### TABLE I SUMMARY OF THE DEPTH MAP RECONSTRUCTION ALGORITHM VIA RELATIVE DIFFUSION.

### Table 1 Diffusion equations used in image processing. The general form of the equations is

1999

"... In PAGE 2: ... JPEG has a characteristic blocking-artefact (Figures 1,2,3 and 6). PDEs of Table1 were tested as perceptually adaptive filters for compression artefact reduction in [8]. The adequate perceptually adaptive filter fell out to be the PMC-AD, since it suppresses noise while performing shape enhancement.... ..."

Cited by 1

### Table 4. Image Annotation via Matching Image Retrieval

2006

"... In PAGE 9: ... We were unable to reproduce the LSI results given in [8] where it performed best. In Table4 we give an example of three query images and the keywords of the retrieved images from the various methods. We do not display the actual retrieved images due to lack of space.... ..."

Cited by 2

### Table 1. Details of the datasets We compare the K-means clustering results between using the full-size image and using tiles of images. In all the experiments, we select the number of dimensions after dimensional reduction, in such a manner that no more than 0.1% information is lost in the reconstructed matrix representation. After that, we apply the K-means clustering technique. We compare the results of diffusion map

### TABLE 2: MEANS ACROSS THE ANTHROPOMORPHISM CONDITIONS.

2007

### Table 2. Square-error cost metric for various test images (error-diffusion initial image).

"... In PAGE 8: ...eeded to define efficient and perceptually meaningful cost functions (i.e., in our case filters) and/or better initial binary images to be used in the context of a DLM halftoning problems. As an example, Table2 illustrates cost metric values when the error diffusion images are used as initial images for the grid algorithm. While the numbers clearly show that our algorithm reduces the overall cost metric with respect to the initial condition {Floyd-Steinberg error diffusion is a one-pass non-iterative algorithm.... ..."

### Table 3. Reconstruction error

"... In PAGE 18: ....1.3 Simulations We have generated 12 different 2-Dimensional data of 100,000 samples each in order to evaluate the performance of the studied classifiers. Table 2 shows the probability of misclassification and Table3 the mean error when reconstructing the image from the ... ..."