Object recognition from local scale-invariant features

doi:10.1109/ICCV.1999.790410

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Object recognition from local scale-invariant features

Proceedings Article•DOI•

Object recognition from local scale-invariant features

David G. Lowe¹•Institutions (1)

University of British Columbia¹

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.

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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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Journal Article•DOI•

Robust point matching method for multimodal retinal image registration

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Gang Wang¹, Zhicheng Wang¹, Yufei Chen¹, Weidong Zhao¹•Institutions (1)

Tongji University¹

01 May 2015-Biomedical Signal Processing and Control

TL;DR: A registration framework based on speed up robust feature (SURF) detector, PIIFD and robust point matching, called SURF–PIIFD–RPM, which outperforms existing algorithms, and it is quite robust to outliers.

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98 citations

Posted Content•

Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks

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Shivang Agarwal, Jean Ogier Du Terrail, Frédéric Jurie

06 Sep 2018-arXiv: Computer Vision and Pattern Recognition

TL;DR: This article reviews the recent literature on object detection with deep CNN, in a comprehensive way, and provides an in-depth view of these recent advances.

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Abstract: Object detection-the computer vision task dealing with detecting instances of objects of a certain class (e.g., 'car', 'plane', etc.) in images-attracted a lot of attention from the community during the last 5 years. This strong interest can be explained not only by the importance this task has for many applications but also by the phenomenal advances in this area since the arrival of deep convolutional neural networks (DCNN). This article reviews the recent literature on object detection with deep CNN, in a comprehensive way, and provides an in-depth view of these recent advances. The survey covers not only the typical architectures (SSD, YOLO, Faster-RCNN) but also discusses the challenges currently met by the community and goes on to show how the problem of object detection can be extended. This survey also reviews the public datasets and associated state-of-the-art algorithms.

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98 citations

Cites methods from "Object recognition from local scale..."

...…initially relied on mechanisms to align a 2D/3D model of the object on the image using simple features, such as edges [Lin et al., 2007], key-points [Lowe, 1999] or templates [Pentland et al., 1994], the arrival of Machine Learning (ML) was the first revolution which had shaken up the area....
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...While the first object detectors initially relied on mechanisms to align a 2D/3D model of the object on the image using simple features, such as edges [217], key-points [224] or templates [278], the arrival of Machine Learning (ML) was the first revolution which had shaken up the area....
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Journal Article•DOI•

Automated Mitosis Detection Using Texture, SIFT Features and HMAX Biologically Inspired Approach

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Humayun Irshad, Sepehr Jalali¹, Ludovic Roux, Daniel Racoceanu, Lim Joo Hwee, Gilles Le Naour, Frédérique Capron - Show less +3 more•Institutions (1)

National University of Singapore¹

01 Jan 2013-Journal of Pathology Informatics

TL;DR: An approach that assists pathologists in automated mitosis detection and counting is proposed, which is based on the most favorable texture features combination and examines the separability between different channels of color space.

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98 citations

Cites methods from "Object recognition from local scale..."

...Method 2: Scale Invariant Feature Transform Scale Invariant Feature Transform (SIFT) feature extraction method is a well‐known method which has produced promising results in classification tasks.[11] Here we investigate its application in classification of mitosis patch....
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Proceedings Article•DOI•

Optasia: A Relational Platform for Efficient Large-Scale Video Analytics

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Yao Lu¹, Aakanksha Chowdhery¹, Srikanth Kandula¹•Institutions (1)

Microsoft¹

05 Oct 2016

TL;DR: Optasia, a dataflow system that employs relational query optimization to efficiently process queries on video feeds from many cameras, shows high accuracy with many fold improvements in query completion time and resource usage relative to existing systems.

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Abstract: Camera deployments are ubiquitous, but existing methods to analyze video feeds do not scale and are error-prone. We describe Optasia, a dataflow system that employs relational query optimization to efficiently process queries on video feeds from many cameras. Key gains of Optasia result from modularizing vision pipelines in such a manner that relational query optimization can be applied. Specifically, Optasia can (i) de-duplicate the work of common modules, (ii) auto-parallelize the query plans based on the video input size, number of cameras and operation complexity, (iii) offers chunk-level parallelism that allows multiple tasks to process the feed of a single camera. Evaluation on traffic videos from a large city on complex vision queries shows high accuracy with many fold improvements in query completion time and resource usage relative to existing systems.

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98 citations

Cites background from "Object recognition from local scale..."

...Traffic Violation Keypoint Extraction - SIFT Extract SIFT keypoints in the given image region [50]....
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Journal Article•DOI•

Eye pupil localization with an ensemble of randomized trees

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Nenad Markuš¹, Miroslav Frljak¹, Igor S. Pandźić¹, Jörgen Ahlberg², Robert Forchheimer² - Show less +1 more•Institutions (2)

University of Zagreb¹, Linköping University²

01 Feb 2014-Pattern Recognition

TL;DR: A framework for eye pupil localization based on an ensemble of randomized regression trees that compares well with reported state-of-the-art and runs in real-time on hardware with limited processing power, such as mobile devices.

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97 citations

Cites methods from "Object recognition from local scale..."

...Additionally, they extend their approach by extracting a SIFT [24] vector for each candidate location and match it with examples in - a database to obtain the final decision....
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...Additionally, they extend their approach by extracting a SIFT [24] vector for each candidate location and match it with examples in a database to obtain the final decision....
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References

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Journal Article•DOI•

Color indexing

[...]

Michael J. Swain, Dana H. Ballard

01 Nov 1991-International Journal of Computer Vision

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.

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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.

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5,672 citations

Journal Article•DOI•

Generalizing the hough transform to detect arbitrary shapes

[...]

Dana H. Ballard¹•Institutions (1)

University of Rochester¹

01 Jan 1987-Pattern Recognition

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.

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4,310 citations

Journal Article•DOI•

Visual learning and recognition of 3-D objects from appearance

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Hiroshi Murase, Shree K. Nayar¹•Institutions (1)

Columbia University¹

01 Jan 1995-International Journal of Computer Vision

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.

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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.

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2,037 citations

Journal Article•DOI•

Local grayvalue invariants for image retrieval

[...]

Cordelia Schmid, Roger Mohr

01 May 1997-IEEE Transactions on Pattern Analysis and Machine Intelligence

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.

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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.

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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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Journal Article•DOI•

A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry

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Zhengyou Zhang, Rachid Deriche, Olivier Faugeras, Quang-Tuan Luong

15 Oct 1995-Artificial Intelligence

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.

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1,574 citations