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Critical Assessment of the Module: AI Seminar

Reviewing my learnings from the AI Seminar As my 5th and final blog, I would like to review what I have learned (and didn't) by critically assessing my own journey. I will also try to incorporate the SMART goals that we wrote down at the start of the course On my first day of taking the seminar, my goals were pretty clear: improve my communication and interpretation skills and improve knowledge of my VT topic. I have now gone through several different papers, reading, reviewing, presenting and writing survey papers about them. I feel like I have made a lot of progress in the following categories : Reading and Understanding a Scientific paper This has become most likely the largest learning for me. Previously, I had a tendency to skim through scientific papers and only have a shallow understanding of the methodology and contents. By reading the same seed paper multiple times by analyzing its main contents, summarizing its contents into a short pitch and especially reviewing it using...
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My take on RGB-D Tracking & Optimal Perception for Deformable Objects and IJCAI review

 Revisiting RGB-D Tracking and Optimal Perception Paper - My thoughts, reception, impact of paper and objective review My Thoughts Honestly, I found this paper to be an excellent starting point into the topic of 3D segmentation of deformable objects. It does not rely on deep learning or tons of training data - so it's very practical to get started.  The authors use super voxel graphs to track texture-less deformable objects in real-time, which is a learning-free approach.  I have actually tried to implement parts of the work into my VT Project of segmenting and tracking a rope in 3D, I have created a dense point cloud and downsampled it into super voxels. Then I tried to create from the point cloud, object segments using Locally Convex Connected Patches (LCCP). During this approach, I found out that the libraries for this approach are only available in C++ and are not well maintained.  As for my project, the additional complexity was not necessary as my problem does ...
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Diving Deeper: Taking a closer look into the scientific content of the paper on tracking of deformable objects

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Looking into the key innovations of the paper on Tracking of Deformable Objects Related Paper: RGB-D Tracking and Optimal Perception of Deformable Objects The tracking of deformable objects is a more challenging task than tracking rigid objects. The reason is that as force is exerted onto a deformable object it can move in more unpredictable ways. There is also an increased possibility of occlusions as the object, for example a rope,  overlaps on itself, which make tracking difficult. Having a great understanding of deformable objects will be necessary in the field of robotics with various important applications such as for medical surgeries or automating routine tasks such as folding laundry.   The main challenge in terms of using supervised ML approaches is that there is not a lot of labelled dataset available for learning. The authors are proposing a learning free method by essentially preprocessing the RBG and Depth data into point clouds and performing a graph-based super...
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Critically analysing writing style of seed paper: RGB-D Tracking and Optimal Perception of Deformable Objects

I have reviewed my seed paper "RGB-D Tracking and Optimal Perception of Deformable Objects " to assess its strengths and weaknesses. Taking such a perspective has helped me in viewing the paper from a new angle, and various strengths and weaknesses of the papers core content have emerged. They are as follows: 1. Clearly structured but sometimes the transitions are not always smooth or explicitly mentioned The paper is structured into Introduction, Method, Results, Conclusion. The method section is not explicitly mentioned which confused me slightly but overall the sections and subsections seem relevant and appropriately used. Occasionally the transitions from section to section seem not as smooth. For example end of Section II.C (Point Cloud Over-Segmentation into Supervoxels) and section III is abrupt without any transitional sentence linking them together. I think such bridge sentences can make the reading smoother.  The paper could also use a Background section to explain ...
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About Me

 Welcome to My Blog.  I’m Melvin Pariyadan, a second-semester data science student with a passion for diving deep into the world of computer vision and robotics. My background is a mix of academic and real-world experience—I completed my bachelor’s degree in Informatics in Vienna, worked at Siemens as a data scientist, and now balance my studies with a part-time job as a scientific assistant at ZHAW. I’m excited to share my journey through the AI Seminar lecture. Why I Chose the AI Seminar I signed up for the AI Seminar because I felt it was the perfect chance to sharpen my skills in reading scientific papers and boost my communication skills. These are areas where I knew I could improve, and the course’s focus aligns well with my personal and professional development goals. It was also the perfect opportunity to learn about my VT Project that I need to do for my university at the same time. Two birds with one stone. Digging Deeper into My Topic For my topic, I decided to expl...
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