• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2013.11a
• /
• pp.213-216
• /
• 2013

본 논문에서는 객체 위주의 컬러 영상 검색을 위하여 영상의 saliency map을 이용해 객체 중심의 영상을 생성하고, 객체와 그 주변 영역에서의 대표 색상이 가지는 통계적 특성과 공간적 분포 정보를 이용하는 방법을 제안한다. 먼저, 영상의 saliency map을 이진화하여 영상을 객체/배경으로 분할하고 객체를 중심으로 객체/배경의 비율이 일정한 일정 크기의 영상을 생성한다. 생성된 영상에서 대표 색상을 추출하고, 각 색상이 영상에서 어떻게 분포하는가를 나타내는 이진 공간분포 지도를 형성한다. 그 후 영상 간의 대표 색상마다 이진 공간분포의 차이를 비교함으로써, 색상의 통계적 특성과 공간적 분포가 동시에 반영된 특징으로 영상을 검색한다. 본 논문에서 제안한 saliency map을 이용한 대표 색상 기반의 영상 검색 기법은 기존의 대표 색상 기반의 영상 검색보다 우수한 성능을 보여준다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.12
• /
• pp.5507-5528
• /
• 2016

In computer vision, salient object is important to extract the useful information of foreground. With active contour analysis acting as the core in this paper, we propose a bottom-up saliency detection algorithm combining with the Bayesian model and the global color distribution. Under the supports of active contour model, a more accurate foreground can be obtained as a foundation for the Bayesian model and the global color distribution. Furthermore, we establish a contour-based selection mechanism to optimize the global-color distribution, which is an effective revising approach for the Bayesian model as well. To obtain an excellent object contour, we firstly intensify the object region in the source gray-scale image by a seed-based method. The final saliency map can be detected after weighting the color distribution to the Bayesian saliency map, after both of the two components are available. The contribution of this paper is that, comparing the Harris-based convex hull algorithm, the active contour can extract a more accurate and non-convex foreground. Moreover, the global color distribution can solve the saliency-scattered drawback of Bayesian model, by the mutual complementation. According to the detected results, the final saliency maps generated with considering the global color distribution and active contour are much-improved.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2014.11a
• /
• pp.63-64
• /
• 2014

본 논문에서는 Saliency map과 Max Filter를 이용한 영상의 자막영역을 추출 한다. Saliency map은 눈에 띄는 영역, 즉 영상에서 주변영역에 비해 밝기 차이가 심한 영역과 윤곽선에 대한 특징이 강한 영역을 돌출하는 것을 말하며, MaxFilter는 중심 픽셀을 최대 윈도우 값을 사용하는 것으로 극단적인 Impulse Noise를 제거하는데 효과적이며 특히 어두운 스파이크를 제거하는데 유용하게 사용된다. 이 두 가지의 특징들을 이용하여 영상의 자막 영역을 추출한다.

• Journal of Broadcast Engineering
• /
• v.18 no.6
• /
• pp.859-871
• /
• 2013

In this paper, we propose an algorithm that generates multi-view images by grid-mesh based image domain warping using occlusion mask and various image features obtained from the stereoscopic images. In the proposed algorithm, we first extract image saliency map, line segments and disparity saliency map from stereo images and then get them through a process that improves the quality of extracted features. This process is accomplished in two steps. In the first step, reliability of disparity saliency map on object boundary regions is enhanced by using occlusion information. And in the second step, we enhance the quality of image features in terms of temporal consistency by using temporal consistency information for stereo images. With these enhanced features, multi-view images are generated by grid-mesh based image domain warping technique. Experimental results show that the proposed algorithm performs better than existing algorithms in terms of visual quality.

• Journal of Korea Multimedia Society
• /
• v.19 no.12
• /
• pp.1951-1959
• /
• 2016

In this paper, we propose a two-scale fusion method for infrared and visible images using saliency and variance. The images are separated into two scales respectively: a base layer of low frequency component and a detailed layer of high frequency component. Then, these are synthesized using weight. The saliencies and the variances of the images are used as the fusion weights for the two-scale images. The proposed method is tested on several image pairs, and its performance is evaluated quantitatively by using objective fusion metrics.

• International Journal of Contents
• /
• v.9 no.1
• /
• pp.6-10
• /
• 2013

Foreground estimation in object segmentation has been an important issue for last few decades. In this paper we propose a GrabCut based automatic foreground estimation method using block clustering. GrabCut is one of popular algorithms for image segmentation in 2D image. However GrabCut is semi-automatic algorithm. So it requires the user input a rough boundary for foreground and background. Typically, the user draws a rectangle around the object of interest manually. The goal of proposed method is to generate an initial rectangle automatically. In order to create initial rectangle, we use Gabor filter and Saliency map and then we use 4 features (amount of area, variance, amount of class with boundary area, amount of class with saliency map) to categorize foreground and background. From the experimental results, our proposed algorithm can achieve satisfactory accuracy in object segmentation without any prior information by the user.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.5
• /
• pp.2576-2589
• /
• 2017

Co-saliency detection is a task of detecting same or similar objects in multi-scene, and has been an important preprocessing step for multi-scene image processing. However existing methods lack efficiency to match similar areas from different images. In addition, they are confined to single image detection without a unified framework to calculate co-saliency. In this paper, we propose a novel model called Superpixel Matching-Cellular Automata (SMCA). We use Hausdorff distance adjacent superpixel sets instead of single superpixel since the feature matching accuracy of single superpixel is poor. We further introduce Cellular Automata to exploit the intrinsic relevance of similar regions through interactions with neighbors in multi-scene. Extensive evaluations show that the SMCA model achieves leading performance compared to state-of-the-art methods on both efficiency and accuracy.

• Journal of Information Processing Systems
• /
• v.14 no.6
• /
• pp.1331-1346
• /
• 2018

Aiming at the problem that the gradient-based edge detection operators are sensitive to the noise, causing the pseudo edges, a triqubit-state measurement-based edge detection algorithm is presented in this paper. Combing the image local and global structure information, the triqubit superposition states are used to represent the pixel features, so as to locate the image edge. Our algorithm consists of three steps. Firstly, the improved partial differential method is used to smooth the defect image. Secondly, the triqubit-state is characterized by three elements of the pixel saliency, edge statistical characteristics and gray scale contrast to achieve the defect image from the gray space to the quantum space mapping. Thirdly, the edge image is outputted according to the quantum measurement, local gradient maximization and neighborhood chain code searching. Compared with other methods, the simulation experiments indicate that our algorithm has less pseudo edges and higher edge detection accuracy.

• IEIE Transactions on Smart Processing and Computing
• /
• v.6 no.4
• /
• pp.262-268
• /
• 2017

Intelligent video surveillance systems have been developed to monitor global areas and find specific target objects using a large-scale database. However, person re-identification presents some challenges, such as pose change and occlusions. To solve the problems, this paper presents an improved person re-identification method using sparse representation and saliency-based dictionary construction. The proposed method consists of three parts: i) feature description based on salient colors and textures for dictionary elements, ii) orthogonal atom selection using cosine similarity to deal with pose and viewpoint change, and iii) measurement of reconstruction error to rank the gallery corresponding a probe object. The proposed method provides good performance, since robust descriptors used as a dictionary atom are generated by weighting some salient features, and dictionary atoms are selected by reducing excessive redundancy causing low accuracy. Therefore, the proposed method can be applied in a large scale-database surveillance system to search for a specific object.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.7
• /
• pp.3245-3271
• /
• 2016

To obtain well-dehazed images at the receiver while sustaining low bit rates in the transmission pipeline, this paper investigates the effects of image dehazing methods using dehazing contrast-enhancement filters on image compression for surveillance systems. At first, this paper proposes a novel image dehazing method by using a new method of calculating the transmission function—namely, the direct denoising method. Next, we deduce the dehazing effects of the direct denoising method and image dehazing method based on dark channel prior (DCP) on image compression in terms of ringing artifacts and blocking artifacts. It can be concluded that the direct denoising method performs better than the DCP method for decompressed (reconstructed) images. We also improve the direct denoising method to obtain more desirable dehazed images with higher contrast, using the saliency map as the guidance image to modify the transmission function. Finally, we adjust the parameters of dehazing contrast-enhancement filters to obtain a corresponding composite peak signal-to-noise ratio (CPSNR) and blind image quality assessment (BIQA) of the decompressed images. Experimental results show that different filters have different effects on image compression. Moreover, our proposed dehazing method can strike a balance between image dehazing and image compression.