Adaptive 3-D object recognition from multiple views

The authors address the problem of generating representations of 3-D objects automatically from exploratory view sequences of unoccluded objects. In building the models, processed frames of a video sequence are clustered into view categories called aspects, which represent characteristic views of an object invariant to its apparent position, size, 2-D orientation, and limited foreshortening deformation. The aspects as well as the aspect transitions of a view sequence are used to build (and refine) the 3-D object representations online in the form of aspect-transition matrices. Recognition emerges as the hypothesis that has accumulated the maximum evidence at each moment. The `winning' object continues to refine its representation until either the camera is redirected or another hypothesis accumulates greater evidence. This work concentrates on 3-D appearance modeling and succeeds under favorable viewing conditions by using simplified processes to segment objects from the scene and derive the spatial agreement of object features     

Object matching using deformable templates

We propose a general object localization and retrieval scheme based on object shape using deformable templates. Prior knowledge of an object shape is described by a prototype template which consists of the representative contour/edges, and a set of probabilistic deformation transformations on the template. A Bayesian scheme, which is based on this prior knowledge and the edge information in the input image, is employed to find a match between the deformed template and objects in the image. Computational efficiency is achieved via a coarse-to-fine implementation of the matching algorithm. Our method has been applied to retrieve objects with a variety of shapes from images with complex background. The proposed scheme is invariant to location, rotation, and moderate scale changes of the template     

Representation and shape matching of 3-d objects

A three-dimensional scene analysis system for the shape matching of real world 3-D objects is presented. Various issues related to representation and modeling of 3-D objects are addressed. A new method for the approximation of 3-D objects by a set of planar faces is discussed. The major advantage of this method is that it is applicable to a complete object and not restricted to single range view which was the limitation of the previous work in 3-D scene analysis. The method is a sequential region growing algorithm. It is not applied to range images, but rather to a set of 3-D points. The 3-D model of an object is obtained by combining the object points from a sequence of range data images corresponding to various views of the object, applying the necessary transformations and then approximating the surface by polygons. A stochastic labeling technique is used to do the shape matching of 3-D objects. The technique matches the faces of an unknown view against the faces of the model. It explicitly maximizes a criterion function based on the ambiguity and inconsistency of classification. It is hierarchical and uses results obtained at low levels to speed up and improve the accuracy of results at higher levels. The objective here is to match the individual views of the object taken from any vantage point. Details of the algorithm are presented and the results are shown on several unknown views of a complicated automobile casting.     

基于模糊逻辑的多相机非重叠场景的物体匹配

An approach based on fuzzy logic for matching both articulated and non-articulated objects across multiple non-overlapping field of views (FoVs) from multiple cameras is proposed. We call it fuzzy logic matching algorithm (FLMA). The approach uses the information of object motion, shape and camera topology for matching objects across camera views. The motion and shape information of targets are obtained by tracking them using a combination of ConDensation and CAMShift tracking algorithms. The information of camera topology is obtained and used by calculating the projective transformation of each view with the common ground plane. The algorithm is suitable for tracking non-rigid objects with both linear and non-linear motion. We show videos of tracking objects across multiple cameras based on FLMA. From our experiments, the system is able to correctly match the targets across views with a high accuracy.     

Recognizing large isolated 3-D objects through next view planning using inner camera invariants.

Most model-based three-dimensional (3-D) object recognition systems use information from a single view of an object. However, a single view may not contain sufficient features to recognize it unambiguously. Further, two objects may have all views in common with respect to a given feature set, and may be distinguished only through a sequence of views. A further complication arises when in an image, we do not have a complete view of an object. This paper presents a new online scheme for the recognition and pose estimation of a large isolated 3-D object, which may not entirely fit in a camera's field of view. We consider an uncalibrated projective camera, and consider the case when the internal parameters of the camera may be varied either unintentionally, or on purpose. The scheme uses a probabilistic reasoning framework for recognition and next-view planning. We show results of successful recognition and pose estimation even in cases of a high degree of interpretation ambiguity associated with the initial view.     

Matching ‘sticks,plates and blobs’ objects using geometric and relational constraints

A recognition scheme using relational and rough geometric information about three-dimensional man-made objects to recognize instances of the objects in single perspectivenormal views of scenes is described. Experiments performed using the matching scheme show that, in most cases, the object in the view can be identified correctly and reasonable estimates can be made of the unknown camera position responsible for generating the given view.The technique is based on the fact that the camera position constrains the appearance of the various parts of the object. The propagation of these constraints from one planar object surface to another through the projection equations is worked out. This constraint propagation guides the matching scheme in the development of the interpretation of the scene. The results provide an estimate of the camera position within 20° of the actual location.     

Evaluation of a hypothesizer for silhouette-based 3-D object recognition

Shape retrieval and shape-based object recognition are closely related problems; however, they have different task contexts, performance criteria, and database characteristics. In previous work, we proposed a method for similarity-based 2-D shape retrieval using scale-space part decompositions, part-frequency distributions, and structural indexing. In this paper, we evaluate the use of that shape retrieval method as the hypothesis generation component of silhouette-based 3-D object recognition systems, using a performance criterion and test database appropriate for the new application.     

An Affine-Invariant Tool for Retrieving Images from Homogeneous Databases

In this paper, we examine the complexities involved in retrieving images from a database comprised of objects of very similar appearance. Such an operation requires a process that can discriminate among images at a very fine level, such as distinguishing among various species of fish. Furthermore, incidental environmental factors such as change in viewpoints and slight, nonessential shape deformation must be excluded from the similarity criteria. To this end, we propose a new method for content-based image retrieval and indexing, one that is well suited for discriminating among objects within the same class in a way that is insensitive to incidental environmental changes. The scheme comprises a global alignment and a local matching process. Affine transform is used to model the different viewpoints associated with positioning the camera, while multi-dimensional indexing techniques are used to make the global alignment scheme efficient. A local matching process based on dynamic programming allows the optimal matching of local structures using cost metrics that may ignore nonessential local shape deformation. Results show the method's ability to cancel out visual distortions caused by a changing viewpoint, and its tolerance to noise, occlusion, and slight deformations of the object.     

Geometry-based image retrieval in binary image databases

In this paper, a geometry-based image retrieval system is developed for multi-object images. We model both shape and topology of image objects using a structured representation called curvature tree (CT). The hierarchy of the CT reflects the inclusion relationships between the image objects. To facilitate shape-based matching, triangle-area representation (TAR) of each object is stored at the corresponding node in the CT. The similarity between two multi-object images is measured based on the maximum similarity subtree isomorphism (MSSI) between their CTs. For this purpose, we adopt a recursive algorithm to solve the MSSI problem and a very effective dynamic programming algorithm to measure the similarity between the attributed nodes. Our matching scheme agrees with many recent findings in psychology about the human perception of multi-object images. Experiments on a database of 13500 real and synthesized medical images and the MPEG-7 CE-1 database of 1400 shape images have shown the effectiveness of the proposed method.     

Image segmentation and similarity of color-texture objects

We aim for content-based image retrieval of textured objects in natural scenes under varying illumination and viewing conditions. To achieve this, image retrieval is based on matching feature distributions derived from color invariant gradients. To cope with object cluttering, region-based texture segmentation is applied on the target images prior to the actual image retrieval process. The retrieval scheme is empirically verified on color images taken from textured objects under different lighting conditions.     

Shape spectrum based view grouping and matching of 3D free-formobjects

We address the problem of constructing view aspects of 3D free-form objects for efficient matching during recognition. We introduce a novel view representation based on “shape spectrum” features, and propose a general and powerful technique for organizing multiple views of objects of complex shape and geometry into compact and homogeneous clusters. Our view grouping technique obviates the need for surface segmentation and edge detection. Experiments on 6,400 synthetically generated views of 20 free-form objects and 100 real range images of 10 sculpted objects demonstrate the good performance of our shape spectrum based model view selection technique     

On the Repeatability and Quality of Keypoints for Local Feature-based 3D Object Retrieval from Cluttered Scenes

3D object recognition from local features is robust to occlusions and clutter. However, local features must be extracted from a small set of feature rich keypoints to avoid computational complexity and ambiguous features. We present an algorithm for the detection of such keypoints on 3D models and partial views of objects. The keypoints are highly repeatable between partial views of an object and its complete 3D model. We also propose a quality measure to rank the keypoints and select the best ones for extracting local features. Keypoints are identified at locations where a unique local 3D coordinate basis can be derived from the underlying surface in order to extract invariant features. We also propose an automatic scale selection technique for extracting multi-scale and scale invariant features to match objects at different unknown scales. Features are projected to a PCA subspace and matched to find correspondences between a database and query object. Each pair of matching features gives a transformation that aligns the query and database object. These transformations are clustered and the biggest cluster is used to identify the query object. Experiments on a public database revealed that the proposed quality measure relates correctly to the repeatability of keypoints and the multi-scale features have a recognition rate of over 95% for up to 80% occluded objects.     

Visual object retrieval via block-based visual-pattern matching

This paper presents an object-based image retrieval using a method based on visual-pattern matching. A visual pattern is obtained by detecting the line edge from a square block using the moment-preserving edge detector. It is desirable and yet remains as a challenge for querying multimedia data by finding an object inside a target image. Given an object model, an added difficulty is that the object might be translated, rotated, and scaled inside a target image. Object segmentation and recognition is the primary step of computer vision for applying to image retrieval of higher-level image analysis. However, automatic segmentation and recognition of objects via object models is a difficult task without a priori knowledge about the shape of objects. Instead of segmentation and detailed object representation, the objective of this research is to develop and apply computer vision methods that explore the structure of an image object by visual-pattern detection to retrieve images from a database. A voting scheme based on generalized Hough transform is proposed to provide object search method, which is invariant to the translation, rotation, scaling of image data, and hence, invariant to orientation and position. Computer simulation results show that the proposed method gives good performance in terms of retrieval accuracy and robustness.     

融合信息熵和CNN 的基于手绘的三维模型检索

基于手绘草图的三维模型检索(SBSR)已成为三维模型检索、模式识别与计算机视 觉领域的一个研究热点。与传统方法相比,基于卷积神经网络(CNN)的三维深度表示方法在三 维模型检索任务中性能优势非常明显。本文提出了一种基于手绘图像融合信息熵和CNN 的三 维模型检索方法。首先,通过计算模型投影图的信息熵得到模型的代表性视图,并将代表性视 图经过边缘检测等处理得到三维模型投影图的轮廓图像;然后,将轮廓图像和手绘草图输入到 CNN 中提取特征描述子,并进行特征匹配。本文方法在Shape Retrieval Contest (SHREC) 2012 数据库和SHREC 2013 数据库上进行实验。实验证明,该方法的效果较其他传统方法检索准确 度更高。