Chapter 1: Camera Modeling And Computer Video

For activity 2, the frame is run through a thresholding function that creates a binary image based on the histogram levels that are similar to that of a stop sign. Any pixel that falls within the levels are then converted to a value of one and the rest are set to zero. The binary image is then put through two morphological operators, open and close. The open morphological operator gets rid of smaller clutters of pixels as defined by the parameters that are passed in to the function. The close morphological operator fills in clusters of pixels that are the shape of the parameter passed to the function. In this case, the shape is an octagon just like a stop sign. The last step is to run the binary image through blob analysis to get the ROIs that are of a specified minimum size. Once the ROIs have been found, they are passed into the cascade object detector for verification of

You do this because, all points have been translated in a straight line, in the same direction (the direction is [7 -1]) by the same distance, the new figure A'B'C'D' would have the same shape, same orientation and same size as the original image. It would just be in a different location on the graph. If you were to join A with A', B with B', and so on, you would see that all 4 of the vectors are parallel and are all equal length.

Answer: - In the raster data model, a grid is used to cover the space and the value of each cell called as pixel in the grid and it corresponds to the characteristic of the geographic feature at the cell location. The cell is the smallest unit in the grid which is a matrix of

I will then compare the limitations of different software packages used in graphics work. (M1)

These data items for recovering a tile image T are integrated as a five-component bit stream of the form in which the bit segments represent the values of the index of B, the rotation angle of T , the means of T and B, the standard deviation quotients, and total number of tiles, respectively. In more detail, the numbers of required bits for the five data items in M are

Another step in pre-processing is to make the image width and height divisible by 8. Let w and h represent the width and height of the image respectively. w and h are converted into w* and h* such that 8|w* and 8|h* are as follows in (1) and (2):

are the pixels used in the feature detection. The pixel at C is the centre of a detected

known components of the image to approximate a real image. Figure 4.5 shows the three

Texture is one of the crucial primitives in human vision and texture features have been used to identify contents of images. Examples are identifying crop fields and mountains from aerial image domain. Moreover, texture can be used to describe contents of images, such as clouds, bricks, hair, etc. Both identifying and describing characteristics of texture are accelerated when texture is integrated with color, hence the details of the important features of image objects for human vision can be provided. One crucial distinction between color and texture features is that color is a point, or pixel, property, whereas texture is a local-neighborhood property. The main motivation for using texture is the identifying and describing

to produce and utilize the information about the original position of each segment from the raw

Digital photography was the reason for the graphics editing software such as Adobe Photoshop were developed, and has changed the way people take pictures. The digital camera has been in development since George Smith and Willard Boyle invented the charged-coupled device (CCD) which is an image sensor. The original intended function of the CCD was to be the new semi-conductor memory for computers. By 1975 image quality improved greatly and was able to be broadcasted over television. Since then cameras have improved eminently through the years, progressively advancing and functioning more dynamically. A digital image is a long string of 1s and 0s that represent all the pixels in an image. Just as with film cameras, the image that is recorded is visible light or flash. When the light bounces off the subject and is captured in the camera, it breaks into light patterns and is converted into a series of pixel values. The light is then converted into electric charges which are then used by the CCD or CMOS (Complementary Metal Oxide Semiconductor) to record images.

If the camera information is precise, there will be excellent correlation between the synthetic scene and the scene from the video frame. When differences exist, ground control points will be generated and a transformation matrix will be derived to determine a more precise camera model. By feeding this precise camera model as well as a position computed using dead-reckoning like position predictions into a filtering algorithm such as a Kalman filter, we can integrate multiple data sources for the most precise camera position information. This will allow the overlay to be precise even when GPS signals are poor, or visibility on the UAV video feed is poor due to dust or clouds.

A two dimensional digital image is a representation of an image as a set of digital values, called pixels. Each pixel is associated to a specific position X_ij in the image and has an intensity value d (X_ij ) with information about the color and intensity at the point〖 X〗_ij. An image of 8 bit is usually stored as a matrix with integer entries between 0 and 255, where 0 is the weakest intensity and 255 is the strongest. Gray scale images are composed of shades of gray. Each pixel has a single value that tells us intensity, varying from 0 corresponding to black and 255 corresponding to white. The RGB color model is one possible model in which to represent a color image. Shades of red, green and blue are combined create other colors. A pixel in color image then consists of three integer values between 0 and 255. The values define the intensity of red, green and blue respectively. It is possible to represent 2563 = 16777216 different colors in this

measures between brightness distributions. Experiments performed on picture with 3 faces display that the 3 bin-by-bin measures along with Minkowski-shape distance, Chi-rectangular distance and Bhattacharyya reserve provide a real positive value of 56.76%, 59.46% and 48.65% respectively, while cross-bin measures such as Earth Movers Distance (EMD), Match distance and Kolmogorov-Smirnov distance deliver decrease genuine fantastic rates of 32.43%, 32.43% and 37.44r% respectively. Results of threshold based totally method for picture with faces display that the three bin-by way of-bin measures which includes Minkowski-shape distance, Chi-square distance and Bhattacharyya distance deliver a true wonderful price of sixty three.16%, 63.16% and 60.53% respectively, whereas go-bin measures which

1.1.1. Image processing: Image processing is a methodology to perform some operations on an image, so as to urge an enhanced image or to extract some helpful data from it. It is treated as an area of signal processing where both the input and output signals are images. Images are portrayed as two dimensional matrix, and we are applying already having signal processing strategies to input matrix. Images processing finds applications in several fields like photography, satellite imaging, medical imaging, and image compression, just to name a few. Basically Image processing includes the following steps:  Reading the image via image acquisition tools like cameras, caners etc.  Analysing and manipulating the acquired image to have enhanced quality and locate the data of interest;  Output in which result can be altered image or report that is based on image analysis. Originally image processing is proposed for space exploration and biomedical field. But later on with the increase in use of digital images in everybody’s lives it considered as powerful tool for arbitrarily manipulating images to gain useful information. It defined as the means of conversion between human visual system and digital imaging devices.The main purpose of image processing are listed below: 1. Visualization - Observe the objects which are not visible. 2. Image sharpening and restoration - To increase quality of image. 3. Image retrieval –