• IEIE Transactions on Smart Processing and Computing
• /
• v.5 no.1
• /
• pp.49-54
• /
• 2016

Regarding global and local factors of a set of features, a given single image or multiple images is a common approach in image processing. This paper introduces an application of an adaptive version of non-local filter whose original version searches non-local similarity for removing noise. Since most images involve texture partner in both foreground and background, extraction of signified regions with texture is a challenging task. Aiming to the detection of visual attention regions for images with texture, we present the contrast analysis of image patches located in a whole image but not nearby with assistance of the adaptive filter for estimation of non-local divergence. The method allows extraction of signified regions with texture of images of wild life. Experimental results for a benchmark demonstrate the ability of the proposed method to deal with the mentioned challenge.

• Journal of Broadcast Engineering
• /
• v.24 no.6
• /
• pp.983-991
• /
• 2019

This study aims to analyze movies which contain various stories according to the size of their shots. To achieve this, it is needed to classify dataset according to the shot size, such as extreme close-up shots, close-up shots, medium shots, full shots, and long shots. However, a typical video storytelling is mainly composed of close-up shots, medium shots, full shots, and long shots, it is not an easy task to construct an appropriate dataset for extreme close-up shots. To solve this, we propose an image cropping method based on the region of interest (ROI) detection. In this paper, we use the face detection and saliency detection to estimate the ROI. By cropping the ROI of close-up images, we generate extreme close-up images. The dataset which is enriched by proposed method is utilized to construct a model for classifying shots based on its size. The study can help to analyze the emotional changes of characters in video stories and to predict how the composition of the story changes over time. If AI is used more actively in the future in entertainment fields, it is expected to affect the automatic adjustment and creation of characters, dialogue, and image editing.

• International Journal of Contents
• /
• v.10 no.4
• /
• pp.1-10
• /
• 2014

In this paper, we propose a method to build an accurate initial trimap for the GrabCut algorithm without the need for human interaction. First, we identify a rough candidate for the label region of a bottle by applying a saliency map to find a salient area from the image. Then, the Hough Transformation method is used to detect the left and right borders of the label region, and the k-means algorithm is used to localize the upper and lower borders of the label of the bottle. These four borders are used to build an initial trimap for the GrabCut method. Finally, GrabCut segments accurate regions for the label. The experimental results for 130 wine bottle images demonstrated that the saliency map extracted a rough label region with an accuracy of 97.69% while also removing the complex background. The Hough transform and projection method accurately drew the outline of the label from the saliency area, and then the outline was used to build an initial trimap for GrabCut. Finally, the GrabCut algorithm successfully segmented the bottle label with an average accuracy of 92.31%. Therefore, we believe that our method is suitable for product label recognition systems that automatically segment product labels. Although our method achieved encouraging results, it has some limitations in that unreliable results are produced under conditions with varying illumination and reflections. Therefore, we are in the process of developing preprocessing algorithms to improve the proposed method to take into account variations in illumination and reflections.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2014.11a
• /
• pp.259-260
• /
• 2014

본 논문에서는 영상에 존재하는 운동정보를 이용하여 관심맵을 얻는 방법을 제안한다. 운동의 크기, 방향, 그리고 영상의 색을 이용한다. 이를 이용함으로써 기존의 static image에서 구하는 관심맵보다 향상된 결과를 얻을 수 있다. 실험에서는 다양한 모션이 존재하는 영상을 이용하여 제안방법의 우수성을 증명하였다.

• Proceedings of the KIEE Conference
• /
• 2006.04a
• /
• pp.306-308
• /
• 2006

We propose an attention based object recognition system, to recognize object fast and robustly. For this we calculate visual stimulus degrees and make saliency maps. Through this map we find a strongly attentive part of image by stimulus degrees, where local features are extracted to recognize objects.

• Journal of information and communication convergence engineering
• /
• v.14 no.2
• /
• pp.129-135
• /
• 2016

In this paper, we present a novel algorithm for image-based maritime obstacle detection using global sparsity potentials (GSPs), in which "global" refers to the entire sea area. The horizon line is detected first to segment the sea area as the region of interest (ROI). Considering the geometric relationship between the camera and the sea surface, variable-size image windows are adopted to sample patches in the ROI. Then, each patch is represented by its texture feature, and its average distance to all the other patches is taken as the value of its GSP. Thereafter, patches with a smaller GSP are clustered as the sea surface, and patches with a higher GSP are taken as the obstacle candidates. Finally, the candidates far from the mean feature of the sea surface are selected and aggregated as the obstacles. Experimental results verify that the proposed approach is highly accurate as compared to other methods, such as the traditional feature space reclustering method and a state-of-the-art saliency detection method.

• Journal of the Korea Computer Graphics Society
• /
• v.13 no.3
• /
• pp.1-6
• /
• 2007

Anticipation effect has been used as a traditional skill to enhance the dynamic motion of the traditional 2D animation. Basically, anticipation means the action of opposite direction which performs before the real action step. In this paper, we propose the perception-based video anticipation method to guide a user's visual attention to the important region. Using the image based attention map, we calculate the visual attention region and then combine this map with temporal saliency of video. We apply the anticipation effect in these saliency regions using the blur kernel. Using our method, we can generate the dynamic video motion which has attentive guidance.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.3
• /
• pp.1264-1286
• /
• 2018

In this paper, a novel background prior-based salient object detection framework is proposed to deal with images those are more complicated. We take the superpixels located in four borders into consideration and exploit a mechanism based on image boundary information to remove the foreground noises, which are used to form the background prior. Afterward, an initial foreground prior is obtained by selecting superpixels that are the most dissimilar to the background prior. To determine the regions of foreground and background based on the prior of them, a threshold is needed in this process. According to a fixed threshold, the remaining superpixels are iteratively assigned based on their proximity to the foreground or background prior. As the threshold changes, different foreground priors generate multiple different partitions that are assigned a likelihood of being foreground. Last, all segments are combined into a saliency map based on the idea of similarity voting. Experiments on five benchmark databases demonstrate the proposed method performs well when it compares with the state-of-the-art methods in terms of accuracy and robustness.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.3
• /
• pp.298-303
• /
• 2014

In this paper, we proposed a robust object extraction and tracking algorithm in the IR image sequence acquired in the sea environment. In order to extract size-invariant object, we detect horizontal and vertical edges by using DWT and combine it to generate saliency map. To extract object region, binarization technique is applied to saliency map. The correspondences between objects in consecutive frames are defined by the calculating minimum weighted Euclidean distance as a matching measure. Finally, object trajectories are determined by considering false correspondences such as entering object, vanishing objects and false object and so on. The proposed algorithm can find trajectories robustly, which has shown by experimental results.

• Journal of KIISE
• /
• v.43 no.2
• /
• pp.186-195
• /
• 2016

This paper proposes a vehicle license plate detection method in real road environments using 8 bit-MCT features and a landmark-based Adaboost method. The proposed method allows identification of the potential license plate region, and generates a saliency map that presents the license plate's location probability based on the Adaboost classification score. The candidate regions whose scores are higher than the given threshold are chosen from the saliency map. Each candidate region is adjusted by the local image variance and verified by the SVM and the histograms of the 8bit-MCT features. The proposed method achieves a detection accuracy of 85% from various road images in Korea and Europe.