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Smart Traffic Light Controller Using Edge Detection in Digital Signal Processing
Published in Anuj Singal, Sandeep Kumar, Sajjan Singh, Ashish Kr. Luhach, Wireless Communication with Artificial Intelligence, 2023
Aayush Chibber, Anuarg, Rohit Anand, Jagtar Singh
Image Scaling/Resizing: Image scaling takes place in all the digital photographs at one stage or another. This scaling may be in the Bayer Demosaicing stage [24] or the photo-enlargement stage. It occurs when we rescale any image concerning the pixel grid. Image scaling is mandatory whenever there is a requirement to enlarge or diminish the various pixels in any image. Even if the same image rescaling is done (i.e., rescaled image is of identical dimensions as the real-time image being captured), the result can change remarkably depending upon the procedure through which the image is rescaled because of the various causes. Since the different cameras have different resolutions, it may not be valid for other camera specifications whenever a system is developed for some particular criteria. Hence, it becomes mandatory to frame the constant resolution specification for the image and hence accomplish image rescaling.
Watermarking Attacks and Tools
Published in Frank Y. Shih, Digital Watermarking and Steganography: Fundamentals and Techniques, 2017
Image scaling can have two types: uniform and nonuniform scaling. Uniform scaling uses the same scaling factors in the horizontal and vertical directions. However, nonuniform scaling applies different scaling factors in the horizontal and vertical directions. In other words, the aspect ratio is changed in nonuniform scaling. An example of nonuniform scaling using x scale = 0.8 and y scale = 1 is shown in Figure 5.8. Note that nonuniform scaling will produce the effect of object shape distortion. Most digital watermarking methods are designed to be flexible only to uniform scaling.
Watermarking Attacks and Tools
Published in Frank Y. Shin, Digital Watermarking and Steganography, 2017
Image scaling can be of one of two types: uniform or nonuniform. Uniform scaling uses the same scaling factors in horizontal and vertical directions. However, nonuniform scaling applies different scaling factors in horizontal and vertical directions. In other words, the aspect ratio is changed in nonuniform scaling. An example of nonuniform scaling using xscale = 0.8 and yscale = 1 is shown in Figure 5.8. Note that the nonuniform scaling will produce the effect of object shape distortion. Most digital watermarking methods are designed to be flexible only to uniform scaling.
A framework for fast automatic image cropping based on deep saliency map detection and gaussian filter
Published in International Journal of Computers and Applications, 2019
Ziaur Rahman, Yi-Fei Pu, Muhammad Aamir, Farhan Ullah
Image cropping is used to remove unwanted area from images. It is an important functionality of many application. It is a fact that images are extensively used in social media as the latest innovations in photography, and it is evident from the use of digital cameras with sophisticated lens, and high-resolution camera as a function of modern smart phones has become one of the requirement of this era. Normally, images are available in the form of personal and internet photo-groups, massive in size with the aim of sharing information at different levels [1]. We need an operation to identify the meaningful parts of images and discard unwanted area from images such that to keep focus on important contents of images. We know that images represent visual information, and thus, it has been an essential and interesting task to identify the common and visible object. However, in many cases, it is quite complex due to some properties of the image i.e. background colors, viewpoint disparity and illumination conditions. Alternatively, a unique attention model that imitate human system is exploited to extract visually clear objects from an image and then output will be a saliency map. Most of the cropping methods are based on attention model to remove the unwanted area from the images as shown in Figure 1. Extracting visual saliency has received rising attention in recent years and has wide range application in image processing and computer vision tasks i.e. Object detection [2], content-aware image resizing [3], person re-identification [4], and there are many different methods proposed to deal with various saliency cues [5–13]. Previously, many models have been presented using patch and region-based because both methods apply well and extract saliency from simple images. Whereas, for complex images there are certain issues. People’s focus on certain objects when they captured photos. To identify the unwanted portion from many images to preserve the visual composition is time-consuming and boring. To solve this issue, automatic methods are used to make it easier to zoom the relevant information. In this paper, deep CNN is used to detect robust saliency map from images having simple and complex background. The cropping technique, which depends on saliency map is used to cut the unwanted part and to enhance the image composition [14]. The use of Gaussian filter, and image scaling for image cropping does not affect the cropping process and the change in Gaussian filter values and scale down the saliency map, preserves more contents of the image, also boost up the cropping process and results.