Local grayvalue invariants for image retrieval

Schmid, Cordelia; Mohr, Roger

doi:10.1109/34.589215

Cited by 1,228 publications

(788 citation statements)

References 35 publications

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“…The detection and representation of image features are essential to many applications, such as image retrieval and recognition of object, texture, and scene categories, as they are more resistant to partial occlusion, background clutter, and viewpoint changes [14, 28]. This has motivated the development of several scale- and rotation-invariant local image detectors and descriptors.…”

Section: Proposed Methodsmentioning

confidence: 99%

Robust and Effective Component-Based Banknote Recognition for the Blind

Hasanuzzaman

Yang

Tian

2012

IEEE Trans. Syst., Man, Cybern. C

106

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We develop a novel camera-based computer vision technology to automatically recognize banknotes for assisting visually impaired people. Our banknote recognition system is robust and effective with the following features: 1) high accuracy: high true recognition rate and low false recognition rate, 2) robustness: handles a variety of currency designs and bills in various conditions, 3) high efficiency: recognizes banknotes quickly, and 4) ease of use: helps blind users to aim the target for image capture. To make the system robust to a variety of conditions including occlusion, rotation, scaling, cluttered background, illumination change, viewpoint variation, and worn or wrinkled bills, we propose a component-based framework by using Speeded Up Robust Features (SURF). Furthermore, we employ the spatial relationship of matched SURF features to detect if there is a bill in the camera view. This process largely alleviates false recognition and can guide the user to correctly aim at the bill to be recognized. The robustness and generalizability of the proposed system is evaluated on a dataset including both positive images (with U.S. banknotes) and negative images (no U.S. banknotes) collected under a variety of conditions. The proposed algorithm, achieves 100% true recognition rate and 0% false recognition rate. Our banknote recognition system is also tested by blind users.

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Section: Proposed Methodsmentioning

confidence: 99%

Robust and Effective Component-Based Banknote Recognition for the Blind

Hasanuzzaman

Yang

Tian

2012

IEEE Trans. Syst., Man, Cybern. C

106

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“…In contrast, object recognition based on dense local "invariant" image features have shown a lot of success recently [8,11,14,19,1,3,6,16,7] for objects with large withinclass variability in shape and appearance. In such approaches objects are modeled as a collection of parts or local features and the recognition is based on inferring object class based on similarity in parts' appearance and their spatial arrangement.…”

Section: Introductionmentioning

confidence: 99%

A Statistically Selected Part-Based Probabilistic Model for Object Recognition

Zhao

Elgammal

2006

Advances in Machine Vision, Image Processing, and Pattern Analysis

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Abstract. In an object recognition task where an image is represented as a constellation of image patches, often many patches correspond to the cluttered background. If such patches are used for object class recognition, they will adversely affect the recognition rate. In this paper, we present a statistical method for selecting the image patches which characterize the target object class and are capable of discriminating between the positive images containing the target objects and the complementary negative images. This statistical method select those images patches from the positive images which, when used individually, have the power of discriminating between the positive and negative images in the evaluation data. Another contribution of this paper is the part-based probabilistic method for object recognition. This Bayesian approach uses a common reference frame instead of reference patch to avoid the possible occlusion problem. We also explore different feature representation using PCA an 2D PCA. The experiment demonstrates our approach has outperformed most of the other known methods on a popular benchmark data set while approaching the best known results.

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“…Concretely, we propose to capture the appearance of salient surface patches using local image descriptors that are invariant under affine transformations of the spatial domain [18,24] and of the brightness signal [20], and to capture their spatial relationships using multi-view geometric constraints related to those studied in the structure from motion literature [39]. This representation is directly related to a number of recent schemes for combining the local surface appearance at "interest points" [12] with geometric constraints in tasks such as wide-baseline stereo matching [44], image retrieval [36], and object recognition [20]. These methods normally either require storing a large number of views for each object, or limiting the range of admissible viewpoints.…”

Section: Introductionmentioning

confidence: 99%

3D Object Modeling and Recognition from Photographs and Image Sequences

Rothganger

Lazebnik

Schmid

et al. 2006

Toward Category-Level Object Recognition

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Abstract. This chapter proposes a representation of rigid three-dimensional (3D) objects in terms of local affine-invariant descriptors of their images and the spatial relationships between the corresponding surface patches. Geometric constraints associated with different views of the same patches under affine projection are combined with a normalized representation of their appearance to guide the matching process involved in object modeling and recognition tasks. The proposed approach is applied in two domains: (1) Photographs -models of rigid objects are constructed from small sets of images and recognized in highly cluttered shots taken from arbitrary viewpoints. (2) Video -dynamic scenes containing multiple moving objects are segmented into rigid components, and the resulting 3D models are directly matched to each other, giving a novel approach to video indexing and retrieval.

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Local grayvalue invariants for image retrieval

Cited by 1,228 publications

References 35 publications

Robust and Effective Component-Based Banknote Recognition for the Blind

Robust and Effective Component-Based Banknote Recognition for the Blind

A Statistically Selected Part-Based Probabilistic Model for Object Recognition

3D Object Modeling and Recognition from Photographs and Image Sequences

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