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605.626.81 - Image Processing

Computer Science

Fall 2023

Description

Fundamentals of image processing are covered, with an emphasis on digital techniques. Topics include digitization, enhancement, segmentation, the Fourier transform, filtering, restoration, reconstruction from projections, and image analysis including computer vision. Concepts are illustrated by laboratory sessions in which these techniques are applied to practical situations, including examples from biomedical image processing. Prerequisite(s): Familiarity with Fourier transforms.

Expanded Course Description

As cameras and digital sensors continue to become dominant components of most of the personal devices used on our daily life, it is crucial for computer and data scientists to become familiar with different methods to analyze and process the data coming from such sensors. The aim of this course is to provide students a solid foundation of digital image processing techniques and algorithms used to enhance images and video streams. Topics that will be covered throughout the semester include image digitization, image representation, convolution filters. 2D and 3D signal processing. Fourier transforms, image sampling and resampling, gray scale operations, segmentation, morphology, thinning, edge detection, image restoration, biomedical imaging, and image compression. Concepts are discussed in didactic sessions and through examples. Projects are used to apply the concepts learned to real-world practical applications.

Instructor

Jesus Caban

jesus.caban@jhu.edu

Course Structure

The course materials are divided into modules which can be accessed by clicking Course Modules on the left menu. A module will have several sections including the overview, lectures and content, readings, discussions, and assignments. You are encouraged to preview all sections of the module before starting. Modules run for a period of seven (7) days, any exceptions are noted on the Course Outline page. You should regularly check the Calendar and Announcements for assignment due dates.

Course Topics

Topics
Introduction to Image Processing
Introduction to Python and Image Processing Libraries
Introduction to OpenCV
Image Formation
Image Histogram and Intensity Transformations
Convolution and Spatial Filtering
Edge Detection
Frequency Domain
Mathematical Morphology
Color Image Processing
Motion, Tracking, and Optical Flow
Image Descriptors and Features
Image Features and Space Scale
Medical Imaging

Course Goals

The primary goal of this course is to learn the fundamentals concepts of image processing. This course will explore the underlying theory and practical concepts of enhancing digital images, will describe the fundamental image processing algorithms commonly used to process digital images, will explain the core concepts of digitization, enhancement, filtering, and segmentation, will discuss state-of-the-art techniques used to solve real-world problems related to images and video processing, and will guide students in the development of software applications that use imaging processing techniques to enhance digital images. The new knowledge acquired during the semester will be used to solve practical image processing and computer vision problems.

Course Learning Outcomes (CLOs)

Describe the fundamental image processing techniques commonly used to enhance digital images.
Explain the core concepts of digitization, enhancement, filtering, and segmentation.
Employ critical thinking to determine which state-of-the-art techniques can be used to solve real-world problems related to images and video processing.
Develop software applications that use imaging processing techniques to enhance digital images.

Textbooks

Not required

Required Software

Python

Student Coursework Requirements

The course will consist of 14 modules. Each module will include a video lecture, corresponding slides, reading assignment, and an online discussion. The grading of this course will be based on:

Item	% of Grade
Class Participation	20%
Programming Assignments	50%
Final Project	30%

Programming assignments are due according to the dates posted in your Blackboard course site. You may check these due dates in the Course Calendar or the Assignments in the corresponding modules. I will post grades 1-2 weeks after assignment due dates. The code for your programming assignment must be submitted and should include enough documentation so we can read your code and understand the approach you are following.

In written sections and the final paper, we generally do not directly grade spelling and grammar. However, egregious violations of the rules of the English language will be noted without comment. Consistently poor performance in either spelling or grammar is taken as an indication of poor written communication ability that may detract from your grade.

Grading Policy

Final grades will be assigned according to the following scale:

100–98 = A+
97–94 = A
93–90 = A−
89–87 = B+
86–83 = B
82–80 = B−
79–70 = C
<70 = F

A final grade of A indicates achievement of consistent excellence and distinction throughout the course—that is, conspicuous excellence in all aspects of assignments and discussion in every week.

A final grade of B indicates work that meets all course requirements on a level appropriate for graduate academic work.

Late policy:

Programming projects will be due at 11:59pm on the due date.
Students may turn in one programming assignment up to 24 hours late, however students must notify the instructor each time the special extension is used. Failure to notify the instructor and any additional day will result in a 10% penalty of the assignment grade.
Penalty of 10% per day for projects submitted late.
Additional exceptions for emergencies and medical conditions may be given if deemed appropriate.

Course Evaluation

Participation (Module Discussions) (20% of Final Grade Calculation)

Most of the discussion of the class will happen online at the discussion board. Each module will have a topic / question that students must answer to obtain credit. To enable student interaction, students will be also required to reply to at least one comment from another student. To receive credit, the discussion of a specific module must happen before the first day of the following module.

Participation is graded as follows:

Criteria	Excellent	Satisfactory	Unsatisfactory
Concise, Critical Thinking/ Reasoning	Student actively stimulates and sustains inquiry by asking or posting thoughtful questions or comments. Student recognizes accuracy, logic, relevance, or clarity of statements. Student has a clear idea of the topic under discussion and sustain inquiry by asking thoughtful questions. Responses are concise and reflect original thinking.	Student posts questions and comments, but relies on momentum of the group to motivate inquiry. Student may be repetitive with comments. Student takes a position but with little evidence or explanation. Responses are somewhat concise and logically organized, and reflect a mixture of original thinking and contributions from others.	Student accepts ideas of others without much thought. Student provides little relevant information or contributes little to the discussion. Student shows little evidence of understanding the topic under discussion. Responses are neither clear nor concise. Little or no original thinking is demonstrated.
Generates learning and engagement among classmates	Post(s) elicit responses and reflections from other learners and responses build upon and integrate multiple views from other learners to take the discussion deeper.	Post(s) attempt to elicit responses and reflections from other learners and responses build upon the ideas of other learners to take the discussion deeper.	Post(s) do not attempt to elicit responses and reflections from other learners and/or responses do not build upon the ideas of other learners to take the discussion deeper.
Demonstrates knowledge of content and applicability to professional practice	Post(s) and responses show evidence of knowledge and understanding of course content and applicability to professional practice, and include other resources that extend the learning of the community.	Post(s) and responses show evidence of knowledge and understanding of course content and applicability to professional practice.	Post(s) and responses show little evidence of knowledge and understanding of course content and applicability to professional practice.
Timeliness and Mechanics	Submits initial response before the end of Day 5 in module week; replies to classmates are meaningful. Posts contain grammatically correct sentences without spelling errors.	Submits initial response before end of Day 6 in module week; replies to classmates are present, but superficial. Posts have one or more grammatical or spelling errors.	Submits initial response after Day 6 in module week; does not respond to classmates. Posts are not in complete sentences and/or contain more than 5 spelling or grammatical errors.

Programming Assignments (50% of Final Grade Calculation)

Programming assignments will count for 50% of the final grade. They will be distributed throughout the semester to enable students in a hands-on experience of implementing image processing techniques to enhance images or video streams. Students will use Python, C/C++ and/or other tools to implement their programming assignments. Programming projects will be graded according to (a) the quality of the results and (b) the clarity of the source code. See generic rubric below that will be used to grade programming assignments.

NOTE: Some programming assignments have a written section. That will be graded based on what is specified in each individual assignment.

Critical Errors Program compiles and/or executes as expected

(0 to 20 points)

Program does not compile or runs correctly. Program executes and sometimes terminates with a segmentation fault.

(0 to 15 points)

Program compiles and runs correctly. Program executes and terminates properly without crashing but produces some run time warnings.

(15 to 20 points)

Program compiles and runs correctly. Program executes and terminates properly without crashing.

(20 points)

Submission Error Project submitted following guidelines

(0 to 10 points)

Submission is incomplete and does not include all requested files.

(0 to 5 points)

Submission is mostly complete. Submission includes all requested files and for the most part follows the naming convention.

(5 to 9 points)

Submission is complete. Submission includes all requested files and follows naming convention.

(10 points)

Correctness

Implementation logical and correct

(0 to 30 points)

Program does not follow most of the requirements and the technical approach does not seem to be logical and correct.

(0 to 14 points)

Program implemented following most of the requirements and the technical approach (with the exception of 1 to 2 components) seems to be logical and correct.

(15 to 25 points)

Program implemented following requirements and the technical approach seems to be logical and correct.

(25 to 30 points)

Efficiency & Design:

Quality of Final Product (0 to 35 points)

Program is not efficient and/or only works with a small set of input images. Overall design is not clear and logical.

(0 to 15 points)

Program is mostly efficient and most with multiple input images. Overall design is mostly clear, simple, and logical.

(15 to 25 points)

Program is efficient and works with multiple input images. Overall design is clear, simple, and logical.

(25 to 30 points)

Documentation: Program Documentation

(0 to 10 points)

The source code is (for the most part) not documented. (0 to 5 points)

Source code documentation is not complete or reasonable. (5 to 8 points)

Source documentation is sufficient and reasonable.

(8-10 points)

Final Project (30% of Final Grade Calculation)

The final project is a significant part of the course. It allows students to synthesize the concepts learned throughout the semester and apply them to their own images and/or to their particular area of interest. The final project grades will be based on the following components:

Draft Proposal (10%)
Revised Proposal (10%)
Literature survey (10%)
Final video presentation (20%)
Final project (paper, code, program, etc...) (50%)

See generic rubric below that will be used to grade the Final Project.

Project Proposal

Draft Proposal and Revised Project Proposal

(0 to 20 points)

Topic of the proposal not necessarily relevant to the class. Proposal does not include detailed description, project plan, or timeline. Revised proposal does not include some of the suggestions received from instructor.

(0 to 15 points)

Topic of the proposal relevant to the class. Proposal includes description, project plan, and a rough timeline. Revised proposal does not include some of the suggestions received from instructor.

(15 to 20 points)

Topic of the proposal relevant to the class. Proposal includes description, project plan, and detailed timeline. Revised proposal include some of the suggestions received from instructor.

(20 points)

Literature Survey

Annotated Bibliography

(0 to 20 points)

Annotated bibliography that includes less than four references and each reference has a short description of the paper, approach and results.

(0 to 9 points)

Annotated bibliography that includes at least four references and each reference has a short description of the paper, approach and results.

(10 to 18 points)

Annotated bibliography that includes at least four references and each reference has a short, but accurate, description of the paper, approach and results.

(18 to 20 points)

Final Project (Paper)

Paper submission with describing final Project

(0 to 30 points)

A paper that describes a good class project, technical approach, and different results. The paper has some grammatical errors or typos and does not include all the suggested sections.

(0 to 15 points)

A well-written paper that describes a good class project, technical approach, and different results. The paper includes sections for introduction, background, approach, results, and conclusion.

(16 to 25 points)

A well-written paper that describes an outstanding and comprehensive class project, technical approach, and different results. The paper includes sections for introduction, background, approach, results, and conclusion.

(26 to 30 points)

Final Project (Code):

Code, images, and data used in final project.

(0 to 20 points)

Program that compiles and performs most of what the student described in his/her final paper. The code has some documentation and the project follows most of the required naming convention.

(0 to 14 points)

Good program that compiles and performs what the student described in his/her final paper. The code has some documentation and the project follows the required naming convention.

(10 to 15 points)

Efficient program that compiles and performs what the student described in his/her final paper. The code has good documentation and the project follows the required naming convention.

(15 to 20 points)

Academic Policies

Deadlines for Adding, Dropping and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.

This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.

For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically.

For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity.

If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.