Catálogo de publicaciones - libros

Ants, bees and other social insects deposit pheromone (a type of chemical) in order to communicate between the members of their community. Pheromone that causes clumping or clustering behavior in a species and brings individuals into a closer proximity is called aggregation pheromone. This paper presents a novel method for image segmentation considering the aggregation behavior of ants. Image segmentation is viewed as a clustering problem which aims to partition a given set of pixels into a number of homogenous clusters/segments. At each location of data point representing a pixel an ant is placed; and the ants are allowed to move in the search space to find out the points with higher pheromone density. The movement of an ant is governed by the amount of pheromone deposited at different points of the search space. More the deposited pheromone, more is the aggregation of ants. This leads to the formation of homogenous groups of data. The proposed algorithm is evaluated on a number of images using different cluster validity measures. Results are compared with those obtained using and clustering algorithms and are found to be better.

The present article proposes a new neuro-fuzzy-fusion (NFF) method for combining the output of a set of fuzzy classifiers in a multiple classifier system (MCS) framework. In the proposed method the output of a set of classifiers (i.e., fuzzy class labels) are fed as input to a neural network, which performs the fusion task. The proposed fusion technique is tested on a set of remote sensing images and compared with existing techniques. Experimental study revealed the improved classification capability of the NFF based MCS as it yielded consistently better results.

There is increasing need for effective delineation of meaningfully different landforms due to the decreasing availability of experienced landform interpreters. Any procedure for automating the process of landform segmentation from satellite images offer the promise of improved consistency and reliality. We propose a hierarchical method for landform classification for classifying a wide variety of landforms. At stage 1 an image is classified as one of the three broad categories of terrain types in terms of its geomorphology, and these are: desertic/rann of kutch, coastal or fluvial. At stage 2, all different landforms within either desertic/rann of kutch , coastal or fluvial areas are identified using suitable processing. At the final stage, all outputs are fused together to obtain a final segmented output. The proposed technique is evaluated on large number of optical band satellite images that belong to aforementioned terrain types.

A central task in image processing is to find the region in the image corresponding to an entity. In a number of problems, the region takes the form of a collection of circles, tree crowns in remote sensing imagery; cells in biological and medical imagery. In [1], a model of such regions, the ‘gas of circles’ model, was developed based on higher-order active contours, a recently developed framework for the inclusion of prior knowledge in active contour energies. However, the model suffers from a defect. In [1], the model parameters were adjusted so that the circles were local energy minima. Gradient descent can become stuck in these minima, producing phantom circles even with no supporting data. We solve this problem by calculating, via a Taylor expansion of the energy, parameter values that make circles into energy inflection points rather than minima. As a bonus, the constraint halves the number of model parameters, and severely constrains one of the two that remain, a major advantage for an energy-based model. We use the model for tree crown extraction from aerial images. Experiments show that despite the lack of parametric freedom, the new model performs better than the old, and much better than a classical active contour.

In the Kalman filter, the state dynamics is specified by the state equation while the measurement equation characterizes the likelihood. In this paper, we propose a generalized methodology of specifying state dynamics using the conditional density of the states given its neighbors without explicitly defining the state equation. In other words, the typically strict linear constraint on the state dynamics imposed by the state equation is relaxed by specifying the conditional density function and using it as the prior in predicting the state. Based on the above idea, we propose a sampling-based Kalman Filter (KF) for the image estimation problem. The novelty in our approach lies in the fact that we compute the mean and covariance of the prior (possibly non-Gaussian) by importance sampling. These apriori mean and covariance are fed to the update equations of the KF to estimate the aposteriori estimates of the state. We show that the estimates obtained by the proposed strategy are superior to those obtained by the traditional Kalman filter that uses the auto-regressive state model.

Boundary detection in natural images is a fundamental problem in many computer vision tasks. In this paper, we argue that early stages in primary visual cortex provide ample information to address the boundary detection problem. In other words, such as object and region boundaries can be extracted using captured by the receptive fields. The anatomy of visual cortex and psychological evidences are studied to identify some of the important underlying computational principles for the boundary detection task. A scheme for boundary detection based on these principles is developed and presented. Results of testing the scheme on a benchmark set of natural images, with associated human marked boundaries, show the performance to be quantitatively competitive with existing computer vision approaches.

In this paper we discuss the speckle reduction in images with the recently proposed Wavelet Embedded Anisotropic Diffusion (WEAD) and Wavelet Embedded Complex Diffusion (WECD). Both these methods are improvements over anisotropic and complex diffusion by adding wavelet based bayes shrink in its second stage. Both WEAD and WECD produces excellent results when compared with the existing speckle reduction filters. The comparative analysis with other methods were mainly done on the basis of Structural Similarity Index Matrix (SSIM) and Peak Signal to Noise Ratio (PSNR). The visual appearance of the image is also considered.

Linear filtering of images is usually performed in the spatial domain using the linear convolution operation. In the case of images stored in the block DCT space, the linear filtering is usually performed on the sub-image obtained by applying an inverse DCT to the block DCT data. However, this results in severe blocking artifacts caused by the boundary conditions of individual blocks as pixel values outside the boundaries of the blocks are assumed to be zeros. To get around this problem, we propose to use the symmetric convolution operation in such a way that the operation becomes equivalent to the linear convolution operation in the spatial domain. This is achieved by operating on larger block sizes in the transform domain. We demonstrate its applications in image sharpening and removal of blocking artifacts directly in the compressed domain.

In this paper, we propose a novel algorithm for robust image watermarking by inserting a single copy of the watermark. Usually, robustness is achieved by embedding multiple copies of the watermark.The proposed method locates and watermarks ‘significant pixels’ of the image in the wavelet domain. Here, the amount of distortion at every pixel is kept within the threshold of perception by adopting ideas from Human Visual System (HVS) model. The robustness of the proposed method was verified under six different attacks. To verify the advantage of selecting the significant pixels over the highest absolute coefficients, simulations were performed under both cases with quantization of pixels as per HVS model. Simulation results show the advantage of selecting the ‘significant pixels’ for watermarking gray images as well as color images.

Evidences favouring a dynamic receptive field model of retinal ganglion cells and the cells of Lateral Geniculate Nucleus (LGN) have been presented based on the perception of some brightness-contrast illusions. Of the different kinds of such stimuli, four, namely the Simultaneous Brightness-contrast, the White effect, the DeValois and DeValois checkerboard illusion and the Howe stimulus have been chosen to establish this model. The present approach attempts to carry forward the works that look upon visual perception as a step-by-step information processing task rather than a rule-based Gestalt approach and provides a new biologically inspired tool for simultaneous smoothing and edge enhancement in image processing.