• 한국정보전자통신기술학회논문지
• /
• 제2권3호
• /
• pp.31-38
• /
• 2009

A process of identifying moving objects from data is typical task in many computer vision applications. In this paper, we propose a motion segmentation method that generally consists from background subtraction and foreground pixel segmentation. The Approximated Median Filter (AMF) was chosen to perform background modelling. To demonstrate the effectiveness of proposed approach, we tested it gray-scale video data as well as RGB color space.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권4호
• /
• pp.938-958
• /
• 2024

Unsupervised Video Object Segmentation (UVOS) is a highly challenging problem in computer vision as the annotation of the target object in the testing video is unknown at all. The main difficulty is to effectively handle the complicated and changeable motion state of the target object and the confusion of similar background objects in video sequence. In this paper, we propose a novel deep Dual-stream Co-enhanced Network (DC-Net) for UVOS via bidirectional motion cues refinement and multi-level feature aggregation, which can fully take advantage of motion cues and effectively integrate different level features to produce high-quality segmentation mask. DC-Net is a dual-stream architecture where the two streams are co-enhanced by each other. One is a motion stream with a Motion-cues Refine Module (MRM), which learns from bidirectional optical flow images and produces fine-grained and complete distinctive motion saliency map, and the other is an appearance stream with a Multi-level Feature Aggregation Module (MFAM) and a Context Attention Module (CAM) which are designed to integrate the different level features effectively. Specifically, the motion saliency map obtained by the motion stream is fused with each stage of the decoder in the appearance stream to improve the segmentation, and in turn the segmentation loss in the appearance stream feeds back into the motion stream to enhance the motion refinement. Experimental results on three datasets (Davis2016, VideoSD, SegTrack-v2) demonstrate that DC-Net has achieved comparable results with some state-of-the-art methods.

• 대한전자공학회논문지SP
• /
• 제40권5호
• /
• pp.303-311
• /
• 2003

MPEG-4 표준에서는 객체 단위의 부호화를 수행하기 위해 우선 자연영상으로부터 비디오 객체론 분리하는 영상분할(Segmentation) 기술이 필요하다. 영상분할 방법은 크게 자동 영상분할(Automatic Segment값ion)과 반자동 영상분할(Semi-automatic Segmentation)의 두 부류로 나눌 수 있다. 대부분의 자동 영상분할 방법은 비디오 객체의 명확한 모델을 수학적으로 제시하기 어려우므로 한 화면에서 개별 객체를 추출하기 어렵기 때문에 그 성능에 한계가 있다. 본 논문에서는 이러한 문제점을 극복하기 위해 기하학적인 Active Contour를 이용한 반자동 영상분할 알고리즘을 제안한다. 매개변수 방식의 Active Contour와 달리, 기하학적인 Active Contour는 곡선의 변화론 Level Set 방법을 이용하여 기술하기 때문에 초기 곡선의 모양을 객체의 모양과 무관하게 그릴 수 있다. 평탄화된 영상으로부터 경계함수를 생성하기 위해 이진화된 3차원 확산 모델을 사용하여 LUV 벡터 공간에서 비등방형 확산을 수행한다. 본 논문에서는 흐름 벡터장(Advection Vector Field)에서 곡선을 수축하고, 움직임 정보를 이용하여 곡선 확장하는 방법을 이용하여 동영상에서 객체를 분리하는 방법을 제안한다.

• 전자공학회논문지B
• /
• 제32B권12호
• /
• pp.1581-1590
• /
• 1995

Recently, advanced video coding techniques using segmentation technique have been actively researched as candidates for video coding of MPEG-4 standard. The conventional segmentation techniques are unsuitable for real-time process because they have sequential structure. In this paper, we propose a new image segmentation technique using competitive learning neural network for vector quantization. The proposed segmentation procedure consist of prefiltering, primary and secondary segmentation, and a small region ellimination process. Primary segmentation segments input image in detail. Secondary segmentation merges similar region using a repetitive FSCL(Frequency sensitive competive learning) neural network. In this process, it is possible to segment an image from high resolution to low resolution by adjusting the number of repetition. Finally, small regions are merged into adjacent regions. Experimental results show that the procedure described yields reconstructed images of reasonably acceptable quality at bit rates of 0. 25 - 0.3 bit/pel.

• 한국정보과학회논문지:소프트웨어및응용
• /
• 제35권12호
• /
• pp.753-763
• /
• 2008

UCC(User Created Contents)의 급격한 증가에 따라 저작권 문제도 크게 대두되고 있다. 자동 로고 인식은 이러한 저작권 문제를 해결하기 위한 효율적인 방법이다. 로고는 다양한 특징을 가지고 있고, 이러한 특징들은 로고 검출과 인식을 어렵게 한다. 특히, 비디오 내에 빈번한 로고 전이가 일어날 경우, 정확한 로고 인식과 로고 기반 분할이 어렵다. 따라서 본 논문에서는 디지털 비디오에서 로고 인식을 위한 정확한 전이 검출 방법과 다양한 로고 타입 인식 방법을 제안한다. 제안한 로고 검출과 로고에 따른 비디오 분할을 이용하여 다양한 비디오에 대한 좋은 실험 결과를 얻을 수 있었다.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2001년도 하계종합학술대회 논문집(4)
• /
• pp.219-222
• /
• 2001

Moving objects in video data are main elements for video analysis and retrieval. In this paper, we propose a new algorithm for tracking and segmenting moving objects in color image sequences that include complex camera motion such as zoom, pan and rotating. The Proposed algorithm is based on the Mean-shift color segmentation and stochastic region matching method. For segmenting moving objects, each sequence is divided into a set of similar color regions using Mean-shift color segmentation algorithm. Each segmented region is matched to the corresponding region in the subsequent frame. The motion vector of each matched region is then estimated and these motion vectors are summed to estimate global motion. Once motion vectors are estimated for all frame of video sequences, independently moving regions can be segmented by comparing their trajectories with that of global motion. Finally, segmented regions are merged into the independently moving object by comparing the similarities of trajectories, positions and emerging period. The experimental results show that the proposed algorithm is capable of segmenting independently moving objects in the video sequences including complex camera motion.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2000년도 ITC-CSCC -1
• /
• pp.429-432
• /
• 2000

The new MPEG-4 video coding standard enables content-based functionalities. In order to support the philosophy of the MPEG-4 visual standard, each frame of video sequences should be represented in terms of video object planes (VOP’s). In other words, video objects to be encoded in still pictures or video sequences should be prepared before the encoding process starts. Therefore, it requires a prior decomposition of sequences into VOP’s so that each VOP represents a moving object. A parallel processing system is required an automatic segmentation to be processed in real-time, because an automatic segmentation is time consuming. This paper addresses the parallel processing: system for an automatic segmentation for separating moving object from the background in image sequences. The proposed parallel processing system comprises of processing elements (PE’s) and a multi-access memory system (MAMS). Multi-access memory system is a memory controller to perform parallel memory access with the variety of types: horizontal, vertical, and block access way. In order to realize these ways, a multi-access memory system consists of a memory module selection module, data routing modules, and an address calculation and routing module. The proposed system is simulated and evaluated by the CADENCE Verilog-XL hardware simulation package.

• 한국컴퓨터산업학회논문지
• /
• 제6권5호
• /
• pp.735-740
• /
• 2005

경사 영상을 사용하는 워터쉐드에서는 영상 내의 잡음이 직접 국부적 최소 점들로 표현되어 영상의 과분할을 초래하게 된다. 특히 분할되어야 할 영역들의 경계에 대한 기울기 크기는 영역 분할의 정확성에 영향을 주어 전체 분할 성능을 좌우할 수 있다. 그러므로 본 논문에서는 기울기 크기를 결정하기 전에 영역들에 대한 경계의 선명도를 보존하면서 잡음을 제거함으로써 영상의 과분할을 줄일 수 있는 ATMF(Adaptive Trimmed Mean Filter)의 적용을 제안하였다.

• IEIE Transactions on Smart Processing and Computing
• /
• 제1권3호
• /
• pp.125-132
• /
• 2012

This paper proposes an automatic object segmentation and background composition method for video communication over consumer mobile phones. The object regions were extracted based on the motion and color variance of the first two frames. To combine the motion and variance information, the Euclidean distance between the motion boundary pixel and the neighboring color variance edge pixels was calculated, and the nearest edge pixel was labeled to the object boundary. The labeling results were refined using the morphology for a more accurate and natural-looking boundary. The grow-cut segmentation algorithm begins in the expanded label map, where the inner and outer boundary belongs to the foreground and background, respectively. The segmented object region and a new background image stored a priori in the mobile phone was then composed. In the background composition process, the background motion was measured using the optical-flow, and the final result was synthesized by accurately locating the object region according to the motion information. This study can be considered an extended, improved version of the existing background composition algorithm by considering motion information in a video. The proposed segmentation algorithm reduces the computational complexity significantly by choosing the minimum resolution at each segmentation step. The experimental results showed that the proposed algorithm can generate a fast, accurate and natural-looking background composition.

• 전기전자학회논문지
• /
• 제13권4호
• /
• pp.1-7
• /
• 2009

본 논문에서는 고정되지 않은 배경의 동영상에서 객체를 추출하는 방법을 제안한다. 제안하는 알고리즘은 추적에 기반을 둔 기법으로 크게 세 단계의 과정으로 이루어져 있다. 첫 번째 단계는 초기 분할로서, 사용자의 반응을 이용하여 첫 프레임의 분할 결과를 획득하는 과정이다. 초기 분할을 통해 획득된 결과 샘플은 커널 밀도 추정을 이용하여 각 매크로 블록별 컬러 확률 밀도 함수를 생성하는데 사용된다. 두 번째 단계에서는 각 프레임에 대해 이전 프레임의 경계 정보와 움직임 벡터를 이용하여 일치성 띠를 생성하고, 생성된 띠에 대한 시공간 확률을 추정한다. 마지막 단계에서는 각 픽셀별 컬러, 시공간, 스무드항의 합으로 구성된 에너지 함수를 최소화하여 최종 결과를 획득한다. 실험 결과를 통해서 본 논문에서 제안하는 기법이 정확한 분할 결과를 추출하는 지 다양한 테스트 영상을 통해 확인한다.