Skip to main content
Engineering LibreTexts

6: Appendix

  • Page ID
    46383
  • Appendix A – Example Literature Review Assignment

    The following is a representative literature review assignment. The course website was hosted by CANVAS with an active discussion board.

    Use the CANVAS discussion board to claim the paper you would like to review – make sure it has not already been claimed. If you would like to consider other papers, that is fine, but email instructor a copy to approve before you do the review. It must be recent and directly relevant to imaging systems whose designs (or novel parts of it) are inspired by natural imaging systems (vision).

    The FSU Libraries are very useful for finding additional publications. If interested:

    Search “FSU Libraries” and then “Find a Database”. Under the option to “Search our A-Z list of Databases” pull down “I” and find IEEExplore. At this point you may have to log in to the FSU Portal. Once in IEEE Xplore, you can search on “biologically-inspired”, “bio-inspired imagers”, or some other related term. On the left under “year” refine your search by moving the range to the most recent year-and-a-half (2018 to 2019), and the click on “Apply refinements”.

    Once you have selected one of the provided papers (or have approval for a different paper) enter the citation (at least the author, title and year) on the course discussion board and confirm no one else has selected that paper. Read the paper and study it well enough to discuss it in class. You are not required to understand all derivations, equations, etc. but should be able to answer the following questions in a Word file. Your answers do not have to be long but should be in your own words and very clear and accurate; there is not a requirement on length. Use sentences and not phrases or bullet points. While presenting in class pull up your Word document (avoid powerpoint, etc.) and you may pull up the paper you are reviewing as well; feel free to go back and forth between your written answers and the paper. You may refer to the figures, tables, diagrams, or anything else in the paper, but your Word file should answer the questions without the reader having to refer to the paper.

    Turn in a Word file or PDF file that answers the questions in this format like the attached example on the next page:

    Reviewed by{your name}

    Paper citation: {citation}

    What is the problem to be addressed or solved?

    What is the natural paradigm being considered?

    What has already been done?

    How is this approach different?

    What accomplishment is claimed?

    What do they plan to do next?

    {Graduate students} Discuss at least one (or more) of the mathematical derivations or equations in the paper. If there are no derivations discussed, then choose a paper which does.

    Paper Abstract (pasted): {paste paper abstract here}

    Post your Word file (or PDF) in the course Assignment folder. If you selected a paper not yet posted, post a copy of it as well with your file.

    Paper citation: H. Wu, K. Zou, T. Zhang, A. Borst, K. Kuhnlenz, Insect-inspired high-speed motion vision system for robot control, Biological Cybernetics, 106:453-463, 2012).

    What is the problem to be addressed or solved?

    Improve the accuracy of velocity estimation in the Hassenstein-Reichardt Elementary Motion Detection (HR-EMD) model. Velocity estimation of the objects in an image is integral to visual perception and will be a necessity for robot control systems performing autonomous navigation and collaboration with other agents (robots). Motion estimation using conventional imaging technology is slow.

    What is the natural paradigm being considered?

    Motion detection at the neuronal level in insect vision, specifically the well-known HR-EMD model.

    What has already been done?

    The basic insect-vision-inspired HR-EMD model is well established. It has been used to address aircraft guidance (collision-avoidance, gorge-following, and landing) and demonstrated in robotic platforms. It has been implemented in VLSI for collision detection and implemented in FPGAs for optic flow detection and motion estimation. It has been applied for course stabilization and altitude control of a blimp-based unmanned aerial vehicle.

    How is this approach different?

    The former applications of the HR-EMD are based on a more qualitative motion detection and not a quantitative motion velocity estimation. Here the authors are using image pattern statistics (brightness, contrast, and a spatial PSD estimation) combined with the HR-EMD output and by a look-up table estimating the velocity of motion instead of simply motion. Here also, as with former efforts of the authors, a conventional temporal low-pass-filter is used as the delay element in the HR-EMD.

    What accomplishment is claimed?

    The average EMD response of the entire image was used for closed-loop yaw-angle control system of a robotic manipulator arm. They demonstrated yaw control using a piece-wise linear motion input and an arbitrary motion input.

    What do they plan to do next?

    They plan to extend to demonstrate motion estimation of 3D objects in a receptive field.

    Paper Abstract (pasted): The mechanism for motion detection in a fly’s vision system, known as the Reichardt correlator, suffers from a main shortcoming as a velocity estimator: low accuracy. To enable accurate velocity estimation, responses of the Reichardt correlator to image sequences are analyzed in this paper. An elaborated model with additional preprocessing modules is proposed. The relative error of velocity estimation is significantly reduced by establishing a real-time response velocity lookup table based on the power spectrum analysis of the input signal. By exploiting the improved velocity estimation accuracy and the simple structure of the Reichardt correlator, a high-speed vision system of 1 kHz is designed and applied for robot yaw-angle control in real-time experiments. The experimental results demonstrate the potential and feasibility of applying insect-inspired motion detection to robot control.

    • Was this article helpful?