Digital Image Processing

S. Mangijao Singh ¹, K. Hemachandran ¹

Affiliations
1 Department of Computer Science, Assam University, Silchar, IN

Abstract

Content based image retrieval (CBIR) systems are used for searching, retrieving and browsing of image databases. In this paper, we propose a color image retrieval method based on color moments. Many indexing techniques are based on global feature distributions. However, these global distributions have limited discriminating power because they are unable to capture local image information. To improve the discriminating power of color indexing techniques, we encode a minimal amount of spatial information in the index. First, an image is divided horizontally into three equal non overlapping regions. From each region in the image, we extract the first three moments (mean, variance and skewness) of the color distribution, from each color channel and store them in the index i.e., for a HSV color space, we store 27 floating point numbers per image. The similarity function which is used for retrieval is a weighted sum of the absolute differences between the corresponding moments. Our experiments demonstrate that the encoding of spatial information in the index significantly increases the discriminating power of the index compared to the color moment, based on global approach.

Keywords

HSV Color Space, Color Moment, Color Channel, Feature Extraction.

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S. Manimala ¹, K. Hemachandran ¹

Affiliations
1 Department of Computer Science, Assam University, Silchar, IN

Abstract

This paper introduces efficient and fast algorithms for unsupervised image segmentation, using low-level features such as color and texture. The proposed approach is based on the clustering technique, using (1) Lab color space and (2) the wavelet transformation technique. The input image is decomposed into two-dimensional Haar wavelets. The features vector, containing the information about the color and texture content for each pixel is extracted. These vectors are used as inputs for the k-means or fuzzy c-means clustering methods, for a segmented image whose regions are distinct from each other, according to color and texture characteristics. Experimental result shows that the proposed method is more efficient and achieves high computational speed.

Keywords

Image Segmentation, K-Means, Fuzzy C-Means, Wavelet Transform, Lab Color Space.

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Raju Rajkumar ¹, K. Hemachandran ²

Affiliations
1 Department of Computer Science, Assam University, IN
2 Department of Computer Science, Assam University, IN

Abstract

Most of the latent fingerprint images are incomplete with poor quality. Therefore to develop an automatic feature extraction of a latent fingerprint is a very challenging problem. A combination of different preprocessing stages will produce a better enhanced Latent fingerprint image. In the present study, an algorithm with a combination of FFT and Gaussian filter has been proposed and implement for image enhancement. The experimental results show that the enhanced images are acceptable for post-processing analysis.

Keywords

Latent Fingerprint, Primary Enhancement, Secondary Enhancement.

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