Complex Deep Learning and Evolutionary Computing Models in Computer Vision

Li Zhang, Chee Peng Lim, Jungong Han
2019 Complexity  
Computer vision, which deals with how computers can be used to gain high-level understanding pertaining to information contained in digital images or videos, is an important, yet challenging, technology. e advent of deep learning and associated paradigms such as evolutionary computing models has propelled computer vision to the next level, solving a variety of complex problems in diverse applications, such as object detection, motion tracking, semantic segmentation, and emotion recognition. In
more » ... his special issue, recent advances with respect to mathematical modeling, simulation, and/or analysis of deep learning and evolutionary computing models for undertaking complex phenomena in computer vision, are presented. A variety of problems, which include automatic object tracking, understanding image content, optical character recognition, personalized recommendation and movie summarization systems, and underwater video streaming, are covered. A description of each article is presented, as follows. e paper titled "Multimodal Deep Feature Fusion (MMDFF) for RGB-D Tracking" addresses the limitation of existing tracking methods in processing geometrical features extracted from depth images by using a multimodal deep feature fusion model. e proposed model consists of four deep convolutional neural networks (CNNs). It extracts RGB (red, green, blue) and depth features from images using RGBspecific and depth-specific CNN models and exploits their correlated relationship using an RGB-depth correlated CNN model. In addition, a motion-specific CNN is used to provide high-level motion information for object tracking. Empirical evaluation with two RGB-depth datasets demonstrates that the proposed method achieves better performances,
doi:10.1155/2019/1671340 fatcat:wars4wlyxbc5rgf2huppco2gmu