Corner Detection Are Useful for Computer Vision Applications Essay

[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

It may be possible to visualise such relationship for two- or three-dimensional problems, yet, a computer algorithm will be needed to optimise the decision boundary in higher-dimensional feature spaces.

Local feature methods are entirely based on descriptors of local regions in a video, no prior knowledge about human positioning nor of any of its limbs is given. In the following subsections, these categories are discussed in further.

The ASM model was proposed by Cootes et al [8]. This was used for feature extraction by characterizing

Inspired by the natural image super-resolution literature [21], [31]–[34], Fang et al. [12], [13] have tried to denoise and interpolate SDOCT images by proposing a fast coupled dictionary learning approach. They clustered image patches space to learn a couple of LR-HR dictionaries for each subspace. Although the idea of adaptive dictionary selection has shown to be very effective for various image restoration problems [22], [23],

In order to evaluate which approach is better in this field, some standardized databases and benchmarks for experiment are designed. Many databases are designed for different kinds of methods, owing that different methods may have different assumptions on shapes. A commonly used database is 99shapes, by Kimia et al. It contains ninety nine planar shapes which classified into nine classes, with eleven shapes in each. Shapes in the same class are in different variant form, including occluded, noised, rotated, etc. Other databases including MPEG-7 Shape Dataset [5], Articulated Dataset, Swedish Leaf Dataset and Brown Dataset are used to have further experiments. Similar to [13], Precision and Recall is used for benchmark for the reason of fair comparisons.

High-level feature matching based on finding shapes and objects implies knowledge of a mathematical model or template of a target shape. This technique is a model-based in which the shape is extracted by searching for the best correlation between a known model and the pixels of the image. Hough transform represents an efficient implementation of computing the correlation between the template and the image by extracting simple shapes such as lines, circles, and ellipses.

After finding K similar patches for each patch, the next step is to combine information from these patches to obtain a better estimate of the sparse representation of the HR patch. To compute a sparse representation for each patch , we need to remove its missing values, and the dictionary rows that are corresponding to the missing values. This can be done by a left matrix multiplication, similar to the previous section. Also, it is important to remove the mean intensity of each patch due to numerical stability issues [33]. We show the vector of observed pixels and the dictionary corresponds to it as , and , respectively. The columns of the must be normalized to unit one. This is a common practice to avoid scaling issues [39]. Thus, we

Abstract—This paper presents a comprehensive comparison of different high pass filtering techniques for Edge detection in both time domain and frequency domain. The paper examines various kernels and compares the efficiency of the filtering technique against the computation time for various sizes of images with various sizes of high-pass filter kernels. We have made use of Sobel filter as the standard filter kernel against which other techniques are compared.

Information gathered at one camera location can be linked to another which makes it a wide-area detection system

In computer vision we can track a human’s movement, we can create a 3D model of an area using large number of photographs, face detection and recognition and large number of other applications. [3]

Abstract- Interactive image segmentation has become more and more popular among researched in recent years. Interactive segmentation, as opposed to fully automatic one, supplies the user with means to incorporate his knowledge into the segmentation process. However, in most of the existing techniques, the suggested user interaction is not good enough since the user cannot intuitively force his knowledge into the tool or edit results easily. Therefore, in ambiguous situation, the user has to revert to tedious manual drawing. Presented method to develop as a combined segmentation and editing tool. It incorporates a simple user interface and a fast and reliable segmentation based on 1D segment matching. The user is required to click just a few "control points" on the desired object border, and let the algorithm complete the rest. The user can then edit the result by adding, removing and moving control points, where each interaction is follows by an automatic, real-time segmentation by the algorithm.

Feature plays a very important role in the area of image processing. Different feature extraction

Using right algorithm, can make image sensor sense or detect practically anything. Image sensors are one of the important sensors been used in robotics industry because they are so flexible, but there are two drawbacks with these kind of sensors: 1)they output lots of data, dozens of megabytes per second, and 2) processing this amount of data can overwhelm many processors. And even if the processor can keep up with the data, much of its processing power won’t be available for other tasks.

Normally the number plate has a rectangular shape with aspect ratio, so number plate can be extracted by finding all possible rectangles from the input vehicle image. Various edge detection methods commonly used to find these