A common set of perceptual observables for grouping, figure-ground discrimination, and texture classification

Hoogs, Anthony; Collins, Roderic; Kaucic, R.; Mundy, Joseph L.

doi:10.1109/tpami.2003.1190572

Cited by 22 publications

(14 citation statements)

References 32 publications

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“…In particular, several different descriptors have been proposed, including Haralick features [6], Gabor filters [12] and histograms of texture elements (textons) [7,8,14]. In this paper we consider Local Binary Patterns [10], which is one of the most used approaches in practical applications [11]; it has the advantage of simple implementation and fast performance, and has been successfully used in a wide variety of different fields.…”

Section: Previous Workmentioning

confidence: 99%

Robust Texture Recognition Using Credal Classifiers

Corani

Giusti

Migliore

et al. 2010

Procedings of the British Machine Vision Conference 2010

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Texture classification is used for many vision systems; in this paper we focus on improving the reliability of the classification through the so-called imprecise (or credal) classifiers, which suspend the judgment on the doubtful instances by returning a set of classes instead of a single class. Our view is that on critical instances it is more sensible to return a reliable set of classes rather than an unreliable single class. We compare the traditional naive Bayes classifier (NBC) against its imprecise counterpart, the naive credal classifier (NCC); we consider a standard classification dataset, when the problem is made progressively harder by introducing different image degradations or by providing smaller training sets. Experiments show that on the instances for which NCC returns more classes, NBC issues in fact unreliable classifications; the indeterminate classifications of NCC preserve reliability but at the same time also convey significant information, reducing the set of possible classes (on most critical instances) from 24 to some 2-3.

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Section: Previous Workmentioning

confidence: 99%

Robust Texture Recognition Using Credal Classifiers

Corani

Giusti

Migliore

et al. 2010

Procedings of the British Machine Vision Conference 2010

View full text Add to dashboard Cite

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“…Yu further improved the results by building a rotated averaging wedge approach that was more robust to the effects of noise while being more discriminating at differing scale values [19]. All of the aforementioned approaches suffer from the same problem as the ODF signature that they produce does not necessarily indicate the underlying pattern sought and that they tend to describe with much information, what diffusion approaches have the potential of describing in a more succinct fashion [5,7]. Also, for the Gabor, end-point and the wedge filter approaches, there are several possible parameter variations, namely the different frequency parameter choices, which must be accounted for, requiring the use of a large bank of such filters to properly account for the various possible patterns [4,12,15].…”

Section: Convolution-based Approachesmentioning

confidence: 99%

An Asymmetrical Diffusion Framework for Junction Analysis

Arseneau

Cooperstock

2006

Procedings of the British Machine Vision Conference 2006

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The diffusion framework exhibits many promising properties for the purposes of junction analysis. In its most common form, images are diffused either isotropically or with respect to gradient information in an anisotropical fashion. This information is then collected into an orientational distribution function (ODF) and the resulting features are modeled as 'X', 'Y' or 'T'-shaped junctions. Specific to the spatio-temporal domain, and in particular, a 2D spatio-temporal slice, points of kinetic-based occlusion are identified by T-junctions while points of kinetic-transparency form X-junctions. The challenge is that most forms of diffusion are symmetric in their representation and are unable to properly distinguish between these two junction types. This work proposes to diffuse information asymmetrically and investigates the differences between weighting the iterative diffusion isotropically versus as an ODF-shaped region of influence function.

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“…In order to simplify object detection and to reduce computational cost, many systems (e.g. [3]) limit the recognition to specific classes of objects. In these cases, a priori knowledge permits to select the most descriptive features for the objects at hand and to circumscribe the search space.…”

Section: Introductionmentioning

confidence: 99%

Object Recognition Technique based on Level Set Method and Neural Network

Pawar¹,

Talbar²

2012

IJCA

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The Object recognition is the task of finding and labeling parts of a two-dimensional (2D) image of a scene that correspond to objects in the scene. In this paper, we have proposed an efficient approach using level set method for extracting object shape contour and convex hull as a shape invariant features to the Feed forward Neural Network classifier for object recognition. We extracted the shape contour by level set method. Then, we have obtained invariant shape feature, convex hull of the objects. This convex hull set serves as a pattern for the Neural Network. Initially Feed forward neural network trained on the odd data set and tested on even data set. Our approach is evaluated on the Amsterdam Library of Object Images collection, a large collection of object images containing 1000 objects recorded under various imaging circumstances. The experimental results clearly demonstrate that our approach significantly outperforms. The proposed method is shown to be effective under a wide variety of imaging conditions.

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A common set of perceptual observables for grouping, figure-ground discrimination, and texture classification

Cited by 22 publications

References 32 publications

Robust Texture Recognition Using Credal Classifiers

Robust Texture Recognition Using Credal Classifiers

An Asymmetrical Diffusion Framework for Junction Analysis

Object Recognition Technique based on Level Set Method and Neural Network

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