## Friday, October 7, 2011

## Thursday, October 6, 2011

### Tool: Google Cloud SQL: your database in the cloud

## Google Cloud SQL: your database in the cloud

*By Navneet Joneja, Product Manager for Google Cloud SQL*

### Reaction: Over Two Decades of Integration Based, Geometric Flow Visualization

The paper attempts to classify the geometric flow-visualization approaches. The author discusses the challenges in the field of flow visualization and the classification itself is based on the challenges. The idea of explaining terminologies like streamline, streakline, paths and surfaces before hand has made the understanding easy and clear. The example of tornado visualization is very interesting. It depicts us how rich a dataset can be and how different visualization techniques can help us perceive multiple dimensions of it.

Overall, this paper has been a great read to get introduction of flow-visualization. Towards the end, the author states that "as the dimensionality of the integral object increases the volume of research decreases" which makes me wonder whether some of the challenges mentioned by the author may not be actually due to lack of computing power.

## Wednesday, October 5, 2011

### Reaction: Marching cubes: A high resolution 3D surface construction algorithm

1.Read four slices

2. Scan two slices and create a cube

3. Calculate an index

4. Find the list of edges from LUT

5. Find the surface-edge intersection

6. Calculate a unit normal at each cube vertex

7. Output: the triangle vertices, vertex normals." The adaptive algorithm produces a very close approximation of the original surface.New methods that extend marching cubes algorithm will likely produce more accurate localization.

### Reaction : Over Two Decades of Integration-Based, Geometric Flow Visualization

### Reaction: A Survey of Algorithms for Volume Visualization

### Reaction: Over Two Decades of Integration Based, Geometric Flow Visualization

This paper mainly talks about the geometry based visualization techniques. It also talks about the side of visualization that deals with motion. Today the field of visualization has advanced to a great extent. Motion and user interaction plays an important role in graphical representation. I think it has been adopted universally. And this is where flow visualization comes into play which has been discussed briefly in this paper. The author also talks about the various challenges faced in flow visualization. I think the biggest challenge now a days is the length and vastness of the data sets. They are so huge that a lot of time is consumed in deciding how to display the available data.

Then the author has discussed various techniques in flow visualization like the direct method, texture based LIC techniques and geometric method which is based on the geometry of objects and last but not the least the feature method.

Another interesting discussion according to me was the example of the tornado using stream surface in comparison to flow volume.

### Reaction: Imaging Vector Fields using Line Integral Convolution

This paper talks about the linear and curvilinear filtering techniques to locally blur textures along a vector field. There is a good comparison made in this paper between DDA convolution and Line integral Convolution. The DDA convolution renders the vector fields unevenly , treating linear portions of the field more accurately than small scaled vertices. This is undesirable for some vector fields as the details in small scaled structures are lost. So for this there is an alternate technique used in the paper called the LIC. This technique uses a completely different approach for approximating a vector . In LIC the author has discussed different things such as periodic motion filters, normalization, implementation and application. LIC has several advantages that have been discussed in the paper like removing the aliasing error, a detailed description of the data and the ability to interface different techniques with LIC.

### Reaction: A survey of Algorithms for volume visualization

Volume visualization, as the paper suggests, is one of the most fast growing areas in the field of visualization. Many graphics techniques are used in volume visualization. This paper discusses the way in which these techniques are applied in fundamental volume visualization and the advantage and disadvantages of the algorithms used in those techniques.

In this paper different algorithms like SF, DVR, Ray Casting, Marching Cube etc algorithms have been discussed. Of all the algorithms I found ‘splatting’ which comes under DVR the most interesting one. Splatting performs a front to back object-order traversal of the voxels in the volumetric dataset. Each voxel’s contribution to the image is calculated and composited using a series of table lookups. It is called splatting because it is similar to throwing a snowball on a glass plate.

The section on photorealism is also very interesting. Here they have questioned if it is appropriate to visualize non plausible objects as people would find it difficult to relate to it. Well I agree to some extent but on the other hand I have never seen a visualization representing a physically non plausible object so I don’t really know how difficult an interpretation would be. Overall it was a nicely written paper with some interesting algorithms.

### Reaction: Marching Cubes: A high resolution 3D surface construction algorithm

This is a very interesting paper that talks about the 3D surface construction algorithm. Basically this model helps to produce an unprecedented detailed models from a 3D array data.

Then the author talk about the information flow for 3D medical algorithms which includes the steps like information acquisition, image processing, model creations, viewing operations and displaying the surface using techniques like ray casting, depth shading and color shading.

Then comes the actual marching cube algorithm which has been explained with the help of several diagrams. There is also a mathematical representation of this algorithm which was a bit confusing but the explanation was enough for me to understand the actual algorithm. Being a student of computer graphics I could easily relate myself to this particular paper as I have read about this particular algorithm earlier as well.

### Reaction: Imaging Vector Fields Using Line Integral Convolution

### Reaction: Marching Cubes: A high resolution 3D surface construction algorithm

### Reaction: Over Two Decades of Integration-Based, Geometric Flow Visualization

### Reaction: Over two decades of Integration-based, Geometric Flow Visualization

### Reaction: Imaging vector fields using line integral convolution

### Reaction: Marching Cubes: A high resolution 3D surface construction algorithm

### Reaction: Imaging Vector Fields using Line Integral Convolution

### Reaction: A Survey of Algorithms for Volume Visualization

### Reaction: Over Two Decades of Integration-Based, Geometric Flow Visualization

### Reaction: Imaging Vector Fields using Line Integral Convolution

### Reaction: A Survey of Algorithms for Volume Visualization

### Reaction: A Survey of Algorithms for Volume Visualization

### Reaction: Imaging Vector Fields Using Line Integral Convolution

This paper focuses on an effective new approach that is to use linear and curvilinear filtering techniques to locally blur textures along a vector field which is capable of imaging arbitrary two and three dimensional vector fields. The technique can also produce novel special effects. Line integral convolution represents a new and general method for imaging two- and three-dimensional vector fields. The algorithm filters an input image along local stream lines defined by an input vector field and generates an output image.

One thing I didn't like about the paper was that its not an easy read as the other papers. Though the mathematical formulas were a requirement, they could have been presented in a much easier format making it a fun to read.

### Reaction: Marching Cubes - A High Resolution 3D Surface Construction Algorithm

### Reaction: Marching Cubes - A High Resolution 3D Surface Construction Algorithm

### Reaction: A Survey of Algorithms for Volume Visualization

Authors have presented the survey by giving an introduction of the fast growing field of volume visualization for the graphics programmer. They have not discussed the graphics techniques, information of which we can get from anywhere but what is explained is the way these techniques are applied in fundamental volume visualization algorithms. Not only the advantages and disadvantages of each algorithm is given but the algorithm's space and time requirements, ease of implementation, and the type of data it handles best is also listed in the peer which is really informative and concise. The survey also reviews the terms, procedures, and heuristics most often used in the field of volume visualization.

I feel they have done quite a good job in putting up all the information together. Even after specifying all possible information, since the field is so large, they have recommended that one should read from the papers listed in bibliography for further information. They have even mentioned which topics they have not covered so as not to confuse reader about anything.

### Reaction : Over Two Decades of Integration-Based, Geometric Flow Visualization

### Reaction: Over Two Decades of Integration-Based, Geometric Flow Visualization

### Over Two Decades of IntegrationBased, Geometric Vector Field Visualization

The author talks about the many challenges that one can face in the process of flow visualization. The challenges include extremely humongous data sets, seeding and placement of objects, computation time for processing. All these factors and more are to be considered before arriving at a visualization technique. The authors enlist and make a comparison about the various techniques present for flow visualization, namely, direct method- which is simple but dos not present high quality images, texture-based-like the LIC technique discussed in one of the previous papers; geometric method that takes the geometry of the objects into consideration and the feature method. The paper discusses how each of these techniques are good in some cases and also mentions the cases to which the technique may not provide the best of results. Overall, this paper acts as a good reference to understand and choose among the various flow visualization techniques.

### Reaction: Imaging Vector Fields using Line Integral Convolution

### Reaction: MARCHING CUBES - A HIGH RESOLUTION 3D SURFACE CONSTRUCTION ALGORITHM

The importance of the algorithm has been very well validated by the authors. They have first described the information flow for 3D medical applications, related work and then the drawbacks of that work. After this they have explained the efficiency and functional enhancements, supported by case studies using three different medical imaging techniques to illustrate the new algorithm's capabilities.

Authors have provided ample number of diagrams to explain marching cubes algorithm. Figure 8. Bone Surface, Figure 9. Soft Tissue Surface, Figure 10. Soft Tissue, Top View and the likewise are not clear enough. Since the paper is old enough it doesn't provide the latest development in the field and hence a little out of date. I am not really sure if reading this paper would benefit anyone other than providing historical information in the field of 3D modelling. But considering that the paper is very old, its amazing that such an advanced algorithm was implemented at that time.

### Reaction: Marching cubes: A high resolution 3D surface construction algorithm

### Reaction: Over Two Decades of Integration-Based, Geometric Flow Visualization

This informative paper does a good job in giving an overview of vector field visualization and its many techniques. It talks about the challenges and parameters to be considered in choosing a suitable technique, which include large data sets, the visualization of both magnitude and direction simultaneously, interactions, computation time and perception. The authors enlist how different visualization techniques can handle these challenges.

What I found fascinating are the different perspectives for the way data is characterized by so many dimensions like spatial, temporal and others like velocity, pressure etc . I also found the geometric flow viz fascinating where discrete objects are computed based on the fact their shape directly relies on the underlying geometry. I wish there were more examples of the cases in which these different techniques could be used though.

### Reaction: A Survey of Algorithms for Volume Visualization

Moving on, I could relate and understand this paper well as I have worked on an image processing project that isolates a desired or target object from its background. So , I had to using the technique of feature extraction where a threshold value is applied to choose or nullify certain pixel values. As I read this paper, I found that this is a popular technique and is called the surface-fitting(SF) algorithm.

I also agree with the authors that data classification is one of the greatest challenges in such visualizations. It is an elaborate iterative trial-and-error process to get the right threshold for image properties.

Among the algorithms, we did read marching cubes in detail in one of the papers. However, I found the ray casting algorithm fascinating as I do feel from the description of the method that it might produce the best images. I assume that it is computationally more intensive because of its detailing

### Reaction: A Survey of Algorithms for Volume Visualization

### Reaction: Over Two Decades of Integration-Based, Geometric Flow Visualization

### Reaction: Imaging Vector Fields Using Line Integral Convolution

The paper explains about how DDA convolution takes the vector field to be a straight line and how this adversely affects the texture and detail of the image in cases where the radius of curvature is small. To overcome this, the LIC (line integral convolution) technique is used to approximate vector fields starting from the center of a pixel.It was fascinating to read how noise is effectively reduced in the whole process to generate lower blur, good quality images. The process of normalization to maintain both brightness and contrast was interesting.It was pretty catchy to read about the implementation of LIC and the impact that the input texture has and how the images are post processed to remove noise. I think I would like to read other related papers to better understand these concepts and the mathematics involved.

### Reaction: Marching Cubes - A High Resolution 3D Surface Construction Algorithm

The paper is an interesting read and I personally was unaware of the importance of image processing and visualization on medical data. The algorithmic approach presented in the paper is easy to comprehend. The steps of marching cubes algorithm and optimizations at each level are explained very well. The reader will need some background in computer graphics to understand some of the optimizations applied to the algorithm. There are some nice pictures which show the effect of using the algorithm. To do justice to the algorithm there should have been a comparative study with one of the algorithms mentioned in the related work.

### Imaging Vector Fields Using Line Integral Convolution

The paper compares two different techniques DDA convolution and Line Integral Convolution. DDA convolution assumes a straight line for every vector field point. In this case, the texture information is lost if the objects in the image have a small radius of curvature. In case of such objects, the border appears to be smudged or blurred with the DDA technique. So, the authors talk about an alternative technique called the LIC. The paper talks in detail about how LIC uses a different approach of approximating a vector from the center of the pixel and it adds negative and positive directions to it to preserve texture information. The authors have expressed the steps involved in LIC by applying low pass filters, normalizing the data points to maintain brightness values and post process the image to eliminate extraneous noise. Although LIC is computationally intensive and tedious, by the explanation of the techniques I assume that LIC proves to be a much better technique than DDA, which is a good trade-off to obtain high quality images.

### Reaction : Imaging Vector Fields Using Line Integral Convolution

for imaging two- and three-dimensional vector fields. The algorithm

filters an input image along local stream lines defined by an

input vector field and generates an output image. Figure 3 is really an interesting one.A two-dimensional vector field showing the local stream line starting in cell (x, y) shows the direction of the flow. When we scroll we actually can see as thought there is motion. It is mainly because of the arrow in different direction differed by a small degree, when scrolled we see the overlapping of arrows and hence we see a motion in the direction of the arrows.

### Reaction: Marching Cubes: A High Resolution 3-d Surface Construction Algorithm

### Reaction: Imaging Vector Fields Using Line Integral Convolution

### Reaction: Over Two Decades of Integration-Based, Geometric Flow Visualization

Authors have reviewed and classified geometric flow visualization literature in this paper. The overall result is a complete collection of solved and unsolved problems in the rapidly evolving branch of geometric flow research. They have analyzed the latest developments and introduced a novel classification scheme based on challenges including seeding. The tabular overview of research and highlights of unsolved problems as well as challenges along with a range of solutions have been provided and authors have put up tremendous effort in it. They have not simply provided an enumeration of related papers in integration-based, geometric flow visualization, but also compared different methods, related them with one another and weighed their relative pros and cons.

The literature has been organized based upon the dimensionality of the geometry-based objects in the domain and the dimensionality of the data domain itself. This organization of literature points to the mature areas where many solutions are offered and even those areas which have a lot of unsolved problems. I even got to know about lot of new terminologies like streamtube, stream ribbon etc through this paper.

Its a good resource for people interested in knowing more about different algorithms implemented in the field of flow visualization.

### Reaction: Imaging Vector Fields Using Line Integral Convolution

### Reaction: A survey of algorithms for volume visualization

### Reaction: Over two decades of Integration Based Geometric Flow Visualization

### Reaction: Over Two Decades of Integration-Based, Geometric Flow Visualization

### Reaction : Marching cubes: A high resolution 3D surface construction algorithm

complements 2D CT, MR, and SPECT data by giving

physicians 3D views of the anatomy.

The steps listed which actually gives a complete overview of the exact steps reading four slices into the memory and finally outputting the triangle vertices and vertex normals, it the best part of the paper.

Another interesting part was the application of visualization into medical scanning and related fields.

### Reaction: Over Two Decades of Integration Based Geometric Flow Visualization

### Reaction: Imaging Vector Fields Using Line Integral Convolution

### Reaction: Over Two Decades of Integration-Based, Geometric Flow Visualization

### Reaction: A Survey of Algorithms for Volume Visualization

### Reaction: A Survey of Algorithms for Volume Visualization

### Reaction: A Survey of Algorithms for Volume Visualization

### Reaction: Marching cubes: A High Resolution 3D Surface Construction Algorithm

### Reaction: A Survey of Algorithms for Volume Visualization

### Reaction: Marching cubes, A high resolution 3D surface construction algorithm

### Reaction:A Survey of Algorithms for Volume Visualization

### Reaction:Marching Cubes: A High Resolution 3D Surface Construction Algorithm

### Reaction: Over Two Decades of Integration-Based, Geometric Flow Visualization

The challenges in flow viz. are easy to understand and the problem with large data sets in not only limited to flow viz. but I believe it can be applied to any visualization technique. The user will certainly not want a delay to occur before his viz. is presented to him. The seeding problem however can be limited only to the geometric viz. as presented by the authors. I however do not completely agree with the authors view that perception is only a problem in 3D and 4D fields because it can be a problem in 2D fields too when we have multiple overlaps. CPU and processing power problem is again generic to all viz. and not only to geometric or flow viz.

This paper has also taught me the techniques of flow viz. which I did not know before. The tabular column presented aids to the ease of reading and understanding of the material. Overall the paper is an enjoyable read and there is a good flow of information from one topic to the other. I can most certainly say that it is one of the well documented research papers that I have read.

### Reaction: Over Two Decades of Integration-Based Geometric Flow Visualization

- Tremendous volume of datasets – Since complex simulations are involved it produces huge amount and data and handling it efficiently becomes an issue.
- Interaction, seeding and placement – Seeding used to place objects within data domain is real challenge
- CPU processing time to handle this volume of data
- Perception – this is like how to visualize 2D and 3D velocity fields and at the same time visualize multi-variate data sets.

I appreciate the effort taken by authors in classifying the work done in this field over the past decades in the table they created. Overall structuring of paper was very informative for someone who had no clue before the paper about flow viz.

### Reaction: Imaging vector fields using line integral convolution

I feel that LIC performs much better when used along with other algorithms than acting stand alone, this is because LIC can take advantage of the post-processing results of other algorithms and act upon them to produce highly filtered results. There is also a striking similarity that I noticed between the results produced by LIC and the photo editing software's we see on mobile phones today. This I guess shows the importance of LIC and how varied its applications can be.

I like the way the authors have paved the path for future work by describing about the varied applications of LIC, specifically the one which talks about how LIC can be used to deblur images which have been blurred by a moving CCD cameras. This paper is a good read for researchers and students who are interested in the field of image processing. I would certainly look into some of the references provided in the paper.

### Reaction: Imaging vector fields using line integral convolution

### Reaction: Imaging Vector Fields using Line Integral Convolution

The author intelligently gave us the required background by explaining DDA convolution method and its drawbacks, which helped us understanding the importance of LIC and its improvements better. DDA is much more efficient in terms of Cells processed per second. But, LIC is much accurate and there is an improvement in quality. So, there will be a tradeoff in using these methods.

I think a parallel implementation of LIC will prove to be both fast and accurate, and will be an excellent way of Imaging vector fields. The authors have mentioned that LIC algorithm is inherently parallel. So, there should not be any problem in implementing the parallelized version of LIC.

Overall, the paper is an excellent read. The concepts are explained quite clearly with the help of before and after images.

### Reaction: Over Two Decades of Integration-Based, Geometric Flow Visualization

There is no wonder that, a lot of progress has been made in this area. But, the challenges of flow visualization have just took a different direction now. It is interesting to know that, specialized vector field compression methods in flow visualization is actively being pursued on the GPU.

Currently, there is a lot of research happening in optimizing the methods of flow visualization on nVidia CUDA GPU architectures. The new nVidia Tegra processor chip has all these encoded in its hardware to give the users ultimate gaming and visualizing experience.

Overall, the paper is very informative with lot of images clearly explaining the methods.

### Reaction: Over Two Decade of Integration based Geometric Flow Visualization

### Reaction: Imaging vector fields using line integral convolution

### Reaction: A survey of algorithms for volume visualization

### Reaction: Marching Cubes - A High Resolution 3d Surface Construction Algorithm

### Reaction: A Survey of Algorithms for Volume Visualization

This paper answers several questions I had when I was reading the paper about "Marching cubes". In the Future work section, much was mentioned about no knowledge about visualizing volume data that is vector, multi-modal, multi-variate,higher dimensional and non-Cartesian. Also hardware implementations of DVR and SF algorithms should be readily available now. Since, the paper is written in the year 1992, I am curious what are the developments in the area of Volume visualization in these 10 years, and I ask the class to let me know if there are any set of papers I could read to understand the advancements.

### Reaction: Marching cubes, A high resolution 3D surface construction algorithm

The idea is to extract a polygonal mesh of an isosurface from a 3-D volume. The algorithm works only with cubic-cells(voxels). The algorithm falls under Isosurface extraction category which still remains one of the most commonly used visualization methods. I am wondering if there are any other interesting and highly used visualization methods existing today. As the paper was published in 1987, I am just curious had there been any developments in this area.

Moreover, the paper mentions that other methods introduce artifacts, whereas the "Marching cubes" algorithm produces models with unprecedented detail. Though the algorithm is explained clearly, nothing is really mentioned about the complexity of the algorithm, and it was not compared with other extraction algorithms in terms of complexity.

### Reaction: A survey of algorithms for volume visualization

### Reaction: A high resolution 3D surface algorithm

Overall I like the presentation of the paper, though I must agree was looking from retro days,and there was lot to learn if you come from non CG background like me. The authors must be applauded for their work in time frame in which this paper was written when CG wasnt as advanced as it is now or even five years before.

### Reaction: A Survey of Algorithms for Volume Visualization

The paper gets somewhat complicated as the content proceeds because this paper is purely based on algorithms used in computer graphics. There will definitely be many unanswered questions by the end of the paper because of the terminology used. I personally do not understand the color codes used for CT data, are these standards? If yes, then over a period of many years with color improvements the standards should have most certainly changed. Also, I feel the authors could have presented a more deeper insight into one of the algorithms so that it can be seen as an attempt to solve one of the many problems which are yet to be solved as described in the summary section. Even though the authors claim that the presented material is just an introduction to the algorithms used in volume visualization, the terminology could have been explained better.

## Tuesday, October 4, 2011

### Reaction: Marching cubes: A high resolution 3D surface construction algorithm

Looking at the fact that the paper was published in 1987, the authors have done a fantastic job by coming up with the algorithm and a working implementation which runs the marching cubes algorithm on the data produced by the CT, MR and SPECT. I seriously wonder how advanced was the field of 3D anatomy at that time because the images shown in the results section on the paper are very impressive. Also, one more point which I would like to make on the algorithm is the amount of space complexity and the processing time it takes and whether the algorithm is still used as a basis on which research work is done today.

Overall, this is a must read paper especially for the curious who would like to know how 3D modelling was done back in those days and how efficient it was (images given in the paper show that it infact did a pretty good job!).

### Reaction: A Survey of Algorithms for volume Visualization

### Reaction : Over Two Decades of Integration-Based, Geometric Flow Visualization

I would have liked if the wind direction imagery had some influence of LIC , as it would have given more reality to the viz. The paper gave insight into some of the terminologies like streamline and critical point. The study for reducing the occlusion problems and possibility of representing these in 3D would be some future work which could be done on the CFD area. So that it would help simulating fluid motion in medical, mechanical or aviation fields.

### Reaction: Imaging vector fields using line integral convolution

### Reaction: Imaging Vector Fields Using Line Integral Convolution

It was interesting to know that visualization was interest of study to fields like fluid dynamics. Hopefully we would be able to see more accurate and better algorithms and deblurring techniques when research goes further in future.

### Reaction: Imaging Vector Fields Using Line Integral Convolution

It talks about DDA covolutions a generalization of traditional line drawing techniques and the spatial convolution algorithms. This summarizes LIC and also its applications.

### Reaction:A Survey of Algorithms for Volume Visualization

### Reaction: Marching cubes: A high resolution 3D surface construction algorithm

### Reaction: A survey of algorithms for volume visualization

### Reaction: A Survey of Algorithms for Volume Visualization

As the paper has mentioned this paper was not able to conclude problems of handling non cartesian girds, multivariate and higher dimensional data. A better classification algorithm also should be figured out. As human minds find it difficult to categorize the volumes just by seeing it , it becomes challenging to visualize the data using some animations and easy to use interfaces. So further study and detailed expiation of these basic points like data acquisition, slicing , data set reconstruction and data classification would improvise volume visualizations in future.

### Reaction:Imaging Vector Fields Using Line Integral Convolution

### Reaction : Over Two Decades of Integration-Based, Geometric Flow Visualization

The jargon's like streamline, streak line are difficult for a novice reader like me. Some sort of description or examples should have been give to those which would have helped me in understanding. The description of various techniques to perform geometric visualization is very impressive. I see that flow visualization has lots of practical usages.

The fact that the last research was done six years back in this field shocked me. It is interesting to know how the challenges like huge amount data can effect the visualization as well how algorithms try to overcome that issue. The differentiation between interactive and automatic seeding is well done. I like the way overall seeding algorithm is presented.

There has been a healthy discussion at every point of the paper. Overall the paper give a good insight about the research done in the last two decades in this field.

### Reaction: Marching cubes: A high resolution 3D surface construction algorithm

For reducing errors they have reduced the 256 patterns to 14 basic patterns , permutation and combination of these patterns produce the 256 cases. Even now in graphics we follow the triangulation approach for developing 3D images, which marching cubes has used, so I found this paper very valuable in this new era. The authors have considered lot of factors in mind thats why I think they have chosen stable operating systems like VMS , VAX and Unix for the implementation of the algorithm.

The possibility of selecting wrong set of three points for generating the image in this algorithm is a disadvantage . We can see that there were future work done in this regard which gave rise to algorithms like marching tetrahedra algorithm which generates more triangles and which needs more memory.

### Reaction: Imaging Vector Fields Using Line Integral Convolution

The description of LIC algorithm, helped me correlating with its practical usage in photo editing software like Photoshop, premiere pro etc. I feel that some more stress should have been give on the influence that this algorithm makes in the medical field, which is a major area where visualization plays a huge role. The growth of this algorithm from DDA algorithm is well mentioned. The drawbacks of the DDA algorithm like loss of accuracy and how the new LIC algorithm solves are described in a very eloquent fashion.

### Reaction: Over Two Decades of Integration-Based, Geometric Flow Visualization

### Reactions: Imaging Vector Fields Using Line Integral Convolution

### Reaction: Marching Cubes

Unfortunately, for someone who is new to 3D rendering, the dictions used by this paper makes it hard to follow at times.

### Reaction: Marching Cubes: A High Resolution 3D Surface Construction Algorithm

The fourteen patterns were well described and the explanation is self contained. As we can see the simplicity of this algorithm, if we implement the same on the fast end machines, it will be of great use. The illustrations clearly describe the practical implementations of the algorithm. The flow of the algorithm is simple and straightforward. The usage of divide and conquer makes this algorithm outperform its counterparts. The paper should be read with the fact that animation is need to represent there visualizations clearly.

### Reaction: A survey of Algorithms for Volume Visualization

The algorithms are explained at depth making intriguing arguments. The concept of using light reflecting materials intrigued me, but they may be carcinogenic agents. The other factors like how easy to implement, which algorithm to use at what situation, drawbacks, positives should have mentioned, which would have made the paper look more practical.It is nice that the author mentions about the applications of the volume visualization, which helps reader in understanding the usefulness of the theory.