Planar Object Recognition Research Articles

키포인트 기반의 시각적 지역 특징을 이용한 평면 물체 인식: 비교 분석

본 논문은 키포인트 기반의 시각적 지역 특징을 이용해 평면 물체를 인식할 때 어떤 점들을 고려해야하는지에 대한 비교 분석을 제시한다. 실제 응용에서 나타날 수 있는 평면 물체 인식에 대한 다양한 시나리오를 고려한 데이터베이스를 이용하여 지역 특징 추출 알고리즘, 매칭 및 정제 알고리즘, 호모그라피 계산 알고리즘을 달리하면서 각 조합에 따른 인식의 정확도 및 전체 연산 속도를 비교 분석한다. 본 논문에서 비교 분석한 결과는 키포인트 기반의 시각적 지역 특징을 이용해 평면 물체를 인식해야하는 컴퓨터비전이나 로봇비전 응용을 개발할 때에 좋은 참고자료로 활용가능하며 특히, 증강현실이나 동시적 위치 추정 및 지도 작성과 같이 지역 특징의 매칭과 호모그라피의 계산을 토대로 카메라의 3차원 자세를 추정해야하는 응용 등에서 높은 가치가 기대된다.

Robust Plane Detection Using Depth Information From a Consumer Depth Camera

The emerging of depth-camera technology is paving the way for a variety of new applications and it is believed that plane detection is one of them. In fact, planes are common in man-made living structures, thus their accurate detection can benefit many visual-based applications. The use of depth information allows detecting planes characterized by complex pattern and texture, where the texture-based plane detection algorithms usually fail. In this paper, we propose a robust depth-driven plane detection (DPD) algorithm which consists of two parts: the growing-based plane detection and a two-stage refinement. The proposed approach starts from the seed patch with the highest planarity and uses the estimated equation of the growing plane and a dynamic threshold function to steer the growing process. Aided with this mechanism, each seed patch can grow to its maximum extent, and then the next seed patch starts to grow. This process is iteratively repeated so as to detect all the planes. Moreover, the refinement is proposed to tackle two common problems suffered by growing-based approaches, the over-growing problem, and the under-growing problem. Validated by extensive experiments, the proposed DPD algorithm is able to accurately detect planes and robust to various testing conditions. In terms of applications, it can be used as the pre-processing step for a variety of applications, such as, planar object recognition, super-resolution of the time-of-flight depth images with intrinsically low resolution.

Depth image-based plane detection

BackgroundThe emerging of depth-camera technology is paving the way for variety of new applications and it is believed that plane detection is one of them. In fact, planes are common in man-made living structures, thus their accurate detection can benefit many visual-based applications. The use of depth data allows detecting planes characterized by complicated pattern and texture, where texture-based plane detection algorithms usually fail. In this paper, we propose a robust Depth Image-based Plane Detection (DIPD) algorithm. The proposed approach starts from the highest planarity seed patch, and uses the estimated equation of the growing plane and a dynamic threshold function to steer the growing process. Aided with this mechanism, each seed patch can grow to its maximum extent, and then next seed patch starts to grow. This process is iteratively repeated so as to detect all the planes.ResultsValidated by extensive experiments on three datasets, the proposed DIPD algorithm can achieve 81% correct detection ratio which doubles the value compared with the state-of-the-art algorithms. Meanwhile, the runtime of the proposed algorithm is around 4 times of the fastest RANdom SAmple Consensus (RANSAC).ConclusionsThe proposed depth image-based plane detection algorithm can achieve state-of-the-art performance. In terms of applications, it could be used as the pre-processing step for planar object recognition, super-resolution of the intrinsically low resolution Time-of-Flight (ToF) depth images, and variety of other applications.

Detection and Recognition of Traffic Planar Objects Using Colorized Laser Scan and Perspective Distortion Rectification

Reliable detection and recognition of planar objects including traffic sign, street sign, and road surface in dynamic cluttered natural scenes are a big challenge for self-driving cars. In this paper, we propose a comprehensive method for planar object detection and recognition. First, the data association of LIDAR and camera is set up to acquire colorized laser scans, which simultaneously contain both color and geometrical information. Second, we combine three color spaces of RGB, HSV, and CIE L*a*b* with laser reflectivity as an aggregation-based feature vector. Third, the 3-D geometrical characteristics of planar objects that contain planarity, size, and aspect ratio are exploited to further reduce false alarm. Fourth, in order to increase robustness to any viewpoint variation, we present a new virtual camera-based rectification method to synthesize fronto-parallel views of refined object descriptors in 3-D space. Finally, experimental results achieved under a variety of challenging conditions show that integration of color space aggregation and laser reflectivity is superior to individuals. Specifically, the proposed perspective distortion rectification method remarkably eliminates false recognition error by 45.5%. Overall, the detection rate of our comprehensive method has up to 95.87% and the recognition rate even reaches 95.07% for traffic signs ranging within 100 m, with about 33.25 ms average running time per frame.

Image Projective Invariants

In this paper, we have proved the existence of projective moment invariants of images using finite combinations of weighted moments, with relative projective differential invariants as weight functions. We have given some instances constructed in that way, and analyzed possible issues could affect the performance. Some procedures are taken to estimate partial derivatives of discrete images, and a new method is designed to normalize the number of pixels for discrete images to minimize the changes before and after the projective transformation. We have carried out experiments using popular image databases and real images to test the performance. And the results show that the invariants proposed in this paper have better stability and discriminability than other previously used moment invariants in image retrieval and classification. Users can directly extract invariant features of images for a given planar object from different viewpoints without knowing the parameters of the 2D projective transformations. Therefore, the projective moment invariant could be potentially useful for planar object recognition, image description and classification.

Planar object recognition based on local feature prediction agreements

Object recognition has drawn great attention in industrial application especially in automated feeding and assembling, for it can greatly improve the line flexibility and save cost. In this paper, a simple but effective method for planar object recognition is presented. This method can deal with objects under complex conditions like occlusion and clutter. The method generates object pose hypothesis from the prediction agreements of different local features in the object. There are two stages contained in our method, offline stage and online stage. At offline stage, the representative parts in the object are chosen as its local features and the recognition template is made. Next at online stage, the matches of different local features are found in the input image. Then the prediction agreements are searched among them in order to generate the final object pose hypothesis. A thin planar object recognition experiment has been conducted under occluded conditions and an improved result is presented compared with the traditional overall matching method.

Contour model based homography estimation of texture-less planar objects in uncalibrated images

This paper proposes a homograhpy estimation algorithm that uses contour model to track and locate the texture-less object in high clutter environments. The proposed method consists of a homography recognition with the correspondences between model and image lines unknown, followed by a nonlinear homography optimization based on the maximum likelihood criterion. In our approach, the initial estimation of the homography is obtained in the framework of RANSAC-like, which contains two stages: hypothesizing and verifying. The first stage generates a number of homograhpy hypotheses from the correspondences of the quadrangle-like structures. Next, the homography hypotheses are quickly ranked according to a redefined distance function. In the refinement procedure, the model sample points are projected into the image plane. After that, 1D search is utilized along the normal direction to obtain the corresponding image point for each model sample point. Finally, the optimized homography is obtained by minimizing the errors between the sample points and their corresponding image points. Experiments show that the proposed method performs robust homography recognition of texture-less planar objects and maintains accurate and stable homography estimation in the cluttered environments as well as the cases of extreme slant angles.

HumanTop: a multi-object tracking tabletop

In this paper, a computer vision based interactive multi-touch tabletop system called HumanTop is introduced. HumanTop implements a stereo camera vision subsystem which allows not only an accurate fingertip tracking algorithm but also a precise touch-over-the-working surface detection method. Based on a pair of visible spectra cameras, a novel synchronization circuit makes the camera caption and the image projection independent from each other, providing the minimum basis for the development of computer vision analysis based on visible spectrum cameras without any interference coming from the projector. The assembly of both cameras and the synchronization circuit is not only capable of performing an ad-hoc version of a depth camera, but it also introduces the recognition and tracking of textured planar objects, even when contents are projected over them. On the other hand HumanTop supports the tracking of sheets of paper and ID-code markers. This set of features makes the HumanTop a comprehensive, intuitive and versatile augmented tabletop that provides multitouch interaction with projective augmented reality on any flat surface. As an example to exploit all the capabilities of HumanTop, an educational application has been developed using an augmented book as a launcher to different didactic contents. A pilot study in which 28 fifth graders participated is presented. Results about efficiency, usability/satisfaction and motivation are provided. These results suggest that HumanTop is an interesting platform for the development of educational contents.

Planar Object Recognition Based on Riemannian Manifold

摘要: 平面目标识别中的几何形变可用射影变换群描述. 与紧致李群SO(n, R)不同, 正则化的射影变换群, 即非紧致李群SL(n, R)上由黎曼度量决定的黎曼指数映射不同于由单参数子群决定的李群指数映射. 基于黎曼流形优化算法得到取值于特殊线性群SL(3, R)的样本的内蕴均值和协方差矩阵, 并依此构建李群正态分布. 利用此先验知识, 根据贝叶斯定理进行简单背景下的平面目标的识别实验. 结果表明, 利用射影变换群的统计特性可有效提高平面目标识别的成功率. 关键词: 黎曼流形 / 目标识别 / 射影变换 / 李群 / 流形优化

Optimal color space for relative color polygons

We aim for detection and recognition of planar objects in natural outdoor scenes under varying illumination conditions. To achieve this, we propose Relative Color Polygons (RCPs) using component colors of objects for color matching. They can be defined on many color spaces, and it is found that a 2D color space (XY space) is the optimal color space for our relative color method compared with other color spaces. To evaluate the invariance to illumination changes for object recognition, experiments have been carried out using 500 outdoor scene images. By using the proposed model, the color matching rate of the input images with the standard one was 95%. This framework is potentially applicable to image retrieval, image segmentation, image recognition, and so on.

Recognition of Planar Objects Using Multiresolution Analysis

By using affine-invariant shape descriptors, it is possible to recognize an unknown planar object from an image taken from an arbitrary view when standard view images of candidate objects exist in a database. In a previous study, an affine-invariant function calculated from the wavelet coefficients of the object boundary has been proposed. In this work, the invariant is constructed from the multiwavelet and (multi)scaling function coefficients of the boundary. Multiwavelets are known to have superior performance compared to scalar wavelets in many areas of signal processing due to their simultaneous orthogonality, symmetry, and short support properties. Going from scalar wavelets to multiwavelets is challenging due to the increased dimensionality of multiwavelets. This increased dimensionality is exploited to construct invariants with better performance when the multiwavelet "detail" coefficients are available. However, with (multi)scaling function coefficients, which are more stable in the presence of noise, scalar wavelets cannot be defeated.

A Hidden Markov Model object recognition technique for incomplete and distorted corner sequences

This paper presents a new technique for planar object recognition based on Hidden Markov Models. First, the contour of the object is processed to extract a sequence of high curvature points. These points are extracted from a new adaptively extracted curvature function which is resistant against noise and transformations. Each corner is characterized by its subtended angle and its distance to the next corner. Then, corner sequences are analyzed by using HMMs. The method has been successfully tested for different databases. Its main advantage is that it can deal with incomplete and distorted corner sequences.

Indexing for local appearance-based recognition of planar objects

Local appearance-based approaches for planar object recognition do not support efficient means for indexing of object models. The size of the feature set which needs to be stored and processed is also large. To reduce database size, we propose an optimal feature extraction technique that selects only the salient features of an object. Since in typical local appearance-based systems, the actual feature information is not isolated from the background, scene clutter causes error in recognition. We propose a mechanism whereby this shortcoming can be alleviated. Further, by indexing onto this space, we propose to improve the performance in terms of computation time and suppression of false positives. This indexing mechanism based on the color histogram and the geometry of the feature, exploits the fact that features tend to form clusters in the feature space based on their similarity of appearances. We have implemented the proposed system and verified its validity through extensive testing.

Recognition of 3D planar objects in canonical frames

An algorithm for recognizing 3D planar objects by their boundaries is presented. Extreme points on a shape are extracted for constructing canonical frames, under which signatures are then generated for determining the similarity between shapes. The method is efficient and yields a high recognition rate.

Recognition of Planar Objects Using the Density of Affine Shape

In this paper, we will study the recognition problem for finite point configurations, in a statistical manner. We study the statistical theory of shape for ordered finite point configurations, or otherwise stated, the uncertainty of geometric invariants. Here, a general approach for defining shape and finding its density, expressed in the densities for the individual points, is developed. No approximations are made, resulting in an exact expression of the uncertainty region. In particular, we will concentrate on the affine shape, where often analytical computations is possible. In this case confidence intervals for invariants can be obtained from a priori assumptions on the densities of the detected points in the images. However, the theory is completely general and can be used to compute the density of any invariant (Euclidean, affine, similarity, projective, etc.) from arbitrary densities of the individual points. These confidence intervals can be used in such applications as geometrical hashing, recognition of ordered point configurations, and error analysis of reconstruction algorithms. Finally, an example will be given, illustrating the theory for the problem of recognizing planar point configurations from images taken by an affine camera. This case is of particular importance in applications, where details on a conveyor belt are captured by a camera, with image plane parallel to the conveyor belt and extracted feature points from the images are used to sort the objects.

Assessing Feature Importance in the Context of Object Recognition

A popular paradigm in computer vision is based on dividing the vision problem into three stages namely segmentation, feature extraction and recognition. For example edge detection followed by line detection followed by planar object recognition. It can be argued that each of these stages needs to be thoroughly described to enable vision systems to be configured with predictable performance. However an alternative view is that the performance of each stage is not in itself important as long as the overall performance is acceptable. This paper discusses feature performance concentrating on the assessmentof edge-based feature detection and object recognition. Evaluation techniques are discussed for assessing arc and line detection algorithmsand for features in the context of verification and pose refinement strategies. These techniques can then be used for the design and integration of indexing and verification stages of object recognition. A theme of the paper is the need to assess feature extraction in the context of the chosen task.

Planar object recognition using projective shape representation

We describe a model based recognition system, called LEWIS, for the identification of planar objects based on a projectively invariant representation of shape. The advantages of this shape description include simple model acquisition (direct from images), no need for camera calibration or object pose computation, and the use of index functions. We describe the feature construction and recognition algorithms in detail and provide an analysis of the combinatorial advantages of using index functions. Index functions are used to select models from a model base and are constructed from projective invariants based on algebraic curves and a canonical projective coordinate frame. Examples are given of object recognition from images of real scenes, with extensive object libraries. Successful recognition is demonstrated despite partial occlusion by unmodelled objects, and realistic lighting conditions.

Viewpoint-invariant Fourier descriptor for 3-D planarobject recognition

A new viewpoint-invariant Fourier descriptor is presented which can be used for 3-D planar object recognition without requiring knowledge of the viewing position. The descriptor is derived from the linear paraperspective projection model which is relatively simple to work with, and the approach has the advantage of providing a close geometric camera model for 3-D applications.

Invariance from the Euclidean geometer's perspective.

It is remarkable how well the human visual system can cope with changing viewpoints when it comes to recognising shapes. The state of the art in machine vision is still quite remote from solving such tasks. Nevertheless, a surge in invariance-based research has led to the development of methods for solving recognition problems still considered hard until recently. A nonmathematical account explains the basic philosophy and trade-offs underlying this strand of research. The principles are explained for the relatively simple case of planar-object recognition under arbitrary viewpoints. Well-known Euclidean concepts form the basis of invariance in this case. Introducing constraints in addition to that of planarity may further simplify the invariants. On the other hand, there are problems for which no invariants exist.

Application of elliptic Fourier descriptors to symmetry detection under parallel projection

In this paper, the method of elliptic Fourier descriptors using arc length parameterization is applied to tackle the problem of detection and recovery of symmetry under parallel projection. A simple and fast iteration algorithm together with the invariants of symmetry provides sufficient information for the detection and recovery of symmetry under parallel projection. The proposed method has been extensively tested using symmetric figures under different parallel projections. Simulation results of the algorithm are presented. The extension of this method for planar object recognition under parallel projection is also addressed. >