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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01 Aug 2017
TL;DR: NoScope as mentioned in this paper cascades two types of models: specialized models that forego the full generality of the reference model but faithfully mimic its behavior for the target video and object; and difference detectors that highlight temporal differences across frames.
Abstract: Recent advances in computer vision---in the form of deep neural networks---have made it possible to query increasing volumes of video data with high accuracy. However, neural network inference is computationally expensive at scale: applying a state-of-the-art object detector in real time (i.e., 30+ frames per second) to a single video requires a $4000 GPU. In response, we present NoScope, a system for querying videos that can reduce the cost of neural network video analysis by up to three orders of magnitude via inference-optimized model search. Given a target video, object to detect, and reference neural network, NoScope automatically searches for and trains a sequence, or cascade, of models that preserves the accuracy of the reference network but is specialized to the target video and are therefore far less computationally expensive. NoScope cascades two types of models: specialized models that forego the full generality of the reference model but faithfully mimic its behavior for the target video and object; and difference detectors that highlight temporal differences across frames. We show that the optimal cascade architecture differs across videos and objects, so NoScope uses an efficient cost-based optimizer to search across models and cascades. With this approach, NoScope achieves two to three order of magnitude speed-ups (265-15,500x real-time) on binary classification tasks over fixed-angle webcam and surveillance video while maintaining accuracy within 1--5% of state-of-the-art neural networks.
291 citations
20 Jun 2005
TL;DR: A feature descriptor is presented that augments SIFT with a global context vector that adds curvilinear shape information from a much larger neighborhood, thus reducing mismatches when multiple local descriptors are similar.
Abstract: Matching points between multiple images of a scene is a vital component of many computer vision tasks. Point matching involves creating a succinct and discriminative descriptor for each point. While current descriptors such as SIFT can find matches between features with unique local neighborhoods, these descriptors typically fail to consider global context to resolve ambiguities that can occur locally when an image has multiple similar regions. This paper presents a feature descriptor that augments SIFT with a global context vector that adds curvilinear shape information from a much larger neighborhood, thus reducing mismatches when multiple local descriptors are similar. It also provides a more robust method for handling 2D nonrigid transformations since points are more effectively matched individually at a global scale rather than constraining multiple matched points to be mapped via a planar homography. We have tested our technique on various images and compare matching accuracy between the SIFT descriptor with global context to that without.
290 citations
Cites methods from "Object recognition from local scale..."
...He then creates a Scale Invariant Feature Transform (SIFT) descriptor to match key points using a Euclidean distance metric in an efficient best-bin first algorithm where a match is rejected if the ratio of the best and second best matches is greater than a threshold....
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...The SIFT (Scale Invariant Feature Transform) [9,10] has been shown to perform better than other local descriptors [13]....
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...This paper presents a feature descriptor that combines a local SIFT descriptor [9] with a global context vector similar to shape contexts [2]....
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19 Dec 2005
TL;DR: A quantitative theory to account for the computations performed by the feedforward path of the ventral stream of visual cortex and the local circuits implementing them and it is shown that a model instantiating the theory is capable of performing recognition on datasets of complex images at the level of human observers in rapid categorization tasks.
Abstract: : We describe a quantitative theory to account for the computations performed by the feedforward path of the ventral stream of visual cortex and the local circuits implementing them. We show that a model instantiating the theory is capable of performing recognition on datasets of complex images at the level of human observers in rapid categorization tasks. We also show that the theory is consistent with (and in some case has predicted) several properties of neurons in V1, V4, IT and PFC. The theory seems sufficiently comprehensive, detailed and satisfactory to represent an interesting challenge for physiologists and modelers: either disprove its basic features or propose alternative theories of equivalent scope. The theory suggests a number of open questions for visual physiology and psychophysics.
289 citations
Cites methods from "Object recognition from local scale..."
...This allowed for a more rigorous comparison at the representation-level (model C2b units vs. computer vision features such as SIFT [Lowe, 1999], component-experts [Heisele et al., 2001; Fergus et al., 2003; Fei-Fei et al., 2004], or fragments [Ullman et al., 2002; Torralba et al., 2004]) rather…...
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TL;DR: The first machine learning method for ground truthing a video is proposed, based on a multi-resolution convolutional neural network with a cascaded architecture, for segmenting foreground moving objects pictured in surveillance videos.
288 citations
06 May 2013
TL;DR: This paper presents a system architecture, implemented prototype, and initial experimental data for a cloud-based robot grasping system that incorporates a Willow Garage PR2 robot with onboard color and depth cameras, Google's proprietary object recognition engine, the Point Cloud Library for pose estimation, Columbia University's GraspIt! toolkit and OpenRAVE for 3D grasping and the prior approach to sampling-based grasp analysis to address uncertainty in pose.
Abstract: Rapidly expanding internet resources and wireless networking have potential to liberate robots and automation systems from limited onboard computation, memory, and software. “Cloud Robotics” describes an approach that recognizes the wide availability of networking and incorporates open-source elements to greatly extend earlier concepts of “Online Robots” and “Networked Robots”. In this paper we consider how cloud-based data and computation can facilitate 3D robot grasping. We present a system architecture, implemented prototype, and initial experimental data for a cloud-based robot grasping system that incorporates a Willow Garage PR2 robot with onboard color and depth cameras, Google's proprietary object recognition engine, the Point Cloud Library (PCL) for pose estimation, Columbia University's GraspIt! toolkit and OpenRAVE for 3D grasping and our prior approach to sampling-based grasp analysis to address uncertainty in pose. We report data from experiments in recognition (a recall rate of 80% for the objects in our test set), pose estimation (failure rate under 14%), and grasping (failure rate under 23%) and initial results on recall and false positives in larger data sets using confidence measures.
288 citations
Cites background from "Object recognition from local scale..."
...Object recognition is a very well-studied topic in computer vision, and there has been significant progress in many aspects of the problem, from the design of features that are invariant to translation, scaling, and rotation [24], to models for the problem [31], as well as links to other fields [12]....
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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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