CS325 Artificial Intelligence Ch. 24, Computer Vision I – Object Recognition Cengiz Günay, Emory Univ. Spring 2013 Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 1 / 27
Computer Vision Done with games, except homework :) Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 2 / 27
Computer Vision Done with games, except homework :) Vision is one of our main perceptions Computer vision is what robots use to understand their surrounding Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 2 / 27
Computer Vision Done with games, except homework :) Vision is one of our main perceptions Computer vision is what robots use to understand their surrounding 3 lectures: 1 Object recognition (today) 2 3D reconstruction 3 Motion analysis Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 2 / 27
Entry/Exit Surveys Exit survey: Advanced Planning Why isn’t classical planning schema adequate for resource planning? What is the advantage gained in abstract plans by having surely-reachable versus potentially-reachable states? Entry survey: Computer Vision I – Image Processing (0.25 points) List three specific tasks where computer vision would be desirable. What do you think are the major hurdles in computer vision? Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 3 / 27
How Machines See: Cameras A charge-coupled device (CCD) photo sensor array: Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 4 / 27
Focal Optics for Determining Distance and Size See the videos, I’ll summarize: X Z = x f Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 5 / 27
Focal Optics for Determining Distance and Size See the videos, I’ll summarize: X Z = x f What can we can figure out from this? Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 5 / 27
Focal Optics for Determining Distance and Size See the videos, I’ll summarize: X Z = x f What can we can figure out from this? Object’s distance ( Z ) & height ( X ) based on projection height ( x ) and focal distance ( f ) Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 5 / 27
We All See a Perspective Projection Vanishing points from parallel lines: Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 6 / 27
We All See a Perspective Projection Vanishing points from parallel lines: Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 6 / 27
We All See a Perspective Projection Vanishing points from parallel lines: Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 6 / 27
We All See a Perspective Projection Vanishing points from parallel lines: Giant panda, or just close? Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 6 / 27
Object Recognition: How Hard Can It Be? Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 7 / 27
Object Recognition: How Hard Can It Be? Problems? Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 7 / 27
Object Recognition: How Hard Can It Be? Problems? Rotation, scale, illumination, occlusion, viewpoint, deformation Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 7 / 27
Not Hard for Us Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 8 / 27
Not Hard for Us Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 8 / 27
Not Hard for Us Diffuse reflection, bright Specularities Diffuse reflection, dark Cast shadow Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 8 / 27
Not Hard for Us Diffuse reflection, bright Specularities Diffuse reflection, dark Cast shadow How does our brain do it? Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 8 / 27
Not Hard for Us Diffuse reflection, bright Specularities Diffuse reflection, dark Cast shadow How does our brain do it? Will have examples later. Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 8 / 27
Invariance is Crucial for Computer Vision Must recognize objects invariant of their: Rotation, scale, illumination, occlusion, viewpoint, deformation Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 9 / 27
Invariance is Crucial for Computer Vision Must recognize objects invariant of their: Rotation, scale, illumination, occlusion, viewpoint, deformation Let’s start by simplifying: 1 Greyscale (monochrome) images 2 Pixels can have values: 0. . . 255 Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 9 / 27
Even Terminator Has Monochrome Vision Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 10 / 27
Extracting Features: Edge Detection Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 11 / 27
Extracting Features: Edge Detection Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 11 / 27
Extracting Features: Edge Detection How to detect the vertical edge? Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 11 / 27
Extracting Features: Edge Detection How to detect the vertical edge? 1 Spatial derivative? Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 11 / 27
Extracting Features: Edge Detection How to detect the vertical edge? 1 Spatial derivative? 2 Filter with mask: + 1 − 1 Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 11 / 27
Extracting Features: Edge Detection How to detect the vertical edge? 1 Spatial derivative? 2 Filter with mask: + 1 − 1 Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 11 / 27
Extracting Features: Edge Detection How to detect the vertical edge? 1 Spatial derivative? Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 11 / 27 2 Filter with mask: + 1 − 1
Extracting Features: Edge Detection How to detect the vertical edge? 1 Spatial derivative? Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 11 / 27 2 Filter with mask: + 1 − 1
Edge Detection: Linear Filter What we did is called convolution : I ′ I ⊗ g = = ⊗ Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 12 / 27
Edge Detection: Linear Filter What we did is called convolution : I ′ I ⊗ g = = ⊗ For each pixel, we multiply by mask and sum: � I ′ ( x , y ) = I ( x − u , y − v ) g ( u , v ) u , v Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 12 / 27
Edge Detection: Linear Filter What we did is called convolution : I ′ I ⊗ g = = ⊗ For each pixel, we multiply by mask and sum: � I ′ ( x , y ) = I ( x − u , y − v ) g ( u , v ) u , v Does that equation look familiar? Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 12 / 27
Edge Detection: Linear Filter What we did is called convolution : I ′ I ⊗ g = = ⊗ For each pixel, we multiply by mask and sum: � I ′ ( x , y ) = I ( x − u , y − v ) g ( u , v ) u , v Does that equation look familiar? Perceptron? Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 12 / 27
Edge Detection: Linear Filter What we did is called convolution : I ′ I ⊗ g = = ⊗ For each pixel, we multiply by mask and sum: � I ′ ( x , y ) = I ( x − u , y − v ) g ( u , v ) u , v Does that equation look familiar? Perceptron? What are the weights? Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 12 / 27
Edge Detection: Linear Filter What we did is called convolution : I ′ I ⊗ g = = ⊗ For each pixel, we multiply by mask and sum: � I ′ ( x , y ) = I ( x − u , y − v ) g ( u , v ) u , v Does that equation look familiar? Perceptron? What are the weights? The mask, g . Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 12 / 27
Edge Detection: Linear Filter What we did is called convolution : I ′ I ⊗ g = = ⊗ For each pixel, we multiply by mask and sum: � I ′ ( x , y ) = I ( x − u , y − v ) g ( u , v ) u , v Does that equation look familiar? Perceptron? What are the weights? The mask, g . What’s the advantage? Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 12 / 27
Edge Detection: Linear Filter What we did is called convolution : I ′ I ⊗ g = = ⊗ For each pixel, we multiply by mask and sum: � I ′ ( x , y ) = I ( x − u , y − v ) g ( u , v ) u , v Does that equation look familiar? Perceptron? What are the weights? The mask, g . What’s the advantage? Works in parallel! Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 12 / 27
Neurons Can Do It Faster? Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 13 / 27
Detect Only Vertical Edges? Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 14 / 27
Detect Only Vertical Edges? Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 14 / 27
Horizontal and Vertical Gradients Original: Günay Ch. 24, Computer Vision I – Object Recognition Spring 2013 15 / 27
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