Crazy 'Bout a Mercury

When we purchase an automobile, we are also acquiring an amorphous but very real image, that is, the statement which the automobile makes about its owner to the public. Such images are forged in popular culture, and Mercury is an automobile brand that had an auspicious post-WWII popular culture debut. In 1948, K.C. Douglas recorded “Mercury Boogie” on a 10-inch 78-RPM, with its memorable line in the chorus “I’m crazy ‘bout a Mercury.” Five years later in 1953, George and Sam Barris transformed a 1951 Mercury Club Coupe into the Hirohata Merc, creating a classic of customization that has been described as “the most famous custom of all time” (Taylor 2006: 56). Ford occasionally attempted to take advantage of these strong roots in popular culture formed in the make’s earliest days, but the company’s efforts were not notably successful. In spite of Mercury’s promising beginnings in media, it has had only a slight presence in music and film. Mercury’s image never influenced the automobile market beyond the first few years, and it was unable to prevent the brand’s 2011 demise.

Semi-supervised learning for raindrop removal on a single image

This article propose s a network that is mainly used to deal with a single image polluted by raindrops in rainy weather to get a clean image without raindrops. In the existing solutions, most of the methods rely on paired images, that is, the rain image and the real image without rain in the same scene. However, in many cases, the paired images are difficult to obtain, which makes it impossible to apply the raindrop removal network in many scenarios. Therefore this article proposes a semi-supervised rain-removing network apply to unpaired images. The model contains two parts: a supervised network and an unsupervised network. After the model is trained, the unsupervised network does not require paired images and it can get a clean image without raindrops. In particular, our network can perform training on paired and unpaired samples. The experimental results show that the best results are achieved not only on the supervised rain-removing network, but also on the unsupervised rain-removing network.

Misrepresentation of Orientals in Dave Eggers’s A Hologram for The King

This study deals with the colonial perspectives in Dave Eggers’s A Hologram for The King (2012), according to the postcolonial approach. Although colonialism era is over by now, colonial perspectives remain strong in some literary works. Since its advent in the second half of the twentieth century, postcolonial theory confronts colonial attitudes and experiences as colonialism has been justified in many works of Western writers and scholars who have distorted the real image of non-Europeans and non-Westerners via different means and techniques in masquerade of orientalism. Postcolonial discourse opposes the misrepresentation of non-Europeans and argues that such falsification is driven by political, social, religious and economic motives. In the current study, the researcher aims at explaining the notions of colonialism, otherization and other falsified images of non-Westerners in A Hologram for the King. This paper mainly questions Eggers’s portrayal of the protagonist, Alan Clay, who after bankruptcy and failure at home, flies to Saudi Arabia and capitalizes on the physical and moral assets of the Orientals in this country to convert his story of failure to a success. The characterization of the oriental world and its setting show Eggers’s being biased against the Eastern world and ironically mirror clear hints of colonialism and eurocentrism.

Number Plate Recognition System using MATLAB

This paper presents a system called NPR (Number Plate Recognition) which is based on image processing and is used to detect the number plates of vehicles and process them to record the information. In a fast-growing world, it has become almost impossible to track illegal vehicles and store vehicle information. This is eventually leading to a rise in the crime rate, especially due to manual errors. The proposed system first captures the vehicle image and the vehicle number plate region is extracted using Image Segmentation in an image. The resulting data is then used to compare with the records on a database to come up with specific information like the vehicle's owner, place of registration, address, etc. Further, the system is implemented and simulated in MATLAB for studying feasibility and accuracy on a real image.

A Pansharpening Generative Adversarial Network with Multilevel Structure Enhancement and a Multistream Fusion Architecture

Deep learning has been widely used in various computer vision tasks. As a result, researchers have begun to explore the application of deep learning for pansharpening and have achieved remarkable results. However, most current pansharpening methods focus only on the mapping relationship between images and the lack overall structure enhancement, and do not fully and completely research optimization goals and fusion rules. Therefore, for these problems, we propose a pansharpening generative adversarial network with multilevel structure enhancement and a multistream fusion architecture. This method first uses multilevel gradient operators to obtain the structural information of the high-resolution panchromatic image. Then, it combines the spectral features with multilevel gradient information and inputs them into two subnetworks of the generator for fusion training. We design a comprehensive optimization goal for the generator, which not only minimizes the gap between the fused image and the real image but also considers the adversarial loss between the generator and the discriminator and the multilevel structure loss between the fused image and the panchromatic image. It is worth mentioning that we comprehensively consider the spectral information and the multilevel structure as the input of the discriminator, which makes it easier for the discriminator to distinguish real and fake images. Experiments show that our proposed method is superior to state-of-the-art methods in both the subjective visual and objective assessments of fused images, especially in road and building areas.