توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Mixture of RLS and LMS Algorithms
اطلاعات انتشار: سیزدهیمن کنفرانس مهندسی برق ایران، سال
تعداد صفحات: ۴
The LMS algorithm has properties of slow convergence and good tracking in low SNR compared to the RLS algorithm, whereas the RLS has a fast convergence property. A new approach based on a dynamic mixture of the RLS and LMS algorithms, RLMS, is presented. The optimum weights of the mixture are derived and it is proved that the MMSE of the proposed system is reduced compared to those of the RLS and LMS algorithms. RLMS algorithm is configured for identification and chirp tracking problems. Experimental results show better performance compared to both the RLS and LMS algorithms in identification problem and noisy chirp tracking.<\div>

۲Vehicle Tracking by a Motion History Graph
اطلاعات انتشار: چهارمین کنفرانس ماشین بینایی و پردازش تصویر، سال
تعداد صفحات: ۹
In this paper, a history–based vehicle tracking algorithm is presented which is a new top–down or learning–based vehicle tracker. History of trajectory is represented by a weighted directed graph (WDG), called motion history graph, MHG. This algorithm includes two phases; spatio–temporal trajectory is stored in a WDG in the learning. In the search phase the spatio–temporal database is employed to increase the performance of the predictor. The spatiotemporal database is also updated. The proposed technique is used for vehicle tracking in highways. It yields a decrease of up to 80% in prediction error relative to a conventional technique.<\div>

۳Dynamic and memory efficient web page prediction model using LZ78 and LZW algorithms
نویسنده(ها): ،
اطلاعات انتشار: چهاردهمین کنفرانس بین المللی سالانه انجمن کامپیوتر ایران، سال
تعداد صفحات: ۶
Web access prediction has attracted significant attention in recent years. Web prefetching and some personalization systems use prediction algorithms. Most current applications that predict the next user web page have an offline component that does the data preparation task and an online section that provides personalized content to the users based on their current navigational activities. In this paper we present an online prediction model that does not have an offline component and fit in the memory with good prediction accuracy. Our algorithm is based on LZ78 and LZW algorithms that are adapted for modeling the user navigation in web. Our model decreases computational complexities which is a serious problem in developing online prediction systems. A performance evaluation is presented using real web logs. This evaluation shows that our model needs much less memory than PPM family of algorithms with good prediction accuracy.<\div>

۴An HMM Model with a Recurrent Kernel in High Dimensional Space Applied to Vehicle Trajectory Recognition
اطلاعات انتشار: سومین کنفرانس ماشین بینایی و پردازش تصویر، سال
تعداد صفحات: ۷
In this paper, a new type of the HMM model is presented with a recurrent kernel in the high dimensional space, HDS. HMM in the HDS have three properties: HDS is a linear space relative to the input space, creation of an interaction between HMM models, and it is a kind of hierarchical classification. Also, recurrent kernel in HMM model has a few advantages as, reducing of noise in noisy data and parameter smoothing in HMM. This approach was applied to synthetic motion and vehicle trajectory recognition; Experimental results show an increase of 32.1% for synthetic motion recognition and 9.7% for vehicle trajectory recognition performance in comparison to conventional HMM<\div>

۵Modeling the Behavior of Drivers
اطلاعات انتشار: سومین کنفرانس ماشین بینایی و پردازش تصویر، سال
تعداد صفحات: ۶
In this paper, a model for normal behavior of drivers is presented. In this model, the mutual effect of vehicles is employed. The spatio–temporal features and motion history are utilized for developing the model. This model has three main parts: The first part concerns with the history of trajectory, in the form of Centers Transition Matrix, CTM. The second part is based on the linguistic features, including forward, turn right and left, line changing to right and left. The third part is constituted from low level features which contain velocity and distance to neighboring vehicles. The average value of the correct recognition obtained is 81.2%, for normal behavior.<\div>

۶Object Recognition: Computational Theories and Models
اطلاعات انتشار: یازدهمین کنفرانس سالانه انجمن کامپیوتر ایران، سال
تعداد صفحات: ۸
In this review we try to outline the principles of object recognition and the way biological visual system performs this task, which is also a goal in computational neuroscience. At the same time we have a focus on theories and computational models of object recognition in which the main idea is somehow taken from biological visual system, or in other words, biologically plausible theories and models.<\div>
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