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Object Recognition in Outdoor Environment: A Deep Learning Framework

Deep Learning has become a rapidly growing research area, redefining state-of-theart performances in a wide range of applications such as object recognition, image segmentation, speech recognition, natural language processing etc. Considering the capability of deep learning to address largescale data and learn high-level representation, deep learning can be a powerful and effective solution for real world challenges in object recognition. Object recognition facilitates many applications, ranging from indoor to outdoor, such as surveillance, security, counting etc. It aims to find and identify objects in a given image. For this purpose, image quality plays a very important role. The image inferior in quality may lead to poor results and thereby degrade the performance of an entire application. The quality degradation can occur due to several noises and environmental effects. Various methodologies exist in the literature which deals with such images.
Recognition of objects in outdoor scenes may degrade during image acquisition and transmission due to various noises and environmental factors such as haze, fog etc. It causes contrast reduction, colour fading, loss of features and many more.


Dr. Nischal K. Verma
Indian Institute of Technology, Kanpur.