Patent•
Analysis of three-dimensional scenes
11 Aug 2010-
TL;DR: In this article, a method for processing data includes receiving a depth map of a scene containing a humanoid form, which is processed so as to identify three-dimensional (3D) connected components in the scene, each connected component including a set of the pixels that are mutually adjacent and have mutually-adjacent depth values.
Abstract: A method for processing data includes receiving a depth map of a scene containing a humanoid form. The depth map is processed so as to identify three-dimensional (3D) connected components in the scene, each connected component including a set of the pixels that are mutually adjacent and have mutually-adjacent depth values. Separate, first and second connected components are identified as both belonging to the humanoid form, and a representation of the humanoid form is generated including both of the first and second connected components.
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Patent•
25 Jan 2013TL;DR: In this article, the authors describe a system for determining dimensions of a physical object using a mobile computer equipped with a motion sensing device, which includes a microprocessor, a memory, a user interface, a motion sensor, and a dimensioning program executable by the microprocessor.
Abstract: Devices, methods, and software are disclosed for determining dimensions of a physical object using a mobile computer equipped with a motion sensing device. In an illustrative embodiment, the mobile computer can comprise a microprocessor, a memory, a user interface, a motion sensing device, and a dimensioning program executable by the microprocessor. The processor can be in communicative connection with executable instructions for enabling the processor for various steps. One step includes initiating a trajectory tracking mode responsive to receiving a first user interface action. Another step includes tracking the mobile computer's trajectory along a surface of a physical object by storing in the memory a plurality of motion sensing data items outputted by the motion sensing device. Another step includes exiting the trajectory tracking mode responsive to receiving a second user interface action. Another step includes calculating three dimensions of a minimum bounding box corresponding to the physical object.
370 citations
Patent•
03 May 2013
TL;DR: In this paper, a method for volume dimensioning packages is described, which can determine from the received image data a number of features in three dimensions of the first 3D object.
Abstract: Systems and methods for volume dimensioning packages are provided. A method of operating a volume dimensioning system may include the receipt of image data of an area at least a first three-dimensional object to be dimensioned from a first point of view as captured using at least one image sensor. The system can determine from the received image data a number of features in three dimensions of the first three-dimensional object. Based at least on part on the determined features of the first three-dimensional object, the system can fit a first three-dimensional packaging wireframe model about the first three-dimensional object. The system can display of an image of the first three-dimensional packaging wireframe model fitted about an image of the first three-dimensional object on a display device.
362 citations
Patent•
15 May 2012TL;DR: In this paper, an actuator is connected to at least one imaging subsystem for moving an angle of the optical axis relative to the terminal to align the object in the second image data with the first image data.
Abstract: A terminal for measuring at least one dimension of an object includes at least one imaging subsystem and an actuator. The at least one imaging subsystem includes an imaging optics assembly operable to focus an image onto an image sensor array. The imaging optics assembly has an optical axis. The actuator is operably connected to the at least one imaging subsystem for moving an angle of the optical axis relative to the terminal. The terminal is adapted to obtain first image data of the object and is operable to determine at least one of a height, a width, and a depth dimension of the object based on effecting the actuator to change the angle of the optical axis relative to the terminal to align the object in second image data with the object in the first image data, the second image data being different from the first image data.
341 citations
Patent•
07 Jun 2013
TL;DR: In this paper, a method for correcting errors in a 3D scanner is presented, where each of a plurality of calibration objects is used to correct the measurements obtained by the scanner.
Abstract: A method is presented for correcting errors in a 3D scanner. Measurement errors in the 3D scanner are determined by scanning each of a plurality of calibration objects in each of a plurality of sectors in the 3D scanner's field of view. The calibration objects have a known height, a known width, and a known length. The measurements taken by the 3D scanner are compared to the known dimensions to derive a measurement error for each dimension in each sector. An estimated measurement error is calculated based on scans of each of the plurality of calibration objects. When scanning target objects in a given sector, the estimated measurement error for that sector is used to correct measurements obtained by the 3D scanner.
298 citations
Patent•
21 Oct 2014
TL;DR: In this article, a system and method for obtaining a dimension measurement that conforms to conformance criteria is disclosed, which provides either feedback to confirm that the measurement complies with the criteria or information on how the measurement geometry could be adjusted in order to provide a compliant measurement in a subsequent dimension measurement.
Abstract: A system and method for obtaining a dimension measurement that conforms to a conformance criteria is disclosed. The dimensioning system provides either (i) feedback to confirm that the measurement complies with the criteria or (ii) information on how the measurement geometry could be adjusted in order to provide a compliant measurement in a subsequent dimension measurement.
257 citations
References
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TL;DR: It is proved the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density.
Abstract: A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.
11,727 citations
21 Jun 1994
TL;DR: A feature selection criterion that is optimal by construction because it is based on how the tracker works, and a feature monitoring method that can detect occlusions, disocclusions, and features that do not correspond to points in the world are proposed.
Abstract: No feature-based vision system can work unless good features can be identified and tracked from frame to frame. Although tracking itself is by and large a solved problem, selecting features that can be tracked well and correspond to physical points in the world is still hard. We propose a feature selection criterion that is optimal by construction because it is based on how the tracker works, and a feature monitoring method that can detect occlusions, disocclusions, and features that do not correspond to points in the world. These methods are based on a new tracking algorithm that extends previous Newton-Raphson style search methods to work under affine image transformations. We test performance with several simulations and experiments. >
8,432 citations
Proceedings Article•
07 Sep 1999TL;DR: Experimental results indicate that the novel scheme for approximate similarity search based on hashing scales well even for a relatively large number of dimensions, and provides experimental evidence that the method gives improvement in running time over other methods for searching in highdimensional spaces based on hierarchical tree decomposition.
Abstract: The nearestor near-neighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing interest in building search/index structures for performing similarity search over high-dimensional data, e.g., image databases, document collections, time-series databases, and genome databases. Unfortunately, all known techniques for solving this problem fall prey to the \curse of dimensionality." That is, the data structures scale poorly with data dimensionality; in fact, if the number of dimensions exceeds 10 to 20, searching in k-d trees and related structures involves the inspection of a large fraction of the database, thereby doing no better than brute-force linear search. It has been suggested that since the selection of features and the choice of a distance metric in typical applications is rather heuristic, determining an approximate nearest neighbor should su ce for most practical purposes. In this paper, we examine a novel scheme for approximate similarity search based on hashing. The basic idea is to hash the points Supported by NAVY N00014-96-1-1221 grant and NSF Grant IIS-9811904. Supported by Stanford Graduate Fellowship and NSF NYI Award CCR-9357849. Supported by ARO MURI Grant DAAH04-96-1-0007, NSF Grant IIS-9811904, and NSF Young Investigator Award CCR9357849, with matching funds from IBM, Mitsubishi, Schlumberger Foundation, Shell Foundation, and Xerox Corporation. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 25th VLDB Conference, Edinburgh, Scotland, 1999. from the database so as to ensure that the probability of collision is much higher for objects that are close to each other than for those that are far apart. We provide experimental evidence that our method gives signi cant improvement in running time over other methods for searching in highdimensional spaces based on hierarchical tree decomposition. Experimental results also indicate that our scheme scales well even for a relatively large number of dimensions (more than 50).
3,705 citations
08 Jun 2004
TL;DR: A novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem under lp norm, based on p-stable distributions that improves the running time of the earlier algorithm and yields the first known provably efficient approximate NN algorithm for the case p<1.
Abstract: We present a novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem under lp norm, based on p-stable distributions.Our scheme improves the running time of the earlier algorithm for the case of the lp norm. It also yields the first known provably efficient approximate NN algorithm for the case p
3,109 citations
Proceedings Article•
01 Jan 2009TL;DR: A system that answers the question, “What is the fastest approximate nearest-neighbor algorithm for my data?” and a new algorithm that applies priority search on hierarchical k-means trees, which is found to provide the best known performance on many datasets.
Abstract: For many computer vision problems, the most time consuming component consists of nearest neighbor matching in high-dimensional spaces. There are no known exact algorithms for solving these high-dimensional problems that are faster than linear search. Approximate algorithms are known to provide large speedups with only minor loss in accuracy, but many such algorithms have been published with only minimal guidance on selecting an algorithm and its parameters for any given problem. In this paper, we describe a system that answers the question, “What is the fastest approximate nearest-neighbor algorithm for my data?” Our system will take any given dataset and desired degree of precision and use these to automatically determine the best algorithm and parameter values. We also describe a new algorithm that applies priority search on hierarchical k-means trees, which we have found to provide the best known performance on many datasets. After testing a range of alternatives, we have found that multiple randomized k-d trees provide the best performance for other datasets. We are releasing public domain code that implements these approaches. This library provides about one order of magnitude improvement in query time over the best previously available software and provides fully automated parameter selection.
2,934 citations