CS代考 Part a – cscodehelp代写

Part a
Module Organisation and Introduction
Contacting Us
Email:
• •
Weekly office hours
Please ask questions via the discussion forum on KEATS
7CCSMCVI / 6CCS3COV: Computer Vision
Lecturers: Dr Michael Spratling & Dr Miaojing
Exam
85% of mark
• In January
• Past Exam Papers – available through KEATS.
Coursework 15% of mark
• Instructions – available through KEATS
• Deadlines – available through KEATS
Course Syllabus
1. Introduction
2. Image Formation
3. Low-Level Vision (Artificial)
4. Low- and Mid-Level Vision (Biological) 5. Mid-Level Vision: Segmentation
6. Mid-Level Vision: Correspondence
7. Mid-Level Vision: Stereo and Depth
8. Mid-Level Vision: Video and Motion
9. High-Level Vision (Artificial)
10. High-Level Vision (Biological)
Teaching Materials
Module Web-Page
• Available through KEATS.
Lectures
• Lecture Slides – available through KEATS.
• Lecture Videos – available through KEATS.
Tutorials
• Questions – available through KEATS.
• Answers – available on KEATS after corresponding tutorial session.
Practical Exercises
• MATLAB exercises – available through KEATS.

Course Outline
Introductory course on computer vision – aiming to provide a comprehensive introduction to the main issues and methods.
● Image formation: the physics of image formation, cameras, the geometry of image formation, image coding and representation, the eye.
● Low-level vision: image processing (filtering, convolution), feature detection (edges), neural representations in V1.
● Mid-level vision: grouping and segmentation, the correspondence problem, stereo and depth, video and motion, Gestalt principles, border ownership.
● High-level vision: object recognition and categorisation.
Prerequisites
Mathematical Knowledge:
• Geometry and Trigonometry
• Matrix and Vector Mathematics
• Differentiation and Integration
Programming knowledge:
• ability to code
• Willingness to learn to programme in MATLAB
Course Syllabus
1. Introduction
2. Image Formation
3. Low-Level Vision (Artificial)
4. Low- and Mid-Level Vision (Biological) 5. Mid-Level Vision: Segmentation
6. Mid-Level Vision: Correspondence
7. Mid-Level Vision: Stereo and Depth
8. Mid-Level Vision: Video and Motion
9. High-Level Vision (Artificial)
10. High-Level Vision (Biological)

This Week
• What is Computer Vision? • Aims
• Relation to other subjects
• Why is it important?
• Need to understand biological vision
• Applications of machine vision
• Why is it difficult?
• One image → many interpretations
• One object → many images
• How do we tackle this problem?
• Computational approach
• Biological approach
Part b
What is Computer Vision
7CCSMCVI / 6CCS3COV: Computer Vision
Introduction to Computer Vision

What is computer vision?
Possible Definitions
• “Computing properties of the 3D world from one or more digital images” (Trucco and Verri).
• “To make useful decisions about real physical objects and scenes based on sensed images” (Stockman and Shapiro).
• “Extracting descriptions of the world from pictures or sequences of pictures” (Forsyth and Ponce).
What is computer vision?
Possible Definitions
• “Computing properties of the 3D world from one or more digital images” (Trucco and Verri).
• “To make useful decisions about real physical objects and scenes based on sensed images” (Stockman and Shapiro).
• “Extracting descriptions of the world from pictures or sequences of pictures” (Forsyth and Ponce).
Extracting information from images
A boat passing under Westminster Bridge
Contents
What is Computer Vision?
• Definitions
• Relationtoothersubjects • Applications

Related Disciplines
Computer vision also has other (equivalent) names: machine vision
image analysis image understanding computational vision
Why is Vision Worth Studying?
Vision is main way in which we experience the world.
• Want machines to interact with world like we do (e.g. robotics).
• Want machines to understand world like we do (e.g. AI).
• Want machines to extract useful information.
Digital images are everywhere.
• Lots of Information available in those images.
• Lots of applications…
Related Disciplines
Image processing – manipulation of an image Computer graphics – digitally synthesizing images
Image Processing: Computer Vision: Computer Graphics:
Image → Image → Description →
Image Description Image
Pattern recognition – recognising and classifying stimuli in images and other datasets
Photogrammetry – obtaining measurements from images
Biological Vision – understanding visual perception in humans and animals (studied in Neuroscience, Psychology, Psychophysics)

Appln: character recognition
Optical character recognition (OCR) converts scanned documents to text.
Combining a camera, OCR, language translation and computer graphics allows for real-time translation of menus, signs, etc.
Google Lens
lens.google.com
Appln: face detection
Many digital cameras use face detection to focus on the likely subject.
Some webcams detect faces so that the camera can automatically pan/tilt and zoom to follow someone.
Appln: character recognition
● Optical character recognition (OCR) converts scanned documents to text.
● Automatic numberplate recognition (ANR) reads car licence plates.
Digit recognition, AT&T labs License plate readers
http://www.research.att.com/~yann/
http://en.wikipedia.org/wiki/Automatic_number_plate_recognition

Appln: face detection / recognition
Detect faces in images to allow user to tag people. Recognise tagged people in new photos.
e.g. Picasa, Facebook
Appln: face recognition
Face recognition
http://www.face-rec.org/
e.g. for controlling access to buildings, identifying terrorists at airports, and for controlling access to computers (e.g. http://www.sensiblevision.com/)
Appln: smile detection
Camera can be set to automatically take photos when a chosen subject laughs, smiles, and grins
e.g. Sony Cyber-shot® T70 Digital Still Camera

Appln: people tracking
People tracking for visual surveillance and crime detection (e.g. generate warning if someone is breaking into a car)
Appln: object tracking
e.g. in sport for instant replay and analysis (http://www.hawkeyeinnovations.co.uk/)
Appln: biometrics
Iris Recognition
http://www.iris-recognition.org/
Fingerprint Recognition. e.g. Fingerprint scanners on many laptops, phones, and other devices

Appln: content-based image retrieval
… also known as query by image content.
“beetle” →
results that look like insect
results that look like car
Appln: reverse image search Find similar images
images.google.com
Google Lens
lens.google.com
Appln: advertising
Detect ground plane in video and introduce pictures on them.

Appln: driver assistance lane departure warning
pedestrian and car detection
collision warning / automatic braking
Appln: space exploration
also driver impairment monitoring e.g. http://www.mobileye.com/
…soon driver replacement, with self-driving cars
Vision systems used for several tasks
• Panorama stitching
• 3D terrain modeling
• Obstacle detection, position tracking
(see “Computer Vision on Mars” by Matthies et al.)
Appln: landmark recognition
Allows someone to point a camera phone at an object or picture and find out more about it.
Point & Find by Nokia
Google Lens
lens.google.com

Appln: 3D models from images
+ + +…
From a set of photos of an object or building, generate a 3D virtual
(CAD) model that can e.g
be viewed from any angle.
Summary
http://photosynth.net/about.aspx http://www.3dsom.com/
• Vision is concerned with determining properties of the world from images.
• Lots of applications.
Appln: medical imaging
automatic measurement and analysis of non-visual images created in MRI, CT and ultrasound scanners

Contents
Why is Computer Vision difficult?
• Oneimage→manyinterpretations • One object → many images
Why is vision difficult?
Note that all the previous examples of vision systems are limited to operating in a specific (small) domain:
• specific task
– e.g. locate a tennis ball, identify a finger print
• specific environment
– e.g. on a road, given a frontal view of a face
Solutions are not robust / do not generalise: • Code that works well for one task will:
– fail for different tasks
– fail for similar tasks
– fail for same task under different conditions
– sometimes fail for the same task under the same conditions
Part c
Why is Computer Vision Challenging

Why is vision difficult?
A boat passing under Westminster Bridge
Vision is easy for us, so it is difficult to appreciate how difficult it is to develop algorithms for computer vision.
Why is vision difficult?
A boat passing under Westminster Bridge
Vision is easy for us, so it is difficult to appreciate how difficult it is to develop algorithms for computer vision.
Only easy because:
• ~ 50% of cerebral cortex is devoted to vision.
• Vision consumes ~10% of entire human energy consumption.
➔ important & difficult.
Why is vision difficult?
Note that all the previous examples of vision systems are limited to operating in a specific (small) domain:
• specific task
– e.g. locate a tennis ball, identify a finger print
• specific environment
– e.g. on a road, given a frontal view of a face
The challenge of developing robust, general purpose vision systems that can match human performance still remains:
• any task
– e.g. recognise many different objects
• any environment
– e.g. under many viewing conditions

Why is vision difficult?
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A boat passing under Westminster Bridge
If we replace the image by its numerical representation (the input to a CV algorithm), the transformation into a description is less obvious.
Major Challenges:
1. One image → many interpretations problem is ill-posed
2. One object → many images problem is exponentially large
Vision is an ill-posed problem
Mapping from world to image (3D to 2D) is unique (well-posed).
• This is a “forward problem” (i.e. imaging). Mapping from image to world (2D to 3D) is
NOT unique (ill-posed)
• This is an “inverse problem” (i.e. vision).
For any given image there are many objects that could have generated that image.
Solved using constraints or priors: which make some interpretations more likely than others (usually the brain produces one interpretation from the many possible ones).
Why is vision difficult?
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A boat passing under Westminster Bridge
If we replace the image by its numerical representation (the input to a CV algorithm), the transformation into a description is less obvious.
Major Challenges:
One image → many interpretations
problem is ill-posed
One object → many images
problem is exponentially large

Multiple interpretations of an image What does this image show?
Three possible interpretations:
One object Two objects Three objects Most likely?
Multiple interpretations of an image What does this image show?
Multiple interpretations of an image What does this image show?

Multiple interpretations of an image
Necker cube Rubin’s Face / Vase illusion
In both case there are two possible interpretations which are both equally likely, so either is perceived spontaneously.
Note, differing interpretations never perceived simultaneously.
Vision scales exponentially
Consider trying to recognize an object.
Suppose the object can:
• appear at any one of l locations in the image
• appear at any one of s different scales (i.e. sizes)
• appear at any one of o orientations
• appear in any one of c colours
• …
This one object can give rise to l x s x o x c different images.
The number of images increases exponentially with the number of parameters.
Solved by using invariant representations and priors.
Multiple interpretations of an image What does this image show?
Three possible interpretations:
Most likely?

Illumination affects appearance
A single object seen under different lighting conditions can vary greatly in appearance.
The resulting images have little similarity.
Non-rigid deformations affect appearance
A single object can undergo deformations which cause it to vary greatly in appearance.
The resulting images have little similarity.
Viewpoint affects appearance
A single object seen from different viewpoints can vary greatly in appearance (object orientation, retinal location, scale, etc. all affect appearance).
The resulting images have very little similarity.

Discrimination despite variation
“Objects that look very similar can be represented and recognized as different objects, whereas objects that look very different can be recognized as the same basic-level objects” (Bar, 2004).
Despite the variation in appearance of a single object, or a single category, it is necessary to be able to distinguish one object/category from another.
Other objects affect appearance
Images usually contain multiple objects.
This leads background clutter and occlusion.
Resulting in images of a single object having little similarity.
Within-category variation in appearance
Objects forming a single category can vary greatly in appearance.
The resulting images have little similarity.

Part d
How do we Tackle the Problem of Vision
Contents
How do we tackle the difficult problem of Vision?
• Biological approach
• Computational approach
Summary
Vision is difficult due to the problem being:
• ill-posed (one image can have many interpretations), and
• exponentially large (one object can generate many images).

Effects of inference (illumination)
Which is darker, A or B?
Prior knowledge about shadows results in perceived intensity not reflecting image intensity.
Effects of inference (illumination)
Which is darker, A or B?
Prior knowledge about shadows results in perceived intensity not reflecting image intensity.
Need for constraints (priors)
Previous slides illustrated the two major challenges for Computer Vision:
• One image → many interpretations • One object → many images
To solve these challenges we need to employ constraints / priors / expectations.
Perception involves inference:
We must combine prior information about the world with evidence from our senses (e.g. vision) to infer what is in the world.
The next slides illustrate some effects that priors have on human visual inference…

Effects of inference (illumination)
Are the central patches the same colour?
Visual system sees them as different due to inference about different lighting conditions
Effects of inference (illumination)
Are the craters convex or concave?
Prior expectation about the direction of illumination (from above) effects the interpretation of a single image.
Effects of inference (illumination)
Are the central patches the same colour?

Effects of inference (perspective)
Effects of inference (perspective) The Ames Room illusion
Effects of inference (perspective)
Which is larger? Which is longer?
Prior expectation about image formation (perspective geometry) effects size/shape perception

Effects of inference (prior knowledge)
Our prior expectation to see a face is so strong that we see them everywhere.
“Virgin Mary” toast fetches $28,000 on eBay!
Effects of inference (prior knowledge)
Who are these people?
What does
this say? Prior expectation about the likely
content of an image prevents us from seeing what is actually there.
Effects of inference (prior knowledge)
What does this image show?
Prior knowledge about the image content enables us to easily see something that was previously invisible.

Effects of inference (prior exposure)
Vision is sensitive to temporal discontinuities, so a sudden change is easy to spot. Disrupting the temporal continuity (with a flicker, or by flashing up some other stimulus) makes us insensitive to significant changes to the scene. This is called “change blindness”.
Effects of inference (context)
Contextual information from the whole image enables us to disambiguate parts of the image.
What is the middle letter in each word?
What does this word say?
Effects of inference (prior exposure)
What is changing in these images?
For better demo see: http://csclab.ucsd.edu/~alan/vision/change_blindness/

Effects of inference (context)
Contextual information from the whole image enables us to disambiguate parts of the image.
Effects of inference (context)
What are the hidden objects?
1
2
Effects of inference (context)
What are these objects?

Illusions as effects of inference
Several of the preceding examples are illusions.
• Illusions are traditionally considered to reveal “mistakes”
made by the visual system.
• However, illusions actually reveal the assumptions that the visual system is making in order to solve the under- constrained problem of vision.
The assumption does not reflect a “flaw” in the visual system but represents an adaptation to the the way things usually are.
Our visual system excels because it has learned rules about our world, that work well in typical situations.
Influence of priors on human vision
Preceding examples demonstrate that Human perception is influenced by prior expectations coming from many sources.
We can categorise these sources as priors from:
prior knowledge / familiarity
» learned familiarity with certain
objects
» knowledge of image formation
process in general
– prior exposure / motion / priming
» recent / preceding sensory input
– current context
» surrounding visual scene (and
concurrent input in other sensory modalities)
Effects of inference (context)
Chance ≈ 1/20000 per image!
Contextual information from the whole image enables us to predict contents of parts of the image.

This course
…Hence, this course is interdisciplinary.
It considers both:
Computational (machine) vision:
How can we get computers to see? Implementing algorithms for perception
Biological (human) vision: How do people see?
Modelling biological perception
Summary
• Vision is difficult due to the problem being ill-posed (one image can have many interpretations) and being exponentially large (one object can generate many images).
• Overcoming these problems requires combining prior information with evidence from the image in order to make inferences about image content.
– Our brains excel at this, so understanding biological vision can inspire solutions in computer vision.
– Prior knowledge about a restricted domain has enabled the development of many impressive vision applications.
How do we tackle the problem of vision?
(Forward) Engineering Approach.
● determine what the system needs to do (requirements). ● design a system to perform this task.
● implement the system, test and refine it.
– “top-down”: start with computational theory and fill out details.
Reverse Engineering Approach.
• find a system that performs the task (e.g. the brain).
• analyse the system to determine how it does it.
• implement a new system using the same mechanisms.
– “bottom-up”: start with mechanisms and build a model. This course will consider both approaches…