Multichannel Gabor filters (MGFs) and Markov random fields (MRFs) are two common methods for texture classification. However, the two above methods make the implicit assumption that textures are acquired in the same viewpoint, which is unsuitable for rotation-invariant texture classification. In this paper, rotation-invariant (RI) texture features are developed based on MGF and MRF. A novel alg...

A Gabor filter based feature extraction scheme for texture classification is proposed. By using a novel set of circularly symmetric filters, rotation invariance is achieved. The scheme offers a high classification performance on textures at any orientation using both fewer features and a smaller area of analysis than most existing schemes. The performance of the scheme on noisy images is also i...

Texture analysis plays an important role in content-based image retrieval and other areas of image processing. It is often desirable for the texture classifier to be rotation and scale invariant. Furthermore, to enable real-time usage, it is desirable to perform the classification efficiently. Toward these goals, we propose several enhancements to the multiresolution Gabor analysis. The first i...

Pixel-by-pixel template matching is very time-consuming. For an input image of size N N × , and the template of size W W × , the computational complexity is O( 2 2 N W‧ ), given that the object orientations in both images are coincident. When we search an object with unknown orientation, the straightforward way to do template matching is to rotate the reference template in every possible orient...

The notion of valuation on convex sets can be considered as a generalization of the notion of measure, which is defined only on the class of convex compact sets. It is well-known that there are important and interesting examples of valuations on convex sets, which are not measures in the usual sense as, for example, the mixed volumes. Basic definitions and some classical examples are discussed ...

A method for object recognition, invariant under translation, rotation, and scaling, is addressed. The first step of the method (preprocessing) takes into account the invariant properties of the normalized moment of inertia and a novel coding that extracts topological object characteristics. The second step (recognition) is achieved by using a holographic nearest-neighbor algorithm (HNN), in wh...

In this paper, we propose a continuous-domain version of principal-component analysis, with the constraint that the underlying family of templates appears at arbitrary orientations. We show that the corresponding principal components are steerable. Our method can be used for designing steerable filters so that they best approximate a given collection of reference templates. We apply this framew...

The emergence of multimedia technology and the rapid growth in the number and type of multimedia assets controlled by several entities, yet because the increasing range of image and video documents showing on the Internet, have attracted vital analysis efforts in providing tools for effective retrieval and management of visual data. So the need for image retrieval system arose. Out of many exis...

In this paper, we propose a new type of local binary pattern (LBP)-based feature, called Rotation Invariant Co-occurrence among adjacent LBPs (RIC-LBP), which simultaneously has characteristics of rotation invariance and a high descriptive ability. LBP was originally designed as a texture description for a local region, called a micropattern, and has been extended to various types of LBP-based ...

We attempt to encode an image in a fashion that is only weakly dependent on rotation of objects within the image, as an expansion of Roger Grosse’s work on Translation Invariant sparse coding. Our approach is to specify only a small set of basis images, from which some reasonably large number rotated bases are calculated. The image is trained to this set of rotated bases, so that ultimately the...