K. Revathy

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The application of fractal geometry to musical signals and instrumental recognition system is not been widely experimented. The fractal dimension D is a very important characteristic of fractals useful for segmentation. This paper introduces an instrument identification system, which uses fractal dimension for segmentation of audio signals. This research is(More)
Automatic animal sound classification and retrieval is very helpful for bioacoustic and audio retrieval applications. In this paper we propose a system to define and extract a set of acoustic features from all archived wild animal sound recordings that is used in subsequent feature selection, classification and retrieval tasks. The database consisted of(More)
Multiple Classifier fusion is an efficient and widely useful method of improving system performance. The classifier fusion approach to musical instrument recognition system is not been widely experimented. This paper explores in depth a classifier combination approach for the instrument classification task, studied over a diverse classifier pool, which(More)
— Texture analysis plays an increasingly important role in computer vision. Since the textural properties of images appear to carry useful information for discrimination purposes, it is important to develop significant features for texture. Various texture feature extraction methods include those based on gray-level values, transforms, auto correlation etc.(More)
for the recognition of isolated handwritten Malayalam (one of the south Indian languages) characters. The key idea is that count of zero crossings of wavelet transform coefficients of an image characterize it. A set of 3000 images of 20 selected characters are used for classification. All images are normalized to have same height, binarized and inverted.(More)
—This paper deals with different techniques for registration and fusion of remote sensed images. In this work the lower spatial resolution multispectral and higher resolution panchromatic images of SPOT satellite are used. These images are registered using a registration algorithm that combines a simple yet powerful search strategy based on stochastic(More)
Accurate classification of images is essential for the analysis of mammograms in computer aided diagnosis of breast cancer. We propose a new approach to classify mammogram images based on fractal features. Given a mammogram image, we first eliminate all the artifacts and extract the salient features such as Fractal Dimension (FD) and Fractal Signature (FS).(More)
This paper presents a novel method based on fractal features for the classification of mammogram images. For recognition of regions and objects in the natural scenes, there is always a need for features, which are invariant, and they provide a good set of descriptive values for the region. There are numerous methods available to estimate parameters from the(More)