Plagiarism detection

Rectification of camera captured document images for Camera-based OCR technology Abstract Due to the rapid progress in digital cameras industry, camera captured documents becomes as another possibility or choice for document capturing and understanding for OCR applications. However, image quality degradations arising from the image acquisition process have severe effect on these applications. The distortion results from digital camera may take the shape of skew, perspective distortion or geometric

human lie detection is fairly accurate, earlier research has found the opposite. According to Wallace (1999), psychological research on deception shows that most of us are poor judges of truthfulness. One may assume that this only applies to only ordinary people and not professionals. However further research shows that ‘this applies to professionals such as police and custom inspectors, whose jobs are supposed to include some expertise at lie detection’ (Wallace, 1999)

Corner detection and its parameters: position, model and orientation are useful for many computer vision applications, such as object recognition, matching, segmentation, 3D reconstruction, motion estimation [2, 3, 4, 34.] indexing, retrieval, robot navigation and in our case edge tracking from geometry design. This need has driven the development of a large number of corner detectors [1, 5, 6, 7, 8, 9, 10, 11, 12, 13.]. Other methods for corner detection are described in [14, 15]. These detectors

be useful, yet still narrow enough to be completed in approximately two years. One of the most frustratingly difficult problems for autonomous vehicles is obstacle detection and avoidance. For cars especially, the diversity of the types of obstacles they may come across is what really breaks down even the most complicated detection methods. A very common example is a machine learning system which has been trained on large sets of obstacle images; the first time it runs

In life you will come across many obstacles, some of these you may have caused, others you may have not. How do we determine which is which? How do we determine what is the truth and what is a lie? In 1917, William M. Marston would say the polygraph test or lie detector as it is often called. In the twenty-first century, it would probably be witnesses and physical evidence. Which is more accurate and efficient? Is it fair to determine a person 's life sentence just by asking a few questions regarding

A lie is defined as a false statement made with deliberate intent to deceive; an intentional untruth; a falsehood. Why do people lie? What is the point of lying? How can liars be spotted? With the help of Pamela Meyer, these questions were able to be answered. The two points and statements that I found the most interesting that Meyer made during her “Ted Talk” was that 1 in every 10 interactions with a spouse. Also, one in every five interactions that college students have with their mom has a lie

There are many types of lies. The numerous kinds helps one to describe a specific sort with only a few words. This paper will examine more closely one of these types, a fabrication. More precisely fabrication lies and their presence in propaganda, such as in a pamphlet produced by “The Flying Squadrons of Speakers”. Fabrication lies are most clearly defined as lies that could be true, but the one who states them is not basing them on any real direct knowledge, they may also be a falsification of

1) automatic service dependency detection, 2) attack prediction, and 3) application-level DDoS attack protection. 1 Automatic Service Dependency Detection Previous work on service dependency detection include many different approaches. In order to compare the previous work, we first introduce some desirable effectiveness criteria for a dependency detection approach. Later, we use these criteria for reviewing and classifying the previous work. 1.1 Dependency Detection Effectiveness Metrics One can

been used to develop both land and aerial based systems for fruit detection and harvesting. Machine vision in harvesting is broadly based on visual cues and properties of fruit. Machine vision based systems have an advantage as apart from recognition of colour, texture and shape of any object, numerical points for the object are also defined. The aim of fruit detection is characterized by the following desirable outcomes: 1. Detection of each distinct fruit. 2. Discrimination of the detected fruit

II FEATURE EXTRACTION Feature extraction is the third stage in medical image processing application, after image pre-processing. In feature extraction, the features like the shape, colour, texture are used to describe an image content[bio2].features can be short relevance or strong relevant ones. Short relevant features give only little information about the image, while strong relevant features provide significant information about the image. Finding these strong relevant features are time consuming