Grass Detection for Picture Quality Enhancement of TV Video

Current image enhancement in televisions can be improved if the image is analyzed, objects of interest are segmented, and each segment is processed with specifically optimized algorithms. In this paper we present an algorithm and feature model for segmenting grass areas in video sequences. The system employs adaptive color and position models for creating a coherent grass segmentation map. Compared with previously reported algorithms, our system shows significant improvements in spatial and temporal consistency of the results. This property makes the proposed system suitable for TV video applications

Grass Detection for Picture Quality Enhancement of TV Video

Current image enhancement in televisions can be improved if the image is analyzed, objects of interest are segmented, and each segment is processed with specifically optimized algorithms. In this paper we present an algorithm and feature model for segmenting grass areas in video sequences. The system employs adaptive color and position models for creating a coherent grass segmentation map. Compared with previously reported algorithms, our system shows significant improvements in spatial and temporal consistency of the results. This property makes the proposed system suitable for TV video applications

Grass Detection for Picture Quality Enhancement of TV Video

Abstract. Current image enhancement in televisions can be improved if the image is analyzed, objects of interest are segmented, and each segment is processed with specifically optimized algorithms. In this paper we present an algorithm and feature model for segmenting grass areas in video sequences. The system employs adaptive color and position models for creating a coherent grass segmentation map. Compared with previously reported algorithms, our system shows significant improvements in spatial and temporal consistency of the results. This property makes the proposed system suitable for TV video applications.