Content Based Image Retrieval System Project

Content Based Image Retrieval System Project An Efficient Content-based Image Retrieval System Integrating Wavelet-based Image Sub-blocks with Dominant Colors and Texture Analysis ABSTRACT Multimedia information retrieval is a part of computer science and it is used for extracting semantic information from multimedia data sources such as image, audio, video and text. Automatic image annotation is called as automatic image tagging or automatic linguistic indexing. It is the process in which a computer system automatically designates metadata in the form of keywords or captioning to a digital image. This application is widely used in image retrieval systems to locate and organize images from database. In this paper we have proposed efficient content based image retrieval (CBIR) systems due to the availability of large image database. The image retrieval system is used to retrieve the images based on color and texture features. Firstly, the image is partition into equal sized non-overlapping tiles. For partitioning images we are applying methods like, Gray level co-occurrence matrix (GLCM), HSV color feature, dominant color descriptor (DCD), cumulative color histogram and discrete wavelet transform. An integrated matching scheme can be used to compare the query images and database images based on the Most Similar Highest Priority (MSHP). Using the sub-blocks of query image and the images in database, the adjacency matrix of a bipartite graph is formed. INTRODUCTION: Automatic image annotation is known as automatic image tagging or automatic linguistic indexing. It is the process in which a computer system automatically designates metadata in the form of keywords or captioning to a digital image. This application is widely used in image retrieval systems to locate and organize images from database. This method can be considered as multi class image classification with a large number of classes. The advantage of automatic image annotation is that the queries that can be specified by the user. Content based image retrieval requires users to search by images based on the color and texture and also is used to find example queries. The traditional methods of image retrieval are used to retrieve annotated images from large image database manually and which is an expensive, laborious and time consuming in existence. Animage retrieval system is a computer system for searching, browsing and retrieving images from a largecollectionofdigital images. Most common and traditional methods of image retrieval use some methods of adding metadata such as captioning or descriptions and keywords to the images so that the retrieval can be performed over the annotation words. Image searchis used to find images from database and a user will provide a query terms as image file/link, keywords or click on some image and the system will return images similar to that query image. The similarity matching is done by using the Meta tags, color distribution in images and region/shape attributes. Image Meta Search: searching the images based on associated metadata such as text, keywords. Content-Based Image Retrieval  (CBIR):- This is the main application of  computer vision  to retrieve the images from image database. The aim of CBIR is used to retrieve images based on the similarities in their contents such as color, texture and shape instead of textual descriptions and comparing a user-specified image features or user-supplied query image. CBIR Engine List: This is used to search images based on image visual contents as color, texture, and shape/object. Image Collection Exploration: It is used to find images using novel exploration paradigms. Content Based Image Retrieval: Content based image retrieval is known asquery by image content(QBIC) andcontent-based visual information retrieval(CBVIR) and it is the application ofcomputer vision techniques to retrieve the images from digital image database. This is the image retrieval problem of finding for images in large image database. Content-based image retrieval is to provide more accuracy as compared to traditionalconcept-based approaches. Content-based is the search that analyzes the contents of the image instead of metadata such as keywords, tags, or descriptions associated with that image. The term content in this context means textures, shapes, colors or any other information about image can be derived from the image itself. CBIR is popular because of its searches are purely dependent on metadata, annotation quality and completeness. If the images are annotated manually by entering the metadata or keywords in a large database can be a time consuming and sometime it cannot be capture the keywords preferred to describe its images. The CBIR method overcomes with the concept based image annotation or textual based image annotation. This is done by automatically. Content Based Image Retrieval Using Image Distance Measures:- In this the image distance measure method is used to compare the two images such as a query image and an image from database. An image distance measure method is used to compare the matching of two images in various dimensions as color, shape, texture and others. Finally these matching results can be sorted based of the distance to the queried image. Color This is used to compute image distance measures based on color similarity. This is achieved by computing the color histogramfor each image and that is used to identify the proportion of each pixel within an image which is holding a specific values. Finally examine the images based on the colors, which contains most widely used techniques and it can be completed without consider to image size or orientation. It is used to segment color by spatial relationship and by region among several color region. Texture Textures are represented as texels and are then located into a number of sets based on a lot of textures and are detected in the images. These sets are used to define texture and also detect where the textures are located in images. Texture measures are used to define visual patterns in images. By using texture such as a two- dimensional gray level variation is to identify specific textures in an image is achieved. Using texture, the relative intensity of pairs of pixels is estimated such as contrast, regularity, coarseness and directionality.Identifying co-pixel variation patterns and grouping them with particular classes of textures like silky, orrough. Different methods of classifying textures are:- Co-occurrence matrix. Laws texture energy. Wavelet transforms. LITERATURE SURVEY: In this paper a multscale context dependent classification algorithm is developed for segmenting collection of images into four classes. They are background, photograph, text, and graph. Here, features are used for categorization based on the distribution patterns of wavelet coefficients in high frequency bands. The important attribute of this algorithm is multscale nature and is used to classifies an image at different resolutions adaptively and enabling accurate classification at class boundaries. The collected context information is used for improving classification accuracy. In this two features are defined for distinguishing local image types in image database according to the distribution patterns of wavelet coefficients rather than the moments of wavelet coefficients as features for classification. The first feature is defined for matching between the empirical distribution of wavelet coefficients in high frequency bands and the Laplacian distribution. The second feature is de fined for measuring the wavelet coefficients in high frequency bands at a few discrete values. This algorithm was developed to calculate the feature efficiently. The multscale structure collects context information from low resolutions to high resolutions. Classification is done on large blocks at the starting resolution to avoid over-localization. Here, only the blocks with extreme features are classified to ensure that the blocks of mixed classes are left to be classified at higher resolutions and the unclassified blocks are divided into smaller blocks at the higher resolution. These smaller blocks are classified based on the context information achieved at the lower resolution. Finally simulations shows that the classification accuracy is significantly improved based on the context information. Multiscale algorithm is also provides both lower classification error rates and better visual results [1]. This paper proposed content based image retrieval technique that can be derived in a number of different domains as Medical Imaging, Data Mining, Weather forecasting, Education, Remote Sensing and Management of Earth Resources, Education. The content based image retrieval technique is used to annotate images automatically based on the features like color and texture known as WBCHIR (Wavelet Based Color Histogram Image Retrieval). Here, color and texture features are extracted using the color histogram and wavelet transformation and the mixture of these two features are strong to scaling and translation of objects in an image. In this, the proposed system i.e. CBIR has demonstrated a WANG image database containing 1000 general-purpose color images for a faster retrieval method. Here, the computational steps are effectively reduced based on the Wavelet transformation. The retrieval speed is increases by using the CBIR technique even though the time taken for retrieving images from 1000 of images in database is only a 5-6 minutes [2]. This paper presents content based image retrieval scheme for medical images. This is an efficient method of retrieving medical images based on the similarity of their visual contents. CBIR-MD system is used to facilitate doctors in retrieving related medical images from the image database to diagnose the disease efficiently. In this a CBIR system is proposed by which a query image is divided into identical sized sub-blocks and the feature extraction of each sub-block is conceded based on Haar wavelet and Fourier descriptor. Finally, matching the image process is provided using the Most Similar Highest Priority (MSHP) principle and by using the sub-blocks of query and target image, an adjacency matrix of bipartite graph partitioning (BGP) created [3]. In this paper a content based image retrieval (CBIR) system is proposed using the local and global color, texture, and shape features of selected image sub-blocks. These image sub-blocks are approximately identified by segmenting the image into small number of partitions of different patterns. Finding edge density and corner density in each image partition using edge thresholding, morphological dilation. The texture and color features of the identified regions are calculated using the histograms of the quantized HSV color space and Gray Level Co- occurrence Matrix (GLCM) and the combination of color and texture feature vector is evaluated for each region. The shape features are computed using the Edge Histogram Descriptor (EHD). The distance between the characteristics of the query image and target image is computed using the Euclidean distance measure. Finally the experimental results of this proposed method provides a improved retrieving result than retrieval using some of the exis ting methods [4]. An efficient content based image retrieval system plays an important role due to the availability of large image database. The Color-Texture and Dominant Color Based Image Retrieval System (CTDCIRS) is used to retrieve images based on the three features such as Dynamic Dominant Color (DDC), Motif Co-Occurrence Matrix (MCM) and Difference between Pixels of Scan Pattern (DBPSP). By using the fast color quantization algorithm, we can divide the image into eight partitions. From these eight partitions we obtained eight dominant colors. The texture of the image is obtained by using the MCM and DBPSP methods. MCM is derived based on the motif transformed image. It is related to color co-occurrence matrix (CCM) and it is the conventional pattern co-occurrence matrix and is used to calculate the possibility of the occurrence of same pixel color between each pixel and its nearby ones in each image, which is the attribute of the image. The drawback of MCM is used to capture the way of textures but not the difficulty of texture. To overcome this, we use DBPSP as texture feature. The combination of dominant color, MCM and DBPSP features are used in image retrieval system. This approach is efficient in retrieving the user interested images [5]. In this paper content based image retrieval approach is used. It consists of two features such as high level and low level features and these features includes color, texture and shape which are present in each image. By extracting these features we can retrieve the images from image database. To obtain better results, RGB space is converted into HSV space and YCbCr space is used for low level features. The low level features are to be used based upon the applications. Color feature in case of natural images and co-occurrence matrix in case of textured images yields better results [6]. OBJECTIVE: To retrieve images more efficiently or accurately. To improve the efficiency and accuracy by using the multi features for image retrieval (discrete wavelet transform). Image classification and accuracy analysis. Time saving. Robustness. METHODOLOGY: Discrete Wavelet Transform. Conversion to HSV Color Space. Color Histogram Generation. Dominant Color Descriptor. Gray-level Co-occurrence Matrix (GLCM). ARCHITECTURE: This architecture consists of two phases: Training phase Testing phase These two phases of the proposed system consists of many blocks like image database, image partitioning, wavelet transform of image sub-blocks, RGB to HSV, non uniform quantization, histogram generation, dominant color description, textual analysis, query feature, similarity matching, feature database, returned images. In training phase, the input image is retrieved from image database and then the image is being partitioned into equal sized sub-blocks. Further, for each sub-block of the partitioned image, wavelet transform is being applied. Then the conversion from RGB to HSV taken place preceded with non uniform quantization, inputted to histogram generation block where a color histogram is generated for the sub-blocks of the image. Then the dominant color descriptors are extracted and texture analysis of each sub-block of the image is done. Finally the image features from the feature database and the input image features are compared for the similarity matching using MSHP principle. Then the matched image is being returned. In testing phase, the processing steps are same as training phase, except the input image is given as the query image by the user not collected from the image database. OUTCOMES: It provides accurate image retrieving. Comparative analysis and graph. Provides better efficiency. CONCLUSION: To retrieve images from image database, we can use discrete wavelet transform method based on color and texture features. The color feature of the pixels in an image can be described using HSV, color histogram and DCD methods, similarly texture distribution can be described using GLCM method. By using these methods we can achieve accurate retrieval of images. REFERENCES: [1] Jia Li, Member, IEEE, and Robert M. Gray, Fellow, IEEE, â€Å"Context-Based Multiscale Classification of Document Images Using Wavelet Coefficient Distributions†, IEEE Transactions on Image Processing, Vol. 9, No. 9, September 2000. [2] Manimala Singha and K.Hemachandran, â€Å"Content Based Image Retrieval using Color and Texture†, Signal Image Processing: An International Journal (SIPIJ) Vol.3, No.1, February 2012. [3] Ashish Oberoi Deepak Sharma Manpreet Singh, â€Å"CBIR-MD/BGP: CBIR-MD System based on Bipartite Graph Partitioning†, International Journal of Computer Applications (0975 – 8887) Volume 52– No.15, August 2012. [4] E. R. Vimina and K. Poulose Jacob, â€Å"CBIR Using Local and Global Properties of Image Sub-blocks†, International Journal of Advanced Science and Technology Vol. 48, November, 2012. [5] M.Babu Rao Dr. B.Prabhakara Rao Dr. A.Govardhan, â€Å"CTDCIRS: Content based Image Retrieval System based on Dominant Color and Texture Features†, International Journal of Computer Applications (0975 – 8887) Volume 18– No.6, March 2011. [6] Gauri Deshpande, Megha Borse, â€Å"Image Retrieval with the use of Color and Texture Feature†, Gauri Deshpande et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 2 (3) , 2011, 1018-1021. [7] Sherin M. Youssef, Saleh Mesbah, Yasmine M. Mahmoud, â€Å"An Efficient Content-based Image Retrieval System Integrating Wavelet-based Image Sub-blocks with Dominant Colors and Texture Analysis†, Information Science and Digital Content Technology (ICIDT), 2012 8th International Conference on Volume:3 .

Self-imposed Estrangement in Pauls Case Essay -- Willa Cather

Self-imposed Estrangement in "Paul's Case," by Willa Cather Many times, we try to separate ourselves from the world around us; we distance ourselves from society that gives us life. What is worse, we are voluntarily subjected to the lonesomeness which precedes wallowing in our own self pity. "Paul's Case," in which the theme of the fatal progression of deliberate seclusion presents the major conflict, centers around a young man, in his alienation, suppressing his need for attention and satisfying himself through his own world established through his seclusion. The author, Willa Cather, renders this main theme by her insinuations of the character, by the point of view she chooses to illuminate Paul"'"s characteristics, and by key symbols that contribute to the overall work. The character presented by Cather through Paul, withdraws himself from his environment creating the base for the theme of his progressively intensifying need for distinct separation. The reasons Paul acts the way he does seems two fold. First, the sequence of events could be caused by psychological damage or some mental condition, possibly stemming from his mothers death, which was only alluded to in the story. Paul was a teenager who displayed certain signs of a mental illness. According to The Medical Advisor#, Paul suffers from many of similar symptoms of a narcissist. Although the personality disorder was not diagnosed until 1977, and was not perfected until 1987 and expanded upon in 1994, Cather"'"s character of 1904 embodies many of the symptoms listed. Of those Paul qualifies for are: highly developed sense of self importance, preoccupation with fantasies of unlimited success, belief that he or she is special, feeling he has the envy of peers... ...in a connection with his mother. This bond further alienates him with the world by association with the departed; his mother is separated by death, thus by professing to the world his connection with his mother brings him one step further from sanity. As he comes to realize that the mere emotional connection with his mother is not enough to isolate him, the flower becomes submersed into a sea of white as it is buried in the snow, and Paul achieves his ultimate escape and suicide creates the desired connection with his mother. Willa Cather, carefully weaving together a deep character to which understanding is complex, an interesting and enlightening twist on the point of view, and multi-dimensional use of symbolic motifs that describe the character"'"s personality and dreams, has created a universal theme of the grave progression of self-imposed estrangement.

Amendment Right

Many American citizens take their civil liberties for granted. Many do not realize how valuable their rights and privileges under the United States Constitution really are, until they begin to be taken away. The Fourth Amendment, essentially the right to privacy, is slowly being stripped from the American citizen. The use of TEMPEST, or sophisticated eavesdropping technology to intercept information, including telephone monitoring and video surveillance, is unconstitutional under the Fourth Amendment of the United States Constitution.The Fourth Amendment of the United States Constitution states that people have the right to privacy in their person, houses, papers, and effects against unreasonable searches and seizures, and that people should not be violated, and no warrants issued, unless there is probable cause. (â€Å"U. S. Constitution: Fourth Amendment†, 2009) The Fourth Amendment clearly outlines that the American citizen has a right to privacy from the government. This i ncludes privacy not only in their homes, but out in public.For instance, anyone can observe another in public, such as walking down the street. However, when law enforcement officials begin to observe regular citizens in their everyday routine, such as going to work, going to the grocery store, picking up their children from school, and the like, that citizen's right to privacy has been violated. To understand how sophisticated eavesdropping technology to intercept information is a violation of the Fourth Amendment, one must realize how it works.TEMPEST is a code name for studies and investigations of compromising emanations. Compromising emanations are unintentional signals that can send information to a remote source. For instance, computers, telephones, and video surveillance cameras release interference into their surrounding environment. This interference creates signals that bear some relationship to what was originally caught. Essentially, TEMPEST equipment can remotely mirro r what is being done on another device. This is, in its purest form, eavesdropping.(Pike, 2000) In the case of Kyllo versus the United States, which was argued on February 20, 2001 and decided on June 11, 2001, is an example of the violation of the Fourth Amendment. Law enforcement was suspicious that marijuana was being grown in petitioner Kyllo's home in a triplex, and therefore, used thermal imaging devices to detect unusual heat sources, perhaps from heat lamps necessary for growing marijuana. Scanning the outside of the house, the agents detected hot spots coming from Kyllo's garage.The agents obtained a search warrant, and did indeed find marijuana plants. The evidence was then seized from Kyllo's home. The Ninth Circuit Court decided that the thermal imaging was not in violation of the Fourth Amendment because Kyllo had shown no attempt to conceal the heat coming from his home, and even if he had, law enforcement agents were still in the clear because the thermal imaging did not expose any intimate details of Kyllo's life. However, law enforcement used devices that were not in general, public use.They used these devices to â€Å"explore details of a private home that would previously have been unknowable without physical intrusion. † On these grounds, Kyllo decided to appeal, holding fast to the claim that the surveillance was a violation of the Fourth Amendment. Ultimately, the Court decided that the use of the thermal imaging device to obtain information was a violation of Kyllo's right to privacy under the Fourth Amendment. The Court rejected law enforcement's argument that the thermal imaging must be upheld because it detected only heat from the exterior of the house.Law enforcement's argument was rejected because it left the homeowner to the mercy of technology. Law enforcement's argument that the thermal imaging must be upheld because it did not detect intimate details was also rejected because all details concerning a home are intimate det ails. (â€Å"Kyllo v. United States†, 2001) Technology has advanced to the point that the public should be aware of possible videotaping and other types of eavesdropping. For example, hidden cameras scanned the faces of all of the Super Bowl attendees as they entered the stadium in January of 2001.The pictures were then compared with local, state, and FBI files of known criminals and terrorists. The attendees had no idea they were being watched. The federal government, in addition to local law enforcement, is beginning to strip away Americans' right to privacy. On September 11, 2001, the attacks on the World Trade Centers exposed the vulnerability of America to terrorism. In response, Congress quickly passed the Patriot Act. The Patriot Act is supposed to provide important national security measures, such as the removal of a statute on limitations for terrorism offenses.However, it also increased the government's ability to conduct unwarranted surveillance on innocent individ uals without making sure that abuses of power were limited. These examples illustrate the tension between preserving national security and preventing unwarranted government infringement on civil liberties. This infringement is a violation of the Fourth Amendment. (Chandler, 2006) In the months following the attacks on September 11, 2001, everyone was quick to point out a possible terrorist.People paid attention to what others said, and how they said it, and individuals paid more attention to what they were saying to others. For instance, it was within the realm of possibility that a man in a grocery store, having a casual conversation with someone else, mentions his disagreement with the United States government. Surprised by the FBI at his home a few hours later, he is informed that the individual he had the conversation with at the grocery store believed that his disagreement with the United States government was grounds for informing federal law enforcement of possible terrorist actions.Not only did scenarios such as this happen, but the government monitored telephone conversations. The law was that telephone conversations can be monitored by law enforcement or by the telephone company. The telephone company can monitor conversations for a number of reasons, including to provide service, inspect the telephone system, monitor the quality of the service, or to protect against service theft or harassment. However, law enforcement can only listen in on telephone conversations with â€Å"probable cause.† (â€Å"Wiretapping/Eavesdropping†, 1993) In other words, if one is known to be a hit man, law enforcement can eavesdrop on that individual's telephone conversations not only to find out if he will go through with committing murder, but also to find out who else is involved. Law enforcement must obtain a court order to eavesdrop on others telephone conversations. However, after September 11th, it was rumored that the federal government monitored all telephone conversations for key words such as bomb, terrorist, etc.The Bush administration repeatedly insisted that the only telephone conversations they eavesdropped on without court orders were those who were suspected of being linked to al Qaida or other terrorist groups. It is true, however, that after September 11th, the Bush administration made efforts to collect vast amounts of information about Americans' travel, tax and medical records, e-mails, and credit card purchases. (Landay, 2008) This was all done under the guise of the Patriot Act, which essentially made the Fourth Amendment null and void.In addition to listening in on telephone conversations, the United States watches the American public through surveillance cameras. Thousands of cameras, both public and private, dot parks and city streets. Once an individual is out in public, the Courts deem those individuals as no longer having any privacy, at least while they are in public. Most people are not aware that they a re being watched. If they do know, they do not control what their images are being used for. Most cameras are mounted in trees, on streetlight and traffic poles, on public buildings, on subway platforms, and installed in buses and subway cars.These cameras are everywhere, and there are more that cannot be seen. Police officials refuse to tell the public about where the other cameras are because they claim that information would â€Å"undermine law enforcement's effectiveness. One of the major problems with hidden cameras in public areas is that cameras penetrate deeper than anyone staring at an individual. If another person is staring at someone, all that person has to do is stare back to discourage the intrusion. However, one cannot stare back at a camera if they do not know where it is. Even if they did know where it was, the eye of the camera would not stop staring.People behave differently when they think they are alone, and even if one does know about the cameras, the cameras then do not fix the problem. Hidden cameras serve as â€Å"super cops. † These cameras can zoom in to single out a particular individual or to read a letter someone is holding, and can see in the dark due to infrared technology. In the past, police could not do this without probable cause and obtaining a search warrant. In addition to these benefits to law enforcement, cameras can be put in places where a human being could not possibly be, such as perched high atop the side of a building.These cameras were originally touted as tools to aid in the catching of terrorists and violent criminals, and to prevent serious crimes. The cameras have not done this. The only criminals these cameras have caught are minor offenders such as petty thieves and concert-ticket scalpers. For example, in Washington D. C. , New York City, and San Diego, cameras that were originally meant to catch serious offenders now only catch red-light runners, speeders, and others who park illegally. The proble m is this: The faces of random people on the street are being compared with those of criminals.All of this is being done with no probable cause. Law enforcement targets ethnic and racial minorities, and that coupled with false-positive matches means that innocent people will be arrested for no apparent reason. Even though the Supreme Court has never tried a case where someone claimed the Fourth Amendment was violated because of public surveillance, the Court would most likely find that electronic monitoring of public areas is not a violation of the Fourth Amendment. Technology is beginning to take over American society.Each intrusion into Americans' privacy is being introduced as a tool to weed out the harmful individuals. Drug testing and EZ Passes are good examples. At first, drug testing was only used for high security jobs, and now students in extracurricular activities at school are subject to them. EZ Passes were introduced in order to lessen traffic congestion, and now they a re being used to issue tickets to speeders. Every tool introduced as being â€Å"important† and â€Å"helpful† in the fight against crime is now being used to trap innocent citizens, citizens who at first thought these tools were a good idea.Sociologist Gary Marx explains, â€Å"Once the new surveillance systems become institutionalized and taken for granted in a democratic society, they can be used against those with the ‘wrong' political beliefs; against racial, ethnic, or religious minorities; and against those with lifestyles that offend the majority. † No one will use public areas if they believe or know they are being watched. The author believes that spaces that are accessible, not defensive, will be used more. The more people use these areas, the safer they will be.There are more good people than bad in the world, therefore, the chances of someone getting attacked in a group of people are extremely slim. Video surveillance creates insecurity, not a sense of safety. Congress has not yet addressed video surveillance. Hawaii and California have laws to limit video surveillance, and a handful of states have heightened protection of the right to privacy written into their state constitutions. However, even though video surveillance is more intrusive than telephone monitoring, there is currently no federal legislation to govern video surveillance. (Smithsimon, 2003)In conclusion, the Fourth Amendment of the United States Constitution is being violated in today's society due to telephone monitoring and video surveillance. Telephone monitoring and video surveillance have secretly crept upon the average American. Most do not give a second thought, or even know, about these types of violations of privacy. Perhaps the average American is aware of the possibility, but tries not to think about such a disturbing and chilling thought. If the average American citizen knew that someone was watching them as they went about their daily business , feelings of paranoia and possibly fear would begin to take root.America would not feel free any longer. This can be likened to driving at the speed limit when a police officer is driving within close proximity. One is on his/her best behavior, however, when the police officer can no longer be seen, that same person that was on their best behavior just moments before, resumes their fast, reckless ways. However, there is no escape from the cameras that could possibly be watching each and every American. It is amazing how much Americans take for granted, including the civil liberties, the rights and privileges, that the American holds so dear.– (1993). Wiretapping/Eavesdropping on Telephone Conversations: Is There Cause for Concern? Retrieved May 18, 2009, from Privacy Rights. http://www. privacyrights. org/fs/fs9-wrtp. htm – (2001). Kyllo v. United States. Retrieved May 18, 2009, from Find Law. http://caselaw. lp. findlaw. com/scripts/getcase. pl? navby=CASE&court=US&v ol=533&page=27 – (2009). U. S. Constitution: Fourth Amendment. Retrieved May 18, 2009, from Find Law. http://caselaw. lp. findlaw. com/data/constitution/amendment04/ – Chandler, S. A. (Fall 2006). Collateral Damage?The Impact of National Security Crises on the Fourth Amendment Protection against Unreasonable Searches. University of Pittsburgh Law Review. 68(1), 217-41. – Landay, Jonathan S. (2008). Did U. S. Government Snoop on Americans' Phone Calls? Retrieved May 18, 2009, from McClatchy Newspapers. http://www. mcclatchydc. com/257/story/53703. html – Pike, John. (2000). TEMPEST. Retrieved May 18, 2009, from Intelligence Resource Program. http://www. fas. org/irp/program/security/tempest. htm – Smithsimon, M. (Winter 2003). Private Lives, Public Spaces: The Surveillance State. Dissent. 50(1), 43-9.

The Long Road to Womens Suffrage 1848 to 1920