Object recognition from local scale-invariant features
20 Sep 1999-Vol. 2, pp 1150-1157
TL;DR: Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.
Abstract: An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low residual least squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.
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Cites methods from "Object recognition from local scale..."
...Our detector is based on a filtering approach (Lowe 1999 [11])....
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...The same mixture of Gaussians classifier has been trained on a different set of features: the Fourier-Mellin based features [7] used by Dahmen et al. (2000) [5]....
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...We use a Bayesian classifier that models the training data as a mixture of Gaussians (MoG) (Bishop 2000 [3])....
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...6 gives an overview at the mixture of Gaussians classifier we have used in our experiments....
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...The best results were achieved using 4 full covariance Gaussians in a 12 dimensional feature space in each mixture....
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TL;DR: A stereo-based vision system framework where aspects of top-down and bottom-up attention as well as foveated attention are put into focus and how the system can be utilized for robotic object grasping is presented.
Abstract: The ability to autonomously acquire new knowledge through interaction with the environment is an important research topic in the field of robotics. The knowledge can only be acquired if suitable perceptionâ action capabilities are present: a robotic system has to be able to detect, attend to and manipulate objects in its surrounding. In this paper, we present the results of our long-term work in the area of vision-based sensing and control. The work on finding, attending, recognizing and manipulating objects in domestic environments is studied. We present a stereo-based vision system framework where aspects of top-down and bottom-up attention as well as foveated attention are put into focus and demonstrate how the system can be utilized for robotic object grasping.
116 citations
Cites methods from "Object recognition from local scale..."
...To represent the appearance, we use scaleinvariant feature transform (SIFT) descriptors (Lowe 1999) and color histograms (Gevers and Smeulders 1999)....
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...The first method is based on color histograms (Gevers and Smeulders 1999) and the other on scale and rotation invariant SIFT features (Lowe 1999)....
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TL;DR: Experimental results over a set of real-world sequences show that the proposed feature weighting procedure outperforms state-of-the-art solutions and thatThe proposed adaptive multifeature tracker improves the reliability of the target estimate while eliminating the need of manually selecting each feature's relevance.
Abstract: In this paper, we propose a tracking algorithm based on an adaptive multifeature statistical target model. The features are combined in a single particle filter by weighting their contributions using a novel reliability measure derived from the particle distribution in the state space. This measure estimates the reliability of the information by measuring the spatial uncertainty of features. A modified resampling strategy is also devised to account for the needs of the feature reliability estimation. We demonstrate the algorithm using color and orientation features. Color is described with partwise normalized histograms. Orientation is described with histograms of the gradient directions that represent the shape and the internal edges of a target. A feedback from the state estimation is used to align the orientation histograms as well as to adapt the scales of the filters to compute the gradient. Experimental results over a set of real-world sequences show that the proposed feature weighting procedure outperforms state-of-the-art solutions and that the proposed adaptive multifeature tracker improves the reliability of the target estimate while eliminating the need of manually selecting each feature's relevance.
116 citations
Cites background or methods from "Object recognition from local scale..."
...To account for half-bin-wide target rotations and spatial discontinuities, we use trilinear interpolation to smooth the estimated histogram [28]....
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...gradient direction with respect to the dominant orientation [28]....
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...could be reduced by using an optimized implementation of the Gaussian scale space [28]....
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References
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TL;DR: In this paper, color histograms of multicolored objects provide a robust, efficient cue for indexing into a large database of models, and they can differentiate among a large number of objects.
Abstract: Computer vision is moving into a new era in which the aim is to develop visual skills for robots that allow them to interact with a dynamic, unconstrained environment. To achieve this aim, new kinds of vision algorithms need to be developed which run in real time and subserve the robot's goals. Two fundamental goals are determining the identity of an object with a known location, and determining the location of a known object. Color can be successfully used for both tasks.
This dissertation demonstrates that color histograms of multicolored objects provide a robust, efficient cue for indexing into a large database of models. It shows that color histograms are stable object representations in the presence of occlusion and over change in view, and that they can differentiate among a large number of objects. For solving the identification problem, it introduces a technique called Histogram Intersection, which matches model and image histograms and a fast incremental version of Histogram Intersection which allows real-time indexing into a large database of stored models. It demonstrates techniques for dealing with crowded scenes and with models with similar color signatures. For solving the location problem it introduces an algorithm called Histogram Backprojection which performs this task efficiently in crowded scenes.
5,672 citations
TL;DR: It is shown how the boundaries of an arbitrary non-analytic shape can be used to construct a mapping between image space and Hough transform space, which makes the generalized Houghtransform a kind of universal transform which can beused to find arbitrarily complex shapes.
4,310 citations
TL;DR: A near real-time recognition system with 20 complex objects in the database has been developed and a compact representation of object appearance is proposed that is parametrized by pose and illumination.
Abstract: The problem of automatically learning object models for recognition and pose estimation is addressed. In contrast to the traditional approach, the recognition problem is formulated as one of matching appearance rather than shape. The appearance of an object in a two-dimensional image depends on its shape, reflectance properties, pose in the scene, and the illumination conditions. While shape and reflectance are intrinsic properties and constant for a rigid object, pose and illumination vary from scene to scene. A compact representation of object appearance is proposed that is parametrized by pose and illumination. For each object of interest, a large set of images is obtained by automatically varying pose and illumination. This image set is compressed to obtain a low-dimensional subspace, called the eigenspace, in which the object is represented as a manifold. Given an unknown input image, the recognition system projects the image to eigenspace. The object is recognized based on the manifold it lies on. The exact position of the projection on the manifold determines the object's pose in the image. A variety of experiments are conducted using objects with complex appearance characteristics. The performance of the recognition and pose estimation algorithms is studied using over a thousand input images of sample objects. Sensitivity of recognition to the number of eigenspace dimensions and the number of learning samples is analyzed. For the objects used, appearance representation in eigenspaces with less than 20 dimensions produces accurate recognition results with an average pose estimation error of about 1.0 degree. A near real-time recognition system with 20 complex objects in the database has been developed. The paper is concluded with a discussion on various issues related to the proposed learning and recognition methodology.
2,037 citations
TL;DR: This paper addresses the problem of retrieving images from large image databases with a method based on local grayvalue invariants which are computed at automatically detected interest points and allows for efficient retrieval from a database of more than 1,000 images.
Abstract: This paper addresses the problem of retrieving images from large image databases. The method is based on local grayvalue invariants which are computed at automatically detected interest points. A voting algorithm and semilocal constraints make retrieval possible. Indexing allows for efficient retrieval from a database of more than 1,000 images. Experimental results show correct retrieval in the case of partial visibility, similarity transformations, extraneous features, and small perspective deformations.
1,756 citations
"Object recognition from local scale..." refers background or methods in this paper
...This allows for the use of more distinctive image descriptors than the rotation-invariant ones used by Schmid and Mohr, and the descriptor is further modified to improve its stability to changes in affine projection and illumination....
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...For the object recognition problem, Schmid & Mohr [19] also used the Harris corner detector to identify interest points, and then created a local image descriptor at each interest point from an orientation-invariant vector of derivative-of-Gaussian image measurements....
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..., Schmid & Mohr [19]) has shown that efficient recognition can often be achieved by using local image descriptors sampled at a large number of repeatable locations....
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...However, recent research on the use of dense local features (e.g., Schmid & Mohr [19]) has shown that efficient recognition can often be achieved by using local image descriptors sampled at a large number of repeatable locations....
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TL;DR: A robust approach to image matching by exploiting the only available geometric constraint, namely, the epipolar constraint, is proposed and a new strategy for updating matches is developed, which only selects those matches having both high matching support and low matching ambiguity.
1,574 citations
"Object recognition from local scale..." refers methods in this paper
...[23] used the Harris corner detector to identify feature locations for epipolar alignment of images taken from differing viewpoints....
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