Graph-based Image Segmentation Algorithm Research Articles

Clustering-segmentation network: a parallel dual-branch synthetic aperture radar image change detection framework

ABSTRACT Synthetic aperture radar (SAR) image change detection is a key technique for such essential applications as flood disaster assessment and forest fire detection. In SAR image change detection, clustering algorithm is the most applied methods, but clustering algorithm only considers the grey features, therefore it is more susceptible to speckle noise. Deep learning model is difficult to be trained by supervised learning because of lack of labels. To alleviate the above-mentioned challenges of SAR image change detection, a parallel dual-branch SAR image change detection network based on clustering and segmentation (Clustering-Segmentation Network) is proposed in this paper. In the clustering branch, the clustering-based change detection results were obtained by fuzzy c-means (FCM) clustering. In the segmentation branch, the Graph-Based Image Segmentation algorithm was used for pre-segmentation. These results were used as labels of the neural network for training for the segmentation-based change detection. After the fusion of these dual-branch, a double sparse dictionary (DSD) discrimination algorithm is proposed to extract the neighbourhood features for the final discrimination, and obtain the final results. By fusing the dual-branch results, the influence of speckle noise on change detection can be suppressed while maintaining a high accuracy. We show that the Clustering-Segmentation Network exhibited better results compared with existing algorithms on several datasets. The accuracy and kappa coefficients are improved by 0.83% and 3.45% respectively, thereby proving the effectiveness of our proposed method.

Using a graph-based image segmentation algorithm for remote vital sign estimation and monitoring

Reliable and contactless measurements of vital signs, such as respiration and heart rate, are still unmet needs in clinical and home settings. Mm-wave radar and video-based technologies are promising, but currently, the signal processing-based vital sign extraction methods are prone to body motion disruptions or illumination variations in the surrounding environment. Here we propose an image segmentation-based method to extract vital signs from the recorded video and mm-wave radar signals. The proposed method analyses time–frequency spectrograms obtained from Short-Time Fourier Transform rather than individual time-domain signals. This leads to much-improved robustness and accuracy of the heart rate and respiration rate extraction over existing methods. The experiments were conducted under pre- and post-exercise conditions and were repeated on multiple individuals. The results are evaluated by using four metrics against the gold standard contact-based measurements. Significant improvements were observed in terms of precision, accuracy, and stability. The performance was reflected by achieving an averaged Pearson correlation coefficient (PCC) of 93.8% on multiple subjects. We believe that the proposed estimation method will help address the needs for the increasingly popular remote cardiovascular sensing and diagnosing posed by Covid-19.

A tutorial on applications of power watershed optimization to image processing

This tutorial review paper consolidates the existing applications of the power watershed (PW) optimization framework in the context of image processing. In the literature, it is known that PW framework when applied to some well-known graph-based image segmentation and filtering algorithms such as random walker, isoperimetric partitioning, ratio-cut clustering, multi-cut and shortest path filters yield faster yet consistent solutions. In this paper, the intuition behind the working of PW framework, i.e. exploitation of contrast invariance on image data is explained. The intuitions are illustrated with toy images and experiments on simulated astronomical images. This article is primarily aimed at researchers working on image segmentation and filtering problems in application areas such as astronomy where images typically have huge number of pixels. Classic graph-based cost minimization methods provide good results on images with small number of pixels but do not scale well for images with large number of pixels. The ideas from the article can be adapted to a large class of graph-based cost minimization methods to obtain scalable segmentation and filtering algorithms.

Graph-Based Horizon Line Detection for UAV Navigation

Perceiving the horizon line is a critical alternative for unmanned aerial vehicle (UAV) autonomous navigation, especially in the presence of noise-induced drift, unavailability of satellite navigation, and multipath errors. However, its quite tough to detect the horizon line, due to the remotely sensed big data, the dynamic changes in flight, and the serious consequences of failure. To address these problems, we propose a graph-based horizon line detection technique that is composed of graph-based image segmentation, connected domain cascade filtering, horizon line extraction, and UAV attitude estimation. We improve the graph-based image segmentation algorithm so that the segmentation results are more conducive to horizon line detection. We then determine the sky-component by cascade filtering and extract the horizon line based on the boundaries of the sky-component. Furthermore, we directly compute the roll and pitch according to the extracted horizon line and eliminate the ambiguity of the angles. To validate our approach qualitatively and quantitatively, we designed a fixed-wing UAV system. We then validated our algorithm through extensive flights under various conditions and compared the estimated rolls and pitches to the IMU ones. Statistical results show that the proposed technique provides unbiased attitude angles with error variance of about 2, which verify the validity and robustness of our method. For engineering, our program runs at approximately 60 frames per second on the fly after optimizing.

Erratum to “Hierarchizing graph-based image segmentation algorithms relying on region dissimilarity: the case of the Felzenszwalb-Huttenlocher method”

Abstract The original version of the article was published in Mathematical Morphology - Theory and Applications 2 (2017) 55–75. Unfortunately, the original version contains a mistake: in the definition of Dif (C 1, C 2) in Section 3.6, max should be replaced by min. In this erratum we correct the formula defining Dif (C 1, C 2).

Hierarchizing graph-based image segmentation algorithms relying on region dissimilarity

AbstractThis article is a first attempt towards a general theory for hierarchizing non-hierarchical image segmentation method depending on a region-dissimilarity parameter which controls the desired level of simpli fication: each level of the hierarchy is “as close as possible” to the result that one would obtain with the non-hierarchical method using the corresponding scale as simplification parameter. The introduction of this hierarchization problem in the form of an optimization problem, as well as the proposed tools to tackle it, is an important contribution of the present article. Indeed, with the hierarchized version of a segmentation method, the user can just select the level in the hierarchy, controlling the desired number of regions or can leverage on any of the tools introduced in hierarchical analysis. The main example investigated in this study is the criterion proposed by Felzenszwalb and Huttenlocher for which we show that the results of the hierarchized version of the segmentation method are better than those of the original one with the added property that it satisfies the strong causality and location principles from scale-sets image analysis. An interesting perspective of thiswork, considering the current trend in computer vision, is obviously, on a specific application, to use learning techniques and train a criterion to choose the correct region.

Linking soft computing to art: introduction of efficient k-continuous line drawing

Continuous line drawing (CLD) is a technique used in the field of art, in which the pen does not leave the paper until the sketch is completed. In this study, a novel technique, k-continuous line drawing (k-CLD), is proposed; this technique enables a visual image to comprise k closed non-intersecting lines. k-CLD involves the following challenges: 1) partitioning the target image into k regions, 2) stippling each region without distorting the target image, and 3) connecting the stippled dots in each region using a single closed, non-intersecting line. This study identifies and implements efficient algorithms to produce high-quality k-CLDs. Further, an improvement to the graph-based image segmentation algorithm has been proposed using the Minkowski distance to evaluate dissimilarity difference and demonstrated its effectiveness to partition the target image into k regions. Next, well-spaced stippled dots were generated in each region using a weighted Voronoi diagram. Finally, the stippled dots were connected by a single non-intersecting line, obtained by solving a traveling salesman problem (TSP) in each region. The metaheuristic to solve the TSP was an ant colony system algorithm. The proposed methodologies were tested on a wide variety of images to demonstrate their effectiveness and efficiency.

Clutter estimation and perception

We present a technique for determining the perceived relative clutter among different images. This experiment involves participants ranking different sets of images in terms of clutter. The law of comparative judgment is then used to determine the relative levels of clutter on the physiological continuum. Also introduced are two metrics for predicting the level of clutter in an image. The first of these metrics uses a graph-based image segmentation algorithm and the second uses the change in gradients across the image. We present how these two metrics along with an existing metric based on wavelets can successfully predict the perceived clutter in an image.

A Graph-Based Image Segmentation Algorithm

According to the problem that classical graph-based image segmentation algorithms are not robust to segmentation of texture image. We propose a novel segmentation algorithm that GBCTRS, which overcame the shortcoming of existed graph-based segmentation algorithms N-cut and EGBIS. It extract feature vector of blocks using color-texture feature, calculate weight between each block using the neighborhood relationship, use minimum spanning tree method to clustering segmentation. The experimental show that the new algorithm is more efficient and robust to segment texture image and strong edges image.

Manual Refinement System for Graph-Based Segmentation Results in the Medical Domain

The basic principle of graph-based approaches for image segmentation is to interpret an image as a graph, where the nodes of the graph represent 2D pixels or 3D voxels of the image. The weighted edges of the graph are obtained by intensity differences in the image. Once the graph is constructed, the minimal cost closed set on the graph can be computed via a polynomial time s-t cut, dividing the graph into two parts: the object and the background. However, no segmentation method provides perfect results, so additional manual editing is required, especially in the sensitive field of medical image processing. In this study, we present a manual refinement method that takes advantage of the basic design of graph-based image segmentation algorithms. Our approach restricts a graph-cut by using additional user-defined seed points to set up fixed nodes in the graph. The advantage is that manual edits can be integrated intuitively and quickly into the segmentation result of a graph-based approach. The method can be applied to both 2D and 3D objects that have to be segmented. Experimental results for synthetic and real images are presented to demonstrate the feasibility of our approach.

모바일 장치에서 에지 선택의 효율성

본 논문은 모바일 환경 하에서 효과적인 그래프 기반의 이미지 분할 적용 시, 실행시간 및 메모리 사용량 향상시키고, 이를 이용해 surface layout의 전처리 작업으로 수퍼 픽셀을 얻기 위한 효율적인 방법을 제안한다. 그래프기반의 이미지 분할은 영상으로부터 오브젝트 영역을 추출하는 알고리즘으로 입력 영상의 각 화소 정보를 이용한 에지 생성 및 그래프를 구성한다. 그래프 구성 시 기준 화소 및 주변 화소 위치 정보 및 색상 강도 차이 값(edgeweight)을 이용하여 에지를 구성하며, 색상 강도 차이 값에 따른 문턱치 값을 기준으로 각 에지를 연결함으로써 객체 영역을 추출하고 있다. 따라서 에지의 수는 그래프 기반의 이미지 분할의 실행시간 및 메모리 사용량을 결정하게된다. 모바일 디바이스는 PC에 비해 프로세서 속도 및 메모리 사용량 등 하드웨어적인 제약사항이 많으며, 프로그램 응답 시간이 주요 이슈가 되고 있다. 이러한 모바일 디바이스 상의 영상 처리 기술 적용 시 빠른 프로그램 응답시간 제공은 반드시 이루어져야 하며, 이미지 분할 단계에서도 보다 빠른 응답 시간을 위한 프로세싱 시간과 메모리사용량을 줄여야만 한다. 본 논문은 그래프 상의 에지의 수를 효과적으로 선택하는 효과적인 에지 선택 알고리즘 적용을 통해 그래프 기반의 이미지 분할 알고리즘을 모바일 디바이스에 적용 시, 프로세싱 시간 및 메모리 사용량의 개선을 보이도록 하고, 수퍼 픽셀 당 정확도가 입력영상 사이즈에 관계없이 70%이상 생성되며, 그 정확도가 96%동일하다는 것을 보인다. This paper proposes the effective edge selection algorithm for the rapid processing time and low memory usage of efficient graph-based image segmentation on mobile device. The graph-based image segmentation algorithm is to extract objects from a single image. The objects are consisting of graph edges, which are created by information of each image's pixel. The edge of graph is created by the difference of color intensity between the pixel and neighborhood pixels. The object regions are found by connecting the edges, based on color intensity and threshold value. Therefore, the number of edges decides on the processing time and amount of memory usage of graph-based image segmentation. Comparing to personal computer, the mobile device has many limitations such as processor speed and amount of memory. Additionally, the response time of application is an issue of mobile device programming. The image processing on mobile device should offer the reasonable response time, so that, the image segmentation processing on mobile should provide with the rapid processing time and low memory usage. In this paper, we demonstrate the performance of the effective edge selection algorithm, which effectively controls the edges of graph for the rapid processing time and low memory usage of graph-based image segmentation on mobile device.

Spatial Object Segmentation Using Stereo Images

The framework of our proposed for segmenting objects using spatial location information from stereo images. An efficient graph-based image segmentation algorithm within this framework for combining changes in optical features and physical location to segment reality scenes into perceptually and semantically uniform regions. Optical and physical location are extracted usingk-means clustering, and we propose a rules table for combining optical and spatial features together. The performance of our proposed framework is demonstrated in a series of reality-scene images using experimental data from the Middlebury stereo image data(http://vision.middlebury.edu/stereo/data/).