Computer
Lafta R. Al-Khazraji; Ayad R. Abbas; Abeer S. Jamil
Abstract
Deep Dream (DD) is a new technology that works as a creative image-editing approach by employing the representations of CNN to produce dreams-like images by taking the benefits of both Deep CNN and Inception to build the dream through layer-by-layer implementation. As the days go by, the DD becomes widely ...
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Deep Dream (DD) is a new technology that works as a creative image-editing approach by employing the representations of CNN to produce dreams-like images by taking the benefits of both Deep CNN and Inception to build the dream through layer-by-layer implementation. As the days go by, the DD becomes widely used in the artificial intelligence (AI) fields. This paper is the first systematic review of DD. We focused on the definition, importance, background, and applications of DD. Natural language processing (NLP), images, videos, and audio are the main fields in which DD is applied. We also discussed the main concepts of the DD, like transfer learning and Inception. We addressed the contributions, databases, and techniques that have been used to build the models, the limitations, and evaluation metrics for each one of the included research papers. Finally, some interesting recommendations have been listed to serve the researchers in the future.
Computer
Tameem Obaida; Nidaa Flaih Hassan; Abeer Salim Jamil
Abstract
This Face detection is considering one of the important topics for recognizing human, it is the first step before the face recognition process, it is considered one of the biggest challenges in the field of vision computer. In recent years Many algorithms for detection have appeared, which depend on ...
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This Face detection is considering one of the important topics for recognizing human, it is the first step before the face recognition process, it is considered one of the biggest challenges in the field of vision computer. In recent years Many algorithms for detection have appeared, which depend on extracting the features of the human face, and works continue to develop them to this day. This paper aims to make a comparison between two of the most commonly face detection methods, Viola Jones (V_J) and YOLO v3. This comparison is made to determine which of the two algorithms is being most useful when used to detect faces in digital video. These algorithms are used in many applications, including image classification, medical analysis of image, and objects detection in real time (especially in surveillance cameras). Both algorithms are applied to detect faces in the real time video. The experimental results of a sample consists of 20 video frames show that V_J algorithm consumes less time in comparison with YOLO v3 algorithm, but its results are less accurate, unlike the YOLO v3 algorithm, which is slower in detect face with high accurate rate.
