Edge replacement in the recognition of occluded objects

The Generalized Hough Transform recognizes objects more successfully when all edges are visible. To facilitate recognition, this paper introduces a preliminary Generalized Hough process that restores edges which were invisible because of occlusion or because of lack of contrast between occluding and occluded areas. Space and time complexity of Generalized Hough processes are reduced by chained hashing techniques. Experimental results show that recognition of occluded objects via a Generalized Hough Transform is improved if occluding objects are first recognized and subtracted out of the input picture, and occluded edges are then reconstructed prior to recognition of occluded objects.     

A generalization of template matching for recognition of real objects

Pattern-recognition methods are discussed under the general headings of feature extraction and template matching. Problems arising from these methods are of two types: those arising in the process of recognition and those arising when making measurements on objects after recognition. It is indicated how template matching can avoid these latter types of problems and how a generalization of the normal notion of template can surmount problems of the first type. Such a generalized template matching procedure for recognizing cells in a microscope slide is discussed. The generalized template, in this case, makes use of the fact that the cells seem “bubble-like”. It expands a “bubble” template and detects matches between this and the visual data.     

Recognition of articulated and occluded objects

A model-based automatic target recognition system is developed to recognize articulated and occluded objects in synthetic aperture radar (SAR) images, based on invariant features of the objects. Characteristics of SAR target image scattering centers, azimuth variation, and articulation invariants are presented. The basic elements of the new recognition system are described and performance results are given for articulated, occluded and occluded articulated objects, and they are related to the target articulation invariance and percent unoccluded     

室性早搏的多模板匹配自适应识别算法

为实现室性早搏的自动判别,提出一种基于多模板匹配的自适应识别算法。采用滤波提高数据信噪比,利用样本数据通过特定的自学习机制建立模板库,设计模板的压缩算法实现待测心搏与模板心搏宽度的对齐,采用模板队列匹配和模板库匹配相结合的匹配策略,设计自适应更新规则实时优化队列与库的结构,利用相关系数检测室早。使用MIT-BIH数据库进行测试,达到99.39%的灵敏度和98.79%的特异度。     

针对遮挡物体的轮廓细化实例分割

目的 遮挡物体实例分割效果的好坏与物体轮廓的预测结果息息相关,但目前算法预测的物体轮廓并不够细化,使得分割掩膜粗糙,物体边界分割效果不佳。为此,以BCNet （bilayer convolutional network）网络为基础,提出一种针对遮挡物体的轮廓细化实例分割算法,预测的物体轮廓更加精细,分割掩膜更加完整。方法 1）提出一种均衡池化注意力模块来提取特征,在传统一维平均池化的基础上,增加一维最大池化操作以突出细节特征,并将最大池化和平均池化结果进行加权融合来提取特征,使提取的特征能更好地兼顾物体的整体和边缘细节;2）将BCNet掩膜头中轮廓预测与掩膜预测分成两个支路来进行,从特征金字塔最高分辨率特征中提取感兴趣区域（region of interest,RoI）特征用于轮廓预测,并提出一种自适应特征融合模块,将轮廓预测支路中的特征与掩膜预测支路的特征进行融合,在轮廓预测支路中,融合掩膜预测支路的特征可以更好地判定轮廓所属物体类别,在掩膜预测支路,融合轮廓预测支路的特征能够更好地辅助掩膜定位。结果 在COCO 2017（common objects in context 2017）数据集上,本文相较于目前同类网络中性能最优的BCNet网络,在骨干网络为ResNet-50/101（deep residual network）时平均精度（average precision,AP）分别提高了1.7%和2.1%。结合可视化结果,本文分割算法对遮挡物体的轮廓分割更加精细,能有效分割出更加完整、精细的掩码。结论 提出的针对遮挡物体的轮廓细化实例分割算法,明显提升了遮挡物体实例分割的效果。     

Recognition of occluded objects by reducing feature interactions

The main difficulty for the recognition of occluded objects lies in the fact that the original feature set is corrupted and no longer reliable to represent the object of interest. This corruption is caused by the interactions between features from different objects, denoted as feature interactions, which is a key issue addressed in our algorithm. In this paper, a local to global strategy is represented for the occlusion recognition problem, which combines the pairwise grouping and graph matching algorithms. Local appearance similarity serves as priors to reduce feature interactions, by which the performance of graph matching algorithms is improved in order to deal with the contaminated data set. With our formulation, a global decision on object recognition can be made based on locally gathered information. Experimental results show that the proposed framework can dramatically reduce incorrect matches and objects under severe occlusions can still be recognized.     

Stochastic models for recognition of occluded targets

Recognition of occluded objects in synthetic aperture radar (SAR) images is a significant problem for automatic target recognition. Stochastic models provide some attractive features for pattern matching and recognition under partial occlusion and noise. In this paper, we present a hidden Markov modeling based approach for recognizing objects in SAR images. We identify the peculiar characteristics of SAR sensors and using these characteristics we develop feature based multiple models for a given SAR image of an object. The models exploiting the relative geometry of feature locations or the amplitude of SAR radar return are based on sequentialization of scattering centers extracted from SAR images. In order to improve performance we integrate these models synergistically using their probabilistic estimates for recognition of a particular target at a specific azimuth. Experimental results are presented using both synthetic and real SAR images.     

基于RGB-D融合的密集遮挡抓取检测

针对当前抓取检测模型对密集遮挡物体的检测效果差以及人工数据标注工作量大的问题,提出基于RGB-D图像融合的目标检测与抓取检测分步骤进行的改进方案.新方案支持将单物体图像训练的抓取检测模型直接应用于密集遮挡的多物体图像场景中.首先,考虑到密集遮挡场景下抓取物具有多尺度的特点,提出子阶段路径聚合(SPA)的多尺度特征融合模块,用于丰富RGB-D特征级别融合的目标检测模型SPA-YOLO-Fusion的高维语义特征信息,以便于检测模型定位所有的抓取物;其次,使用基于RGB-D像素级别融合的GR-ConvNet抓取检测模型估计每个物体的抓取点,并提出背景填充的图像预处理算法来降低密集遮挡物体的相互影响;最后,使用机械臂对目标点进行抓取.在LineMOD数据集上对目标检测模型进行测试,实验结果表明SPA-YOLO-Fusion的mAP比YOLOv3-tiny与YOLOv4-tiny分别提高了10%与7%.从实际场景中采集图像制作YODO_Grasp抓取检测数据集并进行测试,结果表明增加背景填充预处理算法的GR-ConvNet的抓取检测精度比原模型提高了23%.     

Using polygons to recognize and locate partially occluded objects

We present computer vision algorithms that recognize and locate partially occluded objects. The scene may contain unknown objects that may touch or overlap giving rise to partial occlusion. The algorithms revolve around a generate-test paradigm. The paradigm iteratively generates and tests hypotheses for compatibility with the scene until it identifies all the scene objects. Polygon representations of the object's boundary guide the hypothesis generation scheme. Choosing the polygon representation turns out to have powerful consequences in all phases of hypothesis generation and verification. Special vertices of the polygon called ``corners' help detect and locate the model in the scene. Polygon moment calculations lead to estimates of the dissimilarity between scene and model corners, and determine the model corner location in the scene. An association graph represents the matches and compatibility constraints. Extraction of the largest set of mutually compatible matches from the association graph forms a model hypothesis. Using a coordinate transform that maps the model onto the scene, the hypothesis gives the proposed model's location and orientation. Hypothesis verification requires checking for region consistency. The union of two polygons and other polygon operations combine to measure the consistency of the hypothesis with the scene. Experimental results give examples of all phases of recognizing and locating the objects.     

融合注意力特征的遮挡物体6D姿态估计

在机械臂视觉抓取过程中,现有的算法在复杂背景、光照不足、遮挡等条件下,难以对目标物体进行实时、准确、鲁棒的姿态估计。针对以上问题,提出一种基于关键点方法的融合注意力特征的物体6D姿态网络。首先,在跳跃连接（Skip Connection）阶段引入能够聚焦通道空间信息的卷积注意力模块（CBAM）,使编码阶段的浅层特征与解码阶段的深层特征进行有效融合,增强特征图的空间域信息和精确位置通道信息;其次,采用归一化损失函数以弱监督的方式回归每个关键点的注意力图,将注意力图作为对应像素位置上关键点偏移量的权重分数;最后,累加求和得到关键点坐标。实验结果证明,所提网络在LINEMOD数据集和Occlusion LINEMOD数据集上ADD(-S)指标分别达到了91.3%和46.3%。与基于关键点的逐像素投票网络（PVNet）相比ADD(-S)指标分别提升了5.0个百分点和5.5个百分点,验证了所提网络在遮挡场景下有更好的鲁棒性。     

Unsupervised clustering in Hough space for identification ofpartially occluded objects

An automated approach for template-free identification of partially occluded objects is presented. The contour of each relevant object in the analyzed scene is modeled with an approximating polygon whose edges are then projected into the Hough space. A structurally adaptive self-organizing map neural network generates clusters of collinear and/or parallel edges, which are used as the basis for identifying the partially occluded objects within each polygonal approximation. Results on a number of cases under different conditions are provided     

Shape-based recognition of wiry objects

We present an approach to the recognition of complex-shaped objects in cluttered environments based on edge information. We first use example images of a target object in typical environments to train a classifier cascade that determines whether edge pixels in an image belong to an instance of the desired object or the clutter. Presented with a novel image, we use the cascade to discard clutter edge pixels and group the object edge pixels into overall detections of the object. The features used for the edge pixel classification are localized, sparse edge density operations. Experiments validate the effectiveness of the technique for recognition of a set of complex objects in a variety of cluttered indoor scenes under arbitrary out-of-image-plane rotation. Furthermore, our experiments suggest that the technique is robust to variations between training and testing environments and is efficient at runtime.     

Recognition of partially occluded 3-D objects by depth map matching

The problem of 3-D object recognition using depth map representations is tackled. Surface patches are extracted for matching using a spherical window. Matching is done using only the outer boundaries of the patches which are closed curves in 3-space formed by the intersection of the spherical window and the object surface.     

An over-regression suppression method to discriminate occluded objects of same category

Pattern Analysis and Applications - Occlusion is a key challenge in object detection. It is hard to discriminate objects accurately when they gather together and occlude each other, especially when...     

Application of differential geometry to recognize and locate partially occluded objects

A new method has been proposed to recognize and locate partially occluded two-dimensional rigid objects of a given scene. For this purpose we initially generate a set of local features of the shapes using the concept of differential geometry. Finally a computer vision scheme, based upon matching local features of the objects in a scene and the models which are considered as cognitive database, is described using hypothesis generation and verification of features for the best possible recognition.     

一种改进的模版匹配识别算法

通过对现有常用的几种模板匹配算法的分析与研究,提出了一种全区域特征加权模板匹配识别算法,它是对特征加权的模板匹配算法的一种改进。经过理论分析与实际测试,这种改进的识别算法进一步降低了字符的误识率。     

Silhouette-based occluded object recognition through curvature scale space

A complete and practical system for occluded object recognition has been developed which is very robust with respect to noise and local deformations of shape (due to weak perspective distortion, segmentation errors and non-rigid material) as well as scale, position and orientation changes of the objects. The system has been tested on a wide variety of free-form 3D objects. An industrial application is envisaged where a fixed camera and a light-box are utilized to obtain images. Within the constraints of the system, every rigid 3D object can be modeled by a limited number of classes of 2D contours corresponding to the object's resting positions on the light-box. The contours in each class are related to each other by a 2D similarity transformation. The Curvature Scale Space technique [26, 28] is then used to obtain a novel multi-scale segmentation of the image and the model contours. Object indexing [16, 32, 36] is used to narrow down the search space. An efficient local matching algorithm is utilized to select the best matching models. Received: 5 August 1996 / Accepted: 19 March 1997     

On multiple moving objects

This paper explores the motion-planning problem for multiple moving objects. The approach taken consists of assigning priorities to the objects, then planning motions one object at a time. For each moving object, the planner constructs a configuration space-time that represents the time-varying constraints imposed on the moving object by the other moving and stationary objects. The planner represents this space-time approximately, using two-dimensional slices. The space-time is then searched for a collision-free path. The paper demonstrates this approach in two domains. One domain consists of translating planar objects; the other domain consists of two-link planar articulated arms.     

On multiple moving objects

This paper explores the motion-planning problem for multiple moving objects. The approach taken consists of assigning priorities to the objects, then planning motions one object at a time. For each moving object, the planner constructs a configuration space-time that represents the time-varying constraints imposed on the moving object by the other moving and stationary objects. The planner represents this space-time approximately, using two-dimensional slices. The space-time is then searched for a collision-free path. The paper demonstrates this approach in two domains. One domain consists of translating planar objects; the other domain consists of two-link planar articulated arms.This report describes research performed at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Michael Erdmann is supported in part by a fellowship from General Motors Research Laboratories. Tomás Lozano-Pérez is supported by an NSF Presidential Young Investigator grant. Support for the Laboratory's Artificial Intelligence research is provided in part by the System Development Foundation, in part by the Office of Naval Research under Office of Naval Research Contract N00014-81-K-0494, and in part by the Advanced Research Projects Agency under Office of Naval Research Contracts N00014-80-C-0505 and N00014-82-K-0344.