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—Image quality is an important practical challenge that is often overlooked in the design of machine vision systems. Commonly, machine vision systems are trained and tested on high quality image datasets, yet in practical applications the input images can not be assumed to be of high quality. Recently, deep neural networks have obtained state-of-the-art(More)
—Existing saliency models have been designed and evaluated for predicting the saliency in distortion-free images. However, in practice, the image quality is affected by a host of factors at several stages of the image processing pipeline such as acquisition, compression and transmission. Several studies have explored the effect of distortion on human visual(More)
Deep neural networks (DNNs) achieve excellent performance on standard classification tasks. However, under image quality distortions such as blur and noise, classification accuracy becomes poor. In this work, we compare the performance of DNNs with human subjects on distorted images. We show that, although DNNs perform better than or on par with humans on(More)
With the increased focus on visual attention (VA) in the last decade, a large number of computational visual saliency methods have been developed. These models are evaluated by using performance evaluation metrics that measure how well a predicted map matches eye-tracking data obtained from human observers. Though there are a number of existing performance(More)
Visual saliency models have recently begun to incorporate deep learning to achieve predictive capacity much greater than previous unsupervised methods. However, most existing models predict saliency using local mechanisms limited to the receptive field of the network. We propose a model that incorporates global scene semantic information in addition to(More)
This paper introduces a method for analyzing floor plan images using wall segmentation, object detection, and optical character recognition. We introduce a challenging new real-estate floor plan dataset, R-FP, evaluate different wall segmentation methods, and propose fully convolutional networks (FCN) for this task. We explore architectures with different(More)
This paper presents a novel method for static gesture recognition based on visual attention. Our proposed method makes use of a visual attention model to automatically select points that correspond to fixation points of the human eye. Gesture recognition is then performed using the determined visual attention fixation points. For this purpose, shape context(More)
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