DeepMerge: Deep-Learning-Based Region-Merging for Image Segmentation
Northeastern University at Qinhuangdao（东北大学秦皇岛分校）；University of Twente（特文特大学）；Wuhan University（武汉大学）；University of Arkansas（阿肯色大学）；China University of Geosciences (Beijing)（中国地质大学（北京））
Image segmentation aims to partition an image according to the objects in the scene and is a fundamental step in analysing very high spatial-resolution (VHR) remote sensing imagery. Current methods struggle to effectively consider land objects with diverse shapes and sizes. Additionally, the determination of segmentation scale parameters frequently adheres to a static and empirical doctrine, posing limitations on the segmentation of large-scale remote sensing images and yielding algorithms with limited interpretability. To address the above challenges, we propose a deep-learning-based region merging method dubbed DeepMerge to handle the segmentation of complete objects in large VHR images by integrating deep learning and region adjacency graph (RAG). This is the first method to use deep learning to learn the similarity and merge similar adjacent super-pixels in RAG. We propose a modified binary tree sampling method to generate shift-scale data, serving as inputs for transformer-based deep learning networks, a shift-scale attention with 3-Dimension relative position embedding to learn features across scales, and an embedding to fuse learned features with hand-crafted features. DeepMerge can achieve high segmentation accuracy in a supervised manner from large-scale remotely sensed images and provides an interpretable optimal scale parameter, which is validated using a remote sensing image of 0.55 m resolution covering an area of 5,660 km^2. The experimental results show that DeepMerge achieves the highest F value (0.9550) and the lowest total error TE (0.0895), correctly segmenting objects of different sizes and outperforming all competing segmentation methods.
Fermi National Accelerator Laboratory（费米国家加速器实验室）；University of Chicago（芝加哥大学）；Argonne National Laboratory（阿贡国家实验室）；Oak Ridge National Laboratory（橡树岭国家实验室）；Space Telescope Science Institute（太空望远镜科学研究所）；Kavli Institute for Cosmological Physics（卡夫利宇宙物理研究所）
In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial and potentially even detrimental decrease in model accuracy on the new target dataset. Simulated and instrument data represent different data domains, and for an algorithm to work in both, domain-invariant learning is necessary. Here we employ domain adaptation techniques$-$ Maximum Mean Discrepancy (MMD) as an additional transfer loss and Domain Adversarial…
University of Belgrade（贝尔格莱德大学）；Mathematical Institute of the Serbian Academy of Sciences and Arts（塞尔维亚科学与艺术学院数学研究所）；Fermi National Accelerator Laboratory（费米国家加速器实验室）；Space Telescope Science Institute（空间望远镜科学研究所）；University of Chicago（芝加哥大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
We investigate and demonstrate the use of convolutional neural networks (CNNs) for the task of distinguishing between merging and non-merging galaxies in simulated images, and for the first time at high redshifts (i.e. $z=2$). We extract images of merging and non-merging galaxies from the Illustris-1 cosmological simulation and apply observational and experimental noise that mimics that from the Hubble Space Telescope; the data without noise form a "pristine" data set and that with noise form a "noisy" data set. The test set classification accuracy of the CNN is $79\%$ for pristine and $76\%$ …
Object detection is a fundamental task in computer vision and has many applications in image processing. This paper proposes a new approach for object detection by applying scale invariant feature transform (SIFT) in an automatic segmentation algorithm. SIFT is an invariant algorithm respect to scale, translation and rotation. The features are very distinct and provide stable keypoints that can be used for matching an object in different images. At first, an object is trained with different aspects for finding best keypoints. The object can be recognized in the other images by using achieved k…
北京天地智绘科技有限公司；China Institute of Water Resources and Hydropower Research (IWHR)（中国水利水电科学研究院）；北京治元景行科技有限公司
Until now, all single level segmentation algorithms except CNN-based ones lead to over segmentation. And CNN-based segmentation algorithms have their own problems. To avoid over segmentation, multiple thresholds of criteria are adopted in region merging process to produce hierarchical segmentation results. However, there still has extreme over segmentation in the low level of the hierarchy, and outstanding tiny objects are merged to their large adjacencies in the high level of the hierarchy. This paper proposes a region-merging-based image segmentation method that we call it Dam Burst. As a si…
Mask R-CNN has recently achieved great success in the field of instance segmentation. However, weaknesses of the algorithm have been repeatedly pointed out as well, especially in the segmentation of long, sparse objects whose orientation is not exclusively horizontal or vertical. We present here an approach that significantly improves the performance of the algorithm by first pre-segmenting the images with a PSPNet algorithm. To further improve its prediction, we have developed our own cost functions and heuristics in the form of training strategies, which can prevent so-called (early) overfit…
This paper presents an efficient automatic color image segmentation method using a seeded region growing and merging method based on square elemental regions. Our segmentation method consists of the three steps: generating seed regions, merging the regions, and applying a pixel-wise boundary determination algorithm to the resultant polygonal regions. The major features of our method are as follows: the use of square elemental regions instead of pixels as the processing unit, a seed generation method based on enhanced gradient values, a seed region growing method exploiting local gradient value…
In this paper, the task of video panoptic segmentation is studied and two different methods to solve the task will be proposed. Video panoptic segmentation (VPS) is a recently introduced computer vision task that requires classifying and tracking every pixel in a given video. The nature of this task makes the cost of annotating datasets for it prohibiting. To understand video panoptic segmentation, first, earlier introduced constituent tasks that focus on semantics and tracking separately will be researched. Thereafter, two data-driven approaches which do not require training on a tailored VPS…
This paper addresses the automatic image segmentation problem in a region merging style. With an initially over-segmented image, in which the many regions (or super-pixels) with homogeneous color are detected, image segmentation is performed by iteratively merging the regions according to a statistical test. There are two essential issues in a region merging algorithm: order of merging and the stopping criterion. In the proposed algorithm, these two issues are solved by a novel predicate, which is defined by the sequential probability ratio test (SPRT) and the maximum likelihood criterion. Sta…
Merging Context Clustering with Visual State Space Models for Medical Image Segmentation
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；The Hong Kong University of Science and Technology（香港科技大学）；First School of Clinical Medicine, Nanjing Medical University（南京医科大学第一临床医学院）；the First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）
Laboratoire Hubert Curien, Université Jean Monnet（让·莫奈大学于贝尔·居里实验室）
Recent advances in depth imaging sensors provide easy access to the synchronized depth with color, called RGB-D image. In this paper, we propose an unsupervised method for indoor RGB-D image segmentation and analysis. We consider a statistical image generation model based on the color and geometry of the scene. Our method consists of a joint color-spatial-directional clustering method followed by a statistical planar region merging method. We evaluate our method on the NYU depth database and compare it with existing unsupervised RGB-D segmentation methods. Results show that, it is comparable w…
LATIS, ENISo, Sousse University（苏斯大学（Sousse University）、ENISo、LATIS）；Institut Mines-Télécom Atlantique（法国国立高等矿业电信学校大西洋校区（Institut Mines-Télécom Atlantique））；LaTIM UMR 1101, INSERM（INSERM LaTIM UMR 1101（INSERM是法国国家健康与医学研究院，LaTIM是其下属实验室））
This work presents a region-growing image segmentation approach based on superpixel decomposition. From an initial contour-constrained over-segmentation of the input image, the image segmentation is achieved by iteratively merging similar superpixels into regions. This approach raises two key issues: (1) how to compute the similarity between superpixels in order to perform accurate merging and (2) in which order those superpixels must be merged together. In this perspective, we firstly introduce a robust adaptive multi-scale superpixel similarity in which region comparisons are made both at co…
Ultra-High Resolution Segmentation via Boundary-Enhanced Patch-Merging Transformer
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Peng Cheng Laboratory（鹏城实验室）；The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）；SenseTime Research（商汤科技研究院）
Hierarchical image segmentation provides region-oriented scalespace, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Most image segmentation algorithms, such as region merging algorithms, rely on a criterion for merging that does not lead to a hierarchy, and for which the tuning of the parameters can be difficult. In this work, we propose a hierarchical graph based image segmentation relying on a criterion popularized by Felzenzwalb and Huttenlocher. We illustrate with both real and syn…
This paper investigates one of the most fundamental computer vision problems: image segmentation. We propose a supervised hierarchical approach to object-independent image segmentation. Starting with over-segmenting superpixels, we use a tree structure to represent the hierarchy of region merging, by which we reduce the problem of segmenting image regions to finding a set of label assignment to tree nodes. We formulate the tree structure as a constrained conditional model to associate region merging with likelihoods predicted using an ensemble boundary classifier. Final segmentations can then …
University Medical Center Mannheim（曼海姆大学医学中心）；Heidelberg University（海德堡大学）；Volume Graphics（Volume Graphics公司）；Tooploox（Tooploox公司）
We propose a novel approach for automatic extraction (instance segmentation) of fibers from low resolution 3D X-ray computed tomography scans of short glass fiber reinforced polymers. We have designed a 3D instance segmentation architecture built upon a deep fully convolutional network for semantic segmentation with an extra output for embedding learning. We show that the embedding learning is capable of learning a mapping of voxels to an embedded space in which a standard clustering algorithm can be used to distinguish between different instances of an object in a volume. In addition, we disc…
Universidad de la República（共和国大学）；Universitat Pompeu Fabra（庞培法布拉大学）
We present a novel segmentation algorithm based on a hierarchical representation of images. The main contribution of this work is to explore the capabilities of the A Contrario reasoning when applied to the segmentation problem, and to overcome the limitations of current algorithms within that framework. This exploratory approach has three main goals. Our first goal is to extend the search space of greedy merging algorithms to the set of all partitions spanned by a certain hierarchy, and to cast the segmentation as a selection problem within this space. In this way we increase the number of te…
OV-MAP: Open-Vocabulary Zero-Shot 3D Instance Segmentation Map for Robots
Interdisciplinary Program in AI, Seoul National University（人工智能交叉学科项目，首尔国立大学）；Artificial Intelligence Institute, Seoul National University（人工智能研究所，首尔国立大学）；Department of Computer Science, Seoul National University（计算机科学系，首尔国立大学）
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Pallas Ludens GmbH（帕拉斯·卢登斯有限公司）；University Hospital Heidelberg（海德堡大学医院）
With the rapidly increasing interest in machine learning based solutions for automatic image annotation, the availability of reference annotations for algorithm training is one of the major bottlenecks in the field. Crowdsourcing has evolved as a valuable option for low-cost and large-scale data annotation; however, quality control remains a major issue which needs to be addressed. To our knowledge, we are the first to analyze the annotation process to improve crowd-sourced image segmentation. Our method involves training a regressor to estimate the quality of a segmentation from the annotator…
We propose a simple yet effective proposal-free architecture for lidar panoptic segmentation. We jointly optimize both semantic segmentation and class-agnostic instance classification in a single network using a pillar-based bird's-eye view representation. The instance classification head learns pairwise affinity between pillars to determine whether the pillars belong to the same instance or not. We further propose a local clustering algorithm to propagate instance ids by merging semantic segmentation and affinity predictions. Our experiments on nuScenes dataset show that our approach outperfo…
We present a simple solution for segmenting grayscale images using existing Connected Component Labeling (CCL) algorithms (which are generally applied to binary images), which was efficient enough to be implemented in a constrained (embedded automotive) architecture. Our solution customizes the region growing and merging approach, and is primarily targeted for stereoscopic disparity images where nearer objects carry more relevance. We provide results from a standard OpenCV implementation for some basic cases and an image from the Tsukuba stereo-pair dataset.
School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感信息工程学院）
Unsupervised evaluation of segmentation quality is a crucial step in image segmentation applications. Previous unsupervised evaluation methods usually lacked the adaptability to multi-scale segmentation. A scale-constrained evaluation method that evaluates segmentation quality according to the specified target scale is proposed in this paper. First, regional saliency and merging cost are employed to describe intra-region homogeneity and inter-region heterogeneity, respectively. Subsequently, both of them are standardized into equivalent spectral distances of a predefined region. Finally, by an…
MSSP: Multi-Scale Spatially-Constrained Partition for Unsupervised Semantic Segmentation of 3D Point Clouds
College of Electronic Science and Technology, National University of Defense Technology（国防科技大学电子科学学院）；School of Electronics and Communication Engineering, Sun Yat-sen University（中山大学电子与通信工程学院）
In this work, we present an end-to-end network for fast panoptic segmentation. This network, called Fast Panoptic Segmentation Network (FPSNet), does not require computationally costly instance mask predictions or merging heuristics. This is achieved by casting the panoptic task into a custom dense pixel-wise classification task, which assigns a class label or an instance id to each pixel. We evaluate FPSNet on the Cityscapes and Pascal VOC datasets, and find that FPSNet is faster than existing panoptic segmentation methods, while achieving better or similar panoptic segmentation performance. …
Despite the popularity and empirical success of patch-based nearest-neighbor and weighted majority voting approaches to medical image segmentation, there has been no theoretical development on when, why, and how well these nonparametric methods work. We bridge this gap by providing a theoretical performance guarantee for nearest-neighbor and weighted majority voting segmentation under a new probabilistic model for patch-based image segmentation. Our analysis relies on a new local property for how similar nearby patches are, and fuses existing lines of work on modeling natural imagery patches a…
Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；School of Computer Science and Technology, Guangdong University of Technology（广东工业大学计算机科学与技术学院）；School of Information Science and Technology, Guangdong University of Foreign Studies（广东外语外贸大学信息科学与技术学院）；Faculty of Science and Technology, University of Macau（澳门大学科技学院）；The First Affiliated Hospital of Sun Yat-Sen University（中山大学附属第一医院）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）；South China Hospital, Shenzhen University（深圳大学华南医院）
It is well accepted that image segmentation can benefit from utilizing multilevel cues. The paper focuses on utilizing the FCNN-based dense semantic predictions in the bottom-up image segmentation, arguing to take semantic cues into account from the very beginning. By this we can avoid merging regions of similar appearance but distinct semantic categories as possible. The semantic inefficiency problem is handled. We also propose a straightforward way to use the contour cues to suppress the noise in multilevel cues, thus to improve the segmentation robustness. The evaluation on the BSDS500 show…
Eindhoven University of Technology（埃因霍温理工大学）；University of Amsterdam（阿姆斯特丹大学）
In this work, we introduce the new scene understanding task of Part-aware Panoptic Segmentation (PPS), which aims to understand a scene at multiple levels of abstraction, and unifies the tasks of scene parsing and part parsing. For this novel task, we provide consistent annotations on two commonly used datasets: Cityscapes and Pascal VOC. Moreover, we present a single metric to evaluate PPS, called Part-aware Panoptic Quality (PartPQ). For this new task, using the metric and annotations, we set multiple baselines by merging results of existing state-of-the-art methods for panoptic segmentation…
Norwegian Computing Center（挪威计算中心）；UiT the Arctic University of Norway（挪威北极大学）
Land cover classification of remote sensing images is a challenging task due to limited amounts of annotated data, highly imbalanced classes, frequent incorrect pixel-level annotations, and an inherent complexity in the semantic segmentation task. In this article, we propose a novel architecture called the dense dilated convolutions' merging network (DDCM-Net) to address this task. The proposed DDCM-Net consists of dense dilated image convolutions merged with varying dilation rates. This effectively utilizes rich combinations of dilated convolutions that enlarge the network's receptive fields …
Queensland University of Technology（昆士兰科技大学）；ARC Centre of Excellence for Robotic Vision（澳大利亚研究理事会机器人视觉卓越中心）
There has been a recent emergence of sampling-based techniques for estimating epistemic uncertainty in deep neural networks. While these methods can be applied to classification or semantic segmentation tasks by simply averaging samples, this is not the case for object detection, where detection sample bounding boxes must be accurately associated and merged. A weak merging strategy can significantly degrade the performance of the detector and yield an unreliable uncertainty measure. This paper provides the first in-depth investigation of the effect of different association and merging strategi…
RWTH Aachen University（亚琛工业大学）；Shahid Beheshti University（沙希德·贝赫什提大学）；Mila, Quebec AI Institute（米拉魁北克人工智能研究所）；University of Montreal（蒙特利尔大学）；Polytechnique Montreal（蒙特利尔理工学院）；University of Regensburg（雷根斯堡大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）
While CNN-based methods have been the cornerstone of medical image segmentation due to their promising performance and robustness, they suffer from limitations in capturing long-range dependencies. Transformer-based approaches are currently prevailing since they enlarge the reception field to model global contextual correlation. To further extract rich representations, some extensions of the U-Net employ multi-scale feature extraction and fusion modules and obtain improved performance. Inspired by this idea, we propose TransCeption for medical image segmentation, a pure transformer-based U-sha…
This paper addresses the problem of natural image segmentation by extracting information from a multi-layer array which is constructed based on color, gradient, and statistical properties of the local neighborhoods in an image. A Gaussian Mixture Model (GMM) is used to improve the effectiveness of local spectral histogram features. Grouping these features leads to forming a rough initial over-segmented layer which contains coherent regions of pixels. The regions are merged by using two proposed functions for calculating the distance between two neighboring regions and making decisions about th…
The Chinese University of Hong Kong（香港中文大学）；SmartMore（慧模智能）
Video instance segmentation (VIS) aims to segment and associate all instances of predefined classes for each frame in videos. Prior methods usually obtain segmentation for a frame or clip first, and merge the incomplete results by tracking or matching. These methods may cause error accumulation in the merging step. Contrarily, we propose a new paradigm -- Propose-Reduce, to generate complete sequences for input videos by a single step. We further build a sequence propagation head on the existing image-level instance segmentation network for long-term propagation. To ensure robustness and high …
Zhejiang University（浙江大学）；Megvii Inc. (Face++)（旷视科技有限公司（Face++））；Huazhong University of Science and Technology（华中科技大学）；Peking University（北京大学）；The University of Tokyo（东京大学）
Panoptic segmentation, which needs to assign a category label to each pixel and segment each object instance simultaneously, is a challenging topic. Traditionally, the existing approaches utilize two independent models without sharing features, which makes the pipeline inefficient to implement. In addition, a heuristic method is usually employed to merge the results. However, the overlapping relationship between object instances is difficult to determine without sufficient context information during the merging process. To address the problems, we propose a novel end-to-end network for panopti…
St. Petersburg Institute for Informatics and Automation of RAS（俄罗斯科学院圣彼得堡信息学与自动化研究所）
In this paper a hierarchical model for pixel clustering and image segmentation is developed. In the model an image is hierarchically structured. The original image is treated as a set of nested images, which are capable to reversibly merge with each other. An object is defined as a structural element of an image, so that, an image is regarded as a maximal object. The simulating of none-hierarchical optimal pixel clustering by hierarchical clustering is studied. To generate a hierarchy of optimized piecewise constant image approximations, estimated by the standard deviation of approximation fro…
We present a novel end-to-end single-shot method that segments countable object instances (things) as well as background regions (stuff) into a non-overlapping panoptic segmentation at almost video frame rate. Current state-of-the-art methods are far from reaching video frame rate and mostly rely on merging instance segmentation with semantic background segmentation, making them impractical to use in many applications such as robotics. Our approach relaxes this requirement by using an object detector but is still able to resolve inter- and intra-class overlaps to achieve a non-overlapping segm…
Automatic instance segmentation is a problem that occurs in many biomedical applications. State-of-the-art approaches either perform semantic segmentation or refine object bounding boxes obtained from detection methods. Both suffer from crowded objects to varying degrees, merging adjacent objects or suppressing a valid object. In this work, we assign an embedding vector to each pixel through a deep neural network. The network is trained to output embedding vectors of similar directions for pixels from the same object, while adjacent objects are orthogonal in the embedding space, which effectiv…
University of Manitoba（曼尼托巴大学）；Adiyaman University（阿迪亚曼大学）
Color image segmentation is a crucial step in many computer vision and pattern recognition applications. This article introduces an adaptive and unsupervised clustering approach based on Voronoi regions, which can be applied to solve the color image segmentation problem. The proposed method performs region splitting and merging within Voronoi regions of the Dirichlet Tessellated image (also called a Voronoi diagram) , which improves the efficiency and the accuracy of the number of clusters and cluster centroids estimation process. Furthermore, the proposed method uses cluster centroid proximit…
In this paper, we present preliminary results on the use of deep learning techniques to integrate the users self-body and other participants into a head-mounted video see-through augmented virtuality scenario. It has been previously shown that seeing users bodies in such simulations may improve the feeling of both self and social presence in the virtual environment, as well as user performance. We propose to use a convolutional neural network for real time semantic segmentation of users bodies in the stereoscopic RGB video streams acquired from the perspective of the user. We describe design i…
