本文将带您了解关于CSCI4261ComputerVisionFaculty的新内容，另外，我们还将为您提供关于3DComputerVision、42028Faculty容错、androidstudi

本文将带您了解关于CSCI 4261 Computer Vision Faculty的新内容，另外，我们还将为您提供关于3D Computer Vision、42028 Faculty 容错、android studio问题-E / Vision：加载可选模块com.google.android.gms.vision.ocr时出错、awesome computer vision repo的实用信息。

• CSCI 4261 Computer Vision Faculty
• 3D Computer Vision
• 42028 Faculty 容错
• android studio问题-E / Vision：加载可选模块com.google.android.gms.vision.ocr时出错
• awesome computer vision repo

CSCI 4261 Computer Vision Faculty

CSCI 4261 - Introduction to Computer Vision Faculty of Computer Science, Dalhousie University
Practicum 1
Date Given: May 28, 2024 Due Date: May 31, 2024
Plagiarism Policy
• This assignment is an individual task. Collaboration of any type amounts to a violation of the academic integrity policy and will be reported to the AIO.
• Content should not be copied from any source(s). Please understand the concept and write answers in your own words.
• If you wish to learn more Dalhousie Academic Integrity policy , please visit the following link: https://www.dal.ca/dept/university_secretariat/academic-
integrity.html

 CSCI 4261 – Introduction to Computer Vision

Assessment Criteria
Task Assessment:
• • • •
100%-90% marks:
The solution you have provided is correct and match all the expected requirements.
90%-80% marks:
The solution is correct but there are areas of improvement or context missing.
80%-70% marks:
The solution is close to the correct answer but your approach is correct.
70% or less marks:
Your solution is not correct and there are obvious loops in your understanding.
Requirements:
For your Practicum 1, you must implement the Canny Edge Detection algorithm from scratch. This means you are only able to use numpy and matplotlib libraries. You can only use OpenCV to apply the smoothing and sharpening.
Canny Edge Detection (100%)
For the image titled “building.jpg”, perform:

1. Canny Edge Detection:
   a. Apply the Canny Edge Detection algorithm on building.jpg image.
   b. Apply the Canny Edge Detection algorithm on the sharpened building.jpg image (for sharpening the image, follow the same approach of the Task A2 of assignment 2)

2. Share your ideas to improve the algorithm to only find the contour of the image and ignore edges inside the building.jpg image.
   Submission Criteria
   The submission for this assignment will be done on 2 platforms:
   CSCI 4261 – Introduction to Computer Vision

   1. Document submission:

   The documentation should be a PDF that contain the following:
   a. Output to all the practical questions.
   b. Answers to the questions asked along with the practical questions. c. At the end mention the link to your Gitlab repository.
   Submit the PDF on Brightspace before the deadline.

3. Code submission:
   a. Using the repository you created before. (Do not create a new one)
   c. Add a new directory named “practicum1”. d. Add your python files containing the code.
   Upload you code before the practicum deadline, code pushed after the deadline will not be marked.
   You can name the python files according to your preference but the name should clearly indicate the task and subtask they are associated with.
   Failure to follow the submission criteria can result into 10% deduction in marks.
   CSCI 4261 – Introduction to Computer Vision
   WX：codinghelp

3D Computer Vision

3D Computer Vision
Programming Assignment 2 – Epipolar Geometry
You will upload your codes and a short report (PDF) in a zip file to the NewE3 system. Grading will be done
at demo time (face-to-face or Skype).
A C++ Visual Studio project is provided. To build the code, install VS 2019 (Community). When open the
solution file (project2.sln), be sure NOT to upgrade the Windows SDK Version nor the Platform Toolset:
The project should be buildable and runnable on a Windows system. Your tasks are:

1. [2p] For the test stereo images (pictures/stereo1_left.png , stereo1_right.png), find 8 matching pairs of
   2D points. List them as g_matching_left and g_matching_right. Note: x and y are in [-1,1] range. You
   can define the matching manually or
   [Bonus: +1~2p to mid-term] use off-the-shelf matching methods (such as OpenGL feature matching or
   others). The bonus amount depends on how well you understood and explains your matching method.
2. [5p] Implement the normalized eight-point method in EpipolarGeometry() to calculate the fundamental
   matrix (same as essential matrix). Remember to fill your result in g_epipolar_E To verify your result, the
   eight “*multiply:” stdout should output values very close to zero (around e-6 ~ e-7). The rendering
   should look like:
   (Here the 8 matching are the 8 vertices of the “cube”. But your matching can be anything.)
3. [1p] Explain what line 382-389 do? What does the “multiply” result means? Why should all the multiply
   values be (close to) zero?
4. [3p] Download the OpenCV sfm module source code at https://github.com/opencv/ope... Go
   to \modules\sfm\src\libmv_light\libmv\multiview. Explain the following functions:
   FundamentalFromEssential () in fundamental.cc [1p].
   MotionFromEssential() in fundamental.cc [1p].
   P_From_KRt () in projection.cc [1p].
   Note: “HZ” means the textbook “Multiple View Geometry in Computer Vision” by Richard Hartley and
   Andrew Zisserman and a pdf is provided for your reference.
   WX：codehelp

42028 Faculty 容错

42028: Assignment 2 – Autumn 2019 Page 1 of 4
Faculty of Engineering and Information Technology
School of Software
42028: Deep Learning and Convolutional Neural Networks
Autumn 2019
ASSIGNMENT-2 SPECIFICATION
Due date Friday 11:59pm, 31 May 2019
Demonstrations Optional, If required.
Marks 40% of the total marks for this subject
Submission 1. A report in PDF or MS Word document (10-pages)

2. Google Colab/iPython notebooks
   Submit to UTS Online assignment submission
   Note: This assignment is individual work.
   Summary
   This assessment requires you to customize the standard CNN architectures for
   image classification. Standard CNNs such as AlexNet, GoogleNet, ResNet should be
   used to create customized version of the architectures. Students are also required
   to implement a custom CNN architecture for object detection and localization.
   Both the customized CNNs (image classification and object detection) should be
   trained and tested using the dataset provided.
   Students need to provide the code (ipython Notebook) and a final report for the
   assignment, which will outline a brief assumptions/intuitions considered to create
   the customized CNNs and discuss the performance.
   Assignment Objectives
   The purpose of this assignment is to demonstrate competence in the following
   skills.
   To ensure that the student has a firm understanding of CNNs and object
   detections algorithms. This will facilitate the learning of advanced topics for
   research and also assist in completing the project.
   To ensure that the student can develop custom CNN architectures for different
   computer vision related tasks.
   42028: Assignment 2 – Autumn 2019 Page 2 of 4
   Tasks:
   Description:
3. Customize AlexNet/GoogleNet/ResNet and reduce/increase the layers. Train
   and test on image classification.
4. Implement a custom CNN architecture for object detection and localization.
5. Train and test the custom architecture on a given dataset for detection of
   multiple Objects, using Faster RCNN or YOLO object detection methods.
   Training, validation and testing datasets will be provided.
   Write a short report on the implementation, linking the concepts and methods
   learned in class, and also provide assumptions/intuitions considered to create the
   custom CNNs. Provide diagrams for the CNNs architecture where required for
   better illustrations. Provide the model summary, such as input and output
   parameters, etc. Discuss the results clearly and explain the different
   situations/constraints for the better understanding of the results obtained.
   Dataset to be used: Provided separately.
   Report Structure (suggestion only):
   The report may include the following sections:
6. Introduction: Provide a brief outline of the report and also briefly explain
   the baseline CNN architectures used to create the custom CNNs for image
   classification and object detection.
7. Dataset: Provide a brief description of the dataset used with some sample
   images of each class.
8. Proposed CNN architecture for Image classification:
   a. Baseline architecture used.
   b. Customized architecture
   c. Assumptions/intuitions
   d. Model summary
9. Proposed CNN architecture for Object Detection and localization:
   a. Baseline architecture used.
   b. Customized architecture
   c. Assumptions/intuitions
   d. Model summary
10. Experimental results and discussion:
    a. Experimental settings:
    i. Image classification
    ii. Object detection
    b. Experimental Results:
    i. Image classification
    ii. Object detection
    iii. Discussion: Provide your understanding of the performance
    and accuracy obtained. You may also include some image
    samples which were wrongly classified.
    42028: Assignment 2 – Autumn 2019 Page 3 of 4
11. Conclusion: Provide a short paragraph detailing your understanding of the
    experiments and results.
    Deliverables:
12. Project Report (10 pages max)
13. Google Colab or Ipython notebook, with the code
    Additional Information:
    Assessment Submission
    Submission of your assignment is in two parts. You must upload a zip file of the
    Ipython/Colab notebooks and Report to UTS Online. This must be done by the Due
    Date. You may submit as many times as you like until the due date. The final
    submission you make is the one that will be marked. If you have not uploaded your zip
    file within 7 days of the Due Date, or it cannot be run in the lab, then your assignment
    will receive a zeromark. Additionally, the result achieved and shown in the
    ipython/Colab notebooks should match the report. Penalties apply if there are
    inconsistencies in the experimental results and the report.
    PLEASE NOTE 1: It is your responsibility to make sure you have thoroughly tested your
    program to make sure it is working correctly.
    PLEASE NOTE 2: Your final submission to UTS Online is the one that is marked. It does
    not matter if earlier submissions were working; they will be ignored. Download your
    submission from UTS Online and test it thoroughly in your assigned laboratory.
    Return of Assessed Assignment
    It is expected that marks will be made available 2 weeks after the submission via UTS
    Online. You will be given a copy of the marking sheet showing a breakdown of the marks.
    Queries
    If you have a problem such as illness which will affect your assignment submission
    contact the subject coordinator as soon as possible.
    Dr. Nabin Sharma
    Room: CB11.07.124
    Phone: 9514 1835
    Email: Nabin.Sharma@uts.edu.au
    If you have a question about the assignment, please post it to the UTS Online forum
    for this subject so that everyone can see the response.
    If serious problems are discovered the class will be informed via an announcement on UTS
    Online. It is your responsibility to make sure you frequently check UTS Online.
    42028: Assignment 2 – Autumn 2019 Page 4 of 4
    PLEASE NOTE: If the answer to your questions can be found directly in any of the
    following
    Subject outline
    Assignmentspecification
    UTS Online FAQ
    UTS Online discussion board
    You will be directed to these locations rather than given a direct answer.
    Extensions and Special Consideration
    In alignment with Faculty policies, assignments that are submitted after the Due Date
    will lose 10% of the received grade for each day, or part thereof, that the assignment
    is late. Assignments will not be accepted after 5 days after the Due Date.
    When, due to extenuating circumstances, you are unable to submit or present an
    assessment task on time, please contact your subject coordinator before the
    assessment task is due to discuss an extension. Extensions may be granted up to a
    maximum of 5 days (120 hours). In all cases, you should have extensions confirmed in
    writing.
    If you believe your performance in an assessment item or exam has been adversely
    affected by circumstances beyond your control, such as a serious illness, loss or
    bereavement, hardship, trauma, or exceptional employment demands, you may be
    eligible to apply for Special Consideration (https://www.uts.edu.au/curren...).
    Academic Standards and Late Penalties
    Please refer to subject outline.
    WX：codehelp

android studio问题-E / Vision：加载可选模块com.google.android.gms.vision.ocr时出错

W/DynamiteModule: Local module descriptor class for com.google.android.gms.vision.dynamite.ocr not found.
I/DynamiteModule: Considering local module com.google.android.gms.vision.dynamite.ocr:0 and remote module com.google.android.gms.vision.dynamite.ocr:0
W/DynamiteModule: Local module descriptor class for com.google.android.gms.vision.ocr not found.
I/DynamiteModule: Considering local module com.google.android.gms.vision.ocr:0 and remote module com.google.android.gms.vision.ocr:0
E/Vision: Error loading optional module com.google.android.gms.vision.ocr: com.google.android.gms.dynamite.DynamiteModule$LoadingException: No acceptable module found. Local version is 0 and remote version is 0.