VOLOOM Volumizing Hair Straighteners Iron for Woman (UK Edition) - 1 inch Revolutionary Hair Crimpers - Wide Plates Lifter Add Lasting Volume & Body to Hair - Patented Checkerboard Volumiser Design

Product Information

VOLOOM has been designed to help you achieve maximum results while minimizing the potential for hair damage. It is to be used only on the hair near the scalp and a few inches down the hair shaft. This hair is rich in natural protective oils – your own natural heat protection. Unlike other hot tools, it is never used on the ends of hair, most prone to damage.

Most women over-wash and over-style their hair because it falls flat if they don’t. With VOLOOM, you’ll likely find that you can easily go extra days without washing because your hair is never flat! This change in your regular hair routine means that you’ll find that you need to shampoo, color, and heat-style your hair less often with VOLOOM, causing less damage to your hair over time. This work was supported by Academy of Finland [269474]; Tekes [269/31/2015]; Cancer Society of Finland; Emil Aaltonen Foundation; Finnish Foundation for Technology Promotion; KAUTE Foundation; and Orion Research Foundation. Here’s a basic guide. But experiment to find your ideal temperature. The digital controls and readout make finding and choosing the right temperature so easy and accurate. Remember, the first time you use the VOLOOM hair volumizer, select a lower temperature than you think you might need, until you get the hang of the technique! If you just don’t like a lot of heat on your hair, you always have the option of selecting a lower temperature and holding VOLOOM on for slightly longer. Just experiment to find out what temperature works best for your particular hair type. For each section pair, we evaluated the similarity of corresponding pixels. After conversion to grayscale we computed the following measures: root mean squared error (RMSE), normalized cross correlation (NCC), mutual information (MI) and normalized mutual information (NMI) ( Studholme et al., 1999). Only the set of overlapping tissue pixels A∩ B was considered. These indirect metrics provide information from the entire tissue area and complement the TRE evaluation. 2.2.5 Reconstruction smoothnessVOLOOM is an absolute miracle for aging hair, which gets finer and thinner, the older you get. Thin hair is very aging, because it accentuates the sagging of the skin and face that comes with age. By lifting the hair up and away from the scalp and face, you can take years off your appearance. We say, “Don’t get a facelift; get a hair-lift with VOLOOM!” Plus, VOLOOM has protective ceramic coated plates, as well as ionic technology that help to seal the cuticle and protect from damage. All of these features protect the hair. All methods benefited from parameter tuning on both image resolutions based on most of the metrics, using either set of landmarks for evaluation (see Table 1 and Supplementary Results). Of the top three methods, MIM and RVSS obtained better accuracy using high resolution images and ESA worked better on the low resolution images. ESA and MIM reached similar mean TRE values, slightly better than RVSS and approaching or exceeding the accuracy of LS. In terms of maximum TRE and ATRE, the three methods were comparable, but RVSS reached slightly lower ATRE than ESA or MIM. Among all tools, ESA and MIM also obtained the highest Jaccard index values. The RMSE and f 2 metrics do not allow comparison across different image resolutions and one should note that MIM’s output was always stored at the lower resolution for technical reasons. Considering these limitations, we can observe that ESA performed best in terms of these metrics on both image resolutions ahead of RVSS. Changes in tissue area introduced by ESA, MIM and RVSS were moderate. Behind the top three, most other tools reached accuracy comparable to each other. The worst results were obtained using default parameters and for some methods, most notably ESA and RVSS, they were even comparable to the unregistered original images. As with the prostate, the lowest TRE values among the automated methods were achieved by ESA on the lower resolution and MIM on the high resolution data with RVSS being the third best method. The other methods reached TRE values comparable to each other. In terms of maximum TRE and ATRE, the conclusion was less clear. Voloom performed better on the lower resolution, reaching a maximum TRE second only to LS, while ESA and OPT also reached comparable values. On this dataset, MIM suffered from larger maximum errors compared to the higher quality prostate sample. The lowest mean ATRE values among all automated methods were obtained by ESA, MIM and Voloom, while in terms of maximum ATRE Voloom was superior to ESA and MIM. ESA was the top method in terms of RMSE and f 2, and MIM obtained the highest Jaccard index. Again, the poorest results were obtained when using the default values of tunable parameters.

Take a thin section of hair alongside your face – ½ to 1 inch wide -- and clip it off to the side with the top layer of untreated hair. It will remain untreated and smooth. VOLOOM’s digital controls and readout give you complete control over temperature, which can be adjusted every five degrees, starting from as low as 220 degrees Fahrenheit going up to 395 degrees. First, we analyzed whether our metrics depend on image resolution (see Supplementary Results). TRE, ATRE, Jaccard and ΔA-% are essentially invariant to image resolution. They can be compared across different datasets and resolutions, as long as the accumulation of interpolation errors is avoided. RMSE, NCC, MI, NMI, f 2 and f 3 depend both on resolution and image content, and these metrics should thus only be compared within the same dataset and resolution. In all following analyses, we used images subsampled to pixel sizes of 7.36 and 3.68 µm, referred to as low and high resolution, respectively. The pixel sizes are close to the 5 µm section spacing and metrics computed from these images are not distorted by interpolation errors. Furthermore, we will only present RMSE as a measure of pixelwise similarity and f 2 as a measure of reconstruction smoothness due to their strong correlations with NCC, MI, NMI and f 3 (see Supplementary Table S1 for details). 3.2 Automated parameter tuning Repeat this process as you move VOLOOM down the hair shaft, two to three times, stopping at about eye or cheekbone level. You can experiment with more or less, depending on the length of your hair.MIM: Medical Image Manager, trial v. 0.94, was applied using images subsampled by a factor of 4 (magnification of 5×) as input. Sections 130 and 24 were used as references for the prostate and liver, respectively. We varied the initial magnification (0.3125×, 0.625×, 1.25× or 2.5×) and the number of non-rigid levels (1, 2, 3 or 4), thus modifying the image resolution used. Of the evaluated methods, LS, HSR and Voloom do not have tunable parameters. For OPT, SIFT, RVSS, ESA and MIM, we tuned the parameters automatically, minimizing the mean TRE computed for the prostate dataset. Parameter optimization took approximately 1500 hours in total to compute, producing 23 terabytes of data. VOLOOM is made to be used on the under-layers of hair, which are covered by an untreated top layer. Part your hair normally, and then section off the top layer that you would like to stay smooth, and clip it off to the side. This top layer of hair should be about ½ to 1 inch wide and run parallel to your regular part. This layer will stay smooth and untreated. You will also want to make sure that a small section of hair – about ½ to 1 inch wide -- running alongside your face stays smooth and untreated. Based on this study, methods utilizing locally varying transformations (ESA, MIM, RVSS, Voloom) were superior to those constrained to global affine models (OPT, SIFT, HSR). ESA was the only method to consistently outperform or match the other approaches on two datasets based on the majority of metrics. In the case of the higher quality prostate dataset, differences in accuracy between the tools were rather subtle. All three top-performing methods on this dataset incorporate an elastic transformation model: MIM and RVSS use a B-spline grid and ESA is based on a piecewise linear mesh. While methods relying on a global transformation model also performed reasonably well, the additional accuracy offered by elastic transformations could be crucial when microstructure at the cellular scale is of interest. In the case of the liver sample, more profound differences between the methods were observed, likely due to the more challenging tissue content and the presence of deformations, which cannot be compensated for using a global model. ESA, MIM and Voloom stood out from the other methods. While Voloom appeared to be less accurate on average compared to ESA and MIM based on mean TRE, it demonstrated the lowest maximum and accumulated errors of all automated methods, indicating capability to avoid propagation of errors even in the presence of considerable deformations. The ability of the algorithms to tolerate such deformations is a significant benefit. Due to the mostly manual nature of histological sectioning and brittleness of the thin tissue sections, deformations in the form of folds and tears often occur. This challenge is especially encountered in 3D histology, when uninterrupted sequences of sections are desired.