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GVF SnakeBased Ice Floe Boundary Identification and Ice Image Segmentation
Published in Qin Zhang, Roger Skjetne, Sea Ice Image Processing with MATLAB®, 2018
where ux,uy,vx,vy $ u_{x},u_{y},v_{x},v_{y} $ are the derivatives of the vector field in the x and y directions of the image, respectively, and μ $ \mu $ is a regularization parameter that controls the balance between the first and second order terms in the integrand. The edge map f, which is an image gradient taking a larger value on image edges, can be derived by using any edge detector. If the features of interest are other image features rather than edges, f can be redefined to be larger at the desired features.
Analysis of coding unit partitioning and complexity reduction at intra-prediction mode of HEVC
Published in The Imaging Science Journal, 2022
Yogita M. Vaidya, Shilpa P. Metkar
An image gradient is defined as the directional change in image intensity. Thus, the gradient evaluation and estimation can be effectively used to represent content complexity in a video sequence. In the continuous domain, and are the rates of change of function along the x- axis and the y-axis, respectively. The rate of change in any direction θ is expressed as where u is a variable in the direction θ. Accordingly, the direction in which this rate of change has the greatest magnitude is given Similarly the magnitude is given as The vector represented by this magnitude and direction is called the gradient of g(r,c) and denoted by For video frame with random pixel (i,j) the first differences are used instead of the first derivative and expressed as The magnitude of the gradient is given by The direction of maximum change is expressed as To reduce computational complexity is defined as Equations 8 and 10 are summarized as point operator Op such that Thus, the gradient of the given image is obtained by using the transformation as shown by Eq. 11 ‘ is the difference operator.
Smoothing via elliptic operators with application to edge detection
Published in Inverse Problems in Science and Engineering, 2018
Mohammad F. Al-Jamal, A. K. Alomari, Mark S. Gockenbach
The image gradient is a key ingredient in image processing and computer vision, particularly in detecting edges or in processing images with edges. Assuming the image intensities are sampled from a continuous intensity function f, the image gradient at each pixel x is defined by . Since in practice we only have a discrete intensity function, the image gradient is usually approximated using a finite difference operator or other operators such as the Sobel operator.
Multilevel weather detection based on images: a machine learning approach with histogram of oriented gradient and local binary pattern-based features
Published in Journal of Intelligent Transportation Systems, 2021
Md Nasim Khan, Anik Das, Mohamed M. Ahmed, Shaun S. Wulff
The first step of computing the HOG feature is to calculate the gradients of the image. The image gradient is defined as a directional change in pixel intensity in both the x-axis and y-axis. The gradient vector of a pixel at location can be described using Equation 1.