Content Based Image Retrieval Using Low Level Features of Automatically Extracted Regions of Interest

This paper proposes a region based image retrieval system using the local colour and texture features of image sub regions. The regions of interest (ROI) are roughly identified by segmenting the image into fixed partitions, finding the edge map and applying morphological dilation. The colour and texture features of the ROIs are computed from the histograms of the quantized HSV colour space and Gray Level co- occurrence matrix (GLCM) respectively. Each ROI of the query image is compared with same number of ROIs of the target image that are arranged in the descending order of white pixel density in the regions, using Euclidean distance measure for similarity computation. Preliminary experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods.

A Sub-block Based Image Retrieval Using Modified Integrated Region Matching.

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching (IRM) algorithm is used for finding the minimum distance between the sub-blocks of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods

classification and content based retrieval of digital mammograms and placental sonograms

Image processing has been a challenging and multidisciplinary research area since decades with continuing improvements in its various branches especially Medical Imaging. The healthcare industry was very much benefited with the advances in Image Processing techniques for the efficient management of large volumes of clinical data. The popularity and growth of Image Processing field attracts researchers from many disciplines including Computer Science and Medical Science due to its applicability to the real world. In the meantime, Computer Science is becoming an important driving force for the further development of Medical Sciences. The objective of this study is to make use of the basic concepts in Medical Image Processing and develop methods and tools for clinicians’ assistance. This work is motivated from clinical applications of digital mammograms and placental sonograms, and uses real medical images for proposing a method intended to assist radiologists in the diagnostic process. The study consists of two domains of Pattern recognition, Classification and Content Based Retrieval. Mammogram images of breast cancer patients and placental images are used for this study. Cancer is a disaster to human race. The accuracy in characterizing images using simplified user friendly Computer Aided Diagnosis techniques helps radiologists in detecting cancers at an early stage. Breast cancer which accounts for the major cause of cancer death in women can be fully cured if detected at an early stage. Studies relating to placental characteristics and abnormalities are important in foetal monitoring. The diagnostic variability in sonographic examination of placenta can be overlooked by detailed placental texture analysis by focusing on placental grading. The work aims on early breast cancer detection and placental maturity analysis. This dissertation is a stepping stone in combing various application domains of healthcare and technology.

Intelligent Agent-based Multilingual Information Retrieval System

The goal of this work is to develop an Open Agent Architecture for Multilingual information retrieval from Relational Database. The query for information retrieval can be given in plain Hindi or Malayalam; two prominent regional languages of India. The system supports distributed processing of user requests through collaborating agents. Natural language processing techniques are used for meaning extraction from the plain query and information is given back to the user in his/ her native language. The system architecture is designed in a structured way so that it can be adapted to other regional languages of India

English-Malayalam Cross-Lingual Information Retrieval – an Experience

This paper describes about an English-Malayalam Cross-Lingual Information Retrieval system. The system retrieves Malayalam documents in response to query given in English or Malayalam. Thus monolingual information retrieval is also supported in this system. Malayalam is one of the most prominent regional languages of Indian subcontinent. It is spoken by more than 37 million people and is the native language of Kerala state in India. Since we neither had any full-fledged online bilingual dictionary nor any parallel corpora to build the statistical lexicon, we used a bilingual dictionary developed in house for translation. Other language specific resources like Malayalam stemmer, Malayalam morphological root analyzer etc developed in house were used in this work

Content Based Image Retrieval System for Malayalam Handwritten Characters

Content Based Image Retrieval is one of the prominent areas in Computer Vision and Image Processing. Recognition of handwritten characters has been a popular area of research for many years and still remains an open problem. The proposed system uses visual image queries for retrieving similar images from database of Malayalam handwritten characters. Local Binary Pattern (LBP) descriptors of the query images are extracted and those features are compared with the features of the images in database for retrieving desired characters. This system with local binary pattern gives excellent retrieval performance

Content-Based Image Retrieval of Axial Brain Slices Using a Novel LBP with a Ternary Encoding

Retrieval of similar anatomical structures of brain MR images across patients would help the expert in diagnosis of diseases. In this paper, modified local binary pattern with ternary encoding called modified local ternary pattern (MOD-LTP) is introduced, which is more discriminant and less sensitive to noise in near-uniform regions, to locate slices belonging to the same level from the brain MR image database. The ternary encoding depends on a threshold, which is a user-specified one or calculated locally, based on the variance of the pixel intensities in each window. The variancebased local threshold makes the MOD-LTP more robust to noise and global illumination changes. The retrieval performance is shown to improve by taking region-based moment features of MODLTP and iteratively reweighting the moment features of MOD-LTP based on the user’s feedback. The average rank obtained using iterated and weighted moment features of MOD-LTP with a local variance-based threshold, is one to two times better than rotational invariant LBP (Unay, D., Ekin, A. and Jasinschi, R.S. (2010) Local structure-based region-of-interest retrieval in brain MR images. IEEE Trans. Inf. Technol. Biomed., 14, 897–903.) in retrieving the first 10 relevant images

Content Based Image Retrieval of Brain MR Images across Different Classes

Magnetic Resonance Imaging play a vital role in the decision-diagnosis process of brain MR images. For an accurate diagnosis of brain related problems, the experts mostly compares both T1 and T2 weighted images as the information presented in these two images are complementary. In this paper, rotational and translational invariant form of Local binary Pattern (LBP) with additional gray scale information is used to retrieve similar slices of T1 weighted images from T2 weighted images or vice versa. The incorporation of additional gray scale information on LBP can extract more local texture information. The accuracy of retrieval can be improved by extracting moment features of LBP and reweighting the features based on users’ feedback. Here retrieval is done in a single subject scenario where similar images of a particular subject at a particular level are retrieved, and multiple subjects scenario where relevant images at a particular level across the subjects are retrieved

Content Based Video Retrieval using SURF Descriptor

This paper presents a Robust Content Based Video Retrieval (CBVR) system. This system retrieves similar videos based on a local feature descriptor called SURF (Speeded Up Robust Feature). The higher dimensionality of SURF like feature descriptors causes huge storage consumption during indexing of video information. To achieve a dimensionality reduction on the SURF feature descriptor, this system employs a stochastic dimensionality reduction method and thus provides a model data for the videos. On retrieval, the model data of the test clip is classified to its similar videos using a minimum distance classifier. The performance of this system is evaluated using two different minimum distance classifiers during the retrieval stage. The experimental analyses performed on the system shows that the system has a retrieval performance of 78%. This system also analyses the performance efficiency of the low dimensional SURF descriptor.

Assessment of Energy Conservation in Indian Cement Industry and Forecasting of Co2 Emissions

Cement industry ranks 2nd in energy consumption among the industries in India. It is one of the major emitter of CO2, due to combustion of fossil fuel and calcination process. As the huge amount of CO2 emissions cause severe environment problems, the efficient and effective utilization of energy is a major concern in Indian cement industry. The main objective of the research work is to assess the energy cosumption and energy conservation of the Indian cement industry and to predict future trends in cement production and reduction of CO2 emissions. In order to achieve this objective, a detailed energy and exergy analysis of a typical cement plant in Kerala was carried out. The data on fuel usage, electricity consumption, amount of clinker and cement production were also collected from a few selected cement industries in India for the period 2001 - 2010 and the CO2 emissions were estimated. A complete decomposition method was used for the analysis of change in CO2 emissions during the period 2001 - 2010 by categorising the cement industries according to the specific thermal energy consumption. A basic forecasting model for the cement production trend was developed by using the system dynamic approach and the model was validated with the data collected from the selected cement industries. The cement production and CO2 emissions from the industries were also predicted with the base year as 2010. The sensitivity analysis of the forecasting model was conducted and found satisfactory. The model was then modified for the total cement production in India to predict the cement production and CO2 emissions for the next 21 years under three different scenarios. The parmeters that influence CO2 emissions like population and GDP growth rate, demand of cement and its production, clinker consumption and energy utilization are incorporated in these scenarios. The existing growth rate of the population and cement production in the year 2010 were used in the baseline scenario. In the scenario-1 (S1) the growth rate of population was assumed to be gradually decreasing and finally reach zero by the year 2030, while in scenario-2 (S2) a faster decline in the growth rate was assumed such that zero growth rate is achieved in the year 2020. The mitigation strategiesfor the reduction of CO2 emissions from the cement production were identified and analyzed in the energy management scenarioThe energy and exergy analysis of the raw mill of the cement plant revealed that the exergy utilization was worse than energy utilization. The energy analysis of the kiln system showed that around 38% of heat energy is wasted through exhaust gases of the preheater and cooler of the kiln sysetm. This could be recovered by the waste heat recovery system. A secondary insulation shell was also recommended for the kiln in the plant in order to prevent heat loss and enhance the efficiency of the plant. The decomposition analysis of the change in CO2 emissions during 2001- 2010 showed that the activity effect was the main factor for CO2 emissions for the cement industries since it is directly dependent on economic growth of the country. The forecasting model showed that 15.22% and 29.44% of CO2 emissions reduction can be achieved by the year 2030 in scenario- (S1) and scenario-2 (S2) respectively. In analysing the energy management scenario, it was assumed that 25% of electrical energy supply to the cement plants is replaced by renewable energy. The analysis revealed that the recovery of waste heat and the use of renewable energy could lead to decline in CO2 emissions 7.1% for baseline scenario, 10.9 % in scenario-1 (S1) and 11.16% in scenario-2 (S2) in 2030. The combined scenario considering population stabilization by the year 2020, 25% of contribution from renewable energy sources of the cement industry and 38% thermal energy from the waste heat streams shows that CO2 emissions from Indian cement industry could be reduced by nearly 37% in the year 2030. This would reduce a substantial level of greenhouse gas load to the environment. The cement industry will remain one of the critical sectors for India to meet its CO2 emissions reduction target. India’s cement production will continue to grow in the near future due to its GDP growth. The control of population, improvement in plant efficiency and use of renewable energy are the important options for the mitigation of CO2 emissions from Indian cement industries

Zur Reaktionskinetik der Oxidation von Kohlenstoffmaterialien aus Biomasse

Zur Modellierung von Vergasungs- und Verbrennungsprozessen zur energetischen Nutzung von Biomasse ist die Kenntnis von reaktionskinetischen Daten für die Sauerstoff-Oxidation von Biomassepyrolysaten erforderlich. Eine ausführliche Literaturübersicht zeigt den Stand der Forschung bezüglich der experimentellen Ermittlung von reaktionskinetischen Parametern für die Oxidation von Pyrolysaten aus Lignin, Cellulose und pflanzlicher Biomasse sowie der Suche nach einem plausiblen Reaktionsmechanismus für die Reaktion von Sauerstoff mit festen Kohlenstoffmaterialien. Es wird eine Versuchsanlage mit einem quasistationär betriebenen Differentialreaktor konstruiert, die eine Messung der Reaktionskinetik und der reaktiven inneren Oberfläche (RSA) für die Reaktion eines Pyrolysats aus Maispflanzen mit Sauerstoff ermöglicht. Die getrockneten und zerkleinerten Maispflanzen werden 7 Minuten lang bei 1073 K in einem Drehrohrofen pyrolysiert. Das Pyrolysat zeichnet sich vor allem durch seine hohe Porosität von über 0,9 und seinen hohen Aschegehalt von 0,24 aus. Die RSA wird nach der Methode der Messung von Übergangskinetiken (TK) bestimmt. Die Bestimmung der RSA erfolgt für die Reaktionsprodukte CO und CO2 getrennt, für die entsprechend ermittelten Werte werden die Bezeichnungen CO-RSA und CO2-RSA eingeführt. Die Abhängigkeit dieser Größen von der Sauerstoffkonzentration läßt sich durch eine Langmuir-Isotherme beschreiben, ebenso das leichte Absinken der CO-RSA mit der Kohlendioxidkonzentration. Über dem Abbrand zeigen sich unterschiedliche Verläufe für die CO-RSA, CO2-RSA und die innere Oberfläche nach der BET-Methode. Zur Charakterisierung der Oberflächenzwischenprodukte werden temperaturprogrammierte Desorptionsversuche (TPD) durchgeführt. Die Ergebnisse zeigen, daß eine Unterscheidung in zwei Kohlenstoff-Sauerstoff-Oberflächenkomplexe ausreichend ist. Die experimentellen Untersuchungen zum Oxidationsverlauf werden im kinetisch bestimmten Bereich durchgeführt. Dabei werden die Parameter Temperatur, Sauerstoff-, CO- und CO2-Konzentration variiert. Anhand der Ergebnisse der reaktionskinetischen Untersuchungen wird ein Reaktionsmechanismus für die Kohlenstoff-Sauerstoff-Reaktion entwickelt. Dieser Reaktionsmechanismus umfaßt 7 Elementarreaktionen, für welche die reaktionskinetischen Parameter numerisch ermittelt werden. Darüber hinaus werden reaktionskinetische Parameter für einfachere massenbezogene Reaktionsgeschwindigkeitsansätze berechnet und summarische Reaktionsgeschwindigkeitsansätze für die Bildung von CO und CO2 aus dem Reaktionsmechanismus hergeleitet.

Zur Adsorption und Reaktion von Stickstoffmonoxid an Aktivkohle

Die Verwendung von Aktivkohlen und -koksen stellt eine Alternative zu herkömmlichen Prozessen zur Verminderung der NOx-Emissionen in Rauchgasen dar. An diesen Materialien wird Stickstoffmonoxid adsorbiert und katalytisch zu N2 reduziert. Eine einheitliche Erklärung über die ablaufenden Vorgänge und die Reaktionsmechanismen gibt es noch nicht. Die Ergebnisse der bisher veröffentlichten wissenschaftlichen Arbeiten sind sehr unterschiedlich, wenn nicht sogar widersprüchlich. In dieser Arbeit wird, anhand der Messung von NO-Durchbruchskurven und thermischen Desorptionsspektren, die Adsorption und Reaktion von Stickstoffmonoxid an Aktivkohlen und -koksen in Anwesenheit von Sauerstoff und Wasserdampf untersucht. Zur Durchführung der experimentellen Untersuchungen wird eine Versuchsanlage, bestehend aus einer Vorrichtung zur Gasgemischaufbereitung, einem Festbettreaktor und einer Gasanalytik, konzipiert und aufgebaut. Die Untersuchungen erfolgen bei Temperaturen zwischen 100 und 150 °C. Die NO-, O2- und H2O-Konzentrationen werden anhand der Rauchgaszusammensetzung kohlegefeuerter Kraftwerke gewählt. Die experimentellen Untersuchungen konzentrieren sich auf die Verwendung einer Aktivkohle aus Ölpalmschalen, die in einem Drehrohrreaktor am Institut für Thermische Energietechnik der Universität Kassel hergestellt wurde. Die experimentellen Ergebnisse zeigen, dass während des Prozesses NO-Adsorption, -Reduktion und -Oxidation, NO2-Bildung, -Adsorption und -reduktive Desorption, H2O-Adsorption sowie O2-Vergasung gleichzeitig stattfinden. Bei niedrigen Temperaturen werden die NO2-Bildung und die Adsorption bevorzugt. Die NO-Reduktion läuft über adsorbiertes NO mit CO2- und CO-Bildung. Durch O2-Vergasung werden aktive freie Cf-Plätzen für die NO-Reaktion und -Adsorption gebildet. Wasserdampf wird an der Aktivkohle adsorbiert und belegt aktive Plätze für diese Prozesse. Aus den experimentellen Ergebnissen werden kinetische und Gleichgewichtsparameter der NO-Sorption bestimmt. Ein vereinfachtes mathematisches Modell des Festbettreaktors, das zur Berechnung der NO-Durchbruchskurven bei unterschiedlichen Temperaturen dient, wird aufgestellt.

Field measurements of the CO2 evolution rate under different crops during an irrigation cycle in a mountain oasis of Oman

For millennia oasis agriculture has been the backbone of rural livelihood in the desertic Sultanate of Oman. However, little is known about the functioning of these oasis systems, in particular with respect to the C turnover. The objective was to determine the effects of crop, i.e. alfalfa, wheat and bare fallow on the CO2 evolution rate during an irrigation cycle in relation to changes in soil water content and soil temperature. The gravimetric soil water content decreased from initially 24% to approximately 16% within 7 days after irrigation. The mean CO2 evolution rates increased significantly in the order fallow (27.4 mg C m^−2 h^−1) < wheat (45.5 mg C m^−2 h^−1) < alfalfa (97.5 mg C m^−2 h^−1). It can be calculated from these data that the CO2 evolution rate of the alfalfa root system was nearly four times higher than the corresponding rate in the wheat root system. The decline in CO2 evolution rate, especially during the first 4 days after irrigation, was significantly related to the decline in the gravimetric water content, with r = 0.70. CO2 evolution rate and soil temperature at 5 cm depth were negatively correlated (r = -0.56,n = 261) due to increasing soil temperature with decreasing gravimetric water content.

Salinity-induced changes in the microbial use of sugarcane filter cake added to soil

Five laboratory incubation experiments were carried out to assess the salinity-induced changes in the microbial use of sugarcane filter cake added to soil. The first laboratory experiment was carried out to prove the hypothesis that the lower content of fungal biomass in a saline soil reduces the decomposition of a complex organic substrate in comparison to a non-saline soil under acidic conditions. Three different rates (0.5, 1.0, and 2.0%) of sugarcane filter cake were added to both soils and incubated for 63 days at 30°C. In the saline control soil without amendment, cumulative CO2 production was 70% greater than in the corresponding non-saline control soil, but the formation of inorganic N did not differ between these two soils. However, nitrification was inhibited in the saline soil. The increase in cumulative CO2 production by adding filter cake was similar in both soils, corresponding to 29% of the filter cake C at all three addition rates. Also the increases in microbial biomass C and biomass N were linearly related to the amount of filter cake added, but this increase was slightly higher for both properties in the saline soil. In contrast to microbial biomass, the absolute increase in ergosterol content in the saline soil was on average only half that in the non-saline soil and it showed also strong temporal changes during the incubation: A strong initial increase after adding the filter cake was followed by a rapid decline. The addition of filter cake led to immobilisation of inorganic N in both soils. This immobilisation was not expected, because the total C-to-total N ratio of the filter cake was below 13 and the organic C-to-organic N ratio in the 0.5 M K2SO4 extract of this material was even lower at 9.2. The immobilisation was considerably higher in the saline soil than in the non-saline soil. The N immobilisation capacity of sugarcane filter cake should be considered when this material is applied to arable sites at high rations. The second incubation experiment was carried out to examine the N immobilizing effect of sugarcane filter cake (C/N ratio of 12.4) and to investigate whether mixing it with compost (C/N ratio of 10.5) has any synergistic effects on C and N mineralization after incorporation into the soil. Approximately 19% of the compost C added and 37% of the filter cake C were evolved as CO2, assuming that the amendments had no effects on the decomposition of soil organic C. However, only 28% of the added filter cake was lost according to the total C and d13C values. Filter cake and compost contained initially significant concentrations of inorganic N, which was nearly completely immobilized between day 7 and 14 of the incubation in most cases. After day 14, N re-mineralization occurred at an average rate of 0.73 µg N g-1 soil d-1 in most amendment treatments, paralleling the N mineralization rate of the non-amended control without significant difference. No significant net N mineralization from the amendment N occurred in any of the amendment treatments in comparison to the control. The addition of compost and filter cake resulted in a linear increase in microbial biomass C with increasing amounts of C added. This increase was not affected by differences in substrate quality, especially the three times larger content of K2SO4 extractable organic C in the sugarcane filter cake. In most amendment treatments, microbial biomass C and biomass N increased until the end of the incubation. No synergistic effects could be observed in the mixture treatments of compost and sugarcane filter cake. The third 42-day incubation experiment was conducted to answer the questions whether the decomposition of sugarcane filter cake also result in immobilization of nitrogen in a saline alkaline soil and whether the mixing of sugarcane filter cake with glucose (adjusted to a C/N ratio of 12.5 with (NH4)2SO4) change its decomposition. The relative percentage CO2 evolved increased from 35% of the added C in the pure 0.5% filter cake treatment to 41% in the 0.5% filter cake +0.25% glucose treatment to 48% in the 0.5% filter cake +0.5% glucose treatment. The three different amendment treatments led to immediate increases in microbial biomass C and biomass N within 6 h that persisted only in the pure filter cake treatment until the end of the incubation. The fungal cell-membrane component ergosterol showed initially an over-proportionate increase in relation to microbial biomass C that fully disappeared at the end of the incubation. The cellulase activity showed a 5-fold increase after filter cake addition, which was not further increased by the additional glucose amendment. The cellulase activity showed an exponential decline to values around 4% of the initial value in all treatments. The amount of inorganic N immobilized from day 0 to day 14 increased with increasing amount of C added in comparison to the control treatment. Since day 14, the immobilized N was re-mineralized at rates between 1.31 and 1.51 µg N g-1 soil d-1 in the amendment treatments and was thus more than doubled in comparison with the control treatment. This means that the re-mineralization rate is independent from the actual size of the microbial residues pool and also independent from the size of the soil microbial biomass. Other unknown soil properties seem to form a soil-specific gate for the release of inorganic N. The fourth incubation experiment was carried out with the objective of assessing the effects of salt additions containing different anions (Cl-, SO42-, HCO3-) on the microbial use of sugarcane filter cake and dhancha leaves amended to inoculated sterile quartz sand. In the subsequent fifth experiment, the objective was to assess the effects of inoculum and temperature on the decomposition of sugar cane filter cake. In the fourth experiment, sugarcane filter cake led to significantly lower respiration rates, lower contents of extractable C and N, and lower contents of microbial biomass C and N than dhancha leaves, but to a higher respiratory quotient RQ and to a higher content of the fungal biomarker ergosterol. The RQ was significantly increased after salt addition, when comparing the average of all salinity treatments with the control. Differences in anion composition had no clear effects on the RQ values. In experiment 2, the rise in temperature from 20 to 40°C increased the CO2 production rate by a factor of 1.6, the O2 consumption rate by a factor of 1.9 and the ergosterol content by 60%. In contrast, the contents of microbial biomass N decreased by 60% and the RQ by 13%. The effects of the inoculation with a saline soil were in most cases negative and did not indicate a better adaptation of these organisms to salinity. The general effects of anion composition on microbial biomass and activity indices were small and inconsistent. Only the fraction of 0.5 M K2SO4 extractable C and N in non-fumigated soil was consistently increased in the 1.2 M NaHCO3 treatment of both experiments. In contrast to the small salinity effects, the quality of the substrate has overwhelming effects on microbial biomass and activity indices, especially on the fungal part of the microbial community.

Dual retrieval in conceptual information systems

Conceptual Information Systems provide a multi-dimensional conceptually structured view on data stored in relational databases. On restricting the expressiveness of the retrieval language, they allow the visualization of sets of realted queries in conceptual hierarchies, hence supporting the search of something one does not have a precise description, but only a vague idea of. Information Retrieval is considered as the process of finding specific objects (documents etc.) out of a large set of objects which fit to some description. In some data analysis and knowledge discovery applications, the dual task is of interest: The analyst needs to determine, for a subset of objects, a description for this subset. In this paper we discuss how Conceptual Information Systems can be extended to support also the second task.