Titlebook: Soft Computing Techniques in Vision Science; Srikanta Patnaik,Yeon-Mo Yang Book 2012 Springer-Verlag GmbH Berlin Heidelberg 2012 Computati

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书目名称Soft Computing Techniques in Vision Science影响因子(影响力)学科排名




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书目名称Soft Computing Techniques in Vision Science被引频次学科排名




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书目名称Soft Computing Techniques in Vision Science年度引用学科排名




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书目名称Soft Computing Techniques in Vision Science读者反馈学科排名

Platelet

Automatic Localization and Segmentation of Left Ventricle from Short Axis Cine MR Images: An Image segmentation framework developed in this paper is fully automatic, and does not require manually drawn initial contour. The method was evaluated on MRI short axis cine slices of 15 subjects from MICCAI 2009 LV challenge database.

tenosynovitis

替代品

Artificial Neural Network (ANN) Based Object Recognition Using Multiple Feature Sets,equency Domain and Discrete Cosine Transform (DCT) components. The idea is to use these varied components to form a unique hybrid feature set so as to capture relevant details of objects for recognition using a ANN which for the work is a Multi Layer Perceptron (MLP) trained with (error) Back Propagation learning.

Congestion

确保

Prediction of Protein Tertiary Structure Using Genetic Algorithm,tion is used. This algorithm is adapted to search the protein conformational search space to find the lowest free energy conformation. Interestingly, the algorithm was able to find the lowest free energy conformation for a test protein (i.e. Met enkephalin) using ECEPP force fields.

BRUNT

Handwritten Script Recognition Using DCT, Gabor Filter and Wavelet Features at Line Level,of the proposed system at line level for bilingual scripts and later extended to trilingual scripts. We have obtained 100% recognition accuracy for bi-scripts at line level. The classification is done using k-nearest neighbour classifier.

Exuberance

Character Recognition Using 2D View and Support Vector Machine,and testing of SVM classifier. Support Vector Machine is promising recognition method, which is alternative to Neural Network (NN). Experiments show that the proposed method can provide a good recognition result using Support Vector Machines at a recognition rate 82.33%.

antipsychotic

Extort

Protein Structure Prediction Using Multiple Artificial Neural Network Classifier,cids secondary structures are derived. Then based on the majority of the secondary structure final structure is derived. This work shows the prediction of secondary structure of proteins employing ANNs though it is restricted initially to four structures only.