Efficient Poisson Image Editing

Image composition refers to the process of composing two or more images to create a natural output image. It is one of the important techniques in image processing. In this paper, two efficient methods for composing color images are proposed. In the proposed methods, the Poisson equation is solved using image pyramid and divide-and-conquer methods. The proposed methods are more efficient than other existing image composition methods. They reduce the time taken in the composition process while achieving almost identical results using the previous image composition methods. In the proposed methods, the Poisson equation is solved after converting it to a linear system using different methods. The results show that the time for composing color images is decreased using the proposed methods

Efficient Poisson Image Editing

Image composition refers to the process of composing two or more images to create an acceptable output image. It is one of the important techniques of image processing. In this paper, two efficient methods for composing color images are proposed. In the proposed methods, the Poisson equation is solved using image pyramid, and divide-and-conquer methods. The proposed methods are more efficient than other existing image composition methods. They reduce the time taken in the composition process while achieving almost identical results using the previous image composition methods. In the proposed methods, the Poisson equation is solved after converting it to a linear system using different methods. The results show that the time for composing color images is decreased using the proposed methods

A New Approach to Automatic Clothing Matting from Mannequins

It is crucial to extract retail clothes from images of mannequins when building a database of clothing images for virtual try-on systems. However, clothes often have complex texture and translucent material, such as holes and laces. It is thus difficult to extract clothes as foreground by existing generic natural image matting methods. Hence in this paper, we present a novel approach to automatic clothing matting from mannequins, with auxiliary information from a rough background image of the mannequin only. Experiments show that we can achieve remarkable improvement on the alpha matte near challenging regions of complex texture and translucent material of clothes. Moreover, our approach can automatically generate trimaps to facilitate the development and evaluation of other image matting algorithms

Image-based clothes changing system

Abstract Current image-editing tools do not match up to the demands of personalized image manipulation, one application of which is changing clothes in usercaptured images. Previous work can change single color clothes using parametric human warping methods. In this paper, we propose an image-based clothes changing system, exploiting body factor extraction and content-aware image warping. Image segmentation and mask generation are first applied to the user input. Afterwards, we determine joint positions via a neural network. Then, body shape matching is performed and the shape of the model is warped to the user’s shape. Finally, head swapping is performed to produce realistic virtual results. We also provide a supervision and labeling tool for refinement and further assistance when creating a dataset.https://deepblue.lib.umich.edu/bitstream/2027.42/136772/1/41095_2017_Article_84.pd

Weighted color and texture sample selection for image matting

Color information is leveraged by color sampling-based matting methods to find the best known samples for foreground and background color of unknown pixels. Such methods do not perform well if there is an overlap in the color distribution of foreground and background regions because color cannot distinguish between these regions and hence, the selected samples cannot reliably estimate the matte. Similarly, alpha propagation based matting methods may fail when the affinity among neighboring pixels is reduced by strong edges. In this paper, we overcome these two problems by considering texture as a feature that can complement color to improve matting. The contribution of texture and color is automatically estimated by analyzing the content of the image. An objective function containing color and texture components is optimized to choose the best foreground and background pair among a set of candidate pairs. Experiments are carried out on a benchmark data set and an independent evaluation of the results show that the proposed method is ranked first among all other image matting methods

Weighted color and texture sample selection for image matting

Color sampling based matting methods find the best known samples for foreground and background colors of unknown pixels. Such methods do not perform well if there is an overlap in the color distribution of foreground and background regions because color cannot distinguish between these regions and hence, the selected samples cannot reliably estimate the matte. Furthermore, current sampling based matting methods choose samples that are located around the boundaries of foreground and background regions. In this paper, we overcome these two problems. First, we propose texture as a feature that can complement color to improve matting by discriminating between known regions with similar colors. The contribution of texture and color is automatically estimated by analyzing the content of the image. Second, we combine local sampling with a global sampling scheme that prevents true foreground or background samples to be missed during the sample collection stage. An objective function containing color and texture components is optimized to choose the best foreground and background pair among a set of candidate pairs. Experiments are carried out on a benchmark data set and an independent evaluation of the results shows that the proposed method is ranked first among all other image matting methods