Photomosaic Generation Research Articles

Generating Mosaic Images Based On Texture Analysis

Photo-mosaic (mosaic-image) generation is the process of dividing an input image into equal rectangular blocks (image blocks), each of which is replaced with another image (tile image) that matches the features of a corresponding image block. When the produced photo mosaic is seen from a distance, it seemingly forms the input image. Photo mosaic is not a new concept; however, only a few publications on this subject are available because of its commercial nature. The quality of the produced photo mosaic depends on the size of the database and the variety of images. In addition to process time, a bottleneck occurs because of the discrepancy in the match rate between the input image and the produced photo mosaic. This research proposes three intelligence-based approaches for producing photo mosaics in less time: (1) k-means clustering with Manhattan distance, (2) back propagation neural network with Manhattan distance, and (3) hybrid fuzzy logic with Elman neural network. Three groups of features are extracted and then used to find the best matching tile image for each corresponding image block within the container image. The first group comprises statistical features extracted from a 64-gray level quantized histogram. These features are variance, mean, skewness, kurtosis, and energy. The second group is a subset of Tamura features, namely, coarseness, contrast, and directionality. The last group includes a feature called edge rate, which is computed as the percentage of edge pixels within an image that can be detected using a Canny filter. In addition, two methods of color correction are used to adjust the colors of a tile image to match the colors of a corresponding image block. The first method is based on mean, whereas the second is based on histogram specification. Finally, experimental results show that hybrid fuzzy logic with Elman neural network is the best among the three approaches used. This technique needs 10.0 seconds to produce photo mosaics, with a correlation rate of 0.82 between the container image and the produced photo mosaics,using mean-based color correction. By contrast, this approach needs 42.33 seconds to produce photo mosaics, with a correlation rate of 0.86, using histogram specification-based color correction.

Mosaicking UAV orthoimages using bounded Voronoi diagrams and watersheds

ABSTRACTPanoramic mosaicking is a key step in the process of aerial photogrammetry, which combines multiple images to form a single image with a wide field of view. In this article, a refined global seam-line network generation method based on bounded Voronoi diagrams and watershed segmentation algorithm is proposed for mosaicking unmanned aerial vehicle (UAV) orthoimages. First of all, valid range polygons and image centres are extracted from orthoimages. Second, an initial seam-line network is generated by a bounded Voronoi diagrams and polygon Boolean operations. Third, the seam-line network is refined by marker-controlled watershed segmentation algorithm. Finally, a photomosaic is produced by multiband blending mosaic. The panoramic mosaicking approach is comprehensively discussed in terms of data volume and degree of overlapping. Experimental studies performed on several groups of UAV orthoimages show that the proposed approach provides a feasible and promising solution to large-scale UAV photomosaic generation.

적응적 타일링 및 블록 매칭을 통한 포토 모자이크 알고리즘

모자이크란 여러 가지 빛깔의 재료를 조각조각 붙여서 무늬나 영상을 만드는 기법을 말하며, 최근에는 디지털 이미징 기술의 발달로 인하여 사진을 이용하여 영상을 만드는 포토 모자이크 기술들이 활용되고 있다. 본 논문에서는 적응적 타일링 및 블록 매칭을 통하여 포토 모자이크 영상을 만드는 컴퓨터 알고리즘을 제안한다. 제안한 알고리즘은 사진 데이터베이스의 생성 단계와 포토 모자이크 생성 단계로 구성된다. 사진 데이터베이스란 모자이크에 사용되는 사진(또는 타일)을 의미하며, 타일을 <TEX>$4{\times}4$</TEX>로 분할한 후에 각 영역의 RGB 평균값을 특징값으로 저장한다. 포토 모자이크 생성 단계는 입력 영상에 대하여 기 설정된 블록 크기로 분할한 후에 특징을 추출하는 과정, 인접한 블록들 사이의 유사도를 비교하여 병합하는 적응적 타일링 과정, 적응적 타일링을 통해 생성된 블록들을 사진 데이터베이스의 타일들과 유클리드 차이로 유사도를 비교하여 유사한 타일을 찾는 블록 매칭 과정 및 매칭된 타일의 명암값을 해당 블록의 명암값으로 교체하여 영상의 유사도를 높이는 밝기값 조정 과정으로 구성된다. 또한 인접 블록간 타일의 중복성을 최소화하는 기법을 적용하여 영상의 품질도 향상하였다. 제안한 알고리즘의 성능을 분석하기 위하여 안드레아 모자이크 소프트웨어와 비교하였고, 정량적인 분석 및 정성적인 분석에 있어서 제안한 알고리즘이 우수한 것으로 나타났다. Mosaic is to make a big image by gathering lots of small materials having various colors. With advance of digital imaging techniques, photomosaic techniques using photos are widely used. In this paper, we presents an automatic photomosaic algorithm based on adaptive tiling and block matching. The proposed algorithm is composed of two processes: photo database generation and photomosaic generation. Photo database is a set of photos (or tiles) used for mosaic, where a tile is divided into <TEX>$4{\times}4$</TEX> regions and the average RGB value of each region is the feature of the tile. Photomosaic generation is composed of 4 steps: feature extraction, adaptive tiling, block matching, and intensity adjustment. In feature extraction, the feature of each block is calculated after the image is splitted into the preset size of blocks. In adaptive tiling, the blocks having similar similarities are merged. Then, the blocks are compared with tiles in photo database by comparing euclidean distance as a similarity measure in block matching. Finally, in intensity adjustment, the intensity of the matched tile is replaced as that of the block to increase the similarity between the tile and the block. Also, a tile redundancy minimization scheme of adjacent blocks is applied to enhance the quality of mosaic photos. In comparison with Andrea mosaic software, the proposed algorithm outperforms in quantitative and qualitative analysis.

GPU-based SoftAssign for Maximizing Image Utilization in Photomosaics

Photomosaic generation is a popular non-photorealistic rendering technique, where a single image is assembled from several smaller ones. Visual responses change depending on the proximity to the photomosaic, leading to many creative prospects for publicity and art. Synthesizing photomosaics typically requires very large image databases in order to produce pleasing results. Moreover, repetitions are allowed to occur which may locally bias the mosaic. This paper provides alternatives to prevent repetitions while still being robust enough to work with coarse image subsets. Three approaches were considered for the matching stage of photomosaics: a greedy-based procedural algorithm, simulated annealing and SoftAssign. It was found that the latter delivers adequate arrangements in cases where only a restricted number of images is available. This paper introduces a novel GPU-accelerated SoftAssign implementation that outperforms an optimized CPU implementation by a factor of 60 times in the tested hardware.