Polsar time series temporal change detection and analysis with binary partition trees

Scene change detection is the process of identifying the differences between the multi-temporal image scenes at the semantic level, which has significant potential in the application of urban development and land management. In this paper, we propose a novel deep convolution canonical correlation analysis neural network (DCCANet) architecture, which could consider the spectral-spatial-temporal correlation feature for scene change detection in remote sensing images. For this purpose, we put together the convolutional neural networks (CNNs) and the deep canonical correlation analysis (DCCA) into the end-to-end network. The CNN could get the spectral-spatial feature information for scene representation, while the later could enhance the temporal correlation by nonlinear high- dimensional transformations between the multi-temporal image scenes for scene change detection. Experiments with high-resolution remote sensing image scene datasets demonstrated that our proposed approach can get a better performance in scene classification and change detection.

This paper discusses the interest of binary partition trees as a region-oriented image representation. Binary partition trees concentrate in a compact and structured representation a set of meaningful regions that can be extracted from an image. They offer a multiscale representation of the image and define a translation invariant 2-connectivity rule among regions. As shown in this paper, this representation can be used for a large number of processing goals such as filtering, segmentation, information retrieval and visual browsing. Furthermore, the processing of the tree representation leads to very efficient algorithms. Finally, for some applications, it may be interesting to compute the binary partition tree once and to store it for subsequent use for various applications. In this context, the paper shows that the amount of bits necessary to encode a binary partition tree remains moderate.

In this study, binary partition trees are applied to the problem of fruit detection. The fact that binary partition trees are inherently unbiased and independent of flatzones is the main reason for this application. Using only circularity from the shape priors, this system is put to test with 39 images of three classes of fruits and the test results show an average of 0.669 precision and 0.851 recall.

Supervised quality evaluation of binary partition trees for object segmentation

We present a reactive data structure, that is, a spatial data structure with detail levels. The two properties, spatial organization and detail levels, are the basis for a geographic information system (GIS) with a multi-scale database. A reactive data structure is a novel type of data structure catering to multiple detail levels with rapid responses to spatial queries. It is presented here as a modification of the binary space partitioning tree that includes the levels of detail. This tree is one of the few spatial data structures that does not organize space in a rectangular manner. A prototype system has been implemented. An important result of this implementation is that it shows that binary space partitioning trees of real maps have O(n) storage space complexity in contrast to the theoretical worst case O(n2 ), with n the number of line segments in the map.

Rate control plays a key role in a video encoder, and it has been a challenging research topic. In this paper, an adaptive variable length group of picture (GOP) level rate control algorithm for H.264 based on scene change detection is proposed. In the proposed algorithm, the length of current GOP changes adaptively with the occurrence of scene change, so that the entire sequence contains a fixed number of I frame to avoid the actual bit rate increasing. Furthermore, it adjusts the QP of the scene change frame to ease the pressure of the buffer. Simulation results show that the proposed algorithm improves the video quality significantly on the basis of meeting bit rate constraints accurately, which shows better adaptability for sequences with scene change occurrence frequently. When compared with existed schemes, the average peak signal-to-noise ratio (PSNR) improvement is more than 1.4 dB.

대용량 컨텐츠 수요와 공급의 증가에 따라 UHD 비디오의 분석, 색인, 편집 등을 위한 장면전환을 검출하는 방법이 요구되고 있다. 이전까지 많은 연구자들이 다양한 장면전환 검출 방법을 연구해왔지만 카메라의 다양한 움직임과 장면의 변화를 정확하게 검출하기는 어려웠다. 또한, 4K (3820x2160) 해상도 이상의 UHD 비디오들은 데이터 량을 더욱더 증가시키기 때문에 이전의 장면전환 검출 방법은 UHD 비디오 컨텐츠에 적용하기에는 너무 많은 시간이 걸리는 문제점이 발생한다. 따라서, 압축률이 높은 차세대 고효율 코덱 HEVC를 이용하여 장면전환을 검출하는 방법이 요구되고 있다. 본 논문에서는 차세대 고효율 코덱 HEVC의 부호화 부가정보를 이용한 4가지 장면전환 검출 방법을 제안하며, 대용량 비디오의 장면전환 검출을 위한 픽셀 기반의 새로운 장면전환 검출 시스템 구조를 제안한다. 연산량을 줄이기 위해 프레임 특징에 따른 방법을 각각 제시하며, 실험 결과를 통해 HEVC로 부호화 된 UHD 컨텐츠들의 장면전환 검출 가능성을 확인한다. With the increase of mass contents data, a method of a scene change detection is required for analysis, indexing and editing. Although many researchers are studying a variety of scene change detection method, it is too difficult to accurately detect various movements of the cameras and scene changes. Also, earlier scene change detection methods take too much time to apply to UHD video contents. That is because the UHD video contents with 4K (3820x2160) resolution or higher have greater amount of data. Therefore a method for detecting a scene change by using the next-generation codec, HEVC, is required. In this paper, we propose four scene change detection methods using the coding additional information of HEVC, and a new pixel-based scene change detection system. Furthermore, through the experimental results, we check the possibility of detecting the scene changes of UHD videos encoded in HEVC format.

An efficient video de-interlacing technique with scene change detection is proposed and its performances are examined. Scene changes happen quite often in film broadcasting and they tend to destabilize the quality of performance such as jagged effect, blurred effect, and artifacts effect, while de-interlacing technique is utilized. Therefore, the issue of scene change needs to be addressed with de-interlacing process. In the proposed method, de-interlacing begins with scene change detection, which is to ensure that the interfield information is used correctly. To improve the quality of de-interlacing, the factors of scene change are taken into account when de-interlacing techniques are applied. The simulation results show that the proposed algorithm exhibits better performances than other interpolation algorithms

Video is an important and challenging medium and requires sophisticated indexing schemes for efficient retrieval from visual databases. An important step in video indexing is scene change detection. Recently, several scene change detection algorithms in the pixel and compressed (MPEG-2) domains have been reported in the literature. These algorithms are computationally complex and are not very robust in detecting gradual scene changes. The authors propose an efficient technique for detecting scene changes in the MPEG-2 compressed domain. The proposed algorithm has the advantage of fast scene change detection. In addition, this algorithm has the potential to detect gradual scene changes.

An algorithm is proposed to detect abrupt and gradual scene changes in H.264 compressed videos. In H.264, the inter prediction in the encoding process generates the DC component of the prediction residual. In this paper, we use the DC component of inter prediction residuals as well as motion vectors to detect scene changes. Using the proposed algorithm, we can detect scene changes for H.264 compressed videos more effectively, as demonstrated by simulations.

One of the fundamental operations in computer vision applications is change detection, in which moving foreground objects are segmented from a static background. A common approach for change detection is the comparison of an image frame with the stored background model using a matching algorithm, a process known as background subtraction. However, such techniques fail in environments with dynamic backgrounds, illumination changes, or shadow and camera jitters. This study focuses on effectively detecting scene changes in complex environments. To this end, we proposed a new colour descriptor named local colour difference pattern (LCDP) that is insusceptible to shadow and is able to capture both colour and texture features at a pixel location. Furthermore, a scene change detection framework was proposed to handle dynamic scenes based on sample consensus that integrates LCDP and a novel spatial model fusion mechanism. Experiments using the CDnet benchmark dataset demonstrated the effectiveness of the proposed approach to change detection in complex environments.

Binary partition trees (BPTs) are a well known technique used for region-based image representation and analysis. BPTs are usually created as a result of a merging process based on homogeneity properties, such as colour or motion. In this paper, we present a BPT creation technique based on a general merging algorithm, where the homogeneity criteria are neither low-level (pixel oriented, intra-region), nor high-level features (object oriented, semantics), but a combination of several criteria including region-based and structural features such as shape and partial-inclusion. We are thus combining intra-region homogeneity (e.g colour-based) with inter-region homogeneity (structural), with the long term aim of bridging the gap in region-based image analysis from low-level features to a higher level interpretation of the image by the intermediate description of the image structure (which we call visual features). Syntactic visual features are geometric relationships among regions based on shapes and the spatial configuration of neighboring regions in the image and can be found by structure analysis (or syntactic analysis). The aim of this work is to present how the addition of combined pixel-region and structural features leads to better object BPT representation.

Region-based urban road extraction from VHR satellite images using Binary Partition Tree

내용기반 검색을 위한 비디오 데이터 장면전환 검출에서 점진적인 전환을 검출하는 것은 갑작스런 전환을 검출하는 것에 비해 일반적으로 어려운 문제로 알려져 있다. 본 논문에서는 가변형 동적 임계값과 가장 최근에 검출된 컷 프레임과 현재 프레임간의 특징값 차이인 컷 프레임 차를 이용하여 갑작스런 전환과 점진적인 전환을 찾아내는 기법을 제안한다. 이를 위하여 본 논문에서는 점진적인 전환이 갖는 특성과 수학적 모델을 제시하고 컷 프레임 차를 이용하여 점진적인 전환을 검출할 수 있음을 보인다. 그리고 이를 바탕으로 갑작스런 전환과 점진적인 전환을 함께 검출할 수 있는 방법을 제시한다. 실세계 동영상 데이터를 대상으로 한 실험을 통해 제안하는 기법이 점진적인 전환 효과의 종류에 종속적이지 않으며 적은 연산 비용으로 쉽게 점진적인 전환 유무를 검출 할 수 있음을 보인다. Gradual scene change detection is known as more difficult problem then abrupt scene change detection on video data analysis for contents based retrieval. In this paper, we present a new method for scene change detection far both abrupt and gradual change using the variable dynamic threshold arid cut frame difference (CFD). For this, We present the characteristics arid mathematical models of gradual transitions anti then, how can be detected by the CFD. And also we present new scene change detection algorithm based on cut frame difference. By the experimental result using real world video data indicate that the proposed method detect various scene changes both abrupt and gradual change efficiently without time-consuming computation and any dependency on a kind of gradual change effects.

In order to fulfill the requirement of limited channel bandwidth and of growing video demand like streaming media delivery on internet, and digital library, video compression is necessary. In video compression, temporal redundancy between adjacent frames is removed with block based motion estimation algorithms. Video represents a sequence of frames captured from camera. Scene is a series of consecutive frames captured from narrative point of view. In this paper we present an effective scene change detection method for an uncompressed video. We have divided frames in to blocks and applied a canny edge detector in consecutive frames. Count no of pixels (ones) in each block and compare it with consecutive frames. If scene change happens then number of pixels per block will change, based on that change we can detect scene change in consecutive frame. Here we have presented a hybrid approach, in which we have used scene change detection along with block based motion estimation algorithms (BME) to compress video.