IEEE Trans Image Process - Salient region detection improved by principle component analysis and boundary information.

Tópicos

{ imag(2830) propos(1344) filter(1198) }
{ featur(1941) imag(1645) propos(1176) }
{ motion(1329) object(1292) video(1091) }
{ import(1318) role(1303) understand(862) }
{ imag(2675) segment(2577) method(1081) }
{ framework(1458) process(801) describ(734) }
{ error(1145) method(1030) estim(1020) }
{ age(1611) year(1155) adult(843) }
{ analysi(2126) use(1163) compon(1037) }
{ imag(1947) propos(1133) code(1026) }
{ imag(1057) registr(996) error(939) }
{ algorithm(1844) comput(1787) effici(935) }
{ activ(1138) subject(705) human(624) }
{ high(1669) rate(1365) level(1280) }
{ can(774) often(719) complex(702) }
{ measur(2081) correl(1212) valu(896) }
{ take(945) account(800) differ(722) }
{ assess(1506) score(1403) qualiti(1306) }
{ method(1557) propos(1049) approach(1037) }
{ design(1359) user(1324) use(1319) }
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{ data(3963) clinic(1234) research(1004) }
{ research(1085) discuss(1038) issu(1018) }
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{ group(2977) signific(1463) compar(1072) }
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{ data(1737) use(1416) pattern(1282) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Salient region detection is useful for several image-processing applications, such as adaptive compression, object recognition, image retrieval, filter design, and image retargeting. A novel method to determine the salient regions of images is proposed in this paper. The L0 smoothing filter and principle component analysis (PCA) play important roles in our framework. The L0 filter is extremely helpful in characterizing fundamental image constituents, i.e., salient edges, and can simultaneously diminish insignificant details, thus producing more accurate boundary information for background merging and boundary scoring. PCA can reduce computational complexity as well as attenuate noise and translation errors. A local-global contrast is then used to calculate the distinction. Finally, image segmentation is used to achieve full-resolution saliency maps. The proposed method is compared with other state-of-the-art saliency detection methods and shown to yield higher precision-recall rates and F-measures.

Resumo Limpo

salient region detect use sever imageprocess applic adapt compress object recognit imag retriev filter design imag retarget novel method determin salient region imag propos paper l smooth filter principl compon analysi pca play import role framework l filter extrem help character fundament imag constitu ie salient edg can simultan diminish insignific detail thus produc accur boundari inform background merg boundari score pca can reduc comput complex well attenu nois translat error localglob contrast use calcul distinct final imag segment use achiev fullresolut salienc map propos method compar stateoftheart salienc detect method shown yield higher precisionrecal rate fmeasur

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