This flow (fig. 3.1a) shows how 2D content is converted into 3D content, so there is first step is find 3rd dimension which is known as Depth. After generation of depth map for 3D view left & rightimage is generated by depth image based rendering process (DIBR). Generated left and rightimage have some holes so for reduction of this problem hole Figure 3.1a: Flow of conversion process filling process is required. In a next step filtered left and right view are produced 3D output. This is a conversion process of 2D video/image to 3D video/image. Here 2D to 3D conversion process is based on single frame approach.
In this paper, the NCC (normalization cross-compilation) matching algorithm is used to complete the matching of left and rightimage pairs. It is a block match- ing algorithm that compares the original pixels, compensates the average gray value and its covariance in the sub-window, and has a short calculation time and can quickly find the optimal match. The implementation steps are as follows: af- ter extracting two frames of images to be matched, correct them so that their optical centers are on the same horizontal line (polar line level), which can re- duce the consumption of more computing resources. A given point on an image is the pixel position to be matched, and a 3 × 3 neighborhood matching window is constructed at this position, and the target pixel position is searched according to a certain similarity criterion along the polar line where the point is located. Find the subgraph that is most similar to the subwindow image. The similarity S of the two images is:
Abstract - Three dimension (3D) technology increases the visual quality as compared to two-dimensional (2D) technology. In Currently time every multimedia device needs 3D technology. So for generation of 3D content there is need of Depth image based rendering (DIBR) process which will generate left and rightimage through depth image and original image. Basically DIBR is following the concept of actual 3D recording camera setup. Through original camera setup there is virtual camera formula is generated which will create left and rightimage. As we already know for any image processing application time complexity is main issue. So in this work I will propose a fast and approximate DIBR algorithm which will reduce the time complexity issue. For image quality measurement there is some scientific parameter will use which will check the quality of generated left and rightimage through proposed DIBR algorithm. Those parameters are like PSNR, SSIM, RFSIM and FSIM. Algorithm will implement on Matlab.
Purple. None of the pigments shows a tail at longer wavelength or any other exceptional characteristics. Due to the paint selection, this image has a large gamut except in the purple sector which does not play an important role in this image. Figure 8.10 compares the rendered image under Illuminant D50 and Illuminant A. Most image areas are color constant. An area where a color shift occurs is the street that tends to be more reddish under Illuminant A. This color shift is maintained in the reproduction shown in the bottom rightimage. This image shows that depending on the paint palette and the image, spectral color reproduction does not yield a big advantage over colorimetric color reproduction as long as a large gamut can be reproduced. When balancing between colorimetric and spectral color reproduction, this image with its particular paint palette would not gain enough to justify going the extra mile of spectral color reproduction. A wide gamut printing process using colorimetric color management would likely yield a result which would be as good as the spectral workflow for most applications. Observer and illuminant metamerism does not affect every original in the same way.
use approximate smooth filter which is design by approach . In proposed approach we will also create a new formula which will reduce the time complexity issues. In our approach we will not use any hole filling part. To create some new formula but those approaches having some problems. According to that novel way we will create common offset values which will eligible for both Left and Rightimage generation. Using this logic there is no need to devolve two different offsets which results reduction in time complexity. We will perform all implementation on Matlab platform. For quality measurement we will use some very good scientific image quality parameters like PSNR, SSIM, RFSIM, FSIM,GMSD etc. At last we will apply analygraphy approach to convert left & rightimage into 3D image.
The motor network was defined using neurosynth.org, an open-source database of thousands of published functional MRI studies and a platform for large-scale meta- analysis of fMRI data (Yarkoni, 2011). The keyword “motor” generated an automated meta-analysis of 1748 studies that studied brain regions involved in motor function (Figure 7). For each voxel, a z-score was generated that provides the likelihood that activation at that point was caused by motor activity (i.e., reverse inference p(Motor task|Activation)) (Yarkoni, 2011). This map was then parcellated into separate regions using the lowest z-threshold that would separate each region it from neighbouring regions. The final network of R=11 regions comprised the midline supplementary motor area and five regions split into left and right components: the precentral gyri (i.e. motor cortex); rolandic opercula (i.e. premotor cortex); pallidum; thalamus, and cerebella (Figure 8). These regions-of-interest were mapped to the infant space using a two-stage transformation (adult-template -> infant-template; infant-template -> individual infant, see methods).
automatically estimating the midline from the extracted silhouette (Tytell and Lauder, 2002). Other authors have relied on ‘skeletonizing’ algorithms that dissolve a binary image representing the animal’s silhouette down to its midline (Cronin et al., 2005; Geng et al., 2004; McHenry, 2001). Although this approach is automated, it will not estimate the correct midline if other objects are present in the binary image because it cannot distinguish between pixels that belong to the animal’s silhouette and those that belong to a different object. As a result, these algorithms will not correctly estimate the fish’s body posture when there is environmental clutter such as other fish, plants, or a hair used to initiate behavioral responses, as we did in our recordings. Automated kinematic analysis of multiple zebrafish larvae was recently demonstrated. However, this particluar analysis technique utilizes an image filter that is customized for the appearance of zebrafish larvae of a specific age (Burgess and Granato, 2007). This technique does not extend nicely to zebrafish of different ages, other fish species, or when environmental clutter is present.
By projecting the human body in its ephemeral anonymity as merely one interchangeable thing or image among others, the auratic ‘ritual value’ that secures the distinction between person and thing collapses into the aesthetic ‘exhibition value’ that both immanently possess. Benjamin’s ironic commentary on a short opinion piece by literary critic Friedrich Burschell in Die literarische Welt (No 7, 1925) may help in understanding his position. In the piece, Burschell bemoans a recent magazine cover of the then highly popular Berliner Illustrirte Zeitung for showing a miniature photographic portrait in remembrance of esteemed German writer Jean Paul right alongside a series of images depicting, among other things, ‘the children of Thomas Mann, the petty bourgeois hero of a dubious trial, two tarts all done up in feathers and furs, and two cats and a monkey’ (Benjamin, 1972 : 449). It is this leveling juxtaposition of disparate things normally perceived to be categorically distinct, which induces and motivates Burschell’s sentiment (which, Benjamin taunts, reflects an attitude that is ‘kleinbürgerlich’). Instead, Benjamin positively recommends the cover’s ‘higgledy-piggledy’ construction as among the best modern journalism has to offer. It is this reduction of the individual portrait and singular ‘authentic’ face of the person (especially a highly esteemed artist-personality like Jean Paul) to a mere part of a larger visual ensemble of printed matter whose proponents are judged by their exhibition rather than their cult value, that for Benjamin signals the illustrated magazine’s progressive, even emancipatory tendency. What would be more boring, he asks, referring to Burschell’s own cultural paradigm and aesthetic ideal, than a full- blown portrait of the artist on the cover? (Benjamin, 1972 : 449).
left) prolongable by a, if va (resp. av) is also a factor of u. In this case, the word va (resp. av) is called a right (resp. left) extension of v in u; simply we say that the letter a is a right (resp. left) extension of v. The factor v is said to be right (resp. left) special if it admits at least two right (resp. left) extensions in u. If v is both right special factor and left special factor, it is called bispecial factor of u.
37 2002, p. 24). Humans did not evolve eyes to marvel at the aesthetic beauty of the world around us. That is a happy by-product of a sensory system that evolved to distinguish significant objects in the world around us, specifically food and threats, faces, potential mates, etc. You will notice, however, that even trained eyes do not scan the image as a whole (Figure 25). To focus attention on every small detail involves work and energy, much of which is redundant unless we have a reason to examine the minutiae of every passage in an image, such as trying to make two images match. Our eyes, or more precisely our visual system, work very differently to a camera. Having studied both drawing and photography, I am struck by the difference in approach. When we make a drawing we (usually) start with a blank surface and build in the details as we go, starting with the most relevant parts of the image, and the relationship between the parts. It is easy just to leave out the bits we don't want or don't even see. We simply don't put them in. With a camera, the approach is the opposite. Everything in the scene will be recorded by default. Often the photographer's job is to try to see all that is there in order to deliberately excise them from the scene. The work is in simplifying the scene. This is why photographers working in commercial applications such as product and portrait will use a studio or backdrop. The idea is to start with a blank
Hearle and Stevenson 38 , among other things, studied the effect of anisotropy on the tensile properties of the material. Three test groups were examined whose difference was the directional percentage of fibers. The first group was random laid, the second cross laid, and the third parallel laid. The anisotropy of these materials were measured using a visual technique that consisted of projecting the image of the material onto a screen and manually determining the anisotropy. Tensile tests were also carried out by using a standard test on an instron machine. The angles tested varied between zero degrees (machine direction) and ninety degrees (cross direction) in 15-degree intervals. They found that the random group had the highest strength to break in the machine direction and that it this value decreased slightly as it got closer to the cross direction. Although it might be expected that the breaking strength would be the same in every direction given that it is a random laid material and thus essentially isotropic, the processing tends to slightly align the fibers in the machine direction resulting in a slightly higher value in this direction. The cross laid material was found to have the largest breaking strength in the cross direction as expected and then a decreasing value until it reached the machine direction. The parallel laid material on the other hand had the largest breaking strength in the machine direction and decreased until it reached the cross direction. The trends that both the cross and parallel laid materials displayed were expected because it is intuitive that the greater number of fibers in a given direction would result in higher strength. However, the difference in strength in the machine direction and cross direction of the parallel laid materials was much larger than it was for the cross laid materials. Once again this is probably due to the processing parameters.
Abstract: We report one case of yolk sac tumor of the ear and review the literature. The patient was a 9-month boy who scratched his right ear repeatedly one month ago. Computed tomography scan showed an irregular elongated mass image measuring 42×16 mm was found in the right external auditory canal. The tumor was located under- neath of the epidermis with ulceration. Mild or moderate atypical round or oval tumor cells were arranged in nest and reticular pattern around vesicular or cystic spaces. Tumor cells had abundant eosinophilic or clear cytoplasm and marked nucleoli. Mitotic figures were about 7/10HPF. Poorly formed Schiller-Duvall body was occasionally present. The stroma was loose and rich in capillaries. Hyaline globules could be found in the stroma. Immunohistochemistry staining showed that tumor cells were positive for cytokeratin, SALL4, glypican-3, focal positive for EMA, vimentin, CD10, and CD34, but negative for a-fetoprotein, HCG, PLAP. The serum α-fetoprotein was 664.60 ng/mL (normal, ≤25 ng/mL). Yolk sac tumor of the ear is extremely rare, especially α-fetoprotein negative expression in our case. The differential diagnosis includes embryonal rhabdomyosarcoma, paraganglioma, myoepithelioma, carcinoma of skin appendages, and metastatic renal cell carcinoma.
Observing the earth by remote sensing provides a global picture of the earth. This has wide spread use for military and civilian purpose. Remote sensing can be defined as measuring the properties of an object without actually looking at the objects physical characteristic i.e. without any physical contact. When the solar energy is incident on an object, some of it is reflected, some energy is emitted and some of the energy is absorbed while some of the energy is transmitted. Every material has a unique spectral signature. The manner in which an object reflects energy depends primarily on its optical properties. The goal of collecting spectral image data is to extract signatures for material detection, classification, identification, characterization and quantification.
In the LRMSE, Read the sensors attached to the Arduino board and raspberry pi module where it is given as the input along the captured image of the obstacle and displays the temperature, humidity and carbon dioxide. Then the ultrasonic sensors are used measure the distance by using ultrasonic waves. The sensor head emits an ultrasonic wave and receives the wave reflected back from the target. Ultrasonic sensor measures the distance to the target by measuring the time between the emission and reception. If the obstacle found at left it rest over there and capture the image where the series of command is given to the raspberry pi to process the captured image in order to produce the shape and colour of the obstacle. Finally, it displays the colour and shape of the object and move towards the right
Orthonormal wavelets have many applications such as in image processing and image denoising. We are interested in tight wavelet frames that are derived from refinable function through a multiresolution analysis. A tight wavelet frame is an overview of an orthonormal wavelet basis by introducing redundancy into a wavelet system. Tight wavelet frames also called as framelet have advantageous features like shift invariant wavelet frame transforms and it may be helpful in recognize patterns in a redundant/repeative transforms. In order to get fast wavelet frame transform also called framelet transform, tight wavelet frames are generally derived from refinable function through MRA  .
Figure 18. Experiment. Rubber band (blue) covered by a stiffer sticky tape (white). The rubber band, on the upper image, shows few “dislocations” in correspondence of its bending; this can be compared with the dislocations detected in non-image fibers. The pre-tensioned rubber band on the lower image has been covered by a sticky tape and then released. The surface of this composite system appears much more corrugated as it can be detected in the PCW of image-fibers.
Next, SEL was performed by applying light repetitive compression with the hand-held transducer in a perpendicular plan to the tendon. Both B-mode and elastographic images were simultaneously displayed on the same image divided into two panels with the left side showing the B-mode image and the right side showing the elastographic image with superimposed color-coded elasticity features. Between the two images, a strain indicator bar showed whether the displacement was sufficient to obtain local strains within the ROI. The indicator of pressure bar appears as column which in case of adequate compression the full length of the bar became green colored. The elastogram image appeared within a rectangular region of interest (ROI) as a translucent color coded real-time image superimposed on the B-mode image. The ROI is applied to include the subcutaneous fat at the top and the humeral head at the bottom. The color code indicated the relative stiffness of the tissues within the ROI and ranged from red (soft) to blue (hard). Green and yellow indicated medium elasticity.