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85
LDAHash: Improved matching with smaller descriptors
, 2010
"... SIFT-like local feature descriptors are ubiquitously employed in such computer vision applications as content-based retrieval, video analysis, copy detection, object recognition, photo-tourism and 3D reconstruction. Feature descriptors can be designed to be invariant to certain classes of photometri ..."
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Cited by 80 (10 self)
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SIFT-like local feature descriptors are ubiquitously employed in such computer vision applications as content-based retrieval, video analysis, copy detection, object recognition, photo-tourism and 3D reconstruction. Feature descriptors can be designed to be invariant to certain classes of photometric and geometric transformations, in particular, affine and intensity scale transformations. However, real transformations that an image can undergo can only be approximately modeled in this way, and thus most descriptorsareonlyapproximatelyinvariantinpractice. Secondly, descriptors are usually high-dimensional (e.g. SIFT is represented as a 128-dimensional vector). In large-scale retrieval and matching problems, this can pose challenges in storing and retrieving descriptor data. We map the descriptor vectors into the Hamming space, in which the Hamming metric is used to compare the resulting representations. This way, we reduce the size of the descriptors by representing them as short binary strings and learn descriptor invariance from examples. We show extensive experimental validation, demonstrating the advantage of the proposed approach.
Intrinsic shape context descriptors for deformable shapes
- in CVPR
, 2012
"... In this work, we present intrinsic shape context (ISC) descriptors for 3D shapes. We generalize to surfaces the polar sampling of the image domain used in shape con-texts: for this purpose, we chart the surface by shooting geodesic outwards from the point being analyzed; ‘angle’ is treated as tantam ..."
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Cited by 12 (3 self)
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In this work, we present intrinsic shape context (ISC) descriptors for 3D shapes. We generalize to surfaces the polar sampling of the image domain used in shape con-texts: for this purpose, we chart the surface by shooting geodesic outwards from the point being analyzed; ‘angle’ is treated as tantamount to geodesic shooting direction, and radius as geodesic distance. To deal with orientation ambiguity, we exploit properties of the Fourier transform. Our charting method is intrinsic, i.e., invariant to isometric shape transformations. The resulting descriptor is a meta-descriptor that can be applied to any photometric or geo-metric property field defined on the shape, in particular, we can leverage recent developments in intrinsic shape analy-sis and construct ISC based on state-of-the-art dense shape descriptors such as heat kernel signatures. Our experiments demonstrate a notable improvement in shape matching on standard benchmarks. 1.
Using depth and appearance features for informed robot grasping of highly wrinkled clothes
- in Proceedings of the International Conference on Robotics and Automation, 2012
"... Abstract — Detecting grasping points is a key problem in cloth manipulation. Most current approaches follow a multiple regrasp strategy for this purpose, in which clothes are sequentially grasped from different points until one of them yields to a desired configuration. In this paper, by contrast, w ..."
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Cited by 11 (7 self)
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Abstract — Detecting grasping points is a key problem in cloth manipulation. Most current approaches follow a multiple regrasp strategy for this purpose, in which clothes are sequentially grasped from different points until one of them yields to a desired configuration. In this paper, by contrast, we circumvent the need for multiple re-graspings by building a robust detector that identifies the grasping points, generally in one single step, even when clothes are highly wrinkled. We demonstrate our approach for detecting collars in deformed shirts, using a Kinect camera. In order to handle the large variability a deformed cloth may have, we build a Bag of Features based detector that combines appearance and 3D geometry features. An image is scanned using a sliding window with a linear classifier, and the candidate windows are refined using a non-linear SVM and a “grasp goodness ” criterion to select the best grasping point. For this work we have used polo shirt collars as test cloth part. Experimental results show a good performance of the proposed method not only in identifying the same trained textile object part under severe deformations and occlusions, but also the corresponding part in other clothes, exhibiting a degree of generalization. I.
Y.: Scale-Invariant Features for 3-D Mesh Models
- TIP
, 2012
"... Abstract—In this paper, we present a framework for detecting interest points in 3-D meshes and computing their corresponding descriptors. For that, we propose an intrinsic scale detection scheme per interest point and utilize it to derive two scale-in-variant local features for mesh models. First, w ..."
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Cited by 11 (0 self)
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Abstract—In this paper, we present a framework for detecting interest points in 3-D meshes and computing their corresponding descriptors. For that, we propose an intrinsic scale detection scheme per interest point and utilize it to derive two scale-in-variant local features for mesh models. First, we present the scale-invariant spin image local descriptor that is a scale-invariant formulation of the spin image descriptor. Second, we adapt the scale-invariant feature transform feature to mesh data by rep-resenting the vicinity of each interest point as a depth map and estimating its dominant angle using the principal component anal-ysis to achieve rotation invariance. The proposed features were experimentally shown to be robust to scale changes and partial mesh matching, and they were compared favorably with other local mesh features on the SHREC’10 and SHREC’11 testbeds. We applied the proposed local features to mesh retrieval using the bag-of-features approach and achieved state-of-the-art retrieval accuracy. Last, we applied the proposed local features to register models to scanned depth scenes and achieved high registration accuracy. I.
Discriminative sketch-based 3D model retrieval via robust shape matching. Computer Graphics Forum (Proc
- Pacific Graphics
, 2011
"... We propose a sketch-based 3D shape retrieval system that is substantially more discriminative and robust than existing systems, especially for complex models. The power of our system comes from a combination of a contour-based 2D shape representation and a robust sampling-based shape matching scheme ..."
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Cited by 9 (2 self)
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We propose a sketch-based 3D shape retrieval system that is substantially more discriminative and robust than existing systems, especially for complex models. The power of our system comes from a combination of a contour-based 2D shape representation and a robust sampling-based shape matching scheme. They are defined over dis-criminative local features and applicable for partial sketches; robust to noise and distortions in hand drawings; and consistent when strokes are added progressively. Our robust shape matching, however, requires dense sam-pling and registration and incurs a high computational cost. We thus devise critical acceleration methods to achieve interactive performance: precomputing kNN graphs that record transformations between neighboring contour images and enable fast online shape alignment; pruning sampling and shape registration strategically and hierarchically; and parallelizing shape matching on multi-core platforms or GPUs. We demonstrate the effec-tiveness of our system through various experiments, comparisons, and user studies. 1.
Scale Invariant Geometry for Nonrigid Shapes
, 2013
"... In nature, different animals of the same species frequently exhibit local variations in scale. New developments in shape matching research thus increasingly provide us with the tools to answer such fascinating questions as the following: How should we measure the discrepancy between a small dog wit ..."
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Cited by 8 (8 self)
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In nature, different animals of the same species frequently exhibit local variations in scale. New developments in shape matching research thus increasingly provide us with the tools to answer such fascinating questions as the following: How should we measure the discrepancy between a small dog with large ears and a large one with small ears? Are there geometric structures common to both an elephant and a giraffe? What is the morphometric similarity between a blue whale and a dolphin? Currently, there are only two methods that allow us to quantify similarities between surfaces which are insensitive to deformations in size: scale invariant local descriptors and global normalization methods. Here, we propose a new tool for shape exploration. We introduce a scale invariant metric for surfaces that allows us to analyze nonrigid shapes, generate locally invariant features, produce scale invariant geodesics, embed one surface into another despite changes in local and global size, and assist in the computational study of intrinsic symmetries where size is insignificant.
Matching 2D & 3D Articulated Shapes using Eccentricity
- INTERNATIONAL JOURNAL OF COMPUTER VISION
"... Shape matching should be invariant to the typical intra-class deformations present in nature. The majority of shape descriptors are quite complex and not invariant to the deformation or articulation of object parts. Geodesic distances computed over a 2D or 3D shape are articulation insensitive. Th ..."
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Cited by 7 (1 self)
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Shape matching should be invariant to the typical intra-class deformations present in nature. The majority of shape descriptors are quite complex and not invariant to the deformation or articulation of object parts. Geodesic distances computed over a 2D or 3D shape are articulation insensitive. The eccentricity transform considers the length of the longest geodesics. It is robust with respect to Salt and Pepper noise, and minor segmentation errors, and is stable in the presence of holes. We present a method for 2D and 3D shape matching based on the eccentricity transform. Eccentricity histograms make up descriptors insensitive to rotation, scaling, and articulation. The descriptor is highly compact and the method is straight-forward. Experimental results on established 2D and 3D benchmarks show results comparable to more complex state of the art methods. Properties and results are discussed in detail.
Semi-supervised nonlinear hashing using bootstrap sequential projection learning.
- IEEE Transactions on Knowledge and Data Engineering,
, 2012
"... Abstract-In this paper, we study the effective semi-supervised hashing method under the framework of regularized learningbased hashing. A nonlinear hash function is introduced to capture the underlying relationship among data points. Thus, the dimensionality of the matrix for computation is not onl ..."
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Cited by 6 (3 self)
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Abstract-In this paper, we study the effective semi-supervised hashing method under the framework of regularized learningbased hashing. A nonlinear hash function is introduced to capture the underlying relationship among data points. Thus, the dimensionality of the matrix for computation is not only independent from the dimensionality of the original data space but also much smaller than the one using linear hash function. To effectively deal with the error accumulated during converting the real-value embeddings into the binary code after relaxation, we propose a semi-supervised nonlinear hashing algorithm using bootstrap sequential projection learning which effectively corrects the errors by taking into account of all the previous learned bits holistically without incurring the extra computational overhead. Experimental results on the six benchmark datasets demonstrate that the presented method outperforms the state-of-the-art hashing algorithms at a large margin. Index Terms-Hashing, semi-supervised hashing, nearest neighbor search.
Object recognition in probabilistic 3d volumetric scenes
- In ICPRAM, 2012. 6
"... Abstract: A new representation of 3-d object appearance from video sequences has been developed over the past several years (Pollard and Mundy, 2007; Pollard, 2008; Crispell, 2010), which combines the ideas of background modeling and volumetric multi-view reconstruction. In this representation, Gaus ..."
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Cited by 5 (1 self)
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Abstract: A new representation of 3-d object appearance from video sequences has been developed over the past several years (Pollard and Mundy, 2007; Pollard, 2008; Crispell, 2010), which combines the ideas of background modeling and volumetric multi-view reconstruction. In this representation, Gaussian mixture models for in-tensity or color are stored in volumetric units. This 3-d probabilistic volume model, PVM, is learned from a video sequence by an on-line Bayesian updating algorithm. To date, the PVM representation has been applied to video image registration (Crispell et al., 2008), change detection (Pollard and Mundy, 2007) and classifica-tion of changes as vehicles in 2-d only (Mundy and Ozcanli, 2009; Özcanli and Mundy, 2010). In this paper, the PVM is used to develop novel viewpoint-independent features of object appearance directly in 3-d. The resulting description is then used in a bag-of-features classification algorithm to recognize buildings, houses, parked cars, parked aircraft and parking lots in aerial scenes collected over Providence, Rhode Island, USA. Two approaches to feature description are described and compared: 1) features derived from a PCA analysis of model neighborhoods; and 2) features derived from the coefficients of a 3-d Taylor series expansion within each neighborhood. It is shown that both feature types explain the data with similar accuracy. Finally, the effectiveness of both feature types for recognition is compared for the different categories. Encouraging ex-perimental results demonstrate the descriptive power of the PVM representation for object recognition tasks,
COHEN-OR D.: Curve style analysis in a set of shapes
- CGF
"... The word ‘style ’ can be interpreted in so many different ways in so many different contexts. To provide a general analysis and understanding of styles is a highly challenging problem. We pose the open question ‘how to extract styles from geometric shapes?’ and address one instance of the problem. S ..."
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Cited by 5 (0 self)
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The word ‘style ’ can be interpreted in so many different ways in so many different contexts. To provide a general analysis and understanding of styles is a highly challenging problem. We pose the open question ‘how to extract styles from geometric shapes?’ and address one instance of the problem. Specifically, we present an unsupervised algorithm for identifying curve styles in a set of shapes. In our setting, a curve style is explicitly represented by a mode of curve features appearing along the 2D silhouettes of the shapes in the set. Unlike previous attempts, we do not rely on any preconceived conceptual characterisations, for example, via specific shape descriptors, to define what is or is not a style. Our definition of styles is data-dependent; it depends on the input set but we do not require computing a shape correspondence across the set. We provide an operational definition of curve styles which focuses on separating curve features that represent styles from curve features that are content revealing. To this end, we develop a novel formulation and associated algorithm for style-content separation. The analysis is based on a feature-shape association matrix (FSM) whose rows correspond to modes of curve features, columns to shapes in the set, and each entry expresses the extent a feature mode is present in a shape. We make several assumptions to drive style-content separation which only involve properties of, and relations between, rows of the FSM. Computationally, our algorithm only requires row-wise correlation analysis in the FSM and a heuristic solution of an instance of the set cover problem. Results are demonstrated on several data sets showing the identification of curve styles. We also develop and demonstrate several style-related applications
