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Transcript of Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter:...
![Page 1: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/1.jpg)
Probabilistic Object Recognition and Localization
Bernt Schiele, Alex Pentland, ICCV ‘99
Presenter: Matt Grimes
![Page 2: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/2.jpg)
What they did
1. Chose a set of local image descriptors whose outputs are robust to object orientation and lighting.
– Examples:
Laplacian
22),(),(),( yxGyxGyxMag yx
First-derivative magnitude:
![Page 3: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/3.jpg)
What they did
2. Learn a PDF for the outputs of these descriptors given an image of the object:
otherobjectMP ,|
Vector of descriptor
outputsA particular
object
Object orientation,
lighting, etc.
![Page 4: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/4.jpg)
What they did
2. Learn a PDF for the outputs of these descriptors given an image of the object:
objectMP |
Vector of descriptor
outputsA particular
object
![Page 5: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/5.jpg)
• Use Bayes’ rule to obtain the posterior…
• …which is the probability of an image containing an object, given local image measurements M.
• (Not quite this clean)
What they did
MobjectP |
![Page 6: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/6.jpg)
History of image-based object recognition
Two major genres: 1. Histogram-based approaches.
2. Comparison of local image features.
![Page 7: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/7.jpg)
Histogramming approaches
• Object recognition by color histograms (Swain & Ballard, IJCV 1991)– Robust to changes in orientation, scale.– Brittle against lighting changes (dependency on
color).– Many classes of objects not distinguishable by
color distribution alone.
![Page 8: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/8.jpg)
Histogramming approaches
• Combat color-brittleness using (quasi-) invariants of color histograms:– Eigenvalues of matrices of moments of color
histograms – Derivatives of logs of color channels– “Comprehensive color normalization”
![Page 9: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/9.jpg)
Histogramming approaches
• Comprehensive color normalization examples:
![Page 10: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/10.jpg)
Histogramming approaches
• Comprehensive color normalization examples:
![Page 11: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/11.jpg)
Localized feature approaches
• Approaches include:– Using image “interest-points” to index into a
hashtable of known objects.– Comparing large vectors of local filter
responses.
![Page 12: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/12.jpg)
Geometric Hashing
1. An interest point detector finds the same points on an object in different images.
Types of “interest points” include corners, T-junctions, sudden texture changes.
![Page 13: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/13.jpg)
Geometric Hashing
From Schmid, Mohr, Bauckhage, “Comparing and Evaluating Interest Points,” ICCV ‘98
![Page 14: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/14.jpg)
Geometric Hashing
From Schmid, Mohr, Bauckhage, “Comparing and Evaluating Interest Points,” ICCV ‘98
![Page 15: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/15.jpg)
Geometric Hashing
2. Store points in an affine-transform-invariant representation.
3. Store all possible triplets of points as keys in a hashtable.
![Page 16: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/16.jpg)
Geometric Hashing
4. For object recognition, find all triplets of interest points in an image, look for matches in the hashtable, accumulate votes for the correct object.
Hashtable approaches support multiple object recognition within the same image.
![Page 17: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/17.jpg)
Geometric hashing weaknesses
• Dependent on the consistency of the interest point detector used.
From Schmid, Mohr, Bauckhage, “Comparing and Evaluating Interest Points,” ICCV ‘98
![Page 18: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/18.jpg)
Geometric hashing weaknesses
• Shoddy repeatibility necessitates lots of points.
• Lots of points, combined with noise, leads to lots of false positives.
![Page 19: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/19.jpg)
Vectors of filter responses
• Typically use vectors of oriented filters at fixed grid points, or at interest points.
• Pros: – Very robust to noise.
• Cons: – Fixed grid needs large representation, large grid is
sensitive to occlusion.
– If using an interest point detector instead, the detector must be consistent over a variety of scenes.
![Page 20: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/20.jpg)
Also: eigenpictures
• Calculate the eigenpictures of a set of images of objects to be recognized.
• Pros: – Efficient representation of images by their eigenpicture
coefficients. (Fast searches)
• Cons: – Images must be pre-segmented. – Eigenpictures are not local (sensitive to occlusion).– Translation, image-plane rotation must be represented
in the eigenpictures.
![Page 21: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/21.jpg)
This paper:
• Uses vectors of filter responses, with probabilistic object recognition.
otherobjectMP ,|
MobjectP |Bayes’ rule
objectMP |
Learned from training images
Using scene-invariant M
![Page 22: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/22.jpg)
Wins of this paper
• Uses hashtables for multiple object recognition.
• Unlike geometric hashing, doesn’t depend on point correspondence betw. images.– Uses location-unspecific filter responses, not
points.– Inherits robustness to noise of filter response
methods.
![Page 23: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/23.jpg)
Wins of this paper
• Uses local filter responses.– Robust to occlusion compared to global
methods (e.g. eigenpictures or filter grids.)
• Probabilistic matching – Theoretically cleaner than voting.– Combined with local filter responses, allows for
localization of detected objects.
![Page 24: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/24.jpg)
Details of the PDF
• What degrees of freedom are there in the “other” parameters?
otherobjectMP ,|
ILSTRoMP n ,,,,,|on: Object
R: Rotation (3 DOF)
T: Translation(3 DOF)
S: Scene (occlusions, background)
L: Lighting
I: Imaging (noise, pixelation/blur)
![Page 25: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/25.jpg)
P(M|on,R,T,S,L,I)
• Way too many params to get a reliable estimate from even a large image library.
• # of examples needed is exponential in the number of dimensions of the PDF.
• Solution: choose measurements (M) that are invariant with respect to as many params as possible (except on).
![Page 26: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/26.jpg)
Techniques for invariance
• Imaging (noise:) see Schiele’s thesis.
• Lighting: apply a “energy normalization technique” to the filter outputs.
• Scene: probabilistic object recognition + local image measurements.– Gives best estimate using the visible portion of
the object.
![Page 27: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/27.jpg)
Techniques for invariance
• Translation: – Tx, Ty (image-plane translation) are ignored for
non-localizing recognition.– Tz is equivalent to scale. For known scales,
compensate by scaling the filters’ regions of support.
![Page 28: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/28.jpg)
Techniques for invariance
• Fairly robust to unknown scale:
![Page 29: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/29.jpg)
Techniques for invariance
• Rotation:– Rz: rotation in the image plane. Filters invariant
to image-plane rotation may be used.– Rx, Ry must remain in the PDF. Impossible to
have viewpoint- invariant descriptors in the general case.
![Page 30: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/30.jpg)
• 4 parameters.• Still a large amount of training examples
needed, but feasible.• Example: algorithm has been successful
after training with 108 images per object.(108 = 16 orientations * 6 scales)
New PDF
zzyxn trrroMP ,,,,|
![Page 31: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/31.jpg)
Learning & representation of the PDF
• Since the goal is discrimination, overgeneralization is scarier than overfitting.
• They chose multidimensional histograms over parametric representations.
• They mention that they could’ve used kernel function estimates.
![Page 32: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/32.jpg)
Multidimensional Histograms
zzyxn trrrommP ,,,,|, 21
![Page 33: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/33.jpg)
Multidimensional Histograms
• In their experiments, they use a 6-dimensional histogram.– X and Y derivative, at 3 different scales
• …with 24 buckets per axis.– Theoretical max for # of cells: 246=1.9 x 108
• Way too many cells to be meaningfully filled by even 512 x 512 (=262144 ) pixel images.
![Page 34: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/34.jpg)
Multidimensional Histograms
• Somehow, by exploiting dependencies betw. histogram axes, and applying a uniform prior bias, they get the number of calculated cells below 105.
• Factor of 1000 reduction.
• Anybody know how they do this?
![Page 35: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/35.jpg)
(Single) object recognition
k
nnkkn mP
oPomPmoP
||
![Page 36: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/36.jpg)
(Single) object recognition
iiik
nnk
oPomP
oPomP
|
|
k
nnkkn mP
oPomPmoP
||
![Page 37: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/37.jpg)
(Single) object recognition
iiijk
nnjkjkn opommp
opommpmmop
|
||
• A single measurement vector mk is insufficient for recognition.
![Page 38: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/38.jpg)
(Single) object recognition
iiijk
nnjkjkn opommp
opommpmmop
|
||
• A single measurement vector mk is insufficient for recognition.
iiijik
nnjnk
opompomp
opompomp
||
||
![Page 39: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/39.jpg)
(Single) object recognition
iiik
k
nnkk
kk
n opomp
opompmop
)()|(
)()|()|(
ik iki
k nkn
ompop
ompop
)|()(
)|()(
• For k measurement vectors:
![Page 40: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/40.jpg)
(Single) object recognition
![Page 41: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/41.jpg)
(Single) object recognition
![Page 42: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/42.jpg)
(Single) object recognition
![Page 43: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/43.jpg)
(Single) object recognition
• Measurement regions covering 10~20% of an object are usually sufficient for discrimination.
![Page 44: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/44.jpg)
(Single) object recognition
![Page 45: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/45.jpg)
Multiple object recognition
• We can apply the single-object detector to many small regions in the image.
![Page 46: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/46.jpg)
Multiple object recognition
• The algorithm is now O(NKJ)– N = # of known objects– K = # of measurement vectors in each region– J = # of regions
![Page 47: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/47.jpg)
Multiple object recognition
![Page 48: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/48.jpg)
Multiple object recognition
![Page 49: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/49.jpg)
Multiple object recognition
![Page 50: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/50.jpg)
Multiple object recognition
![Page 51: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/51.jpg)
Multiple object recognition
![Page 52: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/52.jpg)
Multiple object recognition
![Page 53: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/53.jpg)
Multiple object recognition
![Page 54: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/54.jpg)
Multiple object recognition
![Page 55: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/55.jpg)
Multiple object recognition
![Page 56: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/56.jpg)
Multiple object recognition
![Page 57: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/57.jpg)
Multiple object recognition
![Page 58: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/58.jpg)
Multiple object recognition
![Page 59: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/59.jpg)
One drawback:
• For a given image, the algorithm calculates a probability for each object it knows of.
• The algorithm lists the objects in its library in decreasing order of probability.
• Need to know beforehand the number of objects in a test image, to know where to stop reading the list.
![Page 60: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/60.jpg)
Failure example
![Page 61: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/61.jpg)
Failure example
![Page 62: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/62.jpg)
Failure example
![Page 63: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/63.jpg)
Failure example
![Page 64: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/64.jpg)
Failure example
![Page 65: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/65.jpg)
Unfamiliar clutter
![Page 66: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/66.jpg)
Unfamiliar clutter
![Page 67: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/67.jpg)
Unfamiliar clutter
![Page 68: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/68.jpg)
Unfamiliar clutter
![Page 69: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/69.jpg)
• Bite the dimensionality bullet and add an object position variable to the PDF:
Object localization
k
jnjnkkjn mp
xopxompmxop
,,||,
![Page 70: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/70.jpg)
• Stop assuming independence of mks, to account for structural dependencies:
Object localization
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|
,,|,,|,|,
![Page 71: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/71.jpg)
Object localization
• Tradeoff between recognition and localization, depending on region size.
![Page 72: Probabilistic Object Recognition and Localization Bernt Schiele, Alex Pentland, ICCV 99 Presenter: Matt Grimes.](https://reader036.fdocuments.net/reader036/viewer/2022062511/55198fbc55034653068b45a4/html5/thumbnails/72.jpg)
Object localization
• Heirarchical discrimination with coarsefine region size refinement: