• IEIE Transactions on Smart Processing and Computing
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• v.5 no.1
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• pp.1-4
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• 2016

In this paper, we present an algorithm for adjusting the position of a user-specified object considering image aesthetics. Specifically, the user-specified object is positioned according to the rule of thirds by inserting or deleting unimportant seam lines from the image. To find such seam lines, a novel weight map is designed using the spatial and color distances from the object. We also design and analyze two approaches to seam carving and insertion. Experimental results show that the proposed method can be used as an effective semi-automatic image recomposition scheme.

• Journal of Broadcast Engineering
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• v.16 no.4
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• pp.573-583
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• 2011

In this paper, we propose an occlusion compensation algorithm which is used for virtual view generation. In general, since occlusion region is recovered from neighboring pixels by taking the mean value or median value of neighbor pixels, the visual characteristics of a given image are not considered and consequently the accuracy of the compensated occlusion regions is not guaranteed. To solve these problem, we propose an algorithm that considers primary visual characteristics of a given image to compensate the occluded regions by using seam carving algorithm. In the proposed algorithm, we first use Sobel mask to obtain the edge map of a given image and then make it binary digit 0 or 1 and finally thinning process follows. Then, the energy patterns of original and thinned edge map obtained by the modified seam carving method are used to compensate the occlusion regions. Through experiments with many test images, we verify that the proposed algorithm performed better than conventional algorithms.

• International journal of advanced smart convergence
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• v.5 no.3
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• pp.1-7
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• 2016

In existing Convolutional Neural Network (CNNs) for object recognition task, there are only few efforts known to reduce the noises from the images. Both convolution and pooling layers perform the features extraction without considering the noises of the input image, treating all pixels equally important. In computer vision field, there has been a study to weight a pixel importance. Seam carving resizes an image by sacrificing the least important pixels, leaving only the most important ones. We propose a new way to combine seam carving approach with current existing CNN model for object recognition task. We attempt to remove the noises or the "unimportant" pixels in the image before doing convolution and pooling, in order to get better feature representatives. Our model shows promising result with CIFAR-10 dataset.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.80-89
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• 2011

In this paper, a new real time video scaling technique which preserved the contents of a movie was proposed. Because in a movie a correlation exists between consecutive frames, in this paper by determining the seam of the current frame considering the seam of the previous frame, it was proposed the real time video scaling technique without the shaking phenomenon of the contents even though the entire video is not analyzed. For this purpose, frames which have similar features in a movie are classified into a scene, and the first frame of a scene is resized by the seam carving at the static images so that it can preserve the contents of the image to the utmost. At this time, the information about the seam extracted to convert the image size is saved, and the sizes of the next frames are controlled with reference to the seam information stored in the previous frame by each frame. The proposed algorithm has the fast processing speed of the extent of being similar to a bilinear method and preserves the main content of an image to the utmost at the same time. Also because the memory usage is remarkably small compared with the existing seam carving method, the proposed algorithm is usable in the mobile terminal in which there are many memory restrictions. Computer simulation results indicate that the proposed technique provides better objective performance and subjective image quality about the real time processing and shaking phenomenon removal and contents conservation than conventional algorithms.

• Journal of the Korea Society of Computer and Information
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• v.18 no.11
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• pp.77-86
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• 2013

In this paper, we propose a fast video retargeting method which preserves the contents of a video and converts the image size. Since the conventional Seam Carving which is the well-known content-aware image retargeting technique uses the dynamic programming method, the repetitive update procedure of the accumulation energy is absolutely needed to obtain seam. The energy update procedure cannot avoid the processing time delay because of many operations by the image full-searching. By applying the proposed method, frames which have similar features in video are classified into a scene, and the first frame of a scene is resized by the modified Seam Carving where multiple seams are extracted from candidate seams to reduce the repetitive update procedure. After resizing the first frame of a scene, all continuous frames of the same scene are resized with reference to the seam information stored in the previous frame without the calculation of the accumulation energy. Therefore, although the fast processing is possible with reducing complexity and without analyzing all frames of scene, the quality of an image can be analogously maintained with an existing method. The experimental results show that the proposed method can preserve the contents of an image and can be practically applied to retarget the image on real time.

• Annual Conference of KIPS
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• 2019.05a
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• pp.579-582
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• 2019

이미지의 특정 객체를 제거할 때, 주변 환경을 고려하면서 제거하기에 어려움이 있다. 본 연구는 특정 객체가 제거되면서 생기는 빈자리를 자연스럽게 보완하기 위해서 이미지 내용을 기반으로 이미지를 변경하는 Seam Carving 알고리즘을 이용하여 보다 자연스러운 결과 이미지를 생성하는 프로그램을 구현했다.

• Journal of the Semiconductor & Display Technology
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• v.14 no.3
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• pp.23-26
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• 2015

A seam carving based micro-crack detection method is proposed which aims at detecting the micro-crack regions in heterogeneously textured surface of polycrystalline solar cells. By calculating the seam which is a connected path of low energy pixels in the image, the micro-crack regions can be detected. Experimental results show that the proposed seam carving based micro-crack detection method has superior efficiency in detecting the micro-crack without background noise pixels and the algorithm's computation time is less than the conventional algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.63-71
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• 2011

In this paper, we propose a new real time image retargeting method which preserves the contents of an image. Since the conventional seam carving which is the well-known content-based image retargeting technology uses the dynamic programming method, the repetitive update procedure of the accumulation minimum energy map is absolutely needed. The energy map update procedure cannot avoid the processing time delay because of many operations by the image full-searching. The proposed method calculates the diffusion region of each seam candidates in the accumulation minimum energy map in order to reduce the update processing time. By using the diffusion region, several seams are extracted at the same time and the update number of accumulation energy map is reduced. Therefore, although the fast processing is possible, the quality of an image can be analogously maintained with an existing method. The experimental results show that the proposed method can preserve the contents of an image and adjust the image size on a real-time.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.11a
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• pp.159-162
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• 2010

본 논문에서는 2D 영상에서 다시점 영상 생성 시 가상 시점을 만드는 과정에서 발생하는 가려짐 영역(occlusion) 보상 기법을 제안한다. 제안된 기법에서는 영상이 가지고 있는 주요한 특성을 유지하면서 가려짐 영역을 보상한다. 기존에 제안되었던 가려짐 영역 보상 방법은 가려짐 영역이 발생한 주변의 화소를 그대로 채워 넣는 방식을 사용하거나 평군값 필터 또는 중간값 필터와 같은 기존의 보상 필터를 이용하기 때문에 시차의 분포 특성을 고려하지 않는다. 따라서 오차는 줄일 수 있으나, 교정된 시차의 정확성은 보장되지 않는다. 본 논문에서 제안하는 가려짐 영역 보상 기법에서는 영상의 주요한 특성을 함께 고려하여 정확성을 높였다. 다양한 영상에 적용하여 제안된 기법의 성능을 테스트하였고, 그 결과 기존의 보상 방법에 비해 가려짐 영역을 비교적 정확하게 보상하는 것을 확인하였다.

• ETRI Journal
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• v.35 no.6
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• pp.980-989
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• 2013

The important requirements for stereo video retargeting are threefold: keeping temporal coherence, preventing depth distortion, and minimizing shape distortions of the retargeted video. To meet these requirements, the left and right video sequences are divided into groups of frames (GoFs), where the GoF is a basic unit for the seam carving and we assign a set of fixed seams for all frames within the GoF. To determine the fixed seams for each GoF, we need to find the GoF boundary in the video first. Then, the representative frame for each GoF is generated by considering the spatial saliency and temporal coherence. Also, the confidence of the stereoscopic correspondence between the left and right frames is considered to prevent depth distortion.