Document Image Segmentation and Compression using Artificial Neural Networks and Evolutionary Methods

The work is intended to study the following important aspects of document image processing and develop new methods. (1) Segmentation ofdocument images using adaptive interval valued neuro-fuzzy method. (2) Improving the segmentation procedure using Simulated Annealing technique. (3) Development of optimized compression algorithms using Genetic Algorithm and parallel Genetic Algorithm (4) Feature extraction of document images (5) Development of IV fuzzy rules. This work also helps for feature extraction and foreground and background identification. The proposed work incorporates Evolutionary and hybrid methods for segmentation and compression of document images. A study of different neural networks used in image processing, the study of developments in the area of fuzzy logic etc is carried out in this work

Motion Segmentation And Meanshift Assisted Contour Refinement For Airborne Video

This paper presents methods for moving object detection in airborne video surveillance. The motion segmentation in the above scenario is usually difficult because of small size of the object, motion of camera, and inconsistency in detected object shape etc. Here we present a motion segmentation system for moving camera video, based on background subtraction. An adaptive background building is used to take advantage of creation of background based on most recent frame. Our proposed system suggests CPU efficient alternative for conventional batch processing based background subtraction systems. We further refine the segmented motion by meanshift based mode association.

Automatic Segmentation Framework for Primary Tumors From Brain MRIs Using Morphological Filtering Techniques

This paper describes a novel framework for automatic segmentation of primary tumors and its boundary from brain MRIs using morphological filtering techniques. This method uses T2 weighted and T1 FLAIR images. This approach is very simple, more accurate and less time consuming than existing methods. This method is tested by fifty patients of different tumor types, shapes, image intensities, sizes and produced better results. The results were validated with ground truth images by the radiologist. Segmentation of the tumor and boundary detection is important because it can be used for surgical planning, treatment planning, textural analysis, 3-Dimensional modeling and volumetric analysis

A semi-automatic method for segmentation and 3D modeling of glioma tumors from brain MRI

This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the pre- operative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These seg- mented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming proc- ess and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of a medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radi- ologist.

Tourist Profiles and Characteristics vis-à-vis Market Segmentation of Ecotourism Destinations in Kerala

Kerala, a classic ecotourism destination in India, provides significant opportunities for livelihood options to the people who depend on the resources from the forest and those who live in difficult terrains. This article analyses the socio-demographic, psychographic and travel behavior patterns and its sub-characteristics in the background of foreign and domestic tourists. The data source for the article has been obtained from a primary survey of 350 randomly chosen tourists, 175 each from domestic and foreign tourists, visiting Kerala’s ecotourists destinations during August-December 2010-11. Several socio-demographic, psychographic and life style factors have been identified based on the inference from field survey. There is considerable divergence in most of the factors identified in the case of domestic and international tourists. Post-trip attributes like satisfaction and intentions to return show that the ecotourism destinations in Kerala have significant potential that can help communities in the region.

The role of Ken&Barbie in JAK/STAT signalling during development of Drosophila melanogaster

Cell-cell interactions during embryonic development are crucial in the co-ordination of growth, differentiation and maintenance of many different cell types. To achieve this co-ordination each cell must properly translate signals received from neighbouring cells, into spatially and temporally appropriate developmental responses. A surprisingly limited number of signal pathways are responsible for the differentiation of enormous variety of cell types. As a result, pathways are frequently 'reused' during development. Thus, in mammals the JAK/STAT pathway is required during early embryogenesis, mammary gland formation, hematopoiesis and, finally, plays a pivotal role in immune response. In the canonical way, the JAK/STAT pathway is represented by a transmembrane receptor associated with a Janus kinase (JAK), which upon stimulation by an extra-cellular ligand, phosphorylates itself, the receptor and, finally, the signal transducer and activator of transcription (STAT) molecules. Phosphorylated STATs dimerise and translocate to the nucleus where they activate transcription of target genes. The JAK/STAT pathway has been conserved throughout evolution, and all known components are present in the genome of Drosophila melanogaster. Besides hematopoietic and immunity functions, the pathway is also required during development for processes including embryonic segmentation, tracheal morphogenesis, posterior spiracle formation etc. This study describes Drosophila Ken&Barbie (Ken) as a selective regulator of JAK/STAT signalling. ken mutations identified in a screen for modulators of an eye overgrowth phenotype, caused by over-expression of the pathway ligand unpaired, also interact genetically with the pathway receptor domeless (dome) and the transcription factor stat92E. Over-expression of Ken can phenocopy developmental defects known to be caused by the loss of JAK/STAT signalling. These genetic interactions suggest that Ken may function as a negative regulator of the pathway. Ken has C-terminal Zn-finger domain, presumably for DNA binding, and N-terminal BTB/POZ domain, often found in transcriptional repressors. Using EGFP-fused construct expressed in vivo revealed nuclear accumulation of Ken. Therefore, it is proposed that Ken may act as a suppresser of STAT92E target genes. An in vitro assay, termed SELEX, determined that Ken specifically binds to a DNA sequence, with the essential for DNA recognition core overlapping that of STAT92E. This interesting observation suggests that not all STAT92E sites may also allow Ken binding. Strikingly, when effects of ectopic Ken on the expression of putative JAK/STAT pathway target genes were examined, only a subset of the genes tested, namely vvl, trh and kni, were down-regulated by Ken, whereas some others, such as eve and fj, appeared to be unresponsive. Further analysis of vvl, one of the genes susceptible to ectopic Ken, was undertaken. In the developing hindgut, expression of vvl is JAK/STAT pathway dependent, but remains repressed in the posterior spiracles, despite the stimulation of STAT92E by Upd in their primordia. Importantly, ken is also expressed in the developing posterior spiracles. Strikingly, up-regulation of vvl is observed in these tissues in ken mutant embryos. These imply that while ectopic Ken is sufficient to repress the expression of vvl in the hindgut, endogenous Ken is also necessary to prevent its activation in the posterior spiracles. It is therefore conceivable that ectopic vvl expression in the posterior spiracles of the ken mutants may be the result of de-repression of endogenous STAT92E activity. Another consequence of these observations is a fine balance that must exist between STAT92E and Ken activities. Apparently, endogenous level of Ken is sufficient to repress vvl, but not other, as yet unidentified, JAK/STAT pathway targets, whose presumable activation by STAT92E is required for posterior spiracle development as the embryos mutant for dome, the receptor of the pathway, show severe spiracle defects. These defects are also observed in the embryos mis-expressing Ken. Though it is possible that the posterior spiracle phenotype caused by higher levels of Ken results from a JAK/STAT pathway independent activity, it seems to be more likely that Ken acts in a dosage dependent manner, and extra Ken is able to further antagonise JAK/STAT pathway target genes. While STAT92E binding sites required for target gene expression have been poorly characterised, the existence of genome data allows the prediction of candidate STAT92E sites present in target genes promoters to be attempted. When a 6kb region containing the putative regulatory domains flanking the vvl locus are examined, only a single potential STAT92E binding site located 825bp upstream of the translational start can be detected. Strikingly, this site also includes a perfect Ken binding sequence. Such an in silico observation, though consistent with both Ken DNA binding assay in vitro and regulation of STAT92E target genes in vivo, however, requires further analysis. The JAK/STAT pathway is implicated in a variety of processes during embryonic and larval development as well as in imago. In each case, stimulation of the same transcription factor results in different developmental outcomes. While many potential mechanisms have been proposed and demonstrated to explain such pleiotropy, the present study indicates that Ken may represent another mechanism, with which signal transduction pathways are controlled. Ken selectively down-regulates a subset of potential target genes and so modifies the transcriptional profile generated by activated STAT92E - a mechanism, which may be partially responsible for differences in the morphogenetic processes elicited by JAK/STAT signalling during development.

Segmentation of Brain Tissue from Magnetic Resonance Images

Segmentation of medical imagery is a challenging problem due to the complexity of the images, as well as to the absence of models of the anatomy that fully capture the possible deformations in each structure. Brain tissue is a particularly complex structure, and its segmentation is an important step for studies in temporal change detection of morphology, as well as for 3D visualization in surgical planning. In this paper, we present a method for segmentation of brain tissue from magnetic resonance images that is a combination of three existing techniques from the Computer Vision literature: EM segmentation, binary morphology, and active contour models. Each of these techniques has been customized for the problem of brain tissue segmentation in a way that the resultant method is more robust than its components. Finally, we present the results of a parallel implementation of this method on IBM's supercomputer Power Visualization System for a database of 20 brain scans each with 256x256x124 voxels and validate those against segmentations generated by neuroanatomy experts.

Segmentation and Alignment of Speech and Sketching in a Design Environment

Sketches are commonly used in the early stages of design. Our previous system allows users to sketch mechanical systems that the computer interprets. However, some parts of the mechanical system might be too hard or too complicated to express in the sketch. Adding speech recognition to create a multimodal system would move us toward our goal of creating a more natural user interface. This thesis examines the relationship between the verbal and sketch input, particularly how to segment and align the two inputs. Toward this end, subjects were recorded while they sketched and talked. These recordings were transcribed, and a set of rules to perform segmentation and alignment was created. These rules represent the knowledge that the computer needs to perform segmentation and alignment. The rules successfully interpreted the 24 data sets that they were given.

Pre-Attentive Segmentation in the Primary Visual Cortex

Stimuli outside classical receptive fields have been shown to exert significant influence over the activities of neurons in primary visual cortexWe propose that contextual influences are used for pre-attentive visual segmentation, in a new framework called segmentation without classification. This means that segmentation of an image into regions occurs without classification of features within a region or comparison of features between regions. This segmentation framework is simpler than previous computational approaches, making it implementable by V1 mechanisms, though higher leve l visual mechanisms are needed to refine its output. However, it easily handles a class of segmentation problems that are tricky in conventional methods. The cortex computes global region boundaries by detecting the breakdown of homogeneity or translation invariance in the input, using local intra-cortical interactions mediated by the horizontal connections. The difference between contextual influences near and far from region boundaries makes neural activities near region boundaries higher than elsewhere, making boundaries more salient for perceptual pop-out. This proposal is implemented in a biologically based model of V1, and demonstrated using examples of texture segmentation and figure-ground segregation. The model performs segmentation in exactly the same neural circuit that solves the dual problem of the enhancement of contours, as is suggested by experimental observations. Its behavior is compared with psychophysical and physiological data on segmentation, contour enhancement, and contextual influences. We discuss the implications of segmentation without classification and the predictions of our V1 model, and relate it to other phenomena such as asymmetry in visual search.

Visual Segmentation without Classification in a Model of the Primary Visual Cortex

Stimuli outside classical receptive fields significantly influence the neurons' activities in primary visual cortex. We propose that such contextual influences are used to segment regions by detecting the breakdown of homogeneity or translation invariance in the input, thus computing global region boundaries using local interactions. This is implemented in a biologically based model of V1, and demonstrated in examples of texture segmentation and figure-ground segregation. By contrast with traditional approaches, segmentation occurs without classification or comparison of features within or between regions and is performed by exactly the same neural circuit responsible for the dual problem of the grouping and enhancement of contours.

A new approach to segmentation based on fusing circumscribed contours, region growing and clustering

One of the major problems in machine vision is the segmentation of images of natural scenes. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. The main contours of the scene are detected and used to guide the posterior region growing process. The algorithm places a number of seeds at both sides of a contour allowing stating a set of concurrent growing processes. A previous analysis of the seeds permits to adjust the homogeneity criterion to the regions's characteristics. A new homogeneity criterion based on clustering analysis and convex hull construction is proposed

Active regions for colour texture segmentation integrating region and boundary information

In this paper a colour texture segmentation method, which unifies region and boundary information, is proposed. The algorithm uses a coarse detection of the perceptual (colour and texture) edges of the image to adequately place and initialise a set of active regions. Colour texture of regions is modelled by the conjunction of non-parametric techniques of kernel density estimation (which allow to estimate the colour behaviour) and classical co-occurrence matrix based texture features. Therefore, region information is defined and accurate boundary information can be extracted to guide the segmentation process. Regions concurrently compete for the image pixels in order to segment the whole image taking both information sources into account. Furthermore, experimental results are shown which prove the performance of the proposed method

Unsupervised active regions for multiresolution image segmentation

An unsupervised approach to image segmentation which fuses region and boundary information is presented. The proposed approach takes advantage of the combined use of 3 different strategies: the guidance of seed placement, the control of decision criterion, and the boundary refinement. The new algorithm uses the boundary information to initialize a set of active regions which compete for the pixels in order to segment the whole image. The method is implemented on a multiresolution representation which ensures noise robustness as well as computation efficiency. The accuracy of the segmentation results has been proven through an objective comparative evaluation of the method

Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach

Enhanced Model Selection for motion segmentation

In this paper a novel rank estimation technique for trajectories motion segmentation within the Local Subspace Affinity (LSA) framework is presented. This technique, called Enhanced Model Selection (EMS), is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built by LSA. The results on synthetic and real data show that without any a priori knowledge, EMS automatically provides an accurate and robust rank estimation, improving the accuracy of the final motion segmentation