Boundary detection using mathematical morphology

Object boundaries contain important shape information in an image. Mathematical morphology is shape sensitive and can be used in boundary detection. In this paper, we propose dynamic mathematical morphology which only operates on the parts of interest in an image and reacts to certain characteristics of the region. The next position of the structuring element is dynamically selected at each step of the operation. The technique is used to detect object boundaries and has produced encouraging results.     

Shape Based Detection and Top-Down Delineation Using Image Segments

We introduce a segmentation-based detection and top-down figure-ground delineation algorithm. Unlike common methods which use appearance for detection, our method relies primarily on the shape of objects as is reflected by their bottom-up segmentation. Our algorithm receives as input an image, along with its bottom-up hierarchical segmentation. The shape of each segment is then described both by its significant boundary sections and by regional, dense orientation information derived from the segment’s shape using the Poisson equation. Our method then examines multiple, overlapping segmentation hypotheses, using their shape and color, in an attempt to find a “coherent whole,” i.e., a collection of segments that consistently vote for an object at a single location in the image. Once an object is detected, we propose a novel pixel-level top-down figure-ground segmentation by “competitive coverage” process to accurately delineate the boundaries of the object. In this process, given a particular detection hypothesis, we let the voting segments compete for interpreting (covering) each of the semantic parts of an object. Incorporating competition in the process allows us to resolve ambiguities that arise when two different regions are matched to the same object part and to discard nearby false regions that participated in the voting process. We provide quantitative and qualitative experimental results on challenging datasets. These experiments demonstrate that our method can accurately detect and segment objects with complex shapes, obtaining results comparable to those of existing state of the art methods. Moreover, our method allows us to simultaneously detect multiple instances of class objects in images and to cope with challenging types of occlusions such as occlusions by a bar of varying size or by another object of the same class, that are difficult to handle with other existing class-specific top-down segmentation methods.     

From Images to Shape Models for Object Detection

We present an object class detection approach which fully integrates the complementary strengths offered by shape matchers. Like an object detector, it can learn class models directly from images, and can localize novel instances in the presence of intra-class variations, clutter, and scale changes. Like a shape matcher, it finds the boundaries of objects, rather than just their bounding-boxes. This is achieved by a novel technique for learning a shape model of an object class given images of example instances. Furthermore, we also integrate Hough-style voting with a non-rigid point matching algorithm to localize the model in cluttered images. As demonstrated by an extensive evaluation, our method can localize object boundaries accurately and does not need segmented examples for training (only bounding-boxes).     

基于水平集的不规则透明表面缺陷检测设计

针对图像采集中,缺陷的灰度值和背景几乎不可区分,导致图像的低对比度的问题,目前,常规的表面缺陷检测图像预处理中常用阈值化和边缘检测技术,易出现漏检现象,即不适用于检测不规则表面边界处的细微瑕疵.为解决这一问题,从低对比度透明材料图像的非高斯统计特性出发,研究提出利用水平集智能分割具有复杂形状的目标对象,并利用小波变换进行缺陷特征提取的高效的智能缺陷检测方法.测试表明,所提出的基于水平集和小波变换的不规则表面缺陷检测系统的有效性,克服了检测区域边缘处的漏检缺陷问题.     

基于散度-形状引导和优化函数的显著性目标检测

为了准确地进行显著性目标检测,本文提出了一种基于散度-形状引导和优化函数的显著性检测有效框架。首先,通过考虑颜色、空间位置和边缘信息,提出了一种有辨别力的相似性度量。接着,利用散度先验剔除图像边界中的前景噪音获得背景集,并结合相似性度量计算得到基于背景显著图。为了提高检测质量,形状完整性被提出并通过统计在分层空间中区域被激活的次数期望生成相应的形状完整显著图。最后,利用一个优化函数对两个显著图融合后的结果进行优化从而获得最终的结果。在公开数据集 ASD、DUT-OMRON和ECSSD上进行实验验证, 结果证明本文方法能够准确有效地检测出位于图像任意位置的显著性物体。     

基于四阶偏微分方程平滑的图像分割新方法

在方向曲率模值描述图像平滑度的泛函基础上，推导出一种四阶偏微分方程(PDE)图像降噪模型，在有效降噪的同时，较好地保持了边缘。该方法处理结果为分段线性图像，且在目标边缘处梯度存在阶跃。利用降噪结果的这一特点，提出一种新测地线主动轮廓模型。实验表明，新模型不但稳定，轮廓提取能力强，而且收敛速度快。     

An active contour model with shape regulation scheme

This paper presents an active method for locating target objects in images, which is aimed at improving the performance of detecting object boundaries by enhancing the behavioral characteristics of an active contour. The proposed active contour model simulates a mechanical system consisting of two main parts: the first is a rigid fixture, called the 'core', specifying the expected shape of target boundaries, while the second is an elastic rod attached to the rigid fixture. The elastic rod deforms or moves relative to the rigid core according to the classical laws of the mechanical system. When the initial contour is applied to an image data, it is attracted near the dominant image features, but tries to keep its home shape and simultaneously make the deformation smooth if a deformation is more natural for force equilibrium. This mechanism significantly improves the performance of detecting object boundaries in the presence of some disturbing image features. The active contour is scale invariant, thereby significantly relieving the difficulty in selecting proper values for the model parameters. The values for the model parameters can be selected to make the contour have the desired behaviors around the equilibrium position through the analysis of the vibration mode of the mechanical system. The performance of the proposed method is validated through a series of experiments, which include detection of heavily degraded objects, tracking of objects under non-rigid motion and comparisons with the original snake models.     

An object‐oriented methodology to detect oil spills

A new automated methodology for oil spill detection is presented, by which full synthetic aperture radar (SAR) high‐resolution image scenes can be processed. The methodology relies on the object‐oriented approach and profits from image segmentation techniques to detected dark formations. The detection of dark formations is based on a threshold definition that is fully adaptive to local contrast and brightness of large image segments. For the detection process, two empirical formulas are developed that also permit the classification of oil spills according to their brightness. A fuzzy classification method is used to classify dark formations as oil spills or look‐alikes. Dark formations are not isolated and features of both dark areas and sea environment are considered. Various sea environments that affect oil spill shape and boundaries are grouped in two knowledge bases, used for the classification of dark formations. The accuracy of the method for the 12 SAR images used is 99.5% for the class of oil spills, and 98.8% for that of look‐alikes. Fresh oil spills, fresh spills affected by natural phenomena, oil spills without clear stripping, small linear oil spills, oil spills with broken parts and amorphous oil spills can be successfully detected.     

基于卷积神经网络和语义相关的协同显著性检测

针对目前协同显著性检测方法中存在的语义特征类相差悬殊的物体被误检测为协同对象等问题,提出了一种基于卷积神经网络和语义相关的协同显著性检测算法CSCCD。首先,采用引导超像素滤波方法对SLIC分割出的超像素区域和DSS生成的显著性区域进行处理,清晰地显示了目标边界轮廓;然后使用Mask R-CNN提取语义特征,给出了图像语义特征和语义一致性的定义,并针对提取语义特征过程中出现的同一语义类别的物体在不同形态下被检测为不同语义类别的问题,提出了图像组语义相关类的概念,在此概念的基础上定义了图像组语义关联类,解决了多幅图像的语义关联问题;最后融合显著性检测区域和图像组语义一致性区域得到协同显著性检测结果。在公开基准数据集上的实验结果表明,该算法能够有效凸显目标整体及轮廓,在客观量化方面的综合性能有明显提升。     

ASOD: Arbitrary shape object detection

Arbitrary shape object detection, which is mostly related to computer vision and image processing, deals with detecting objects from an image. In this paper, we consider the problem of detecting arbitrary shape objects as a clustering application by decomposing images into representative data points, and then performing clustering on these points. Our method for arbitrary shape object detection is based on COMUSA which is an efficient algorithm for combining multiple clusterings. Extensive experimental evaluations on real and synthetically generated data sets demonstrate that our method is very accurate and efficient.     

Oscillation analysis for salient object detection

Salient object detection from an image is important for many multimedia applications. Existing methods provide good solutions to saliency detection; however, their results often emphasize the high-contrast edges, instead of regions/objects. In this paper, we present a method for salient object detection based on oscillation analysis. Our study shows that salient objects and their backgrounds have different amplitudes of oscillation between the local minima and maxima. Based on this observation, our method analyzes the oscillation in an image by estimating its local minima and maxima and computes the saliency map according to the oscillation magnitude contrast. Our method detects the local minima and maxima and performs extreme interpolation to smoothly propagate these information to the whole image. In this way, the oscillation information is smoothly assigned to regions, retaining well-defined salient boundaries as there are large variations near the salient boundaries (edges between objects and their backgrounds). As a result, our saliency map highlights salient regions/objects instead of high-contrast boundaries. We experiment with our method on two large public data set. Our results demonstrate the effectiveness of our method. We further apply our salient object detection method to automatic salient object segmentation, which again shows the success.     

一种利用形状片段的物体检测方法

针对物体检测中传统方法计算量大, 对复杂背景敏感, 且大部分物体检测方法只能得到物体所在区域而不能精确定位物体边缘等问题, 本文提出一种基于形状片段特征的物体检测方法. 该方法首先根据训练集得到具有多尺度特性的物体形状片段模型; 对测试图像按照和模型中边缘片段提取一致方法, 根据形状片段间的相似度, 选取出候选形状片段; 然后根据候选形状片段估计出模型中的片段与测试图像中片段之间的旋转角; 最后结合具有全局约束的概率Hough变换, 将物体检测问题转换为Hough空间概率问题; 根据Hough空间求解出的物体中心位置, 对候选形状片段验证, 得到实际物体轮廓片段. 理论分析和实验结果表明, 本文提出的算法具有较好的效果.     

Adaptive and optimal difference operators in image processing

Differential operators are essential in many image processing applications. Previous work has shown how to compute derivatives more accurately by examining the image locally, and by applying a difference operator which is optimal for each pixel neighborhood. The proposed technique avoids the explicit computation of fitting functions, and replaces the function fitting process by a function classification process using a filter bank of feature detection templates. Both the feature detectors and the optimal difference operators have a specific shape and an associated cost, defined by a rigid mathematical structure, which can be described by Gröbner bases. This paper introduces a cost criterion to select the operator of the best approximating function class and the most appropriate template size so that the difference operator can be locally adapted to the digitized function. We describe how to obtain discrete approximates for commonly used differential operators, and illustrate how image processing applications can benefit from the adaptive selection procedure for the operators by means of two example applications: tangent computation for digitized object boundaries and the Laplacian of Gaussian edge detector.     

Prior-based Segmentation and Shape Registration in the Presence of Perspective Distortion

Challenging object detection and segmentation tasks can be facilitated by the availability of a reference object. However, accounting for possible transformations between the different object views, as part of the segmentation process, remains difficult. Recent statistical methods address this problem by using comprehensive training data. Other techniques can only accommodate similarity transformations. We suggest a novel variational approach to prior-based segmentation, using a single reference object, that accounts for planar projective transformation. Generalizing the Chan-Vese level set framework, we introduce a novel shape-similarity measure and embed the projective homography between the prior shape and the image to segment within a region-based segmentation functional. The proposed algorithm detects the object of interest, extracts its boundaries, and concurrently carries out the registration to the prior shape. We demonstrate prior-based segmentation on a variety of images and verify the accuracy of the recovered transformation parameters.     

Salient object detection using content-sensitive hypergraph representation and partitioning

As an important problem in image understanding, salient object detection is essential for image classification, object recognition, as well as image retrieval. In this paper, we propose a new approach to detect salient objects from an image by using content-sensitive hypergraph representation and partitioning. Firstly, a polygonal potential Region-Of-Interest (p-ROI) is extracted through analyzing the edge distribution in an image. Secondly, the image is represented by a content-sensitive hypergraph. Instead of using fixed features and parameters for all the images, we propose a new content-sensitive method for feature selection and hypergraph construction. In this method, the most discriminant color channel which maximizes the difference between p-ROI and the background is selected for each image. Also the number of neighbors in hyperedges is adjusted automatically according to the image content. Finally, an incremental hypergraph partitioning is utilized to generate the candidate regions for the final salient object detection, in which all the candidate regions are evaluated by p-ROI and the best match one will be the selected as final salient object. Our approach has been extensively evaluated on a large benchmark image database. Experimental results show that our approach can not only achieve considerable improvement in terms of commonly adopted performance measures in salient object detection, but also provide more precise object boundaries which is desirable for further image processing and understanding.     

Detection and Segmentation of Multiple,Partially Occluded Objects by Grouping,Merging, Assigning Part Detection Responses

We propose a method that detects and segments multiple, partially occluded objects in images. A part hierarchy is defined for the object class. Both the segmentation and detection tasks are formulated as binary classification problem. A whole-object segmentor and several part detectors are learned by boosting local shape feature based weak classifiers. Given a new image, the part detectors are applied to obtain a number of part responses. All the edge pixels in the image that positively contribute to the part responses are extracted. A joint likelihood of multiple objects is defined based on the part detection responses and the object edges. Computation of the joint likelihood includes an inter-object occlusion reasoning that is based on the object silhouettes extracted with the whole-object segmentor. By maximizing the joint likelihood, part detection responses are grouped, merged, and assigned to multiple object hypotheses. The proposed approach is demonstrated with the class of pedestrians. The experimental results show that our method outperforms the previous ones.     

Image Warping for Shape Recovery and Recognition

We demonstrate that, for a large class of reflectance functions, there is a direct relationship between image warps and the corresponding geometric deformations of the underlying three-dimensional objects. This helps explain the hidden geometrical assumptions in object recognition schemes which involve two-dimensional image warping computed by matching image intensity. In addition, it allows us to propose a novel variant of shape from shading which we call shape from image warping. The idea is that the three-dimensional shape of an object is estimated by determining how much the image of the object is warped with respect to the image of a known prototype shape. Therefore, detecting the image warp relative to a prototype of known shape allows us to reconstruct the shape of the imaged object. We derive properties of these shape warps and illustrate the results by recovering the shapes of faces.     

基于区域对比信息混合编码的图像显著性检测方法

图像显著性检测是为了检测到能够引起视觉注意力的对象区域,利用混合的特征编码能够避免单一的特征编码在检测图像中对象显著性和显著区域精确边界时候的不足。提出一种基于图像区域对比信息和图像语义信息混合编码的图像显著性检测方法。结合图像对比信息编码以及原始图像的语义信息编码,通过卷积神经网络来进行图像显著性检测,保证对显著对象进行有效的检测以及对显著区域边缘细节的处理能力。实验结果表明,在主流的显著性检测数据集上,采用该方法能够有效地检测到图像中的显著对象以及显著区域的精确边界。