By Kousuke Imamura, Hideo Kuroda (auth.), Tai-hoon Kim, Hojjat Adeli, Carlos Ramos, Byeong-Ho Kang (eds.)
This publication includes chosen papers of the foreign convention on sign Processing, photo Processing and trend popularity, SIP 2011, held as a part of the long run iteration details know-how convention, FGIT 2011, at the side of GDC 2011, at the side of GDC 2011, Jeju Island, Korea, in December 2011. The papers awarded have been rigorously reviewed and chosen from a number of submissions and concentrate on a few of the features of sign processing, snapshot processing and development recognition.
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Extra info for Signal Processing, Image Processing and Pattern Recognition: International Conference, SIP 2011, Held as Part of the Future Generation Information Technology Conference FGIT 2011, in Conjunction with GDC 2011, Jeju Island, Korea, December 8-10, 2011. Proc
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In addition, through the introduction of LDP previously, we know in the application of LDP method, it needs to divide image region into blocks. Assuming there are nblock blocks, and for the LDP histogram as feature vector formed by every block, it is relative to the selection of k value, namely C8k . So finally the dimension of LDP feature vector formed is nblock × C8k , which is relatively high. Therefore, this paper proposes a variation method based LDP, where we set the first larger k edge response values of eight neighborhood pixel (r , c ) to 1, other to 0.
H_FCM still exhibits the highest performance. It manifests good performance as it overcomes the problem of equating cluster sizes. Table 3. 0876 Enhanced Fuzzy-Based Models for ROI Extraction in Medical Images 33 Table 4 demonstrates the segmentation accuracy of the explained algorithms on various MR phantoms sections. Segmentation accuracy shows different results in terms of ordering the relative performance of the clustering algorithms. 6% increase in average accuracy over FCM while S_FCM segmentation accuracy fell slightly below the proposed algorithms.
M. Fathalla pepper noise. Salt and Pep pper noise can be considered as spike noise relative to the gray levels of MR phanto oms. S_FCM treats salt noise as a separate cluster tthus exhibit low segmentation acccuracy. In H_FCM clustering centers are guided by exppert help thus avoid the effect of o randomness and floating centers position which leadd to S_FCM results. Fig. 3. Segmentation Results of o H_FCM Fig. 4. Segmentation Results of the illustratted on sample images algorithms under Gaussian(b) and Salt&Pepper (cc) Fig.
