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Object Recognition Using Alignment. Brian J. Stankiewicz. Approaches to Human Object Recognition. Alignment Approach Store image(s) in memory Use image transformations to bring new view into alignment with viewed image. Approaches to Human Object Recognition. Alignment Approach.

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approaches to human object recognition
Approaches to Human Object Recognition
  • Alignment Approach
    • Store image(s) in memory
    • Use image transformations to bring new view into alignment with viewed image.
approaches to human object recognition1
Approaches to Human Object Recognition
  • Alignment Approach

Template matching Failures

approaches to human object recognition2
Approaches to Human Object Recognition
  • Alignment Approach

Many different exemplars of category of object. How does one handle this type of variability?

approaches to human object recognition3
Approaches to Human Object Recognition
  • Structural Description
    • Pre-process image before storing in memory
    • Decompose object into simple parts
    • Describe the object’s shape in terms of their parts
      • Parts are described using specific non-accidental properties
structural descriptions
Structural Descriptions
  • Objects are decomposed into “parts”.
  • Objects are described by specifying configuration of parts and their relations.
structural descriptions1
Structural Descriptions
  • Each part is describe by specifying the values of particular shape parameters.
  • Varying parameter varies the shape.
structural descriptions2
Structural Descriptions
  • Challenge.
    • How do you decompose image into objects and objects into parts?
    • How do you determine the shape parameters of a part given an image.
      • This topic will be covered next week in Biederman and Biederman & Cooper papers.
  • Begin by investigating the effect of viewpoint on object recognition.
    • Look for evidence of alignment approach
    • Shepard & Metzler
      • Mental rotation of 3d shapes
      • Picture Plane and Depth rotations
    • Tarr & Pinker
      • Mental rotation of 2d shapes
      • Picture plane rotation only
      • Multiple-Views Hypothesis
shepard metzler
Shepard & Metzler
  • Wanted to understand how humans recognize different views of the same object.
    • Different images of same 3D shape can be produced by manipulating viewpoint
  • Investigated the effect of depth and picture-plane rotations.
shepard metzler stimuli
Shepard & Metzler: Stimuli
  • “Novel” stimuli: Not a lot of previous experience
  • Fairly difficult task
    • Cannot simply use simple features
  • Able to carefully control view information.
shepard metzler procedure
Shepard & Metzler: Procedure
  • Two images presented simultaneously
    • Images of identical or “mirror reflected” objects
  • Subjects indicated whether two images depicted same object
    • Responded by pulling a “lever”
  • Record response times
shepard metzler results
Shepard & Metzler: Results
  • Response times increased linearly with orientation
  • Suggests that subjects are “mentally rotating” images to determine match.

RT To “Same” Responses

Angle of Rotation

shepard metzler results1
Shepard & Metzler: Results
  • Reaction times increased linearly with depth orientation
  • Suggests a similar mechanism
shepard metzler results2
Shepard & Metzler: Results
  • Not only are both depth and picture-plane rotations linearly increasing, but they have very similar slopes.
  • Suggestive of a single “mental rotation” mechanism.
object recognition
Object recognition
  • Two fundamental approaches to human object recognition
    • Alignment approaches
      • Object recognition through alignment process
    • Structural description approach
      • Decomposition of features included in an object
      • Describe the objects’ shape in terms of their parts and relation among the parts.
what is alignment
What is alignment
  • Definition
    • A process that transform stored images to bring new view into alignment with viewed image.
  • Why we need alignment?
    • We cannot recognize object exactly only by template matching
    • Need for some process which transform input images or data  alignment
2 studies in alignment approaches
2 studies in alignment approaches
  • Shepard & Metzler
    • Mental rotation of 3D objects shapes
    • A single mental rotation mechanism
    • Evidence*: same results from rotated depth and picture-plane pairs.
  • Tarr & Pinker
    • Multiple view hypothesis (?)
tarr pinker
Tarr & Pinker
  • Wanted to investigate “mental rotation” in more detail
    • Two hypotheses
      • Single canonical image stored in memory and all new images are aligned to that single representation
      • Multiple-Views stored in memory.
        • Align new view to closest stored view
tarr pinker method
Tarr & Pinker: Method
  • Train subjects to recognize small set of novel, letter-like objects.
    • Did a “handedness” task
    • Is the image the trained image (standard)or its mirror reversal?
tarr pinker stimuli
Tarr & Pinker: Stimuli
  • Novel, letter-like images.
  • Subjects trained on 3 of the images
    • Reduce stimuli specific effects
tarr pinker procedure
Tarr & Pinker: Procedure
  • Trained subjects on 4 different orientations
    • (0°,45°,-90°,135°)
  • Tested on trained and “surprise orientations”
  • Measured response times
tarr pinker exp 1 results

Initial reaction times similar to S&M

Performance improves after 13 blocks

Surprise orientations slower than trained

Tarr & Pinker: Exp. 1 Results

Block 1~12: practice

Block 13: practice + surprise

tarr pinker exp 1 results1
Tarr & Pinker: Exp. 1 Results

Compute best fittingline to compute slope

Surprise orientations’ required degree to be rotated 90 : 45 - 135: 45 - 45 : 45 but 180: 90

“4 different orientation- images stored in memory?”

tarr pinker exp 1 results2
Tarr & Pinker: Exp. 1 Results

High slope = much rotation = single canonical image

tarr pinker exp 1 summary
Tarr & Pinker: Exp. 1 Summary
  • Stimuli showed a similar result to previous findings
    • Increased RT with disparate orientations from training
    • Subjects showed improvement following training
    • Even after training, subjects were slower on non-trained (intermediate) orientations
tarr pinker exp 2 motivation
Tarr & Pinker: Exp. 2 Motivation
  • Demonstrated an improvement in recognition times with training.
    • Not a demonstration of canonical or multiple views.
    • Experiment 2, train on a few orientations and test on multiple orientations.
    • See if there is evidence for rotating to the “nearest” trained orientation.
tarr pinker methods
Tarr & Pinker: Methods
  • Similar to Experiment 1
    • However, classification task rather than “handedness” task.
      • Three objects: “Kip”, “Kef”, “Kor”, and distractors
    • Record response times
tarr pinker exp 2 procedure
Tarr & Pinker: Exp. 2 Procedure
  • Train on 3 orientations
  • Test on multiple intervening orientations
  • Look for rotation functions to nearest trained orientation
tarr pinker exp 2 summary
Tarr & Pinker: Exp. 2 Summary
  • Investigated whether subjects show a linearly increasing RT to canonical view or closest trained view.
    • Showed mixed evidence.
    • For 0° and 210° it appears that there is a dip in the surrounding RTs
      • Suggests rotation to nearest orientation
    • For 105° no evidence of alignment.
tarr pinker exp 2 results1

Mental Rotation in Block 1

By block 13 trained orns are fast

Mental rotation rate for untrained orns slower.

Tarr & Pinker: Exp. 2 Results
tarr pinker study 3
Tarr & Pinker: Study 3
  • Wanted to see if “handedness” played a role in recognition times.
    • Experiment 1 showed effect for handedness judgment.
    • Subjects might engage in handedness judgment unnecessarily.
    • Trained on both “standard” and “reversed” images
    • Tested on both set of images
      • No handedness judgment required

90 

-135 


- 45