Accurate classification of Pap smear images becomes the challenging task in medical image processing. This can be improved in two ways. One way is by selecting suitable well defined specific features and the other is by selecting the best classifier. This paper presents a nominated texture based cervical cancer (NTCC) classification system which classifies the Pap smear images into any one of t...

In order to improve the classification accuracy of the high resolution remote sensing images, the algorithm based on texture features is proposed in this paper. The image is classified initially by texture features which are derived from gray level co-occurrence matrices. And the suitable weights of these features are given to form the feature vector. Secondly, the edge fining method, which can...

-Texture analysis is significant field in image processing and computer vision. Shape and texture has groovy correlation and texture can be defined by shape descriptor. Three individual approach Zernike moment, which is orthogonal shape signifier, Gabor features and Haralick features are utilized for texture analysis. Another approach is applied by aggregating all the features for texture analy...

We present a new method for texture image classification using Basic Gray Level Aura Matrices (BGLAMs). Given an unseen texture image, our approach classifies it into one of the pre-learned classes, each of which is characterized using BGLAMs. There are two stages in our algorithm: a learning stage and a classification stage. In the first stage, models of texture classes are learned from the BG...

This paper introduces a new classification scheme called “open-ended texture classification.” The standard approach for texture classification is to use a closed n-class classifier based on the Bayesian paradigm. These perform supervised classification, whereby all the texture classes have to be predefined. We propose a new texture classification scheme, one that does not require a complete set...

Iris texture is commonly thought to be highly discriminative between eyes and stable over individual lifetime, which makes iris particularly suitable for personal identification. However, iris texture also contains more information related to genes, which has been demonstrated by successful use of ethnic and gender classification based on iris. In this paper, we propose a novel ethnic classific...

We introduce a view-dependent level of detail rendering system designed with modern GPU architectures in mind. Our approach keeps the data in static buffers and geomorphs between different LODs using per-vertex weights for seamless transition. Our method is the first out-of-core system to support texture mapping, including a mechanism for texture LOD. This approach completely avoids LOD pops an...

This paper looks at the nonparametric, multiscale, Markov Random Field (MRF) model and its application in classifying the MeasTex Test Suite. The MeasTex Test Suite is a standard by which various texture classification algorithms can be compared. Typically, todays texture classification algorithms have been based on supervised classification, whereby all the classification classes have been pre...

Assistive robotic applications require systems capable of interaction in the human world, a workspace which is highly dynamic and not always predictable. Mobile assistive devices face the additional and complex problem of when and if intervention should occur; therefore before any trajectory assistance is given, the robotic device must know where it is in real-time, without unnecessary disrupti...

We propose a canonical model for object classes in aerial images. This model is motivated by the observation that geographic regions of interest are characterized by collections of texture motifs corresponding to the geographic processes that generate them. We show that this model is effective in learning the common texture themes, or motifs, of the object classes.