Trimap Generation
218777548
Interactive Trimap Generation for Digital Matting Based on Single Sample Learning
Image matting refers to the task of estimating the foreground of images, which is an important problem in image processing. Recently, trimap generation has attracted considerable attention because designing a trimap for every image is labor intensive. In this paper, a two step algorithm is proposed to generate trimaps. To use the proposed algorithm, users must only provide some clicks (foreground clicks and background clicks) which are employed as the input to generate a binary mask. One shot learning technique achieves remarkable progress on semantic segmentation, we extend this technique to perform the binary mask prediction task. The mask is further used to predict the trimap using image dilation. Extensive experiments were performed to evaluate the proposed algorithm. Experimental results show that the trimaps generated using the proposed algorithm are visually similar to the user annotated ones. Comparing with the interactive matting algorithms, the proposed algoritm is less labor intensive than trimap based matting algorithm and achieved more accuate results than scribble based matting algorithm.
Zhenpeng Chen Yuanjie Zheng Xiaojie Li Rong Luo Weikuan Jia Jian Lian Chengjiang Li
212419919
Robust trimap generation based on manifold ranking
Abstract In this paper, we propose a simple and effective method for creating accurate trimaps based on input images. Most advanced matting algorithms require the user to provide prior information to estimate high quality alpha masks, where the prior information is primarily in the form of trimaps. A precise trimap is one of the most important factors affecting the performance of the matting algorithm. It is a very tedious task for users to specify a large number of accurate trimaps, and it is even impractical in some applications. Based on manifold ranking, we use strokes to mark the superpixel nodes to create high quality trimaps. The experimental results show that the method given in this paper can generate high quality trimaps, thus ensuring the accuracy of the alpha masks that are estimated by the matting algorithm. We verify the performance of the trimaps that are created using the method given in this paper for various matting algorithms.
Inf. Sci.
Jinjiang Li Genji Yuan Hui Fan
182628543
Automatic trimap generation and artifact reduction in alpha matte using unknown region detection
Abstract Image Matting is an actively researched topic due to its massive impact on the filmmaking industry. It refers to the accurate extraction of the foreground from an image and is a severely under constrained problem. To constrain this issue, user input in the form of a trimap is required. However, users must generate this trimap manually, which is an exhausting and time consuming process. To tackle this issue, we develop an expert system which is capable of generating trimaps automatically without any user interaction. We utilize three different techniques which use machine learning at the core. Our expert system receives knowledge from these three techniques which are processed by our system to generate trimaps automatically. We strongly believe that our study will have a strong impact on the filmmaking industry. Our study lays the groundwork for automatic trimap generation. Furthermore, our study can be applied to expert and intelligent systems related to image retrieval and automatic foreground/primary object segmentation in images or videos. We propose a simple yet effective approach to generate optimal trimaps automatically by combining image saliency, graph cut segmentation (lazy snapping) and fuzzy c means clustering (FCM) Lazy snapping is an interactive segmentation technique that requires foreground and background scribbles as input. Instead of using user provided foreground scribbles, we utilize a saliency map as foreground scribbles and input it to the lazy snapping. This results in a coarsely segmented foreground object. We use the corners as the background scribbles based on the assumption that most foreground objects are located in the center of the image. To generate an optimal trimap, we locally cluster the boundary region of the foreground segmentation using FCM. We tested our algorithm on alpha matting evaluation and salient object datasets. In addition to generating accurate trimaps automatically, the alpha mattes generated by our optimal trimaps contain fewer artifacts as compared to trimaps generated by previous works. Moreover, the alpha mattes computed using our optimal trimaps were computed faster as compared to computing alpha mattes with trimaps from previous works. We showed in our experiments that optimal trimaps can improve alpha matte quality by reducing artifacts. Finally, our approach does not rely on depth data like the previous methods. Our experiments show the effectiveness of our method.
Expert Syst. Appl.
Chris Henry Sang-Woong Lee
7902556
Automatic Trimap Generation and Consistent Matting for Light Field Images
In this paper, we introduce an automatic approach to generate trimaps and consistent alpha mattes of foreground objects in a light field image. Our method first performs binary segmentation to roughly segment a light field image into foreground and background based on depth and color. Next, we estimate accurate trimaps through analyzing color distribution along the boundary of the segmentation using guided image filter and KL divergence. In order to estimate consistent alpha mattes across sub images, we utilize the epipolar plane image (EPI) where colors and alphas along the same epipolar line must be consistent. Since EPI of foreground and background are mixed in the matting area, we propagate the EPI from definite foreground/background regions to unknown regions by assuming depth variations within unknown regions are spatially smooth. Using the EPI constraint, we derive two solutions to estimate alpha when color samples along epipolar line are known, and unknown. To further enhance consistency, we refine the estimated alpha mattes by using the multi image matting Laplacian with an additional EPI smoothness constraint. In experimental evaluations, we have created a dataset where the ground truth alpha mattes of light field images were obtained by using the blue screen technique. A variety of experiments show that our proposed algorithm produces both visually and quantitatively high quality alpha mattes for light field images.
IEEE Transactions on Pattern Analysis and Machine Intelligence
Donghyeon Cho Sunyeong Kim Yu-Wing Tai In-So Kweon
195675055
Automatic Trimap Generation and Consistent Matting for Light Field Images.
In this paper, we introduce an automatic approach to generate trimaps and consistent alpha mattes of foreground objects in a light field image. Our method first performs binary segmentation to roughly segment a light field image into foreground and background based on depth and color. Next, we estimate accurate trimaps through analyzing color distribution along the boundary of the segmentation using guided image filter and KL divergence. In order to estimate consistent alpha mattes across sub images, we utilize the epipolar plane image (EPI) where colors and alphas along the same epipolar line must be consistent. Since EPI of foreground and background are mixed in the matting area, we propagate the EPI from definite foreground/background regions to unknown regions by assuming depth variations within unknown regions are spatially smooth. Using the EPI constraint, we derive two solutions to estimate alpha when color samples along epipolar line are known, and unknown. To further enhance consistency, we refine the estimated alpha mattes by using the multi image matting Laplacian with an additional EPI smoothness constraint. In experimental evaluations, we have created a dataset where the ground truth alpha mattes of light field images were obtained by using the blue screen technique. A variety of experiments show that our proposed algorithm produces both visually and quantitatively high quality alpha mattes for light field images.
IEEE transactions on pattern analysis and machine intelligence
290018
Automatic trimap generation for image matting
Image matting is an important problem in computational photography. Although, it has been studied for more than two decades, yet there is a challenge of developing an automatic matting algorithm which does not require any human intervention. Most of the state of the art matting algorithms require human intervention in the form of trimap or scribbles to generate the alpha matte form the input image. In this paper, we present a simple and efficient approach to automatically generate the trimap from the input image and make the whole matting process free from human in the loop. We use learning based matting method to generate the matte using the automatically generated trimap. Experimental results demonstrate that our method produces good quality trimap which results into accurate matte estimation. We validate our results by replacing the automatically generated trimap by manually created trimap while using the same image matting algorithm.
2016 International Conference on Signal and Information Processing (IConSIP)
Vikas Gupta Shanmuganathan Raman
206508013
Automatic trimap generation for digital image matting
Digital image matting is one of the most popular topics in image processing in recent years. For most matting methods, trimap serves as one of the key inputs, and the accuracy of the trimap affects image matting result a lot. Most existing works did not pay much attention to acquiring a trimap; instead, they assumed that the trimap was given, meaning the matting process usually involved users' inputs. In this paper, an automatic trimap generation technique is proposed. First, the contour of the segmentation result is dilated to get an initial guess of the trimap followed by alpha estimation. Then, a smart brush with dynamic width is performed by analyzing the structure of the foreground object to generate another trimap. In other words, the brush size is enlarged if the object boundary contains fine details like hair, fur, etc. On the contrary, the brush size gets smaller if the contour of the object is just a simple curve or straight line. Moreover, by combining the trimap obtained in step one and downsampling the image, the uncertainty is defined as the blurred region, and the third trimap is formed. The final step is to combine these three trimaps together by voting. The experimental results show that the trimap generated by the proposed method effectively improves the matting result. Moreover, the enhancement of the accuracy of the trimap results in a reduction of regions to be processed, so that the matting procedure is accelerated.
2013 Asia Pacific Signal and Information Processing Association Annual Summit and Conference
Chang-Lin Hsieh Ming-Sui Lee
12322090
Human Identification with Finger Veins Using Repeated Line Tracking Even Gabor and Automatic Trimap Generation Algorithms
Finger vein is a unique physiological biometric which is used for identification of individuals based on the physical characteristics and parameters of the vein patterns in the human body. This technology is at present in use or development for a wide range of applications, which includes credit card authentication [1] security in automobile, employee time and tracking attendance [1] computer and network authentication, security at end points and automated teller machines [1] In this paper, different level of techniques are discussed and analyzed i.e. Repeated Line Tracking, Gabor Filter, Automatic Trimap generation and Graph Matching a comparatively evaluate them with more popular score level fusion approaches to ascertain their effectiveness in this proposed system. The basic principle, different feature extraction techniques and performance measuring are analyzed. Mostly the existing work is functionally described and compared in three parts (i.e. Finger vein acquired image, pre processing and feature extraction) Keywords Finger vein, Gabor Filter, Repeated line Tracking and Automatic Trimap Generation Algorithm. INTRODUCTION Image processing is a method to convert an image into digital form and perform some operations on it, in order to get an enhanced image or to extract some useful information from it. It is a type of signal dispensation in which input is image, like video frame or photograph and output may be image or characteristics associated with that image. Usually Image Processing system includes treating images as two dimensional signals while applying already set signal processing methods to them. Image Processing forms core research area within engineering and computer science disciplines too. Image processing basically includes the following three steps: Importing the image with optical scanner or by digital photography. Analyzing and manipulating the image which includes data compression and image enhancement and spotting patterns that are not to human eyes like satellite photographs. Output is the last stage in which result can be altered image or report that is based on image analysis. Finger vein is a unique physiological biometric for identifying individuals based on the physical characteristics and attributes of the vein patterns in the human finger [5] It is a fairly recent technological advance in the field of biometrics that is being applied to different fields such as medical [5] financial, law enforcement facilities and other applications where high levels of security or privacy is very vital. And this technology is very impressive because it requires only small and relatively cheap single chip design, and has an identification process that is contact less and of higher accuracy when compared with other identification biometrics like fingerprint, iris, facial and others. This higher accuracy rate of finger vein is not unconnected with the fact that finger vein patterns are virtually impossible to forge thus it has become one of the fastest growing new biometric technology that is quickly finding its way from research labs to commercial development. Historically, R&D at Hitachi of Japan (1997 2000) discovered that finger vein pattern recognition was a viable biometric for personal authentication technology and by 2000 2005 were the first to commercialize the technology into different product forms, such as ATMs. Their research reports false acceptance rate (FAR) of as low as 0.0001 and false reject rate (FRR) of 0.1% Today 70% of major financial institutions in Japan are using finger vein authentication. Finger vein based biometric system has several benefits when compared with other hands based on the biometric methods. Figure 1: Hitachi of Japan history of research development on finger vein recognition Technology Firstly, the finger vein pattern is very hard to replicate. In addition, the quality of the captured vein pattern is not easily influenced by their conditions of skin. As compared with palm vein based verification system, the size of the device can be made smaller. In the end, finger vein recognition does not require contact between the finger and the sensor, which is desirable for a hygienic viewpoint. Most of the current available approaches for finger vein recognition have similarities on the feature extraction method which utilized the features from the segmented blood vessel network for recognize output. However, due to the optical blurred and skin scattered Prabjot kaur et al, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (6) 2014, 8003 8007
Prabjot Kaur Mr Prince Verma
37681350
A novel framework for automatic trimap generation using the Gestalt laws of grouping
In this paper, we are concerned with unsupervised natural image matting. Due to the under constrained nature of the problem, image matting algorithms are usually provided with user interactions, such as scribbles or trimaps. This is a very tedious task and may even become impractical for some applications. For unsupervised matte calculation, we can either adopt a technique that supports an unsupervised mode for alpha map calculation, or we may automate the process of acquiring user interactions provided for a matting algorithm. Our proposed technique contributes to both approaches and is based on spectral matting. The latter is the only technique in the literature that supports automatic matting but it suffers from critical limitations among which is the unreliable unsupervised operation. Stressing on that drawback, spectral matting may produce erroneous mattes in the absence of guiding scribbles or trimaps. Using the Gestalt laws of grouping, we propose a method that automatically produces more truthful mattes than spectral matting. In addition, it can be used to generate trimaps, eliminating the required user interactions and making it possible to harness the powers of matting techniques that are better than spectral matting but don't support unsupervised operation. The main contribution of this research is the introduction of the Gestalt laws of grouping to the matting problem.
Electronic Imaging
Ahmad Al-Kabbany Eric Dubois
15200024
Automatic Trimap Generation with Structured Lighting and Chromakey
This project has as an objective of the extraction of humans in the foreground of image by creating a trimap which combines a depth map analysis and the Chromakey technique. The trimap is generated automatically, differing from the manual implementations which require user interaction. The extraction is based on extra information deriving from a structured lighting device (Kinect) integrated with a high resolution camera. With the junction of the monochromatic Kinect camera and the high definition camera, the results so far have been more expressive than only using the RGB and monochromatic cameras from the Kinect.
Antonio C T Blaia Junior Daniel Zuniga Vielmas Henrique Ferraz de Arruda Ivan Abdo Aguilar Antonio Carlos Sementille