---
# System prepended metadata

title: CA03
tags: [Research Methodology]

---

###### tags: `Research Methodology`

# CA03

<div style="text-align: center">
    <span style="font-size: 3em; font-weight: 700; font-family: Consolas">
        Class Assignment 03: <br>
        The three-pass approach
    </span>
    <br><br>
    <span style="">
        An assignment for <code>CSC100010</code> "English for Information Technology" @ 21VP
    </span>
</div>

<div style="page-break-after: always"></div>

---
This is an individual assigment.

# Collaborator
- `19126028` - Trần Nguyễn Huế Như - [@2power9](https://github.com/2power9)


# Requirement:
Apply strategy of `the first part` in the article "How to read a paper"
<!-- |![](https://i.imgur.com/DCdUczJ.png)|
|:---:|
|Requirement|
 -->
# 1. CrackIT - An imgage proccessing toolbox
## 1.1. Category: 
- What type of paper is this? 
    → Toolbox, road survey, crack detection and characterization, image processing, pattern recognition
- A measurement paper? 
    → efficient
- An analysis of an existing system? 
    → The authors proposed a toolbox that applies the available algorithms for image processing including crack detection and characterization.
- A description of a research prototype?
    → To pre-process images, to detect cracks and characterize them into types, based on image processing and pattern recognition techniques, as well as modules devoted to the performance evaluation of crack detection and characterization solutions.

## 1.2. Context: 
- Which other papers is it related to? 

[1] VIT, "State of the art; Automatisk Sprickmätning av Vägbanor," Swedish Road and Transport Research Institute 2002. [Online]. Available: http://www.vti.se/en/publications/pdf/state-of-the-art-automatisk-sprickmatning-av-vagbanor.pdf. [Accessed 26 March 2013].

[2] Y. Tsai, C. Jiang and Z. Wang, "Pavement Crack Detection Using High-Resolution 3D Line Laser Imaging Technology," in 7th RILEM International Conference on Cracking in Pavements, Delft, Netherlands, June 20, 2012.

[3] S. Chambon e J. Moliard, “Automatic Road Pavement Assessment with Image Processing: Review and Comparison,” International Journal of Geophysics, vol. 2011, p. 20, 2011.

...

- Which theoretical bases were used to analyze the problem?
    → Nevertheless, high-speed image acquisition systems produce very large amounts of data (images) that need to be efficient and accurately processed, to get a reliable assessment about the road condition.

## 1.3. Correctness: 
- Do the assumptions appear to be valid? → yes

## 1.4. Contributions: 
- What are the paper’s main contributions?
    → Henrique Oliveira

## 1.5. Clarity: 
- Is the paper well written?
    → This paper has a clear structure.

# 2. Classifying food images represented as bag of textons 
## 2.1. Category: 
- What type of paper is this? 
    → Food Classification, Bag of Words, Textons
- A measurement paper? 
    → effective
- An analysis of an existing system? 
    → The classification of food images is an interesting and challenging problem since the high variability of the image content which makes the task difficult for current state-of-the-art classification methods. The image representation to be employed in the classification engine plays an important role.
- A description of a research prototype?
    → This paper points out, through a set of experiments, that textures are fundamental to properly recognize different food items.

## 2.2. Context: 
- Which other papers is it related to? 

[1] R. Spector, “Science and pseudoscience in adult nutrition research and practice,” in Skeptical Inquirer, 2009.

[2] Shulin Yang, Mei Chen, Dean Pomerleau, and Rahul Sukthankar, “Food recognition using statistics of pairwise local features,” in IEEE Conference on Computer Vision and Pattern Recognition, 2010, pp. 2249–2256.

[3] Mei Chen, Kapil Dhingra, Wen Wu, Lei Yang, Rahul Sukthankar, and Jie Yang, “Pfid: Pittsburgh fast-food image dataset,” in IEEE International Conference on Image Processing, 2009, pp. 289–292.

...

- Which theoretical bases were used to analyze the problem?
    → Based on Bag of Textons is more accurate than existing (and more complex) approaches in classifying the 61 classes of the Pittsburgh Fast-Food Image Dataset.

## 2.3. Correctness: 
- Do the assumptions appear to be valid? → yes

## 2.4. Contributions: 
- What are the paper’s main contributions?
    → Giovanni Maria Farinella

## 1.5. Clarity: 
- Is the paper well written?
    → This paper has a clear structure.


