Human Ecology in India Some Historical Perspectives

Before the spread of extensive settled cultivation, the Indian subcontinent would have been inhabited by territorial hunter–gatherers and shifting cultivators with cultural traditions of prudent resource use. The disruption of closed material cycles by export of agricultural produce to centres of non-agricultural population would have weakened these traditions. Indeed, the fire-based sacrificial ritual and extensive agricultural settlements might have catalysed the destruction of forests and wildlife and the suppression of tribal peoples during the agricultural colonization of the Gangetic plains. Buddhism, Jainism and later the Hindu sects may have been responses to the need for a reassertion of ecological prudence once the more fertile lands were brought under cultivation. British rule radically changed the focus of the country's resource use pattern from production of a variety of biological resources for local consumption to the production of a few commodities largely for export. The resulting ecological squeeze was accompanied by disastrous famines and epidemics between the 1860s and the 1920s. The counterflows to tracts of intensive agriculture have reduced such disasters since independence. However, these are quite inadequate to balance the state-subsidized outflows of resources from rural hinterlands. These imbalances have triggered serious environmental degradation and tremendous overcrowding of the niche of agricultural labour and marginal cultivator all over the country.

Monumenttikuvista elämyskuviin : Helsinki matkaesitteiden kuvituksessa 1895 - 2005

The purpose of this study is to define how Helsinki has been presented in the pictures of tourist brochures and how their illustration has changed over time. Attention is also paid to the values and meanings that the pictures mediate, as well as their historical and societal connections. The pictures are approached as representations selectively interpreting and illustrating the reality of Helsinki, while constructing mental images of it. An iconological framework structures the study. It proceeds from the description and classification of the physical features towards an analysis of time- and culture-specific meanings. The emergence of meanings and their historical and cultural underpinnings are examined from the perspectives of humanistic geography, semiotics and constructionism. In the analysis attention is paid to the discourses, myths and ideologies that underlie the representations. Information on the physical features of the pictures and their changes is collected with a content analysis. The classified data consists of 1377 photographs. These pictures are collected from 75 tourist brochures of Helsinki that have been published between 1895 and 2005. The deeper meanings of the pictures are studied qualitatively, by paying attention to the mental images that the content elements and visual effects evoke. Research studies, contemporary literature and the texts of the tourist brochures are utilised in the interpretation of the meanings. There has been a permanent core to objects of the pictures during the entire study period. It has consisted mainly of sights that are located close to the Senate and Market Squares. In addition, marine elements have been popular. The area of Helsinki represented in the brochures has extended from the Senate Square towards Töölö Bay. Pictures of monumental buildings and statues have been complemented with snapshots and portraits. In the beginning of the 20th century, brochures were mainly produced for the travelling, educated elite. The style of the pictures was declaratory and educative. They aimed at medating an objective image of the reality that prevailed in Helsinki. In practice, the pictures were connected to a patriotic ideology and the corresponding myth of Finnishness. In the second half of the 20th century the improvement of the standard of living led to a democratisation of consumers and an increase in the tourism demand. Local culture and the everyday life of "ordinary" people became popular themes in the pictures. A new welfare ideology manifested itself in the people of the local residential areas, for instance. The increase in the cultural diversity has led to the recognition of new target groups, expecially since the 1980s. The human figures in the pictures have started to function as objects of identification and a means of constructing mental images. A pronounced emphasis on experience and individuality in the illustration of the tourist brochures mirrors the post-modern change and a new ideology based on consumption. The construction and consumption of the pictures in the tourist brochures is governed by the conventions of representation and interpretaion that are typical of the genre of tourist brochures. The pictures emphasize the perceived positive characteristics of Helsinki and thus construct a skewed view of the reality. However, consumers can knowingly use the pictures as a means of dreaming and detaching themselves from their everyday reality.

Strip based coding for large images using wavelets

Thanks to advances in sensor technology, today we have many applications (space-borne imaging, medical imaging, etc.) where images of large sizes are generated. Straightforward application of wavelet techniques for above images involves certain difficulties. Embedded coders such as EZW and SPIHT require that the wavelet transform of the full image be buffered for coding. Since the transform coefficients also require storing in high precision, buffering requirements for large images become prohibitively high. In this paper, we first devise a technique for embedded coding of large images using zero trees with reduced memory requirements. A 'strip buffer' capable of holding few lines of wavelet coefficients from all the subbands belonging to the same spatial location is employed. A pipeline architecure for a line implementation of above technique is then proposed. Further, an efficient algorithm to extract an encoded bitstream corresponding to a region of interest in the image has also been developed. Finally, the paper describes a strip based non-embedded coding which uses a single pass algorithm. This is to handle high-input data rates. (C) 2002 Elsevier Science B.V. All rights reserved.

Reconstructing Solid Model from 2D Scanned Images of Biological Organs for Finite Element Simulation

This work presents a methodology to reconstruct 3D biological organs from image sequences or other scan data using readily available free softwares with the final goal of using the organs (3D solids) for finite element analysis. The methodology deals with issues such as segmentation, conversion to polygonal surface meshes, and finally conversion of these meshes to 3D solids. The user is able to control the detail or the level of complexity of the solid constructed. The methodology is illustrated using 3D reconstruction of a porcine liver as an example. Finally, the reconstructed liver is imported into the commercial software ANSYS, and together with a cyst inside the liver, a nonlinear analysis performed. The results confirm that the methodology can be used for obtaining 3D geometry of biological organs. The results also demonstrate that the geometry obtained by following this methodology can be used for the nonlinear finite element analysis of organs. The methodology (or the procedure) would be of use in surgery planning and surgery simulation since both of these extensively use finite elements for numerical simulations and it is better if these simulations are carried out on patient specific organ geometries. Instead of following the present methodology, it would cost a lot to buy a commercial software which can reconstruct 3D biological organs from scanned image sequences.

A DCT Based Filtering of Biomedical Images

Image filtering techniques have numerous potential applications in biomedical imaging and image processing. The design of filters largely depends on the a-priori knowledge about the type of noise corrupting the image and image features. This makes the standard filters to be application and image specific. The most popular filters such as average, Gaussian and Wiener reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high frequency details making the image non-smooth. An integrated general approach to design filters based on discrete cosine transform (DCT) is proposed in this study for optimal medical image filtering. This algorithm exploits the better energy compaction property of DCT and re-arrange these coefficients in a wavelet manner to get the better energy clustering at desired spatial locations. This algorithm performs optimal smoothing of the noisy image by preserving high and low frequency features. Evaluation results show that the proposed filter is robust under various noise distributions.

Comparison of 10 Multi-Sensor Image Fusion Paradigms for IKONOS Images

Fusion of multi-sensor imaging data enables a synergetic interpretation of complementary information obtained by sensors of different spectral ranges. Multi-sensor data of diverse spectral, spatial and temporal resolutions require advanced numerical techniques for analysis and interpretation. This paper reviews ten advanced pixel based image fusion techniques – Component substitution (COS), Local mean and variance matching, Modified IHS (Intensity Hue Saturation), Fast Fourier Transformed-enhanced IHS, Laplacian Pyramid, Local regression, Smoothing filter (SF), Sparkle, SVHC and Synthetic Variable Ratio. The above techniques were tested on IKONOS data (Panchromatic band at 1 m spatial resolution and Multispectral 4 bands at 4 m spatial resolution). Evaluation of the fused results through various accuracy measures, revealed that SF and COS methods produce images closest to corresponding multi-sensor would observe at the highest resolution level (1 m).

Hierarchical Clustering Algorithm for Land Cover Mapping Using Satellite Images

This paper presents hierarchical clustering algorithms for land cover mapping problem using multi-spectral satellite images. In unsupervised techniques, the automatic generation of number of clusters and its centers for a huge database is not exploited to their full potential. Hence, a hierarchical clustering algorithm that uses splitting and merging techniques is proposed. Initially, the splitting method is used to search for the best possible number of clusters and its centers using Mean Shift Clustering (MSC), Niche Particle Swarm Optimization (NPSO) and Glowworm Swarm Optimization (GSO). Using these clusters and its centers, the merging method is used to group the data points based on a parametric method (k-means algorithm). A performance comparison of the proposed hierarchical clustering algorithms (MSC, NPSO and GSO) is presented using two typical multi-spectral satellite images - Landsat 7 thematic mapper and QuickBird. From the results obtained, we conclude that the proposed GSO based hierarchical clustering algorithm is more accurate and robust.

Quasi-based hierarchical clustering for land cover mapping using satellite images

This paper presents an improved hierarchical clustering algorithm for land cover mapping problem using quasi-random distribution. Initially, Niche Particle Swarm Optimization (NPSO) with pseudo/quasi-random distribution is used for splitting the data into number of cluster centers by satisfying Bayesian Information Criteria (BIC). Themain objective is to search and locate the best possible number of cluster and its centers. NPSO which highly depends on the initial distribution of particles in search space is not been exploited to its full potential. In this study, we have compared more uniformly distributed quasi-random with pseudo-random distribution with NPSO for splitting data set. Here to generate quasi-random distribution, Faure method has been used. Performance of previously proposed methods namely K-means, Mean Shift Clustering (MSC) and NPSO with pseudo-random is compared with the proposed approach - NPSO with quasi distribution(Faure). These algorithms are used on synthetic data set and multi-spectral satellite image (Landsat 7 thematic mapper). From the result obtained we conclude that use of quasi-random sequence with NPSO for hierarchical clustering algorithm results in a more accurate data classification.