Obtaining Motion Blur Parameters Form The Frequency Spectrum

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):

Frequency means the number of cycles per second and depending on the amount of cycles per second determines how high or low pitched the sound is and the time that it takes to complete one cycle is called the period. Frequency is measured in Hertz (Hz). And An average human is able to hear sounds between 20Hz and 20,000Hz.

The simplest way to put is Frequency means how fast is the signal is changing. In this graph what we have used is a small sample of an audio recording of my voice. Sound waves can be examined in relationships of their amplitude and frequency. In the frequency analysis graph the recoding is then plotting a graph of frequency (x-axis, in Hz) versus intensity (y-axis, in dB). Thus allowing us to be able to analyze the recording signals with respect to frequency. As in which the other graph allows us to measure the voice recording in respect with time. So in the frequency analysis graph we can see the the higher the dB’s are the higher the pitch is within that portion of the audio compared to another fragment of the recording where the dBs are lower. The frequency of a audio wave affects the pitch of the sound we hear. In the first graph we are able to see the length and how much time/space the sinusoidal signals recoding takes by the amplitude and the

The measure of velocity is distance divided by time. We also need to establish that the term “vehicle” in our case means a sedan or light truck. We also need to know the relationship between mph and fps. Mph =miles per hour, fps = feet per second. We use mph mutiplied by (1.467)=fps.

The recent development ensures the popularity of CBIR, since it has been applied in many real world applications such as life sciences, environmental and health care, digital libraries and social media such as facebook, youtube, etc. CBIR understands and analyzes the visual content of the images [20]. It represents an image using the renowned visual information such as color, texture, shape, etc [11, 12]. These are often referred as basic features of the image, which undergoes lot of variations according to the need and specifications of the image [7-9]. Since the image acquisition varies with respect to illumination, angle of acquisition, depth, etc, it is a challenging task to define a best limited set of features to describe the entire image library.

It covers most of the posterior region of the wall. When a human eye focuses on an object, the light that is reflected by the object falls on the retina and the image is formed on the wall. The discrete light receptors that are spread over the wall of the retina forms the image. These receptors are of two types namely cones and rods. Cones are less in number approximately 6 to 7 million and are concentrated in the region called fovea. These cones gives the clear image with fine details because these have their individual nerve connection where as, the rods give the general pattern of the image because many rods are connected to one nerve. Rods are numerous approximately 75 to 150 million which are spread over the wall of the retina. The interesting phenomena here is that the objects appear as colorless in the nighttime because only rods get stimulated as the cones are color

These relate to each other. When the speed is increased, frequency is increased as well. When wavelength is increased, frequency is decreased. Wavelength is speed divided by frequency. If frequency is equal to a whole number, the sound will appear fuller. Musicians use these techniques to make pleasing music.

The new value is obtained by applying a certain function to each input pixel and its direct neighbors. These neighbors are usually the 8 adjacent pixels (in a 3 x 3 filter) or the 24 surrounding pixels (in a 5 x 5 filter).

bBiomedical Engineering, Vanderbilt University, Nashville, TN USA, cInstitute of Image Science, Vanderbilt University, Nashville, TN USA, dComputer Science, Vanderbilt University, Nashville, TN USA.

The usual technique is to find points on the floor of the target image, which is used as the base of image, and then to give an initial two-dimensional curve and make it astringe to the edge of the target according to dynamics mechanism. This approach is appropriate when the general form of the target is fixed. But human tongue is not always flat when it is extended out of the mouth. There will be some variations in the forms and therefore it is not suitable for this algorithm. We adopt the component H (Hue) and V (Value) of HSV space to decide the initial position of tongue.

[4] Sayyed Mohammad Hosseini (2016) et. al. present that In this paper a new method for detection of camera tampering is proposed. Some examples for camera tampering are: shaking the camera, movement of the camera, occlusion, and rotation of the camera. The tampering may be intentional or unintentional. In the proposed algorithm, in addition to detection of the exact nature of tampering, the exact amount of tampering also can be detected (i.e. the amount and direction of movement). This will help operator in diction making for management in surveillance system. The proposed algorithm detect the shaking using current and previous frames, as well as by constructing a total background based on all frames and building a temporary background based on last 10 frames. The proposed method employs the SURF feature detector to find interest points in both of two backgrounds and compare and match them using MSAC algorithm. The transformation matrix can be obtained to detect the camera movement; camera image zoom and camera rotate. Finally, using the method sobel edge detection the camera occlusion and defocus can be detected. The method also detect the sudden shut downs in camera or images loss. Another feature of the

3D calibration: After the 2D track is generated the matchmoving program solve the camera using these

Although mathematics and film are not commonly associated together, most films’ success is dependent on math. Various formulas are required in many aspects of film, such as budgeting and creating a consistent pattern of camera shots. Film editing, specifically, very rigorously involves mathematics through the use of algorithms in filters, percentages in creating an effect sequence, adjusting the x-axis and y-axis when motion tracking an effect to an object in a shot to create a special effect.

The idea behind this technique is to set a background as a reference or model and then compare the current frame with the reference background in a pixel-by-pixel manner to detect the moving foreground object. The background model is updated with a new image from time to time in order to track dynamic changes. Figure 4 shows background subtraction

Satellites send images to earth. The information from these images cannot be accessed directly. The images sent are related by a 4 dof similarity transformation [1], because the camera motion is of a translation to the image plane and the rotational motion is about the principle axis. To view the complete 3D image i.e. the full view of the objects in space, proper resolution, less blurring Image Mosaicing (IM) is used for these satellite