Currency Recognition on Mobile Phones Proposed system modules Segmentation Feature Extraction Instance Retrieval 1. Building a Visual Vocabulary 2. Image Indexing Using Text Retrieval Methods 3. Retrieval Stage 4. Spatial re-ranking 5. Classification Adaptation to Mobile Performance analysisModule descriptionA. SegmentationThe images might be captured in a wide variety of environments, in terms of lighting condition and background while the bill in the image itself could be deformed. Image segmentation is important not just for reducing the data to process but also for reducing irrelevant features (background region) that would affect the decision-making. This work starts with a fixed rectangular region of interest (ROI) which is forty pixels smaller from all four sides than the image itself. This work assumes that a major part of the bill will be present inside this region. Everything outside this ROI is a probable background. Once this region is obtained, it must be extended to a segmentation of the entire object. Let x be an image and let y be a partition of the image into foreground (object) and background components. Let xi R3 be the color of the ith pixel and let yi be equal to +1 if the pixel belongs to the object and to -1, otherwise. For segmentation this work use a graph cut based energy minimization formulation. The cost function is given by

The edge system E determines the pixel neighborhoods and is the popular eight-way connection. The pair wise potential S(yi , yj|x) favors neighbor pixels with similar color to have the same label. Then the segmentation is defined as the minimize arg miny E(x,y). We use the Grab Cut algorithm, which is based on iterative graph cuts, to carry out foreground/ background segmentation of the images captured by the user. The system should be able to segment the foreground object correctly and quickly without any user interaction. Whenever the foreground area is smaller than a pre-decided threshold, a fixed central region of the image is marked as foreground.B. Instance Retrieval5.3.1. Building a Visual VocabularyThis work first locates keypoints in the foreground region of the image (obtained from segmentation) and describes the key point regions, using any descriptor extractor like SIFT, SURF or ORB-FREAK . This work obtains a set of clusters of features using hierarchical K-means algorithm. The distance function between two descriptors x1 and x2 is given by

Where is the covariance matrix of descriptors. As is standard, the descriptor space is affine transformed by the square root of so that Euclidean distance may be used. The set of clusters forms the visual vocabulary of image. 5.3.2. Image Indexing Using Text Retrieval MethodsFor every training image, after matching each descriptor to its nearest cluster, we get a vector of frequencies (histogram) of visual words in the image. Instead of directly using visual word frequencies for indexing, we employ a standard term frequency - inverse document frequency (tf-idf ) weighting. Suppose there is a vocabulary of k words, then each image is represented by a k-vector , of weighted word frequencies with components

Here nid is the number of occurrences of word i in document d, nd is the total number of words in the document d, ni is the total number of occurrences of term i in the whole database and N is the total number of documents in the whole database. The weighting is a product of two terms: the word frequency, and the inverse document frequency log .However, retrieval on this representation is slow and requires lots of memory. This makes it impractical for applications on mobile phones. Therefore, we use an inverted index for instance retrieval. The inverted index contains a posting list, where each posting contains the occurrences information (e.g. frequencies, and positions) for documents that contain the term. To rank the documents in response to a query, the posting lists for the terms of the query must be traversed, which can be costly, especially for long posting lists.5.3.3. Retrieval Stage At the retrieval stage, this work obtains a histogram of visual words (query vector) for the test image. Image retrieval is performed by computing the normalized scalar product (cosine of the angle) between the query vector and all tf-idf weighted histograms in the database. They are then ranked according to decreasing scalar product. This work selects the first 10 images for further processing.5.3.4. Spatial re-rankingThe Bag of Words (BoW) model fails to incorporate the spatial information into the ranking of retrieved images. In order to confirm image similarity, this work checks whether the key points in the test image are in spatial consistency with the retrieved images. This work use the popular method of geometric verification (GV) by fitting fundamental matrix to find out the number of key points of the test image that are spatially consistent with those of the retrieved images.5.3.5. ClassificationIn the voting mechanism, each retrieved image adds votes to its image class (type of bill) by the number of spatially consistent key points it has (computed in the previous step). The class with the highest vote is declared as the result.C. Adaptation to MobileThe recognition model needed for retrieval cannot be used directly on a mobile phone because of the memory requirement. The system was able to adapt the above solution to a mobile environment by making very significant reductions in complexity, as much as possible, without sacrificing the effective accuracy. This allows us to achieve the best possible performance, given the severe restrictions in various aspects of the pipeline that we have to contend with.D. Performance analysisIn this step evaluate the performance metrics such as accuracy, and precision for the proposed system..

CHAPTER 2INTRODUCTION2.1 Computer ImagingIt can be defined a acquisition and processing of visual information by computer. Computer representation of an image requires the equivalent of many thousands of words of data, so the massive amount of data required for image is a primary reason for the development of many sub areas with field of computer imaging, such as image compression and segmentation. Another important aspect of computer imaging involves the ultimate receiver of visual information in some case the human visual system and in some cases the human visual system and in others the computer itself.Computer imaging can be separate into two primary categories:1. Computer Vision.2. Image Processing

Fig 1. Computer ImagingHistorically, the field of image processing grew from electrical engineering as an extension of the signal processing branch, whereas are the computer science discipline was largely responsible for developments in computer vision.2.2 Computer Vision1. Image Analysis: involves the examination of the image data to facilitate solving vision problem.The image analysis process involves two other topics: Feature Extraction: is the process of acquiring higher level image information, such as shape or color information. Pattern Classification: is the act of taking this higher level information and identifying objects within the image.Computer vision systems are used in many and various types of environments, such as:1. Manufacturing Systems2. Medical Community3. Law Enforcement4. Infrared Imaging5. Satellites Orbiting.2.3 Image ProcessingThe major topics within the field of image processing include:1. Image restoration.2. Image enhancement.3. Image compression.1.Image RestorationIs the process of taking an image with some known, or estimated degradation, and restoring it to its original appearance. Image restoration isoften used in the field of photography or publishing where an image was somehow degraded but needs to be improved before it can be printed

Fig 2. Image restoration2. Image EnhancementInvolves taking an image and improving it visually, typically by taking advantages of human Visual Systems responses. One of the simplest enhancement techniques is to simply stretch the contrast of an image.Enhancement methods tend to be problem specific. For example, a method that is used to enhance satellite images may not suitable for enhancing medical images.Although enhancement and restoration are similar in aim, to make an image look better. They differ in how they approach the problem. Restoration method attempt to model the distortion to the image and reverse the degradation, where enhancement methods use knowledge of the human visual systems responses to improve an image visually.

Fig 3. Image Enhancement3.Image CompressionInvolves reducing the typically massive amount of data needed to represent an image. This done by eliminating data that are visually unnecessary and by taking advantage of the redundancy that is inherent in most images. Image processing systems are used in many and various types of environments, such as:1. Medical community2. Computer Aided Design3. Virtual Reality4. Image Processing.

Fig 4. Image Enhancement2.4 Computer Imaging SystemsComputer imaging systems are comprised of two primary components types, hardware and software. The hard ware components can be divided into image acquiring sub system (computer, scanner, and camera) and display devices (monitor, printer).The software allows us to manipulate the image and perform any desired processing on the image data.2.5 DigitizationThe process of transforming a standard video signal into digital image .This transformation is necessary because the standard video signal in analog (continuous) form and the computer requires a digitized or sampled version of that continuous signal. The analog video signal is turned into a digital image by sampling the continuous signal at affixed rate. The value of the voltage at each instant is converted into a number that is stored, corresponding to the brightness of the image at that point. Note that the image brightness of the image at that point depends on both the intrinsic properties of the object and the lighting conditions in the scene.2.6. Image RepresentationWe have seen that the human visual system (HVS) receives an input image as a collection of spatially distributed light energy; this is form is called an optical image. Optical images are the type we deal with every day cameras captures them, monitors display them, and we see them [we know that these optical images are represented as video information in the form of analog electrical signals and have seen how these are sampled to generate the digital image I(r , c).The digital image I (r, c) is represented as a two- dimensional array of data, where each pixel value corresponds to the brightness of the image at the point (r, c). in linear algebra terms , a two-dimensional array like our image model I( r, c ) is referred to as a matrix , and one row ( or column) is called a vector.The image types we will consider are:1. Binary ImageBinary images are the simplest type of images and can take on two values, typically black and white, or 0 and 1. These types of images are most frequently in computer vision application where the only information required for the task is general shapes, or outlines information. For example, to position a robotics gripper to grasp ) ) an object or in optical character recognition (OCR). Binary images are often created from gray-scale images via a threshold value is turned white (1), and those below it are turned black (0).

Fig 5. Binary Image2. Gray Scale ImageGray _scale images are referred to as monochrome, or one-color image. They contain brightness information only brightness information only, no color information. The number of different brightness level available. The typical image contains 8 bit/ pixel (data, which allows us to have (0- 255) different brightness (gray) levels. The 8 bit representation is typically due to the fact that the byte, which corresponds to 8-bit of data, is the standard small unit in the world of digital computer.

Fig 6. Gray Scale Images3. Color ImageColor image can be modeled as three band monochrome image data, where each band of the data corresponds to a different color.

Fig 7. Color ImagesThe actual information stored in the digital image data is brightness information in each spectral band. When the image is displayed, the corresponding brightness information is displayed on the screen by picture elements that emit light energy corresponding to that particular color.Typical color images are represented as red, green ,and blue or RGB images .using the 8-bit monochrome standard as a model , the corresponding color image would have 24 bit/pixel 8 bit for each color bands (red, green and blue ). 2.7. Introduction to the projectVisual object recognition on a mobile phone has many applications. In this paper, we focus on the problem of recognition of currency bills on a low-end mobile phone. This is an immediate requirement for the visually impaired individuals. There are around 285 Million people estimated to be visually impaired worldwide, out of which 39 Million are blind and 246 Million have low vision. The differences in texture or length of currency bills are not really sufficient for identification by the visually impaired. Moreover, bills are not as easy to distinguish by touch as coins. Certain unique engravings are printed on the bills of different currencies but they tend to wear away.We adopt an approach based on computer vision on mobile devices, and develop an application that can run on low end smart phones. We consider the bills of Indian National Rupee (|) as a working example, but the method can be extended to a wide variety of settings. Our problem is challenging due to multiple reasons. We want all the computations to happen on the phone itself and this requires appropriate adaptation of the recognition architectures to a mobile device. Since our application is desired to be usable in a wide variety of environments (such as in presence of background clutter, folded bills etc.), we need a robust recognition scheme that can address these challenges. Also, visually impaired users may not be able to cooperate with the imaging process by realizing the environmental parameters (like clutter, pose and illumination).2.8. Problem Definition Of the ProjectWorking on a mobile platform brings with it a number of unique challenges that need to be taken care of. Primarily, the restrictions are in the memory, the application size, and the processing time. Currently, the average size of an iOS application is 23MB, while the RAM limit for a Windows phone application is 150MB. For an application to run on a mobile phone without affecting the others, it should not use more than 100MB of storage and 50MB of RAM. Our application recognizes the bills in two major steps. First we segment the bill from the clutter. Then we look at the most similar bill in the database. Though both these problems can be solved with good performance using many state-of-the-art computer vision algorithms, they are not really mobile friendly. The recognition model and other necessary information for our application would typically require more than 500MB of storage and 200MB of RAM with a direct implementation.This exceeds practical limits by a large amount. To be practically useful, the applications response time should not be more than 4 seconds keeping in mind that the current average response time is 3.28 seconds.

CHAPTER 3LITERATURE SURVEY1) Monitoring of the Rice Cropping System in the Mekong Delta Using ENVISAT/ASAR Dual Polarization Data-Alexandre Bouvet, Thuy Le Toan, and Nguyen Lam-Dao, 2009.Introduction In recent years, changes in cultural practices have been observed in different regions of the world. The rice growth region in the Mekong Delta in Vietnam is a good example of changes from the traditional to modern rice cultivation system in the last ten years. A multiple cropping system is implemented, increasing the number of crops per year from one or two to two, three, or even more. Dike infrastructures have been built and intensified after 2000 to block the flood way into the fields during the flood season so as to allow an additional crop cycle. Short-cycle rice varieties (80100 days) are planted in order to harvest three crops per year instead of one or two. Finally, modern water management has been partly introduced in the last three years, consisting in intermittent drainage between two irrigation operations. For those changes in cultural practices, the intensity temporal change method for rice mapping and monitoring needs to be upgraded. In this work, a method using polarization information is developed and assessed for this purpose. Because of the vertical structure of rice plants, the difference between HH and VV backscattering is expected to be higher than that of other crop or land cover types, and through the relation with wave attenuation in the canopy, the ratio of the HH and VV backscattering coefficients (hereafter called HH/VV) can be related to the vegetation biomass. A joint analysis of ERS and RADARSAT-1 data , and the modeling of C-band HH and VV revealed that HH is significantly higher than VV, and the difference can reach 67 dB at the peak growth stage. Based on these findings, HH/VV is potentially a good classifier for rice monitoring, and methods using HH/VV need to be developed and assessed. Specifically, in this work, the method is developed using a time series of dual polarization (HH and VV) ASAR data and tested in the province of An Giang in the Mekong Delta.Advantages This promising result shows that methods using SAR data can be timely and cost effective. The method is well-suited to regions where fields have multiple crops and shifted calendars.Disadvantages Need to consider the improvement of the method by using HH/VV and the temporal change of HH and/or VV in the multi date approach.2) Rice Phenology Estimation With Multitemporal Terrasar-X Data Using Dynamic System Concepts F. Vicente-Guijalba, T. Martinez-Marin, J.M. Lopez-Sanchez,IntroductionPrecision farming has been an important subject during the last decades. The aim of these agricultural techniques is to optimize the field-level management regarding to the crop needs, the environmental impact and the economical competitiveness of the yield. Remote sensing tools based on SAR have improved coverage and temporal information resolution for these agricultural practices. Due to its importance in the human diet, rice has been subject of study in a wide set of remote sensing works. The first studies with SAR were aimed to detect and classify rice fields. More recent works have demonstrated that by means of a set of PolSAR variables it is possible to obtain a coarse estimation of the phenological stage in rice fields. Phenology provides a measure of the biological progress within a crop field and the estimation of this parameter allows farm managers to plan crop activities in an optimized way. Based on the previous approaches, where each estimation is obtained for a single acquisition without using any other information, This work focused the estimation problem from a dynamic system view. The main objective is to employ the temporal information provided by the time series of SAR images to infer the phenological stage in a particular field and date. The estimation approach consists in two main stages: the dynamical model generation and the estimation itself.Advantages The proposed method is able to provide estimation on rice fields based on dual-pol SAR imagery. It achieves results with higher resolution ground truth data in order to validate this methodology.Disadvantages Need to study the generation of models for other kind of crops that behaves in a similar way and try to apply an analogous approach to the phonological estimation.3) Rice Phenology Monitoring by Means of SAR Polarimetry at X-Band- Juan M. Lopez-Sanchez, Senior Member, IEEE, Shane R. Cloude, Fellow, IEEE, and J. David Ballester-Berman,2012.Introduction The feasibility of retrieving the phenological stage of rice fields at a particular date by employing coherent copular dual-pol X-band radar images acquired by the TerraSAR-X sensor has been investigated in this paper. A set of polarimetric observables that can be derived from this data type has been studied by using a time series of images gathered during the whole cultivation period of rice. Among the analyzed parameters, besides backscattering coefficients and ratios, we have observed clear signatures in the correlation (in magnitude and phase) between channels in both the linear and Pauli bases, as well as in parameters provided by target decomposition techniques, like entropy and alpha from the eigenvector decomposition. A new model-based decomposition providing estimates of a random volume component plus a polarized contribution has been proposed and employed in interpreting the radar response of rice. By exploiting the signatures of these observables in terms of the phenology of rice, a simple approach to estimate the phonological stage from a single pass has been devised. This approach has been tested with the available data acquired over a site in Spain, where rice is cultivated, ensuring ground is flooded for the whole cultivation cycle, and sowing is carried out by randomly spreading the seeds on the flooded ground.Advantages The proposed method is simple. It provide better estimation accuracyDisadvantages The main drawback of using dual-pol TerraSAR-X images for this application is their narrow swath (around 15 km on the ground), which is too small for devising a monitoring scheme on large-scale rice plantations. Noise level of the system (NESZ around 19 dB), which may result very close or even higher than the backscattering from rice fields, especially at the early stages of the cultivation cycle.4) A Kalman Filter Based Mtinsar Methodology For Derving 3d Surface Displacement Evolutions- Hu J. , Ding X.L. , Li Z.W., Zhu J.J. , Sun Q., Zhang L., Omura M., 2012.Introduction Multi-temporal InSAR (MTInSAR) have been used widely for studying earth surface deformations related to many geophysical processes. However, MTInSAR techniques have been able to measure one-dimensional (1D) surface deformations in the direction of the line-of-sight (LOS) of the radar. As surface deformations are usually three-dimensional, one-dimensional observation apparently cannot always fully reflect the actual deformations. In addition, the temporal resolution of MTInSAR measurements is limited by the satellite orbit repeat period. The number of SAR satellites has been increasing rapidly in recent years. It is therefore very desirable to combine the observations from the different SAR satellites and orbits to derive more comprehensive surface deformation measurements. This work present a novel new MTInSAR approach for exploiting multi-sensor, multi-track and multi-temporal interferograms to infer three-dimensional (3D) surface displacements. The proposed approach is based on Kalman filter that has been widely used for modeling various dynamic processes. First, the 1D LOS measurements are estimated from multi-sensor, multi-track and multi-temporal interferograms. The observation model and state models of the Kalman filter are then constructed by considering the imaging geometry and temporal correlation. The 3D surface displacement at all the acquisition times can be estimated based on the models and a weighting scheme that reflects the noise levels of the observations and the deformations. The accuracy of the measurements in the north-south directions is low due to the polar orbits of the current SAR satellites. In order to ensure the accuracy of the results in the up and east-west directions, we assume that the deformation in the north-south direction is negligible in the case study carried out for the Los Angeles area. The experiment uses 21 SAR acquisitions from ENVISAT ascending and descending orbits and PALSAR ascending obits. The results are compared with GPS measurements in the area.Advantages This work can fully utilize the available interferograms Significantly increase the temporal monitoring frequency.Disadvantages The 3D instaneous rate vectors and correspondingly variances are usually difficult to exactly identify without any priori information.5) Kalman-Filter-Based Approach for Multisensor, Multitrack, and Multitemporal InSAR - Jun Hu, Xiao-Li Ding, Zhi-Wei Li, Jian-Jun Zhu, Qian Sun, and Lei Zhang, Member, IEEE, 2013.Introduction Differential interferometric synthetic aperture radar (SAR) (InSAR) (DInSAR) has been widely used for monitoring ground deformation associated with various geophysical and engineering processes. However, the applications of DInSAR have been limited by the effects of temporal and spatial decorrelation, atmospheric artifacts, and the inability of the method in providing 3-D measurements. Several multitemporal InSAR (MTInSAR) methods have been developed in recent years to reduce the effects of temporal and spatial decorrelation and atmospheric artifacts, including the persistent scatterers, the small-baseline (SB) subset, and the temporarily coherent point. The measurements from the MTInSAR approaches are, however, 1-D too, i.e., along the line of sight (LOS) of the SAR satellite. When the ground moves not in this direction only, which is, in fact, the case most of the time, the InSAR measurements cannot fully reflect the actual deformation. Some efforts have been made to derive 2-D or 3-D displacement information by combining InSAR measurements from different orbits or combining InSAR measurements with other types of measurements such as those from the Global Positioning System (GPS). This work present a Kalman-filter-based approach for retrieving 3-D surface displacement from multisensor, multitrack, and multitemporal SAR interferograms. This approach allows InSAR measurements from different directions to be integrated sequentially as they become available so that high-temporalresolution results can be achieved. The approach is tested with both simulated and real SAR data sets to verify its performance.Advantages The method works well when the measurement noise is low. The proposed approach can be potentially used to include other measurements, such as GPS and leveling, in the solutions. It achieves the improved accuracyDisadvantages It becomes unstable when the measurement noise is high due to the polar-orbiting imaging geometries of the current satellite SAR sensors.6) Estimating near future regional corn yields by integrating multi-source observations into a crop growth model -Jing Wang, Xin Li Ling Lu, Feng Fang, 2013.IntroductionRegional crop yield estimations play important roles in the food security of a society. Crop growth models can simulate the crop growth process and predict crop yields, but significant uncertainties can be derived from the input data, model parameters and model structure, especially when applied at the regional scale. Abundant observational information provides the relative true value of surface conditions, and this information includes those areal data from remote sensors and ground observations. The objective of this study was to present a data fusion framework used to calibrate a crop growth model at the plot scale and to estimate yield at the regional scale on the basis of two types of data fusion algorithms, which reduces the uncertainty of regional yield estimations. First, based on local intensive observation, the simulated annealing algorithm was applied to obtain a parameter vector that was suited to the local crop variety. This scheme reduces model parameter uncertainty. Then, the ensemble Kalman filter (EnKF), a sequence filter algorithm, was adopted to integrate the areal crop growth information that was derived from remote sensing technologies into a crop growth model for precise regional yield estimation, which reduces uncertainties in the model structure or input data related to meteorological, soil, or filed management information. This proposed scheme and technology will provide an operational method for precisely estimating crop yields at regional scales. Advantages The WOFOST model can simulate the growth curve and yield of corn, especially with respect to crop carbon absorption in agri-ecological systems This study aimed to assess the feasibility of assimilating areal observation data into a crop growth model to improve spatial estimates of crop yields and carbon pools.Disadvantages Estimation uncertainty also arises from parameter uncertainty, and an accurate parameter set is critical for accurate yield predictions.7) Efficient Spatio-temporal Mining of Satellite Image Time Series for Agricultural Monitoring- Andreea Julea, Nicolas Meger, Christophe Rigotti, Emmanuel Trouve, Romain Jolivet, and Philippe Bolon, 2012.IntroductionThis work presents an unsupervised technique to support SITS analysis in agricultural monitoring. The presented approach relies on frequent sequential pattern extraction along the temporal dimension, combined with a spatial connectivity criterion. It allows to uncover sets of pixels satisfying two properties of cultivated areas: they are spatially connected/grouped and share similar temporal evolutions. The approach requires no prior knowledge of the objects (identified regions) to monitor and needs no user-supplied aggregate functions nor distance definitions. It is based on the extraction of patterns, called Grouped Frequent Sequential patterns (GFS-patterns), satisfying a support constraint and a pixel connectivity constraint. In this paper, we extend the general framework of GFS-patterns. This work proposed in two directions, when applied to agricultural monitoring. Firstly, we show that, even though the connectivity constraint does not belong to any typical constraint family (e.g., monotonic, anti-monotonic), it can be pushed partially in the search space exploration. This leads to significant reduction of execution times on real Satellite Image Time Series of cultivated areas. Secondly, we show that a simple post-processing using a maximality constraint over the patterns is very effective. Indeed, it restricts the number of patterns to a human-browsable collection, while still retaining highly meaningful patterns for agro-modelling. This property is confirmed even for poor quality inputs (rough image quantization, raw noisy images).Advantages GFS-patterns is used to extract sets of pixels sharing similar evolution from Satellite Image Time Series over cultivated areas It achieves reduced GFS-patterns extraction times Even on poor quality inputs (i.e., noisy images, rough quantization), the method can exhibit various level of details of primary interest in agro-modellingDisadvantages The contribution due to the stratified atmosphere can be roughly estimated by using DEMs and meteorological data, but the effects of the turbulent atmosphere still degrade interferograms.8) Integrating Vegetation Indices Models and Phenological Classification with Composite SAR and Optical Data for Cereal Yield Estimation in Finland (Part I)- Heikki Laurila, Mika Karjalainen, Juha Hyypp and Jouko Kleemola, 2010.IntroductionThe aim of the present study was to estimate actual non-potential grain yield levels for high latitude spring cereals (spring wheat, barley and oats, Avena Sativa L.) in large area field conditions in southern Finland. The cereal theoretical maximum yielding capacity is limited by environmental and vegetation stresses (e.g., drought periods, nutrient deficiencies, pathogen epidemics) during growing season in actual field growing conditions. These stress factors result to reduced non-potential baseline yield levels (yb, kg/ha) on field parcel level. The objectives of the present study were: (i) to construct a dynamic SatPhenClass phonological classification model, which classifies both optical and SAR satellite data based on cereal actual phenological development in both vegetative and generative phases (ii) to calibrate and validate multispectral Composite Vegetation Indices (VGI) models, which integrate both phenologically preclassified optical (Models III) and microwave SAR data (Composite SAR and NDVI Model III), and finally (iii) VGI models were used to estimate cereal non-potential baseline yield (yb) levels in growing zones (IIV) in southern Finland during 19962006.Advantages The proposed method is validated to estimate cereal yield levels using solelyoptical and SAR satellite data. The averaged composite SAR modeled grain yield level was 3,750 kg/ha (RMSE = 10.3%, 387 kg/ha) for high latitude spring cereals.Disadvantages The early emergence in vegetative phase (ap, BBCH 012) in two leaf stage before double ridge induction and the senescence phase after full maturity and harvest (dp), BBCH > 90) were difficult to estimate.9) Multi-temporal MODISLandsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data David P. Roy, Junchang Ju, Philip Lewis , Crystal Schaaf , Feng Gao, Matt Hansen, Erik Lindquist, 2008.IntroductionA semi-physical fusion approach that uses the MODIS BRDF/Albedo land surface characterization product and Landsat ETM+ data to predict ETM+ reflectance on the same, an antecedent, or subsequent date is presented. The method may be used for ETM+ cloud/cloud shadow and SLC-off gap filling and for relative radiometric normalization. It is demonstrated over three study sites, one in Africa and two in the U.S. (Oregon and Idaho) that were selected to encompass a range of land cover land use types and temporal variations in solar illumination, land cover, land use, and phenology. Specifically, the 30 m ETM+ spectral reflectance is predicted for a desired date as the product of observed ETM+ reflectance and the ratio of the 500 m surface reflectance modeled using the MODIS BRDF spectral model parameters and the sun-sensor geometry on the predicted and observed Landsat dates. The difference between the predicted and observed ETM+ reflectance (prediction residual) is compared with the difference between the ETM+ reflectance observed on the two dates (temporal residual) and with respect to the MODIS BRDF model parameter quality. For all three scenes, and all but the shortest wavelength band, the mean prediction residual is smaller than the mean temporal residual, by up to a factor of three. The accuracy is typically higher at ETM+ pixel locations where the MODIS BRDF model parameters are derived using the best quality inversions. The method is most accurate for the ETM+ near-infrared (NIR) band; mean NIR prediction residuals are 9%, 12% and 14% of the mean NIR scene reflectance of the African, Oregon and Idaho sites respectively.Advantages The proposed method Achieves best quality Also achieves higher accuracyDisadvantages Significant reflectance changes of this nature are difficult to accommodate using conventional relative radiometric normalization and gap filling techniques.10) An automated algorithm to detect timing of urban conversion of agricultural land with high temporal frequency MODIS NDVI data - Bhartendu Pandey, Qingling Zhang and Karen C. Seto, 2013.Introduction Urban expansion is one of the major drivers of agricultural lands loss. However, current remote sensing-based efforts to monitor this process are limited to small scale case studies that require much user input. Given the rate and magnitude of contemporary urbanization, there is a need to develop a land change algorithm that can characterize the loss of agricultural land at large scales over long time periods. Moreover, characterizing agricultural land conversion trajectories from remote sensing images is complex due to farm size, climatic variability, changes in cropping patterns, and variations in the rate of development processes. Here This work propose an econometric time series approach to identify agricultural land loss due to urban expansion, utilizing high temporal frequency MODIS NDVI data between 2000 and 2010. The algorithm is comprised of two main components: 1) detrending the time series, and 2) testing for the presence of a breakpoint in the detrended time series and estimating the date of the breakpoint. Evaluations of the algorithm with simulated and actual MODIS NDVI data confirm that the method can successfully detect when and where urban conversions of agricultural lands occur. The algorithm is simple, robust, and highly automated, thus is valuable for monitoring agricultural land loss at regional and even global scalesAdvantages The proposed method enables processing of very large datasets, either in spatial extent or through time It reduces mistakes due to interpretation or human error.Disadvantages This step-wise land-use transitions result into deviation from the assumption of sequential phases in land conversion process and limit the application of most change detection algorithms.

CHAPTER 4CONCLUSION AND FUTURE WORK CONCLUSIONVisual object recognition is an recent trend which is used to recognize the objects visually through the systems. Currency recognition through mobile phones will be a most effective methodology which will be most useful for visually impaired persons. In this work, we have ported the system to a mobile environment, working around like limited processing power and memory, while achieving high accuracy and low reporting time. Currency retrieval and thereafter recognition is an example of fine-grained retrieval of instances which are highly similar. Thus the result of our experimental results proves that it is more robust to illumination changes than the SIFT descriptor.FUTURE WORKThe system implemented in our work is used to implement on Indian currency rupees whereas in further research it can be implemented to support a world level currency notes.

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