Visibility Estimation of Traffic Signals under Rainy Weather Conditions for Smart Driving Support

Abstract-The aim of this work is to support a driver by notifying the information of traffic signals in accordance with their visibility. To avoid traffic accidents, the driver should detect and recognize surrounding objects, especially traffic signals. However, when driving a vehicle under rainy weather conditions, it is difficult for drivers to detect or to recognize objects existing in the road environment in comparison with fine weather conditions. Therefore, this paper proposes a method for estimating the visibility of traffic signals for drivers under rainy weather conditions by image processing. The proposed method is based on the concept of visual noise known in the field of cognitive science, and extracts two types of visual noise features which ware considered that they affect the visibility of traffic signals. We expect to improve the accuracy of visibility estimation by combining the visual noise features with the texture feature introduced in a previous work. Experimental results showed that the proposed method could estimate the visibility of traffic signals more accurately under rainy weather conditions

Recognition of Very Low-resolution Characters from Motion Images

Abstract. Many kinds of digital devices can easily take motion images such as digital video cameras or camera-equipped cellular phones. If an image is taken with such devices under everyday situations, the resolution is not always high; moreover, hand vibration can cause blurring, making accurate recognition of characters from such poor images difficult. This paper presents a new character recognition algorithm for very low-resolution video data. The proposed method uses multi-frame images to integrate information from each image based on a subspace method. Experimental results using a DV camera and a phone camera show that our method improves recognition accuracy

Reconstructing Face Image from the Thermal Infrared Spectrum to the Visible Spectrum

During the night or in poorly lit areas, thermal cameras are a better choice instead of normal cameras for security surveillance because they do not rely on illumination. A thermal camera is able to detect a person within its view, but identification from only thermal information is not an easy task. The purpose of this paper is to reconstruct the face image of a person from the thermal spectrum to the visible spectrum. After the reconstruction, further image processing can be employed, including identification/recognition. Concretely, we propose a two-step thermal-to-visible-spectrum reconstruction method based on Canonical Correlation Analysis (CCA). The reconstruction is done by utilizing the relationship between images in both thermal infrared and visible spectra obtained by CCA. The whole image is processed in the first step while the second step processes patches in an image. Results show that the proposed method gives satisfying results with the two-step approach and outperforms comparative methods in both quality and recognition evaluations