Project

There are 12 topics in this course project. Students can collaborate as a team to complete the project. Each team should: choose a topic, complete the topic, and present the topic. The following are rules and policy of the project:

Team work

  • AT MOST 10 teams. Each team has 1 ~ 4 members.

Project topics (Choose 1 from 12 topics)

Requirement

  • Each team has to choose one topic and complete the followings

    • Paper reading

    • Program code writing

    • Data experimenting

    • Oral presentation (10~15 minutes)

    • Written report (10 ~ 40 pages)

Schedule

  • Week 11, 5/04 : Project announcement

  • Week 13, 5/17 : Confirmation of team members and project title (Google Sheet)

  • Week 14, 5/24 : Team proposal (1 page, web page/docx)

  • Week 18, 6/15 : Project presentation (ppt)

  • Week 19, 6/20 : Project report (web page/docx)

OpenCV

Goal

  • Complete a complex opencv project with C/C++.

Description

  • There are 6 OpenCV projects from the book

  • You have to choose at least one chapter of the book as your team project.

    • Cartoonifier and Skin Changer for Android (Chapter 1)

    • Marker-based Augmented Reality on iPhone or iPad (Chapter 2)

    • Number Plate Recognition Using SVM and Neural Networks (Chapter 3)

    • Non-rigid Face Tracking (Chapter 4)

    • 3D Head Pose Estimation Using AAM and POSIT (Chapter 5)

    • Face Recognition using Eigenfaces or Fisherfaces (Chapter 6)

Requirement

  • You have to read the book chapter of the program code.

  • You have to successfully compile and execute the code.

  • You have to experiment the code with your images, data, and different parameters.

  • You have to extend the code with at least EXTRA USEFUL 50 lines.

  • You have to extend the code with more OpenCV functions not used in the program codes: At least two functions or two algorithms.

Human Pose Estimation

Goal

  • Complete a human pose estimation project.

Description

Requirement

  • You have to complete a human pose estimation project with either OpenPose or BlazePose.

  • You have to experiment the code with your images, videos, and different parameters.

  • You have to extend the code for some applications, for example Build a personal AI Trainer.

Holistic Tracking

Goal

  • Complete a face/hand/body tracking project. The project may need to be implemented in embedded hardware.

Description

Requirement

  • You have to complete a holistic tracking with at least two objects.

  • You have to experiment the code with your images, videos, and different parameters.

  • You have to extend the code for some applications.

Fundus Image Segmentation

Goal

  • Complete a medical image segmentation project for fundus images.

Related papers

You can choose at least one of the following papers.

  • (MNet) Huazhu Fu, Jun Cheng, Yanwu Xu, Damon Wing Kee Wong, Jiang Liu, and Xiaochun Cao, "Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation", IEEE Transactions on Medical Imaging, vol. 37, no. 7, pp. 1597-1605, 2018. [TensorFlow 1.14 + Keras) + Matlab] [PDF]

  • (DENet) Huazhu Fu, et al., "Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image," IEEE Transactions on Medical Imaging, vol. 37, no. 11, pp. 2493-2501, Nov. 2018. [Keras/Tensorflow] [PDF]

Requirement

  • You have to read papers, complete the code of the paper, and write a report.

OCT Layer Segmentation

Goal

  • Complete a medical image segmentation project for OCT (Optical Coherence Tomography) images.

Related papers

You can choose at least one of the following papers.

  • S. Motamedi, et al., "Normative Data and Minimally Detectable Change for Inner Retinal Layer Thicknesses Using a Semi-automated OCT Image Segmentation Pipeline," Frontiers in Neurology, 25 November 2019. URL. SAMIRIX: Matlab @ GitHub, NeuroDIal @ GitHib for OCT analysis. [PDF]

  • A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, J. L. Prince, "Retinal layer segmentation of macular OCT images using boundary classification." Biomedical Optics Express 4, 1133-1152, 2013. OCTLayerSegmentation by AURA Tools on NITRC [PDF]

Requirement

  • You have to read papers, complete the code of the paper, and write a report.

Goal (議題簡介)

  • 電腦刀(cyberknife)是利用精密的高能量直線加速器架設於電腦操控精準的機械手臂上進行放射治療,破壞腫瘤細胞並阻止腫瘤生長,同時避免對健康組織的損害。術前需規畫個人化治療計劃,評估腫瘤的形狀,大小和位置,及需要保護的器官組織。

  • 臨床應用的考驗:

    • 電腦刀主要用於微小腫瘤。

    • 這是一個非常耗時的過程,由兩名專業人員(一名醫生和一名醫學物理學家)負責電腦操控規劃,通常需要花費數小時,較為複雜的病例,甚至需要一天的時間為患者進行規劃。

  • 為了提升工作流程及品質,本專案將訓練AI來識別腦腫瘤的位置與輪廓勾畫。

  • 透過本議題,希望鼓勵解題者互相學習,並優化訓練過程,以獲得最佳演算模型。同時也為了沒來得及參加第三屆亞洲盃的高手們提供個平台切磋一下,做為2021年第四屆世界盃的暖身。

Description (資料說明)

Requirement

Goal (議題簡介)

  • 自動光學檢查(簡稱 AOI),為高速高精度光學影像檢測系統,運用機器視覺做為檢測標準技術,可改良傳統上以人力使用光學儀器進行檢測的缺點,應用層面包括從高科技產業之研發、製造品管,以至國防、民生、醫療、環保、電力…等領域。工研院電光所投入軟性電子顯示器之研發多年,在試量產過程中,希望藉由 AOI 技術提升生產品質。本資料集由工研院提供,請同學針對所提供的 AOI 影像資料,來判讀瑕疵的分類,藉以提升透過數據科學來加強 AOI 判讀之效能。

Description (資料說明)

  • 本議題所提供之影像資料,包含 6 個類別(正常類別 + 5 種瑕疵類別)。

  • 下載資料 aoi_data.zip 檔案包含:

    • train_images.zip:訓練所需的影像資料(PNG格式),共計 2,528 張。

    • train.csv:包含 2 個欄位,ID 和 Label。

      • ID:影像的檔名。

      • Label:瑕疵分類類別(0 表示 normal,1 表示 void,2 表示 horizontal defect,3 表示 vertical defect,4 表示 edge defect,5 表示 particle)。

    • test_images.zip:測試所需的影像資料(PNG格式),共計 10,142 張。

    • test.csv:包含 2 個欄位,ID 和 Label。

      • ID:影像的檔名。

      • Label:瑕疵分類類別(其值只能是下列其中之一:0、1、2、3、4、5)。

  • 其他說明請請詳見網站

Requirement