A new HMM topology for shape recognition

This study deals with the shape recognition problem using the Hidden Markov Model (HMM). In many pattern recognition applications, selection of the size and topology of the HMM is mostly done by heuristics or using trial and error methods. It is well known that as the number of states and the non-zero state transition increases, the complexity of the HMM training and recognition algorithms increases exponentially. Oil the other hand, many Studies indicate that increasing the size and non-zero state transition does not always yield better recognition rate. Therefore, designing the HMM topology and estimating the number of states for a specific problem is still all unsolved problem and requires initial investigation on the test data.


A generative model for multi class object recognition and detection
Ulusoy, İlkay (2006-01-01)
In this study, a generative type probabilistic model is proposed for object recognition. This model is trained by weakly labelled images and performs classification and detection at the same time. When test on highly challenging data sets, the model performs good for both tasks (classification and detection).
A Favorable Weight-Based Evolutionary Algorithm for Multiple Criteria Problems
SOYLU, Banu; Köksalan, Mustafa Murat (Institute of Electrical and Electronics Engineers (IEEE), 2010-04-01)
In this paper, we present a favorable weight-based evolutionary algorithm for multiple criteria problems. The algorithm tries to both approximate the Pareto frontier and evenly distribute the solutions over the frontier. These two goals are common for many multiobjective evolutionary algorithms. To achieve these goals in our algorithm, each member selects its own weights for a weighted Tchebycheff distance function to define its fitness score. The fitness scores favor solutions that are closer to the Pareto...
A new numerical solver for simulating porous media flow based on immersed boundary method
Güler, Hasan Gökhan; Liu, Xiaofeng; Jensen, Bjarne; Tomaselli, Pietro D; Baykal, Cüneyt; Arıkawa, Taro; Yalçıner, Ahmet Cevdet (null; 2018-12-14)
In this study, we present and validate a new numerical solver in OpenFOAM®-v1706 called "ibmPorFoam" that is developed modifying IHFOAM (Higuera et al., 2014) with Immersed Boundary Method. IHFOAM is previously developed modifying interFoam solver of OpenFOAM® to solve flow properties in porous media. IHFOAM solves Volume Averaged Reynolds Averaged Navier-Stokes Equations, captures the free surface using Volume of Fluid method, and is capable of generating and absorbing waves. Immersed boundary method imple...
A Modified Parallel Learning Vector Quantization Algorithm for Real-Time Hardware Applications
Alkim, Erdem; AKLEYLEK, SEDAT; KILIÇ, ERDAL (2017-10-01)
In this study a modified learning vector quantization (LVQ) algorithm is proposed. For this purpose, relevance LVQ (RLVQ) algorithm is effciently combined with a reinforcement mechanism. In this mechanism, it is shown that the proposed algorithm is not affected constantly by both relevance-irrelevance input dimensions and the winning of the same neuron. Hardware design of the proposed scheme is also given to illustrate the performance of the algorithm. The proposed algorithm is compared to the corresponding...
A genetic algorithm for 2d shape optimization
Chen, Wei Hang; Oral, Süha; Department of Mechanical Engineering (2008)
In this study, an optimization code has been developed based on genetic algorithms associated with the finite element modeling for the shape optimization of plane stress problems. In genetic algorithms, constraints are mostly handled by using the concept of penalty functions, which penalize infeasible solutions by reducing their fitness values in proportion to the degrees of constraint violation. In this study, An Improved GA Penalty Scheme is used. The proposed method gives information about unfeasible ind...
Citation Formats
N. Arica and F. T. Yarman Vural, “A new HMM topology for shape recognition,” 1999, p. 756, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/62643.