Towards virtual keyboard design and realization, the work in this paper presents a robust key input method for deployment in virtual keyboard systems. The proposed scheme harnesses the information contained within shadows towards robustifying virtual key input. This scheme allows for input efficiency to be guaranteed in situations of relatively lower illumination, a core challenge associated with virtual keyboards. Contributions of the paper are twofold. Firstly the paper presents an approach towards effectively applying shadow information towards robustifying virtual key input systems; Secondly, through morphological operations, the performance of this input method is boosted by means of effectively alleviating noise and its impacts on overall algorithm performance, while highlighting the necessary features towards an efficient performance. While previous contributions have followed a similar trend, the contribution of this paper stresses on the intensification and improvement of both shadow and fingertip feature highlighting schemes towards overall performance improvement. Experimental results presented in the paper demonstrate the efficiency and robustness of the approach. The attained results suggest that the scheme is capable of attaining high performances in terms of accuracy while being capable of addressing false touch situations.

Images acquired under the influence of bad weather conditions such as haze, fog and other aerosols are deteriorated due to the dispersal of atmospheric particles which lead to color fading and contrast reduction, making it challenging for human interpretation and object feature recognition. Several methods for image haze removal have been introduced over the recent years, which consist of approaches used to extrapolate information such as contrast, scene depth, color channels and so on. In this paper, we present a concise review of the current image dehazing methods. A comprehensive assessment and development on existing methods and related techniques is conducted based on their individual characteristics and principles. Qualitative and quantitative experimental evaluation of the state-of-the-art methods are conducted and discussed in depth. The paper further puts forward an overview of future trends within the research area.

In this paper, we address the image dehazing problem through a global feature-restoration pipeline. We propose a dark channel prior-based global image dehazing algorithm which captures and restores the true features of pixels within haze-degraded regions by applying scene depth selection and adaptive filtering. Our scheme harnesses haze and depth features intuitively across a given image without the prior scene depth information. This allows our scheme to sustain a high dehazing efficiency across all image regions irrespective of the local depth variations. We prove that haze degradation is linearly correlated with scene depth and based on this nuance, propose a depth selection and cropping scheme, which guides the adaptive filter iteratively across the image. Secondly, we put forward haze relevant image features and highlight the dark-channel prior for image dehazing. We merge the dark channel prior and scene depth-cropping schemes into a unified dehazing pipeline which is capable of sustaining uniform and robust results across all image regions, in real-time. We verify the superiority of the proposed scheme in terms of speed and robustness through computer-based experiments. Finally, we present comparison results with state-of-the-art and further highlight the comparative superiority of our scheme.

Images acquired under deprived weather environment are frequently corrupted due to the presence of haze, mist, fog or other aerosols in a form of noise. Haze elimination is essential in computer vision and computational photography applications. Generally, there is the existence of numerous approaches towards haze removal which are mostly meant for hazy images under daytime environments. Although the potency of these proposed approaches has been comprehensively established on daylight hazy images. However these procedures inherit significant limitations on images influenced by night-time hazy environments. Since night time haze removal dehazing remains an ill-posed problem, we proposed a novel method for night-time single image dehazing which is efficient under night-time environments. The proposed scheme is a dark channel-based local image dehazing procedure that locally estimates the atmospheric intensity for each selected mask on a corrupted image independently and not the entire image. This is done in order to overcome the challenge of night-scenes that are exposed to multiple/artificial lights source and spatially non-uniform environmental illumination. We performed an adaptive filtering on the combined dehazed masks to improve the degraded image. We validated the supremacy of the proposed approach in terms of speed and robustness through computer-based experiments. Conclusively, we displayed comparison results with state-of-the-art and extensively emphasized the comparative advantage of our scheme.