Generally, for the face image recognition, we must cope with the image shift and image rotation problem. To cope with
the image-shifting problem, this research uses one pixel inside the sample image to compare with the around pixels that
surrounding the corresponding pixel that inside the unknown image. The "ring rotation invariant transform" technique is
used to transfer the geometry feature of the face image to another more salient feature. By this approaching one can
obtain more salient geometry feature of the face image. By this more salient geometry feature, one can judge whether or
not the sample image and the unknown image are the same image. The "ring rotation invariant transform" technique can
solve the image rotation problem. In this research, three different kinds of extracted ring signals are generated. The
extracted ring signals are generated by the following ring-circles - ring-radius-31-circle, ring-radius-22-circle, and ringradius-
13-circle. These extracted ring-signals are used to generate the rotation invariant vector magnitude quantities.
These rotation invariant vector magnitude quantities are combined as one entity and this entity is saved inside one
specific corresponding pixel in the BMP file. By this approach, one pixel will possess more geometry-features of the face
images. The obtained entity of the combined signals of one specific pixel inside the sample image will be compared to
the entities of the combined signals of the entire pixels located in the corresponding radius-6-cake in the unknown image.
By this comparison, one can find the most-matching point of the geometry-feature of the pixels between the sample
image and the unknown image.
This research uses the object extracting technique to extract the -thumb, index, middle, ring, and small fingers from the
hand images. The algorithm developed in this research can find the precise locations of the fingertips and the finger-to-finger-
valleys. The extracted fingers contain many useful geometry features. One can use these features to do the person
identification. The geometry descriptor is used to transfer geometry features of these finger images to another feature-domain
for image-comparison. Image is scaled and the reverse Wavelet Transform is performed to the finger image to
make the finger image has more salient feature. Image subtraction is used to exam the difference of the two images. This
research uses the finger-image and the palm image as the features to recognize different people. In this research, totally
eighteen hundred and ninety comparisons are conducted. Within these eighteen hundred and ninety comparisons, two
hundred and seventy comparisons are conducted for self-comparison. The other sixteen hundred and twenty comparisons
are conducted for comparisons between two different persons' finger images. The false accept rate is 0%, the false reject
rate is 1.9%, and the total error rate is 1.9%.
KEYWORDS: Magnetic resonance imaging, Medical imaging, Image processing, Algorithm development, Feature extraction, Inspection, 3D image processing, Roads, Medical research, Neuroimaging
This research investigates the techniques using the image subtraction to find the discrepancy between the healthy and illness MRI images. The technique developed in this research moves the healthy MRI image to overlap with the illness MRI image. Then, the healthy MRI image and the illness MRI image are aligned to the same orientation. After the healthy MRI image overlapped with the illness MRI image, the illness MRI image is subtracted from the healthy MRI image. If there is discrepancy in the illness MRI image, after the image subtraction, the discrepancy will remain in the subtracted result. From beginning to end the inspection is done by the machine automatically. There is no further human effort involved. The technique developed in this research can very accurately find the discrepancy of the healthy and illness images. This paper explains the method using the second moment to find the orientations of the MRI images. By the orientations of the MRI images, the healthy MRI image and the illness MRI image can be aligned to the same orientation. The detailed process of image rotation is addressed in this paper.
In this face recognition research, the head is fixed when a photograph is taken. The infrared diodes provide the only illumination. In front of the CCD camera, a light filter lens is used to filter all other light. After the photograph is taken, the eyebrows, eyes, nostrils, lips, and face contour are extracted separately. The shape, size, object-to-object distance, center and orientation are found for each extracted object. The techniques to solve the object shifting and rotating problems are investigated. Image subtraction is used to examine the geometric differences of the two different faces. The obtained classifying data in this research can accurately classify different people's faces.
In the two-dimension or three-dimension object recognition research, the shapes, the sizes, and the geometry of the objects are usually compared to recognize different objects. However, the quality of the lenses of the CCD cameras is different between manufacturing company. If the quality is poor, the mapping ranges in the camera lenses itself in the X and Y directions are not equal. The mapping range discrepancy will cause the distortion of the object image after the CCD camera takes the picture of the object. In order to get the correct geometry comparison of two images; the distorted images need to be restored to its original images. This paper invests the method to correct the distorted image to its original image. In this paper, the second moment is used to find the center point and the orientation of the object. the distortion correction coefficients are introduced to the second moment equations to find the actual center point and the orientation of the object. Image rotation and correction are introduced in this paper, too. The distortion correction coefficient work well to correct the distorted image. By using the correction coefficient, the image can be restored to its original image and the image rotation equations can rotate the image to its proper position.
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