A system and a method are provided for displaying text in low-light environments. An original image of text is captured in a low-light environment using a camera on a mobile device, whereby the imaged text comprising images of characters. A brightness setting and a contrast setting of the original image are adjusted to increase the contrast of the imaged text relative to a background of the original image. Optical character recognition is applied to the adjusted image to generate computer readable text or characters corresponding to each of the imaged text. The original image of text is displayed on the mobile device. The computer readable text is also displayed, overlaid the original image, wherein the computer readable text is aligned with the corresponding imaged text.

System and method for displaying text in augmented reality

An image enhancement technique and its evaluation through bimodality analysis

This paper proposes a novel road sign detection and recognition method. The position of a road sign in an image is detected using the projection technique. The hidden Markov model is then used to match the detected road sign with those in the database so that the goal of road sign recognition can be achieved. More specifically, the color images in terms of the RGB color system are first converted to the HSV color system and then quantized into specific colors. The horizontal and vertical projections of whole images in the specific colors existing in road signs are then used to detect the positions of road signs. In the recognition stage, only local features around the detected positions are used and a two-step strategy is adopted. The horizontal and vertical projections of background in the local area are used to prune irrelevant road signs. The candidate road signs are then sorted using the hidden Markov model. The one with the first rank is regarded as the recognition result. The effectiveness of the proposed method has been demonstrated by various experiments.

Road Sign Detection and Recognition Using Hidden Markov Model

In this paper, we present a real-time hardware implementation of an exponent-based tone mapping algorithm of Hore et al., that uses both local and global image information for improving the contrast and increasing the brightness of tone-mapped images. Although there are several tone mapping algorithms available in the literature, most of them require manual tuning of their rendering parameters. However, in our implementation, the algorithm has an embedded automatic key parameter estimation block that controls the brightness of the tone-mapped images. We also present the implementation of a Gaussian-based halo-reducing filter. The hardware implementation is described in Verilog and synthesized for a field programmable gate array device. Experimental results performed on different wide dynamic range images show that we are able to get images which are of good visual quality and have good brightness and contrast. The good performance of our hardware architecture is also confirmed quantitatively with the high peak signal-to-noise ratio and structural similarity index.

An FPGA implementation of a tone mapping algorithm with a halo-reducing filter

Projection for pattern recognition

It is asserted that, in many instances, the statistical properties of images do not vary significantly from one TV frame period to the next. It should be possible therefore to produce inexpensive hardware for high-speed histogram modification of TV-rate images. The system described performs TV-rate image enhancement of the current frame using picture statistics collected several frame periods earlier. The total materials cost of the unit is less than £300.

P. A. Daniels

Papers

A histogram modification unit for real-time image enhancement