Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing

<p>Example of source (a) image of class G and image segmentation for the class G wrench image by using the PCA and feature extraction-based method with principal components (b) <svg height="12.7178pt" id="M85" style="vertical-align:-3.42947pt" version="1.1" viewbox="-0.0498162 -9.28833 68.2925 12.7178" width="68.2925pt" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M293 -169V-141C218 -131 209 -90 209 -44C209 19 222 85 222 152C222 207 198 255 139 269V273C199 286 222 334 222 388C222 454 209 523 209 588C209 632 218 671 293 681V709C234 709 148 695 148 577C147 513 155 438 155 372C155 337 152 291 64 285V256C152 250 155 204 155 169C156 105 148 31 148 -41C148 -157 234 -169 293 -169Z"></path></g><g transform="matrix(.013,0,0,-0.013,4.511,0)"><path d="M570 304C570 398 525 448 414 448C385 448 343 445 312 434L329 511L321 518C297 504 262 482 244 460L233 411C195 397 159 381 128 358L135 332C160 347 189 360 224 373L111 -147C97 -210 84 -218 17 -231L13 -257L254 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Journal of Sensors

fig5

Figure 5

Figure 5: Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing 