Off-Line Global Approach to Pattern Recognition for Bubble Chamber Pictures

Bazin, M.; Benoît, J.

doi:10.1109/tns.1965.4323870

Cited by 24 publications

(4 citation statements)

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“…HT was first introduced in 1962 by Paul Hough [11] for curve detection in bubble chamber photographs [12,13] and reached the image processing community's attention in 1969, thanks to the work of Rosenfeld [14] who studied the simplest and most used HT application of line finding in binary images. The classical form of HT has been formalized some years later by Duda and Hart [15] with the so-called "normal parametrization".…”

Section: Early Development In 2d Image Processingmentioning

confidence: 99%

3D Hough transform for sphere recognition on point clouds

Camurri

Vezzani

Cucchiara

2014

Machine Vision and Applications

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Three-dimensional object recognition on range data and 3D point clouds is becoming more important nowadays. Since many real objects have a shape that could be approximated by simple primitives, robust pattern recognition can be used to search for primitive models. For example, the Hough transform is a well-known technique which is largely adopted in 2D image space. In this paper, we systematically analyze different probabilistic/randomized Hough transform algorithms for spherical object detection in dense point clouds. In particular, we study and compare four variants which are characterized by the number of points drawn together for surface computation into the parametric space and we formally discuss their models. We also propose a new method that combines the advantages of both single-point and multi-point approaches for a faster and more accurate detection. The methods are tested on synthetic and real datasets

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Section: Early Development In 2d Image Processingmentioning

confidence: 99%

3D Hough transform for sphere recognition on point clouds

Camurri

Vezzani

Cucchiara

2014

Machine Vision and Applications

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“…The Hough transform [228][229][230] and the Radon transform [299] are related, and the results are equivalent, in the opinion of many; [295][300] see Figure 3-26. The Radon transform is an integral transform, while the Hough transform is a discrete method, therefore much faster.…”

Section: Hough Transform and Radon Transformmentioning

confidence: 99%

Computer Vision Metrics

Krig¹

2014

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Distance From Sensor in meters Depth granularity or resolution in mm Depth granularity or resolution in mm Raw Z depth map Raw YYZ vertex map & Surface normal map 6DOF pose via ICP Volume surface integration 3D surface rendering Volume YYZ vertex map & Surface normal map a. Method of volume integration, 6DOF camera pose, and surface rendering used in KinectFusion [474][475]. Name Middlebury Computer Vision Datasets Description Scholarly and comprehensive datasets, and algorithm comparisons over most of the datasets Categories Stereo vision (excellent), multi-view stereo (excellent), MRF, Optical Flow (excellent), Color processing Contributions Algorithm benchmarks over the datasets can be submitted Key papers Several; see website Owner Middlebury College Link http://vision.middlebury.edu/ Name ADL Activity Recognition Dataset Description Annotated scenes for activity recognition of common living scenes Categories Daily life Tools and apps Activity recognition code available (see link below) Key papers [73] Link http://deepthought.ics.uci.edu/ADLdataset/adl.html Name MIT Indoor Scenes 67, Scene Classification Description Annotated dataset specifically containing diverse indoor scenes Categories 15,620 images organized into 67 indoor categories, some annotations in Labelme format Key papers [74] Link http://web.mit.edu/torralba/www/indoor.html Name RGB-D Object Recognition Dataset, U of W Description Dataset contains RGB and corresponding depth images Categories 300 common household objects, 51 categories using Wordnet similar to Imagenet style (Imagenet dataset reviewed above), each object recorded in RGB and Kinect depth at various rotational angles and viewpoints Key papers [75] Link http://www.cs.washington.edu/rgbd-dataset/ APPENDIX B ■ SurvEy of GrouND TruTh DATASETS 405 Name NYU Depth Datasets Description Annotated dataset of indoor scenes using RGB-D datasets + accelerometer data Categories Over 500,000 frames, many different indoor scenes and scene types, thousands of classes, accelerometer data, inpainted and raw depth information Tools and apps Matlab toolbox + g++ code Key papers [76] Link http://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html Name Intel Labs Seattle -Egocentric Recognition of Handled Objects Description Annotated dataset for egocentric handled objects using a wearable camera Categories Over 42 everyday objects under varied lighting, occlusion, perspectives; over 6GB total video sequence data Key papers [77] [78] Link http://seattle.intel-research.net/~xren/egovision09/ Name Georgia Tech GTEA Egocentric Activities -Gaze(+) Description Annotated dataset for egocentric handled objects using a wearable camera Categories Many everyday objects under varied lighting, occlusion, perspectives Tools and apps Code library of vision functions and mathematical functions Key papers [79] Link http://www.cc.gatech.edu/~afathi3/GTEA_Gaze_Website/ Name CUReT: Columbia-Utrecht Reflectance and Texture Database Description Extensive texture sample and illumination datasets directions Categories Over 60 di...

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“…Use of the Radon transform for shape detection dates back to 1965 [13]. The technique these authors describe is essentially a Radon transform.…”

Section: The Radon Transformmentioning

confidence: 99%

“…Rosenfeld [14] describes this technique (for straight lines) in Section 8.4.e "Coordinate Conversion". Neither [13] nor [14] identify this technique as the Radon transform. Our study of the discretization of the Radon transform in the next section also applies to the popular Hough transform which is often used for shape detection.…”

Section: The Radon Transformmentioning

confidence: 99%

The generalized Radon transform: Sampling, accuracy and memory considerations

Hendriks

Ginkel

Verbeek

et al. 2005

Pattern Recognition

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The generalized Radon (or Hough) transform is a well-known tool for detecting parameterized shapes in an image. The Radon transform is a mapping between the image space and a parameter space. The coordinates of a point in the latter correspond to the parameters of a shape in the image. The amplitude at that point corresponds to the amount of evidence for that shape. In this paper we discuss three important aspects of the Radon transform. The first aspect is discretization. Using concepts from sampling theory we derive a set of sampling criteria for the generalized Radon transform. The second aspect is accuracy. For the specific case of the Radon transform for spheres, we examine how well the location of the maxima matches the true parameters. We derive a correction term to reduce the bias in the estimated radii. The third aspect concerns a projection-based algorithm to reduce memory requirements.

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Off-Line Global Approach to Pattern Recognition for Bubble Chamber Pictures

Cited by 24 publications

References 0 publications

3D Hough transform for sphere recognition on point clouds

3D Hough transform for sphere recognition on point clouds

Computer Vision Metrics

The generalized Radon transform: Sampling, accuracy and memory considerations

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