Changwon Jeon

Adaptive height-modified histogram equalization and chroma correction in YCbCr color space for fast backlight image compensation☆

Abstract Automatic exposure controls in commercially available cameras often encounter difficulties in capturing scenes with backlight luminance which dominates the entire image. An Adaptive Height-Modified Histogram Equalization (AHMHE) algorithm is proposed as a compensation technique for backlight images. It simultaneously enhances contrast in both the dark and the bright areas without creating regions of degraded local contrast. Moreover AHMHE is an adaptive algorithm: thus it requires minimal user input, and its reduced computational requirement makes it suitable for real-time application. In addition to AHMHE, a chroma correction technique was applied to chroma components in the YCbCr color space to produce more vivid color images. A series of subjective and index evaluations were conducted to measure the resultant image quality improvements by the AHMHE and the chroma correction algorithms.

Design of Audio-Visual Interface for Aiding Driver’s Voice Commands in Automotive Environment

This chapter describes an information-modeling and integration of an embedded audio-visual speech recognition system, aimed at improving speech recognition under adverse automobile noisy environment. In particular, we employ lip-reading as an added feature for enhanced speech recognition. Lip motion feature is extracted by active shape models and the corresponding hidden Markov models are constructed for lip-reading . For realizing efficient hidden Markov models, tied-mixture technique is introduced for both visual and acoustical information. It makes the model structure simple and small while maintaining suitable recognition performance. In decoding process, the audio-visual information is integrated into the state output probabilities of hidden Markov model as multistream features . Each stream is weighted according to the signal-to-noise ratio so that the visual information becomes more dominant under adverse noisy environment of an automobile. Representative experimental results demonstrate that the audio-visual speech recognition system achieves promising performance in adverse noisy condition, making it suitable for embedded devices.