requires a different kind of threadingthat should reflect how a sequence of emails is caused bya few related user actions. For example, purchasing goodsfrom an online store will result in a receipt or a conï¬rma-tion message, which may be followed, possibly after a fewdays, by a shipment notiï¬cation message from an expressshipping service. In today_s mail systems, they will not bea part of the same thread, while we believe they should.In this paper, we focus on this type of threading that wecoin “causal threadingâ€. We demonstrate that, by analyzingrecurring patterns over hundreds of millions of mail users,we can infer a causality relation between these two indi-vidual messages. In addition, by observing multiple causalrelations over common messages, we can generate “causalthreads†over a sequence of messages. The four key stagesof our approach consist of: (1) identifying messages that areinstances of the same email type or“template†(generated bythe same machine process on the sender side) (2) building acausal graph, in which nodes correspond to email templatesand edges indicate potential causal relations (3) learning acausal relation prediction function, and (4) automatically“threading†the incoming email stream. We present detailedexperimental results obtained by analyzing the inboxes of12.5 million Yahoo! Mail users, who voluntarily opted-in forsuch research. Supervised editorial judgments show thatwe can identify more than 70% (recall rate) of all “causalthreadsâ€at a precision level of 90%. In addition, for a searchscenario we show that we achieve a precision close to 80%at 90% recall. We believe that supporting causal threads inPermission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee.
Publish Year: 2013
Publisher: ACM-WSDM
موضوع: یادگیری ماشین (Machine Learning)
ایران سای – مرجع علمی فنی مهندسی
حامی دانش بومی ایرانیان
به نام خدا
Title: A logical approach to fuzzy truth hedges
Authors: Francesc Esteva, Llus Godo, Carles Noguera
Abstract: The starting point of this paper are the works of H jek and Vychodil on the axiomatization of truth-stressing and-depressing hedges as expansions of H jek s BL logic by new unary con- nectives. They showed that their logics are chain-complete, but standard completeness was only proved for the expansions over G del logic. We propose weaker axiomatizations over an arbitrary core fuzzy logic which have two main advantages: (i) they preserve the standard completeness properties of the original logic and (ii) any subdiagonal (resp. super- diagonal) non-decreasing function on [0, 1] preserving 0 and 1 is a sound interpretation of the truth-stresser (resp. depresser) connectives. Hence, these logics accommodate most of the truth hedge functions used in the literature about of fuzzy logic in a broader sense.
Publish Year: 2013
Publisher: Information Sciences - Science Direct
موضوع: منطق فازی
ایران سای – مرجع علمی فنی مهندسی
حامی دانش بومی ایرانیان
به نام خدا
Title: Vertical-Edge-Based Car-License-Plate Detection Method
Authors: Abbas M AlGhaili, Syamsiah Mashohor, Abdul Rahman Ramli, and Alyani Ismail
Abstract: This paper proposes a fast method for car-license-plate detection (CLPD) and presents three main contributions.The first contribution is that we propose a fast vertical edgedetection algorithm (VEDA) based on the contrast between thegrayscale values, which enhances the speed of the CLPD method.After binarizing the input image using adaptive thresholding (AT),an unwanted-line elimination algorithm (ULEA) is proposed toenhance the image, and then, the VEDA is applied. The sec-ond contribution is that our proposed CLPD method processesvery-low-resolution images taken by a web camera. After thev ertical edges have been detected by the VEDA, the desired plate details based on color information are highlighted. Then, thecandidate region based on statistical and logical operations will beextracted. Finally, an LP is detected. The third contribution is thatwe compare the VEDA to the Sobel operator in terms of accuracy,algorithm complexity, and processing time. The results show accu-rate edge detection performance and faster processing than Sobelby five to nine times. In terms of complexity, a big-O-notationmodule is used and the following result is obtained: The VEDAhas less complexity byK2 times, whereasK2 represents the masksize of Sobel. Results show that the computation time of the CLPDmethod is 47.7 ms, which meets the real-time requirements.
Publish Year: 2013
Publisher: IEEE TRANS-VEHICULAR TECHNOLOGY
موضوع: پردازش تصویر
ایران سای – مرجع علمی فنی مهندسی
حامی دانش بومی ایرانیان