Mit6870 template matching and histograms
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Mit6870 template matching and histograms Presentation Transcript

  • 1. Object Recognition andMIT Scene Understanding student presentation6.870
  • 2. 6.870Template matching and histograms Nicolas Pinto
  • 3. Introduction
  • 4. Hosts
  • 5. Hosts a guy...(who has big arms)
  • 6. Hosts a guy... Antonio T...(who has big arms) (who knows a lot about vision)
  • 7. Hosts a guy... Antonio T... a frog...(who has big arms) (who knows a lot about vision) (who has big eyes)
  • 8. Hosts a guy... Antonio T... a frog...(who has big arms) (who knows a lot about vision) (who has big eyes) and thus should know a lot about vision...
  • 9. rs p e pa3 yey!!
  • 10. Object Recognition from Local Scale-Invariant Features David G. Lowe Lowe Computer Science Department University of British Columbia s Vancouver, B.C., V6T 1Z4, Canada r (1999) lowe@cs.ubc.ca p e Abstract translation, scaling, and rotation, and partially invariant to illumination changes and affine or 3D projection. Previous a An object recognition system has been developed that uses a approaches to local feature generation lacked invariance to new class of local image features. The features are invariant scale and were more sensitive to projective distortion and p to image scaling, translation, and rotation, and partially in- illumination change. The SIFT features share a number of variant to illumination changes and affine or 3D projection. properties in common with the responses of neurons in infe- These features share similar properties with neurons in in- rior temporal (IT) cortex in primate vision. This paper also3 ferior temporal cortex that are used for object recognition describes improved approaches to indexing and model ver- in primate vision. Features are efficiently detected through ification. a staged filtering approach that identifies stable points in The scale-invariant features are efficiently identified by scale space. Image keys are created that allow for local ge- using a staged filtering approach. The first stage identifies ometric deformations by representing blurred image gradi- key locations in scale space by looking for locations that ents in multiple orientation planes and at multiple scales. are maxima or minima of a difference-of-Gaussian function. The keys are used as input to a nearest-neighbor indexing Each point is used to generate a feature vector that describes method that identifies candidate object matches. Final veri- the local image region sampled relative to its scale-space co- fication of each match is achieved by finding a low-residual ordinate frame. The features achieve partial invariance to least-squares solution for the unknown model parameters. local variations, such as affine or 3D projections, by blur- Experimental results show that robust object recognition ring image gradient locations. This approach is based on a can be achieved in cluttered partially-occluded images with model of the behavior of complex cells in the cerebral cor- a computation time of under 2 seconds. tex of mammalian vision. The resulting feature vectors are called SIFT keys. In the current implementation, each im- 1. Introduction age generates on the order of 1000 SIFT keys, a process that requires less than 1 second of computation time. Object recognition in cluttered real-world scenes requires The SIFT keys derived from an image are used in a local image features that are unaffected by nearby clutter or nearest-neighbour approach to indexing to identify candi- partial occlusion. The features must be at least partially in- date object models. Collections of keys that agree on a po- variant to illumination, 3D projective transforms, and com- tential model pose are first identified through a Hough trans- mon object variations. On the other hand, the features must form hash table, and then through a least-squares fit to a final also be sufficiently distinctive to identify specific objects estimate of model parameters. When at least 3 keys agree among many alternatives. The difficulty of the object recog- on the model parameters with low residual, there is strong nition problem is due in large part to the lack of success in evidence for the presence of the object. Since there may be finding such image features. However, recent research on dozens of SIFT keys in the image of a typical object, it is the use of dense local features (e.g., Schmid & Mohr [19]) possible to have substantial levels of occlusion in the image has shown that efficient recognition can often be achieved and yet retain high levels of reliability. by using local image descriptors sampled at a large number The current object models are represented as 2D loca- of repeatable locations. tions of SIFT keys that can undergo affine projection. Suf- This paper presents a new method for image feature gen- ficient variation in feature location is allowed to recognize eration called the Scale Invariant Feature Transform (SIFT). perspective projection of planar shapes at up to a 60 degree This approach transforms an image into a large collection rotation away from the camera or to allow up to a 20 degree of local feature vectors, each of which is invariant to image rotation of a 3D object. Proc. of the International Conference on 1 Computer Vision, Corfu (Sept. 1999) yey!!
  • 11. Object Recognition from Local Scale-Invariant Features David G. Lowe Lowe Computer Science Department University of British Columbia s Vancouver, B.C., V6T 1Z4, Canada r (1999) lowe@cs.ubc.ca p e Abstract translation, scaling, and rotation, and partially invariant to illumination changes and affine or 3D projection. Previous a An object recognition system has been developed that uses a approaches to local feature generation lacked invariance to new class of local image features. The features are invariant scale and were more sensitive to projective distortion and p to image scaling, translation, and rotation, and partially in- illumination change. The SIFT features share a number of variant to illumination changes and affine or 3D projection. properties in common with the responses of neurons in infe- These features share similar properties with neurons in in- rior temporal (IT) cortex in primate vision. This paper also3 ferior temporal cortex that are used for object recognition describes improved approaches to indexing and model ver- Histograms of Oriented Gradients for Human Detection in primate vision. Features are efficiently detected through ification. a staged filtering approach that identifies stable points in The scale-invariant features are efficiently identified by scale space. Image keys are created that allow for local ge- using a staged filtering approach. The first stage identifies ometric deformations by representing blurred imageDalal and Bill locations in scale space by looking for locations that Navneet gradi- key Triggs INRIA Rhˆ ne-Alps, 655 avenue de l’Europe, Montbonnot 38334, France o ents in multiple orientation planes and at multiple scales. are maxima or minima of a difference-of-Gaussian function. The keys are used as input to a nearest-neighbor indexing Each http://lear.inrialpes.fr {Navneet.Dalal,Bill.Triggs}@inrialpes.fr,point is used to generate a feature vector that describes method that identifies candidate object matches. Final veri- the local image region sampled relative to its scale-space co- Nalal and Triggs fication of each match is achieved by finding a low-residual ordinate frame. The features achieve partial invariance to least-squares solution for the unknown model parameters. Abstract Experimental results show that robust object recognition We briefly discusssuch as affine or 3D projections, by blur- local variations, previous work on human detection in We study the question of feature sets for robust visual with §2, give an overview of our method §3, describe our data ring image gradient locations. This approach is based on a can be achieved in cluttered partially-occluded imagesob- (2005) ject recognition,time of under 2 seconds. a computation adopting linear SVM based human detec- tion as a test case. After reviewing existing edge and gra- dient based descriptors, we show experimentally that grids setsmodel and give a detailedcomplex cells in experimental cor- in §4 of the behavior of description and the cerebral evaluation of each stage of the process in §5–6. The main tex of mammalian vision. The resulting feature vectors are conclusions are summarized in §7. implementation, each im- called SIFT keys. In the current 1. Introduction of Histograms of Oriented Gradient (HOG) descriptors sig- age generates on the order of 1000 SIFT keys, a process that 2 requires lessWork second of computation time. Previous than 1 nificantly outperform existing feature sets for human detec- tion. We recognition in cluttered real-world scenes requires Object study the influence of each stage of the computation There is SIFT keys derived from an image are used in a The an extensive literature on object detection, but local image features that are that fine-scale nearby clutter or here we mention just a approach to papers on to identify candi- on performance, concluding unaffected by gradients, fine nearest-neighbour few relevant indexing human detec- orientation binning, relatively coarse be at least partially in- tiondate object models.See [6] for a survey. Papageorgiou et po- partial occlusion. The features must spatial binning, and [18,17,22,16,20]. Collections of keys that agree on a variant to illumination, 3D projective transforms, and com- al [18] describe apose are first identified throughpolynomial high-quality local contrast normalization in overlapping de- tential model pedestrian detector based on a a Hough trans- mon object variations. On the other hand, the features must SVM using rectified and then through input descriptors, withfinal scriptor blocks are all important for good results. The new form hash table, Haar wavelets as a least-squares fit to a also be sufficiently distinctive to identify specific objects a parts (subwindow) based variant in [17]. at least 3 keysal approach gives near-perfect separation on the original MIT estimate of model parameters. When Depoortere et agree among many alternatives. The difficulty of the challenging give anthe model parameters with[2]. Gavrila &there is strong pedestrian database, so we introduce a more object recog- on optimized version of this low residual, Philomen nition problem is overin large part to the lack images within [8] evidence fordirect approach, extracting edge images and be dataset containing due 1800 annotated human of success take a more the presence of the object. Since there may finding such of pose features. However, recent research on matching them to a set of in the image of a typicalchamfer it is a large range image variations and backgrounds. dozens of SIFT keys learned exemplars using object, the use of dense local features (e.g., Schmid & Mohr [19]) distance. This has been used in levels of occlusion inpedes- possible to have substantial a practical real-time the image 1 Introduction and yet retain high levels of et al [22] has shown that efficient recognition can often be achieved trian detection system [7]. Viola reliability.build an efficient byDetecting humans in images is sampled at a large owing moving person detector, using AdaBoost to train a chain of using local image descriptors a challenging task number The current object models are represented as 2D loca- to their variable appearance and the wide range of poses that of repeatable locations. progressively more complexcan undergo affine projection. Suf- tions of SIFT keys that region rejection rules based on ficient variation in space-time differences. Ronfard et can adopt. The first need is a robust feature set gen- Haar-like wavelets andfeature location is allowed to recognize theyThis paper presents a new method for image featurethat allows the human form to be discriminated cleanly, even in eration called the Scale Invariant Feature Transform (SIFT). al [19] build anprojection of planar shapesby incorporating perspective articulated body detectornd at up to a 60 degree st difficult illumination. collection SVM based away classifierscamera or to allow up to a 20 degree cluttered backgrounds underan image into a largeWe study This approach transforms rotation limb from the over 1 and 2 order Gaussian the issue of feature sets foreach of which is invariant to image filters in a dynamic programming framework similar to those of local feature vectors, human detection, showing that lo- rotation of a 3D object. cally normalized Histogram of Oriented Gradient (HOG) de- of Felzenszwalb & Huttenlocher [3] and Ioffe & Forsyth scriptors provide excellent performance relative to other ex- [9]. Mikolajczyk et al [16] use combinations of orientation- isting feature sets including wavelets [17,22]. The proposed Proc. of the International Conference on position histograms with binary-thresholded gradient magni- 1 descriptorsVision, Corfu (Sept. 1999) orientation histograms tudes to build a parts based method containing detectors for Computer are reminiscent of edge [4,5], SIFT descriptors [12] and shape contexts [1], but they faces, heads, and front and side profiles of upper and lower yey!! are computed on a dense grid of uniformly spaced cells and body parts. In contrast, our detector uses a simpler archi- tecture with a single detection window, but appears to give they use overlapping local contrast normalizations for im- proved performance. We make a detailed study of the effects significantly higher performance on pedestrian images. of various implementation choices on detector performance, taking “pedestrian detection” (the detection of mostly visible 3 Overview of the Method
  • 12. Object Recognition from Local Scale-Invariant Features David G. Lowe Lowe Computer Science Department University of British Columbia s Vancouver, B.C., V6T 1Z4, Canada r (1999) lowe@cs.ubc.ca p e Abstract translation, scaling, and rotation, and partially invariant to illumination changes and affine or 3D projection. Previous a An object recognition system has been developed that uses a approaches to local feature generation lacked invariance to new class of local image features. The features are invariant scale and were more sensitive to projective distortion and p to image scaling, translation, and rotation, and partially in- illumination change. The SIFT features share a number of variant to illumination changes and affine or 3D projection. properties in common with the responses of neurons in infe- These features share similar properties with neurons in in- rior temporal (IT) cortex in primate vision. This paper also3 ferior temporal cortex that are used for object recognition describes improved approaches to indexing and model ver- Histograms of Oriented Gradients for Human Detection in primate vision. Features are efficiently detected through ification. a staged filtering approach that identifies stable points in The scale-invariant features are efficiently identified by scale space. Image keys are created that allow for local ge- using a staged filtering approach. The first stage identifies ometric deformations by representing blurred imageDalal and Bill locations in scale space by looking for locations that Navneet gradi- key Triggs INRIA Rhˆ ne-Alps, 655 avenue de l’Europe, Montbonnot 38334, France o ents in multiple orientation planes and at multiple scales. are maxima or minima of a difference-of-Gaussian function. The keys are used as input to a nearest-neighbor indexing Each http://lear.inrialpes.fr {Navneet.Dalal,Bill.Triggs}@inrialpes.fr,point is used to generate a feature vector that describes method that identifies candidate object matches. Final veri- the local image region sampled relative to its scale-space co- Nalal and Triggs fication of each match is achieved by finding a low-residual ordinate frame. The features achieve partial invariance to least-squares solution for the unknown model parameters. Abstract Experimental results show that robust object recognition We briefly discusssuch as affine or 3D projections, by blur- local variations, previous work on human detection in We study the question of feature sets for robust visual with §2, give an overview of our method §3, describe our data ring image gradient locations. This approach is based on a can be achieved in cluttered partially-occluded imagesob- (2005) ject recognition,time of under 2 seconds. a computation adopting linear SVM based human detec- tion as a test case. After reviewing existing edge and gra- dient based descriptors, we show experimentally that grids setsmodel and give a detailedcomplex cells in experimental cor- in §4 of the behavior of description and the cerebral evaluation of each stage of the process in §5–6. The main tex of mammalian vision. The resulting feature vectors are conclusions are summarized in §7. implementation, each im- called SIFT keys. In the current 1. Introduction of Histograms of Oriented Gradient (HOG) descriptors sig- age generates on the order of 1000 SIFT keys, a process that 2 requires lessWork second of computation time. Previous than 1 nificantly outperform existing feature sets for human detec- tion. We recognition in cluttered real-world scenes requires Object study the influence of each stage of the computation There is SIFT keys derived from an image are used in a The an extensive literature on object detection, but local image features that are that fine-scale nearby clutter or here we mention just a approach to papers on to identify candi- on performance, concluding unaffected by gradients, fine nearest-neighbour few relevant indexing human detec- orientation binning, relatively coarse be at least partially in- tiondate object models.See [6] for a survey. Papageorgiou et po- partial occlusion. The features must spatial binning, and [18,17,22,16,20]. Collections of keys that agree on a variant to illumination, 3D projective transforms, and com- al [18] describe apose are first identified throughpolynomial high-quality local contrast normalization in overlapping de- tential model pedestrian detector based on a a Hough trans- A Discriminatively Trained, Multiscale, Deformable Part Model fit to a mon object variations. On the other hand, the features must SVM using rectified and then through input descriptors, withfinal scriptor blocks are all important for good results. The new form hash table, Haar wavelets as a least-squares also be sufficiently distinctive to identify specific objects a parts (subwindow) based variant in [17]. at least 3 keysal approach gives near-perfect separation on the original MIT estimate of model parameters. When Depoortere et agree among many alternatives. The difficulty of the challenging give anthe model parameters with[2]. Gavrila &there is strong pedestrian database, so we introduce a more object recog- on optimized version of this low residual, Philomen nition problem is overin large part to the lack images within [8] evidence fordirect approach, extracting edge images and be dataset containing due 1800 annotated human of success take a more the presence of the object. Since there may Pedro Felzenszwalb David McAllester Deva Ramanan finding such of pose features. However, recent research on matching them to a set of in the image of a typicalchamfer it is a large range image variations and backgrounds. dozens of SIFT keys learned exemplars using object, the University of Chicago (e.g.,Toyota Technological Institute to has been used in levels ofUC Irvine pedes- use of dense local features possible at Chicago Schmid & Mohr [19]) distance. This have substantial a practical real-time the image occlusion in 1 Introduction Felzenszwalb et al. has pff@cs.uchicago.edu shown that efficient recognition can often be achieved trian detection system [7]. Viola dramanan@ics.uci.edu mcallester@tti-c.org et al [22] and yet retain high levels of reliability.build an efficient byDetecting humans in images is sampled at a large owing moving person detector, using AdaBoost to train a chain of using local image descriptors a challenging task number The current object models are represented as 2D loca- to their variable appearance and the wide range of poses that of repeatable locations. progressively more complexcan undergo affine projection. Suf- tions of SIFT keys that region rejection rules based on ficient variation in space-time differences. Ronfard et can adopt. The firstnew method for image feature gen- Haar-like wavelets andfeature location is allowed to recognize (2008) theyThis paper presents aAbstract robust feature set that need is a allows the human form to be discriminated cleanly, even in eration called the Scale Invariant Feature Transform (SIFT). al [19] build anprojection of planar shapesby incorporating perspective articulated body detectornd at up to a 60 degree st difficult illumination. collection SVM based away classifierscamera or to allow up to a 20 degree cluttered backgrounds underan image into a largeWe study This approach describes a discriminatively trained, multi- rotation limb from the over 1 and 2 order Gaussian This paper transforms the issue of feature sets foreach of which is invariant to image filters in a dynamic programming framework similar to those of local feature vectors, human detection, showing that lo- scale, deformable part model for object detection. Our sys- of Felzenszwalb3D object. cally normalized Histogram of Oriented Gradient (HOG) de- rotation of a & Huttenlocher [3] and Ioffe & Forsyth scriptors providetwo-fold improvement relative to other ex- tem achieves a excellent performance in average precision [9]. Mikolajczyk et al [16] use combinations of orientation- isting thethe International Conference 2006 PASCAL person de- position histograms with binary-thresholded gradient magni- over feature sets including wavelets [17,22]. The proposed 1 Proc. of best performance in the on tection challenge. It also outperforms the best results in the tudes to build a parts based method containing detectors for descriptorsVision, Corfu (Sept. 1999) orientation histograms Computer are reminiscent of edge [4,5], SIFT descriptors [12]of twenty categories. The system faces, heads, and front and side profiles of upper and lower 2007 challenge in ten out and shape contexts [1], but they yey!! are computed on adeformableof uniformly spaced cells and relies heavily on dense grid parts. While deformable part body parts. In contrast, our detector uses a simpler archi- models overlapping quite contrast their value had not been tecture with a single detection obtained with the person model. The Figure 1. Example detection window, but appears to give they usehave become local popular, normalizations for im- model is defined by a coarse template, several higher resolution proved performance. We make a detailedsuch as the PASCAL significantly higher performance on for the location of each part. demonstrated on difficult benchmarks study of the effects part templates and a spatial model pedestrian images. challenge. Our system also relies heavily on new methods of various implementation choices on detector performance, for discriminative training. We detection of mostly visible 3 Overview of the Method taking “pedestrian detection” (thecombine a margin-sensitive
  • 13. Object Recognition from Local Scale-Invariant Features David G. Lowe Lowe Computer Science Department University of British Columbia Vancouver, B.C., V6T 1Z4, Canada (1999) lowe@cs.ubc.ca Abstract translation, scaling, and rotation, and partially invariant to illumination changes and affine or 3D projection. Previous An object recognition system has been developed that uses a approaches to local feature generation lacked invariance to new class of local image features. The features are invariant scale and were more sensitive to projective distortion and to image scaling, translation, and rotation, and partially in- illumination change. The SIFT features share a number of variant to illumination changes and affine or 3D projection. properties in common with the responses of neurons in infe- These features share similar properties with neurons in in- rior temporal (IT) cortex in primate vision. This paper also ferior temporal cortex that are used for object recognition describes improved approaches to indexing and model ver- Histograms of Oriented Gradients for Human Detection in primate vision. Features are efficiently detected through ification. a staged filtering approach that identifies stable points in The scale-invariant features are efficiently identified by scale space. Image keys are created that allow for local ge- using a staged filtering approach. The first stage identifies ometric deformations by representing blurred imageDalal and Bill locations in scale space by looking for locations that Navneet gradi- key Triggs INRIA Rhˆ ne-Alps, 655 avenue de l’Europe, Montbonnot 38334, France o ents in multiple orientation planes and at multiple scales. are maxima or minima of a difference-of-Gaussian function. The keys are used as input to a nearest-neighbor indexing Each http://lear.inrialpes.fr {Navneet.Dalal,Bill.Triggs}@inrialpes.fr,point is used to generate a feature vector that describes method that identifies candidate object matches. Final veri- the local image region sampled relative to its scale-space co- Nalal and Triggs fication of each match is achieved by finding a low-residual ordinate frame. The features achieve partial invariance to least-squares solution for the unknown model parameters. Abstract Experimental results show that robust object recognition We briefly discusssuch as affine or 3D projections, by blur- local variations, previous work on human detection in We study the question of feature sets for robust visual with §2, give an overview of our method §3, describe our data ring image gradient locations. This approach is based on a can be achieved in cluttered partially-occluded imagesob- (2005) ject recognition,time of under 2 seconds. a computation adopting linear SVM based human detec- tion as a test case. After reviewing existing edge and gra- dient based descriptors, we show experimentally that grids setsmodel and give a detailedcomplex cells in experimental cor- in §4 of the behavior of description and the cerebral evaluation of each stage of the process in §5–6. The main tex of mammalian vision. The resulting feature vectors are conclusions are summarized in §7. implementation, each im- called SIFT keys. In the current 1. Introduction of Histograms of Oriented Gradient (HOG) descriptors sig- age generates on the order of 1000 SIFT keys, a process that 2 requires lessWork second of computation time. Previous than 1 nificantly outperform existing feature sets for human detec- tion. We recognition in cluttered real-world scenes requires Object study the influence of each stage of the computation There is SIFT keys derived from an image are used in a The an extensive literature on object detection, but local image features that are unaffected by nearby clutter or nearest-neighbour approach to indexing to identify candi- partial occlusion. The features must be at least partially in- date object models. Collections of keys that agree on a po- variant to illumination, 3D projective transforms, and com- tential model pose are first identified through a Hough trans- A Discriminatively Trained, Multiscale, Deformable Part Model fit to a final mon object variations. On the other hand, the features must form hash table, and then through a least-squares also be sufficiently distinctive to identify specific objects estimate of model parameters. When at least 3 keys agree among many alternatives. The difficulty of the object recog- on the model parameters with low residual, there is strong nition problem is due in large part to the lack of success in Pedro Felzenszwalb David McAllester for the presence of the object. Since there may be evidence Deva Ramanan finding such image features. However, recent research on dozens of SIFT keys in the image of a typical object, it is the University of Chicago (e.g.,Toyota Technological Institute to have substantial levels ofUC Irvine the image use of dense local features Schmid & Mohr [19]) possible at Chicago occlusion inFelzenszwalb et al. has pff@cs.uchicago.edu mcallester@tti-c.org shown that efficient recognition can often be achieved and yet retain high levels of dramanan@ics.uci.edu reliability. by using local image descriptors sampled at a large number The current object models are represented as 2D loca- of repeatable locations. tions of SIFT keys that can undergo affine projection. Suf- (2008) This paper presents aAbstract for image feature gen- new method ficient variation in feature location is allowed to recognize eration called the Scale Invariant Feature Transform (SIFT). perspective projection of planar shapes at up to a 60 degree This approach describes a an image into a large collection This paper transforms discriminatively trained, multi- rotation away from the camera or to allow up to a 20 degree of local feature vectors, each of which isdetection. to image scale, deformable part model for object invariant Our sys- rotation of a 3D object. tem achieves a two-fold improvement in average precision over thethe International Conference 2006 PASCAL person de- 1 Proc. of best performance in the on tection challenge. It also outperforms the best results in the Computer Vision, Corfu (Sept. 1999) 2007 challenge in ten out of twenty categories. The system relies heavily on deformable parts. While deformable part models have become quite popular, their value had not been Figure 1. Example detection obtained with the person model. The demonstrated on difficult benchmarks such as the PASCAL model is defined by a coarse template, several higher resolution
  • 14. Scale-Invariant Feature Transform (SIFT) adapted from Kucuktunc
  • 15. Scale-Invariant Feature Transform (SIFT) adapted from Brown, ICCV 2003
  • 16. SIFT local features areinvariant... adapted from David Lee
  • 17. like me they are robust... Text
  • 18. like me they are robust... Text... to changes in illumination,noise, viewpoint, occlusion, etc.
  • 19. I am sure you want to knowhow to build them Text
  • 20. I am sure you want to know how to build them1. find interest points or “keypoints” Text
  • 21. I am sure you want to know how to build them1. find interest points or “keypoints” Text2. find their dominant orientation
  • 22. I am sure you want to know how to build them1. find interest points or “keypoints” Text2. find their dominant orientation3. compute their descriptor
  • 23. I am sure you want to know how to build them1. find interest points or “keypoints” Text2. find their dominant orientation3. compute their descriptor4. match them on other images
  • 24. 1. find interest points or “keypoints” Text
  • 25. keypoints are taken as maxima/minimaof a DoG pyramid Text in this settings, extremas are invariant to scale...
  • 26. a DoG (Difference of Gaussians) pyramidis simple to compute... even him can do it! before after adapted from Pallus and Fleishman
  • 27. then we just have to findneighborhood extremasin this 3D DoG space
  • 28. then we just have to findneighborhood extremasin this 3D DoG space if a pixel is an extrema in its neighboring region he becomes a candidate keypoint
  • 29. too manykeypoints? adapted from wikipedia
  • 30. too manykeypoints?1. removelow contrast adapted from wikipedia
  • 31. too manykeypoints?1. removelow contrast adapted from wikipedia
  • 32. too manykeypoints?1. removelow contrast2. removeedges adapted from wikipedia
  • 33. too manykeypoints?1. removelow contrast2. removeedges adapted from wikipedia
  • 34. Text2. find their dominant orientation
  • 35. each selected keypoint isassigned to one or more“dominant” orientations...
  • 36. each selected keypoint isassigned to one or more“dominant” orientations...... this step is important toachieve rotation invariance
  • 37. How?
  • 38. How?using the DoG pyramid to achievescale invariance:
  • 39. How?using the DoG pyramid to achievescale invariance:a. compute image gradientmagnitude and orientation
  • 40. How?using the DoG pyramid to achievescale invariance:a. compute image gradientmagnitude and orientationb. build an orientation histogram
  • 41. How?using the DoG pyramid to achievescale invariance:a. compute image gradientmagnitude and orientationb. build an orientation histogramc. keypoint’s orientation(s) = peak(s)
  • 42. a. compute image gradientmagnitude and orientation
  • 43. a. compute image gradientmagnitude and orientation
  • 44. b. build an orientation histogram adapted from Ofir Pele
  • 45. c. keypoint’s orientation(s) = peak(s) * * the peak ;-)
  • 46. Text3. compute their descriptor
  • 47. SIFT descriptor= a set of orientation histograms 16x16 neighborhood 4x4 array x 8 bins of pixel gradients = 128 dimensions (normalized)
  • 48. Text4. match them on other images
  • 49. How to atch?
  • 50. How to atch?nearest neighbor
  • 51. How to atch?nearest neighborhough transform voting
  • 52. How to atch?nearest neighborhough transform votingleast-squares fit
  • 53. How to atch?nearest neighborhough transform votingleast-squares fitetc.
  • 54. SIFT is great! Text
  • 55. SIFT is great! Text invariant to affine transformations
  • 56. SIFT is great! Text invariant to affine transformations easy to understand
  • 57. SIFT is great! Text invariant to affine transformations easy to understand fast to compute
  • 58. Extension example:Spatial Pyramid Matching using SIFT Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories Svetlana Lazebnik1 Cordelia Schmid2 Jean Ponce1,3 slazebni@uiuc.edu Cordelia.Schmid@inrialpes.fr ponce@cs.uiuc.edu 1 Beckman Institute 2 Text INRIA Rhˆ ne-Alpes o 3 Ecole Normale Sup´ rieure e University of Illinois Montbonnot, France Paris, France CVPR 2006
  • 59. Object Recognition from Local Scale-Invariant Features David G. Lowe Lowe Computer Science Department University of British Columbia Vancouver, B.C., V6T 1Z4, Canada (1999) lowe@cs.ubc.ca Abstract translation, scaling, and rotation, and partially invariant to illumination changes and affine or 3D projection. Previous An object recognition system has been developed that uses a approaches to local feature generation lacked invariance to new class of local image features. The features are invariant scale and were more sensitive to projective distortion and to image scaling, translation, and rotation, and partially in- illumination change. The SIFT features share a number of variant to illumination changes and affine or 3D projection. properties in common with the responses of neurons in infe- Histograms of Oriented Gradients for Human Detection Navneet Dalal and Bill Triggs INRIA Rhˆ ne-Alps, 655 avenue de l’Europe, Montbonnot 38334, France o {Navneet.Dalal,Bill.Triggs}@inrialpes.fr, http://lear.inrialpes.fr Nalal and Triggs Abstract We study the question of feature sets for robust visual ob- We briefly discuss previous work on human detection in §2, give an overview of our method §3, describe our data (2005) ject recognition, adopting linear SVM based human detec- tion as a test case. After reviewing existing edge and gra- dient based descriptors, we show experimentally that grids sets in §4 and give a detailed description and experimental evaluation of each stage of the process in §5–6. The main conclusions are summarized in §7. of Histograms of Oriented Gradient (HOG) descriptors sig- 2 Previous Work nificantly outperform existing feature sets for human detec- tion. We study the influence of each stage of the computation There is an extensive literature on object detection, but on performance, concluding that fine-scale gradients, fine here we mention just a few relevant papers on human detec- orientation binning, relatively coarse spatial binning, and tion [18,17,22,16,20]. See [6] for a survey. Papageorgiou et high-quality local contrast normalization in overlapping de- al [18] describe a pedestrian detector based on a polynomial A Discriminatively Trained, Multiscale, Deformable Part Model with scriptor blocks are all important for good results. The new SVM using rectified Haar wavelets as input descriptors, approach gives near-perfect separation on the original MIT a parts (subwindow) based variant in [17]. Depoortere et al pedestrian database, so we introduce a more challenging give an optimized version of this [2]. Gavrila & Philomen [8] take a more direct approach, extracting edge images and dataset containing over 1800 annotated human images with McAllester Pedro Felzenszwalb David matching them to a set of learned exemplarsRamanan Deva using chamfer a large range of pose variations and backgrounds. University of Chicago Toyota Technological Institute athas been used in a practical real-time pedes- distance. This Chicago UC Irvine 1 IntroductionFelzenszwalb et al. pff@cs.uchicago.edu mcallester@tti-c.org system [7]. Viola dramanan@ics.uci.edu trian detection et al [22] build an efficient Detecting humans in images is a challenging task owing moving person detector, using AdaBoost to train a chain of to their variable appearance and the wide range of poses that progressively more complex region rejection rules based on Haar-like wavelets and space-time differences. Ronfard et (2008) they can adopt. The first need is a robust feature set that Abstract allows the human form to be discriminated cleanly, even in al [19] build an articulated body detector by incorporating cluttered backgrounds under difficult illumination. We study SVM based limb classifiers over 1st and 2nd order Gaussian This paper describes a discriminatively trained, multi- filters in a dynamic programming framework similar to those the issue of feature sets for human detection, showing that lo- scale, deformable part model for object detection. Our sys- of Felzenszwalb & Huttenlocher [3] and Ioffe & Forsyth cally normalized Histogram of Oriented Gradient (HOG) de- scriptors providetwo-fold improvement relative to other ex- tem achieves a excellent performance in average precision [9]. Mikolajczyk et al [16] use combinations of orientation- isting the bestsets including wavelets [17,22]. The person de- position histograms with binary-thresholded gradient magni- over feature performance in the 2006 PASCAL proposed descriptors are reminiscent of edge orientation results in the tudes to build a parts based method containing detectors for tection challenge. It also outperforms the best histograms [4,5], SIFT descriptors [12]of twenty categories. The system faces, heads, and front and side profiles of upper and lower 2007 challenge in ten out and shape contexts [1], but they are computed on adeformableof uniformly spaced cells and relies heavily on dense grid parts. While deformable part body parts. In contrast, our detector uses a simpler archi- models overlapping quite contrast their value had not been tecture with a single detection obtained with the person model. The they usehave become local popular, normalizations for im- Figure 1. Example detection window, but appears to give model is defined by a coarse template, several higher resolution proved performance. We make a detailedsuch as the PASCAL significantly higher performance on pedestrian images. demonstrated on difficult benchmarks study of the effects of various implementation choices on detector performance, taking “pedestrian detection” (the detection of mostly visible 3 Overview of the Method
  • 60. Histograms of Oriented Gradients for Human Detection Navneet Dalal and Bill Triggs INRIA Rhˆ ne-Alps, 655 avenue de l’Europe, Montbonnot 38334, France o {Navneet.Dalal,Bill.Triggs}@inrialpes.fr, http://lear.inrialpes.fr Abstract We briefly discuss previous work on human detection in We study the question of feature sets for robust visual ob- §2, give an overview of our method §3, describe our dataject recognition, adopting linear SVM based human detec- sets in §4 and give a detailed description and experimentaltion as a test case. After reviewing existing edge and gra- evaluation of each stage of the process in §5–6. The maindient based descriptors, we show experimentally that grids conclusions are summarized in §7.of Histograms of Oriented Gradient (HOG) descriptors sig- 2 Previous Worknificantly outperform existing feature sets for human detec-tion. We study the influence of each stage of the computation There is an extensive literature on object detection, buton performance, concluding that fine-scale gradients, fine here we mention just a few relevant papers on human detec-orientation binning, relatively coarse spatial binning, and tion [18,17,22,16,20]. See [6] for a survey. Papageorgiou ethigh-quality local contrast normalization in overlapping de- al [18] describe a pedestrian detector based on a polynomialscriptor blocks are all important for good results. The new SVM using rectified Haar wavelets as input descriptors, withapproach gives near-perfect separation on the original MIT a parts (subwindow) based variant in [17]. Depoortere et alpedestrian database, so we introduce a more challenging give an optimized version of this [2]. Gavrila & Philomendataset containing over 1800 annotated human images with [8] take a more direct approach, extracting edge images anda large range of pose variations and backgrounds. matching them to a set of learned exemplars using chamfer distance. This has been used in a practical real-time pedes-1 Introduction trian detection system [7]. Viola et al [22] build an efficient Detecting humans in images is a challenging task owing moving person detector, using AdaBoost to train a chain ofto their variable appearance and the wide range of poses that progressively more complex region rejection rules based onthey can adopt. The first need is a robust feature set that Haar-like wavelets and space-time differences. Ronfard et first of all, let me put this paper inallows the human form to be discriminated cleanly, even incluttered backgrounds under difficult illumination. We study al [19] build an articulated body detector by incorporating SVM based limb classifiers over 1st and 2nd order Gaussian contextthe issue of feature sets for human detection, showing that lo-cally normalized Histogram of Oriented Gradient (HOG) de- filters in a dynamic programming framework similar to those of Felzenszwalb & Huttenlocher [3] and Ioffe & Forsyth
  • 61. Histograms of Oriented Gradients for Human Detection Navneet Dalal and Bill Triggs INRIA Rhˆ ne-Alps, 655 avenue de l’Europe, Montbonnot 38334, France o {Navneet.Dalal,Bill.Triggs}@inrialpes.fr, http://lear.inrialpes.fr λ λ λ Abstract We briefly discuss previous work on human detection in Swain & Ballard 1991 - Color an overview of our method §3, describe our data §2, give Histograms We study the question of feature sets for robust visual ob-ject recognition, adopting linear SVM based human detec- sets in §4 and give a detailed description and experimentaltion as a test case. After reviewing& Crowley 1996 evaluation of each stage of the process in §5–6. The main Schiele existing edge and gra- conclusions are summarized in §7. - Receptive Fields Histogramsdient based descriptors, we show experimentally that gridsof Histograms of Oriented Gradient (HOG) descriptors sig- 2 Previous Worknificantly outperform existing feature sets - SIFT detec- Lowe 1999 for humantion. We study the influence of each stage of the computation There is an extensive literature on object detection, buton performance, concluding that fine-scale gradients, fine here we mention just a few relevant papers on human detec- Schneiderman & Kanade 2000 - Localized for a survey. PapageorgiouWavelets tion [18,17,22,16,20]. See [6] Histograms of etorientation binning, relatively coarse spatial binning, andhigh-quality local contrast normalization in overlapping de- al [18] describe a pedestrian detector based on a polynomial SVM using rectified Haar wavelets as input descriptors, withscriptor blocks are all Leung for good results. The new Texton Histograms important & Malik 2001 -approach gives near-perfect separation on the original MIT a parts (subwindow) based variant in [17]. Depoortere et alpedestrian database, so we introduce a more challenging give an optimized version of this [2]. Gavrila & Philomendataset containing over 1800 annotated human images with Shape Context approach, extracting edge images and Belongie et al. 2002 - [8] take a more directa large range of pose variations and backgrounds. matching them to a set of learned exemplars using chamfer distance. This has been used in a practical real-time pedes-1 Introduction Dalal & Triggs 2005 - Dense Orientation Histogramsan efficient trian detection system [7]. Viola et al [22] build Detecting humans in images is a challenging task owing moving person detector, using AdaBoost to train a chain ofto their variable appearance and the wide range of poses that ... progressively more complex region rejection rules based onthey can adopt. The first need is a robust feature set that Haar-like wavelets and space-time differences. Ronfard et histograms of local image measurementallows the human form to be discriminated cleanly, even incluttered backgrounds under difficult illumination. We study al [19] build an articulated body detector by incorporating SVM based limb classifiers over 1st and 2nd order Gaussian have been quite successfulthe issue of feature sets for human detection, showing that lo-cally normalized Histogram of Oriented Gradient (HOG) de- filters in a dynamic programming framework similar to those of Felzenszwalb & Huttenlocher [3] and Ioffe & Forsyth
  • 62. Histograms of Oriented Gradients for Human Detection Navneet Dalal and Bill Triggs features INRIA Rhˆ ne-Alps, 655 avenue de l’Europe, Montbonnot 38334, France o {Navneet.Dalal,Bill.Triggs}@inrialpes.fr, http://lear.inrialpes.fr Abstract We briefly discuss previous work on human detection in We study the question of feature sets for robust visual ob- §2, give an overview of our method §3, describe our data Gravrila & Philomen 1999 - Edgegive a detailed description and experimentalject recognition, adopting linear SVM based human detec- sets in §4 and Templates + Nearest Neighbortion as a test case. After reviewing existing edge and gra- evaluation of each stage of the process in §5–6. The maindient based descriptors, we show experimentally that grids conclusions are summarized in §7. Papageorgiou & Poggio 2000, Mohan et al. 2001, DePoortere et al.of Histograms of Oriented Gradient (HOG) descriptors sig- 2002 - Haar Wavelets 2 Previous Worknificantly outperform existing feature sets for human detec- + SVMtion. We study the influence of each stage of the computation There is an extensive literature on object detection, buton performance, concluding that fine-scale gradients, - Rectangular Differentialpapers on human + here we mention just a few relevant Viola & Jones 2001 fine tion [18,17,22,16,20]. See [6] for a survey. Papageorgiou et Features detec-orientation binning, relatively coarse spatial binning, and AdaBoosthigh-quality local contrast normalization in overlapping de- al [18] describe a pedestrian detector based on a polynomialscriptor blocks are all important for good results. The new SVM using rectified Haar wavelets as input descriptors, withapproach gives near-perfect separation on the original MIT - parts (subwindow) based variant in [17]. Depoortere et al a Mikolajczyk et al. 2004 give an optimized version of this [2]. Gavrila & Philomen Parts Based Histograms + AdaBoostpedestrian database, so we introduce a more challengingdataset containing over 1800 annotated human images with [8] take a more direct approach, extracting edge images anda large range of pose variations Sukthankar 2004 - PCA-SIFT set of learned exemplars using chamfer Ke & and backgrounds. matching them to a distance. This has been used in a practical real-time pedes-1 Introduction trian detection system [7]. Viola et al [22] build an efficient ... Detecting humans in images is a challenging task owing moving person detector, using AdaBoost to train a chain ofto their variable appearance and the wide range of poses that progressively more complex region rejection rules based onthey can adopt. The first need is a robust feature set that Haar-like wavelets and space-time differences. Ronfard etallows the human form to be discriminated cleanly, even in al [19] build an articulated body detector by incorporating tons of “feature sets” have been proposedcluttered backgrounds under difficult illumination. We studythe issue of feature sets for human detection, showing that lo- SVM based limb classifiers over 1st and 2nd order Gaussian filters in a dynamic programming framework similar to thosecally normalized Histogram of Oriented Gradient (HOG) de- of Felzenszwalb & Huttenlocher [3] and Ioffe & Forsyth
  • 63. Histograms of Oriented Gradients for Human Detection Navneet Dalal and Bill Triggs difficult! INRIA Rhˆ ne-Alps, 655 avenue de l’Europe, Montbonnot 38334, France o {Navneet.Dalal,Bill.Triggs}@inrialpes.fr, http://lear.inrialpes.fr Abstract We briefly discuss previous work on human detection in We study the question of feature sets for robust visual ob- §2, give an overview of our method §3, describe our dataject recognition, adopting linearvariety human detec- Wide SVM based of articulated poses a detailed description and experimental sets in §4 and givetion as a test case. After reviewing existing edge and gra- evaluation of each stage of the process in §5–6. The maindient based descriptors, we show experimentally that grids conclusions are summarized in §7. Variable appearance/clothingof Histograms of Oriented Gradient (HOG) descriptors sig- 2 Previous Worknificantly outperform existing feature sets for human detec- Complex backgroundstion. We study the influence of each stage of the computation There is an extensive literature on object detection, buton performance, concluding that fine-scale gradients, fine here we mention just a few relevant papers on human detec-orientation binning, relatively coarse spatial binning, and tion [18,17,22,16,20]. See [6] for a survey. Papageorgiou et Unconstrained illuminationshigh-quality local contrast normalization in overlapping de- al [18] describe a pedestrian detector based on a polynomialscriptor blocks are all important for good results. The new SVM using rectified Haar wavelets as input descriptors, withapproach gives near-perfect separation on the original MIT a parts (subwindow) based variant in [17]. Depoortere et al Occlusionspedestrian database, so we introduce a more challenging give an optimized version of this [2]. Gavrila & Philomendataset containing over 1800 annotated human images with [8] take a more direct approach, extracting edge images and Different scalesa large range of pose variations and backgrounds. matching them to a set of learned exemplars using chamfer distance. This has been used in a practical real-time pedes-1 Introduction trian detection system [7]. Viola et al [22] build an efficient ... Detecting humans in images is a challenging task owing moving person detector, using AdaBoost to train a chain ofto their variable appearance and the wide range of poses that progressively more complex region rejection rules based onthey can adopt. The first need is a robust feature set that Haar-like wavelets and space-time differences. Ronfard et localizing humans in images is aallows the human form to be discriminated cleanly, even incluttered backgrounds under difficult illumination. We study al [19] build an articulated body detector by incorporating SVM based limb classifiers over 1st and 2nd order Gaussian challenging task...the issue of feature sets for human detection, showing that lo-cally normalized Histogram of Oriented Gradient (HOG) de- filters in a dynamic programming framework similar to those of Felzenszwalb & Huttenlocher [3] and Ioffe & Forsyth
  • 64. Approach
  • 65. Approach• robust feature set (HOG)
  • 66. Approach• robust feature set (HOG)
  • 67. Approach• robust feature set (HOG)• simple classifier(linear SVM)
  • 68. Approach• robust feature set (HOG)• simple classifier(linear SVM)• fast detection(sliding window)
  • 69. adapted from Bill Triggs
  • 70. • Gamma normalization• Space: RGB, LAB or Gray• Method: SQRT or LOG
  • 71. • Filtering with simple masks centered centered * diagonal uncentered uncenteredcubic-corrected cubic-corrected Sobel * centered performs the best
  • 72. remember SIFT ?• Filtering with simple masks centered uncentered cubic-corrected
  • 73. ...after filtering, each “pixel” representsan oriented gradient...
  • 74. ...pixels are regrouped in “cells”,they cast a weighted vote for anorientation histogram... HOG (Histogram of Oriented Gradients)
  • 75. a window can berepresented likethat
  • 76. then, cells are locally normalizedusing overlapping “blocks”
  • 77. they used two types of blocks
  • 78. they used two types of blocks• rectangular• similar to SIFT (but dense)
  • 79. they used two types of blocks• rectangular • circular• similar to SIFT (but dense) • similar to Shape Context
  • 80. and four different types of blocknormalization
  • 81. and four different types of blocknormalization
  • 82. like SIFT, they gain invariance......to illuminations, smalldeformations, etc.
  • 83. finally, a sliding window isclassified by a simple linear SVM
  • 84. during the learning phase, thealgorithm “looked” for hard examples Training adapted from Martial Hebert
  • 85. average gradientspositive weights negative weights
  • 86. Example
  • 87. Example adapted from Bill Triggs
  • 88. Example adapted from Martial Hebert
  • 89. FurtherDevelopment
  • 90. FurtherDevelopment • Detection on Pascal VOC (2006)
  • 91. FurtherDevelopment • Detection on Pascal VOC (2006) • Human Detection in Movies (ECCV 2006)
  • 92. FurtherDevelopment • Detection on Pascal VOC (2006) • Human Detection in Movies (ECCV 2006) • US Patent by MERL (2006)
  • 93. FurtherDevelopment • Detection on Pascal VOC (2006) • Human Detection in Movies (ECCV 2006) • US Patent by MERL (2006) • Stereo Vision HoG (ICVES 2008)
  • 94. Extension example:Pyramid HoG++
  • 95. Extension example:Pyramid HoG++
  • 96. Extension example:Pyramid HoG++
  • 97. A simple demo...
  • 98. A simple demo...
  • 99. A simple demo... VIDEO HERE
  • 100. A simple demo... VIDEO HERE
  • 101. so, it doesn’t work ?!?
  • 102. so, it doesn’t work ?!? no no, it works...
  • 103. so, it doesn’t work ?!? no no, it works... ...it just doesn’t work well...
  • 104. Object Recognition from Local Scale-Invariant Features David G. Lowe Lowe Computer Science Department University of British Columbia Vancouver, B.C., V6T 1Z4, Canada (1999) lowe@cs.ubc.ca Abstract translation, scaling, and rotation, and partially invariant to illumination changes and affine or 3D projection. Previous An object recognition system has been developed that uses a approaches to local feature generation lacked invariance to new class of local image features. The features are invariant scale and were more sensitive to projective distortion and to image scaling, translation, and rotation, and partially in- illumination change. The SIFT features share a number of variant to illumination changes and affine or 3D projection. properties in common with the responses of neurons in infe- Histograms of Oriented Gradients for Human Detection Navneet Dalal and Bill Triggs INRIA Rhˆ ne-Alps, 655 avenue de l’Europe, Montbonnot 38334, France o {Navneet.Dalal,Bill.Triggs}@inrialpes.fr, http://lear.inrialpes.fr Nalal and Triggs Abstract We study the question of feature sets for robust visual ob- We briefly discuss previous work on human detection in §2, give an overview of our method §3, describe our data (2005) ject recognition, adopting linear SVM based human detec- tion as a test case. After reviewing existing edge and gra- dient based descriptors, we show experimentally that grids sets in §4 and give a detailed description and experimental evaluation of each stage of the process in §5–6. The main conclusions are summarized in §7. of Histograms of Oriented Gradient (HOG) descriptors sig- 2 Previous Work nificantly outperform existing feature sets for human detec- tion. We study the influence of each stage of the computation There is an extensive literature on object detection, but A Discriminatively Trained, Multiscale, Deformable Part Model Pedro Felzenszwalb David McAllester Deva Ramanan University of Chicago Toyota Technological Institute at Chicago UC IrvineFelzenszwalb et al. pff@cs.uchicago.edu mcallester@tti-c.org dramanan@ics.uci.edu (2008) Abstract This paper describes a discriminatively trained, multi- scale, deformable part model for object detection. Our sys- tem achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person de- tection challenge. It also outperforms the best results in the 2007 challenge in ten out of twenty categories. The system relies heavily on deformable parts. While deformable part models have become quite popular, their value had not been Figure 1. Example detection obtained with the person model. The demonstrated on difficult benchmarks such as the PASCAL model is defined by a coarse template, several higher resolution part templates and a spatial model for the location of each part. challenge. Our system also relies heavily on new methods for discriminative training. We combine a margin-sensitive
  • 105. This paper describes oneof the best algorithm inobject detection...
  • 106. They used the following methods: s Mo del e atur Part t SV MHO G Fe able Laten De form
  • 107. They used the following methods: Introduced by Dalal & Triggs (2005) e s aturHO G Fe
  • 108. They used the following methods: Mo del Part ableDe form Introduced by Fischler & Elschlager (1973)
  • 109. They used the following methods:Introduced by the authors M ten t SV La
  • 110. e s aturHO G Fe
  • 111. Model Overview deformation detection root filter part filters models
  • 112. t ures G FeaHO // 8x8 pixel blocks window // features computed at different resolutions (pyramid)
  • 113. id Py ramHOG
  • 114. Mo del Part ableDe form
  • 115. l M ode Part mableD efor // each part is a local property // springs capture spatial relationships // here, the springs can be “negative”
  • 116. l M ode artDefor mable P detection score = sum of filter responses - deformation cost
  • 117. l M ode artDefor mable P detection score = sum of filter responses - deformation cost root filter
  • 118. l M ode artDefor mable P detection score = sum of filter responses - deformation cost root filter part filters
  • 119. l M ode artDefor mable P detection score = sum of filter responses - deformation cost root filter deformable part filters model
  • 120. l M ode Part mable eforD score of a placement filters feature vector coefficients of a position relative (at position p quadratic function on to the root location in the pyramid H) the placement
  • 121. M ten t SVLa
  • 122. VM ate nt SL filters and deformation features part displacements parameters
  • 123. VM ate nt SL
  • 124. s B onu// Data Mining Hard Negatives// Model Initialization
  • 125. s ResultPascal VOC 2006
  • 126. sResultModels learned
  • 127. m entsExperi ~ Dalal’s model ~ Dalal’s + LSVM
  • 128. am plesEx errors
  • 129. em o... d im pleAs
  • 130. em o... d im pleAs
  • 131. em o... d im pleAs
  • 132. em o... d im pleAs
  • 133. ns cl usioCon
  • 134. ns cl usioCon so, it doesn’t work ?!?
  • 135. ns cl usioCon so, it doesn’t work ?!? no no, it works...
  • 136. ns cl usioCon so, it doesn’t work ?!? no no, it works... ...it just doesn’t work well...
  • 137. ns cl usioCon so, it doesn’t work ?!? no no, it works... ...it just doesn’t work well... ...or there is a problem with the seat-computer interface...
  • 138. Conclusion