Computer Vision Topics
Since computer vision is a relatively new subject, it is hard for educators to decide what topics should be taught in a computer vision course. This page contains information regarding what topics are being taught and how popular they are among computer vision courses.
The Process of Obtaining the Results on this page
The first type of information provided on this page is a list of topics taught by computer vision courses that were provided to CVED.org. The initial task required the compilation of syllabi of the computer vision courses. Each topic, whether is was considered a core topic or subtopic by the instructor, was entered into an Excel spreadsheet. With the use of a Python script, each topic of every course was sifted into a master topic list. Some difficulty was faced when sorting because the topic names varied greatly from course to course; however, if the topic names were suffiently close to each other (this was determined by paramters incuded in the Python script), then it was considered that the topics were the same.
The second type of included on this page is the popularity of each topic. Popularity in this case refers to how many courses include a particualr topic as a part of the syllabus. To do this, the same Python script was used to find the intersection of all the course syllabi, i.e., the script kept a count of how many courses were found to contain each topic in the master topic list. The next process was to combine the topics into their respective categories. The topics in each category were considered to be taught together. The table shown below shows the popularity of each category. The data for the table was acquired by using the list of categories as a reference list and going through the list of courses once again. This time, if a course contained one or more of the topics in a category, that category would be considered to have occurred once.
List3 Frequency of Occurrence of Groups
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"Created Aug. 5
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2004: Total column indicates the index number assigned to the category"
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Total
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Category Name
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Frequency of Occurrence
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%of Courses teaching at least one topic in category
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Total 0 :
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optical flow
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10
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0.385
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Total 1 :
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motion detection
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3
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0.115
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Total 2 :
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motion
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15
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0.577
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Total 3 :
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aperture problem
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1
|
0.038
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|
Total 4 :
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sensitivity error
|
1
|
0.038
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|
Total 5 :
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applications
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8
|
0.308
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|
Total 6 :
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image compression
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6
|
0.231
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Total 7 :
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tracking
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11
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0.423
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Total 8 :
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human-computer interfaces
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2
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0.077
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Total 9 :
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images streams
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1
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0.038
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Total 10 :
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cameras
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4
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0.154
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Total 11 :
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video processing
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5
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0.192
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Total 12 :
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image processing
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9
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0.346
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Total 13 :
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regions
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2
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0.077
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Total 14 :
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color
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10
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0.385
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Total 15 :
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imaging geometry
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21
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0.808
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Total 16 :
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illumination
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8
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0.308
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Total 17 :
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handling occlusion
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1
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0.038
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Total 18 :
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pose
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5
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0.192
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|
Total 19 :
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modalities
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1
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0.038
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Total 20 :
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edges
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15
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0.577
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Total 21 :
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snakes
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4
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0.154
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Total 22 :
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texture
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9
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0.346
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Total 23 :
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feature composition
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1
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0.038
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Total 24 :
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approximate 2d position
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1
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0.038
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Total 25 :
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image transforms
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12
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0.462
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Total 26 :
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surface geometry
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1
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0.038
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Total 27 :
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camera calibration
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10
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0.385
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Total 28 :
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reconstruction
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5
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0.192
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|
Total 29 :
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image analysis
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4
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0.154
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|
Total 30 :
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features classification
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2
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0.077
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Total 31 :
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segmentation
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16
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0.615
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Total 32 :
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stereo
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17
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0.654
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Total 33 :
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structure from motion
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8
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0.308
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Total 34 :
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image-based rendering
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3
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0.115
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Total 35 :
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object recognition
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17
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0.654
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Total 36 :
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object representations
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14
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0.538
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Total 37 :
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image acquisition
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12
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0.462
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Total 38 :
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image-based modeling
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3
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0.115
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Total 39 :
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mosaics
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5
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0.192
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Total 40 :
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shape from x
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5
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0.192
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Total 41 :
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image alignment
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2
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0.077
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Total 42 :
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invariants
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5
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0.192
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Total 43 :
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content-based image retrieval
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4
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0.154
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Total 44 :
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automatic automobile steering
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1
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0.038
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Total 45 :
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filtering
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13
|
0.500
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Total 46 :
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pattern recognition
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8
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0.308
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Total 47 :
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face detection
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7
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0.269
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Total 48 :
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marr-hildreth theory of low level vision
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2
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0.077
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Total 49 :
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computational theory and lightness
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2
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0.077
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Total 50 :
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biological vision
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5
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0.192
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Total 51 :
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reflectance
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3
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0.115
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Total 52 :
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morphology
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7
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0.269
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Total 53 :
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pyramids
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4
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0.154
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Total 54 :
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interest points
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6
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0.231
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Total 55 :
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harris detector
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1
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0.038
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Total 56 :
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image enhancement
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5
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0.192
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Total 57 :
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image restoration
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3
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0.115
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Total 58 :
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image composition
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2
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0.077
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Total 59 :
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matting
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2
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0.077
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Total 60 :
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sensors
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1
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0.038
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Total 61 :
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data-density/ultrasound/cat/noise
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1
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0.038
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Total 62 :
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single view metrology
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1
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0.038
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Total 63 :
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graphics
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3
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0.115
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|
Total 64 :
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robot vision
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2
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0.077
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Total 65 :
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contouring
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2
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0.077
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Total 66 :
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data structures for image analysis
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1
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0.038
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Total 67 :
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thresholding
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3
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0.115
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Total 68 :
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advanced surface detection approaches
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1
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0.038
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Total 69 :
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techniques
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1
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0.038
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Total 70 :
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optics
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1
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0.038
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Total 71 :
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neural networks for vision
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2
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0.077
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Total 72 :
|
hidden markov models for vision
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1
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0.038
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|
Total 73 :
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3dsensing
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2
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0.077
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Total 74 :
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grayscale correlation
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1
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0.038
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Total 75 :
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active templates
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1
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0.038
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Total 76 :
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model-based vision
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1
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0.038
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Total 77 :
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processing
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1
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0.038
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|
Total 78 :
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enhancement
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2
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0.077
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Total 79 :
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representation of object location
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1
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0.038
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Total 80 :
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intensity data
|
1
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0.038
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Total 81 :
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binary vision
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1
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0.038
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Total 82 :
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hardware
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1
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0.038
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Total 83 :
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math methods
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2
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0.077
|
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Total 84 :
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bundle adjustment(nonlinear optimization)
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1
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0.038
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Total 85 :
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layers
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1
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0.038
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Total 86 :
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space carving
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1
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0.038
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Total 87 :
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active vision
|
1
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0.038
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Total 88 :
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image representation
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8
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0.308
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Total 89 :
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resampling
|
3
|
0.115
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List Results
Course Topics