融合颜色属性和空间信息的显著性物体检测

摘 要：目的：提出了一种基于颜色属性和空间信息的显著性物体检测算法,并将其用于交通标志的检测。方法：首先,训练颜色属性得到颜色-像素值分布,据此将图像划分为不同的颜色聚簇,每个聚簇的显著性取决于其空间紧致性。其次,将每个聚簇分割为多个区域,用颜色属性描述子表示每个区域,计算区域的全局对比度。最后,同时考虑区域对比度和相应聚簇的空间紧致性,得到最终的显著图。在此基础上,将交通标志的先验知识转换为自上而下的显著性图,形成任务驱动的显著性模型,对交通标志进行检测。结果：在公开数据集上的测试结果表明,该算法取得最高92%的查准率,优于其它流行的显著性算法。在交通标志数据集上的检测取得了90.7%的正确率。结论：本文提出了一种新的显著性检测算法,同时考虑了区域的颜色和空间信息,在公开数据集上取得了较高的查准率和查全率。在对交通标志的检测中也取得了较好的结果。     

Geometric and illumination invariants for object recognition

We propose invariant formulations that can potentially be combined into a single system. In particular, we describe a framework for computing invariant features which are insensitive to rigid motion, affine transform, changes of parameterization and scene illumination, perspective transform, and view point change. This is unlike most current research on image invariants which concentrates on either geometric or illumination invariants exclusively. The formulations are widely applicable to many popular basis representations, such as wavelets, short-time Fourier analysis, and splines. Exploiting formulations that examine information about shape and color at different resolution levels, the new approach is neither strictly global nor local. It enables a quasi-localized, hierarchical shape analysis which is rarely found in other known invariant techniques, such as global invariants. Furthermore, it does not require estimating high-order derivatives in computing invariants (unlike local invariants), whence is more robust. We provide results of numerous experiments on both synthetic and real data to demonstrate the validity and flexibility of the proposed framework     

Face recognition across illumination

Illumination variation on images of faces is one of the most difficult problems in face recognition systems. The performance of a self-organizing map-based face recognition system is highly degraded when the illumination in test images differs from that of the training images. Illumination normalization is a way to solve this problem. Both global and local image enhancement methods are studied in this article. A local histogram equalization method strongly improves the recognition accuracy of the CMU-PIE face database. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

Fusion of color, local spatial and global frequency information for face recognition

This paper presents a novel face recognition method by means of fusing color, local spatial and global frequency information. Specifically, the proposed method fuses the multiple features derived from a hybrid color space, the Gabor image representation, the local binary patterns (LBP), and the discrete cosine transform (DCT) of the input image. The novelty of this paper is threefold. First, a hybrid color space, the RC_rQ color space, is constructed by combining the R component image of the RGB color space and the chromatic component images, C_r and Q, of the YC_bC_r and YIQ color spaces, respectively. The RC_rQ hybrid color space, whose component images possess complementary characteristics, enhances the discriminating power for face recognition. Second, three effective image encoding methods are proposed for the component images in the RC_rQ hybrid color space to extract features: (i) a patch-based Gabor image representation for the R component image, (ii) a multi-resolution LBP feature fusion scheme for the C_r component image, and (iii) a component-based DCT multiple face encoding for the Q component image. Finally, at the decision level, the similarity matrices generated using the three component images in the RC_rQ hybrid color space are fused using a weighted sum rule. Experiments on the Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 show that the proposed method improves face recognition performance significantly. In particular, the proposed method achieves the face verification rate (ROC III curve) of 92.43%, at the false accept rate of 0.1%, compared to the FRGC baseline performance of 11.86% face verification rate at the same false accept rate.     

Using eigencolor normalization for illumination-invariant color object recognition

Color is one of salient features for color object recognition, however, the colors of object images sensitively depend on scene illumination. To overcome the lighting dependency problem, a color constancy or color normalization method has to be used. This paper presents a color image normalization method, called eigencolor normalization, which consists of two phases as follows. First, the compacting method, which was originally used for compensating the adverse effect due to shape distortion for 2-D planar objects, is exploited for 3-D color space to make the color distribution less correlated and more compact. Second, the compact color image is further normalized by rotating the histogram to align with the reference axis computed. Consequently, the object colors are transformed into a new color space, called eigencolor space, which reflects the inherent colors of the object and is more invariant to illumination changes. Experimental results show that our eigencolor normalization method is superior to other existing color constancy or color normalization schemes on achieving more accurate color object recognition.     

Appearance-based visual learning and object recognition with illumination invariance

This paper describes a method for recognizing partially occluded objects under different levels of illumination brightness by using the eigenspace analysis. In our previous work, we developed the “eigenwindow” method to recognize the partially occluded objects in an assembly task, and demonstrated with sufficient high performance for the industrial use that the method works successfully for multiple objects with specularity under constant illumination. In this paper, we modify the eigenwindow method for recognizing objects under different illumination conditions, as is sometimes the case in manufacturing environments, by using additional color information. In the proposed method, a measured color in the RGB color space is transformed into one in the HSV color space. Then, the hue of the measured color, which is invariant to change in illumination brightness and direction, is used for recognizing multiple objects under different illumination conditions. The proposed method was applied to real images of multiple objects under various illumination conditions, and the objects were recognized and localized successfully.     

Robust object recognition using a color co-occurrence histogram and the spatial relations of image patches

We propose a robust object recognition system where patch-based pyramid images and the spatial relationships among patches are utilized for our image model. In particular, both a color histogram (CH) and a color co-occurrence histogram (CCH) are applied to obtain image features for each patch. The locations of subregions to be tested are decided by a particle filter in our matching process. We show that the performance of object recognition can be improved by using the spatial relationships among patches. To show the validity of our proposed method, we employ input images from various environments as test images. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

A biologically inspired spatio-chromatic feature for color object recognition

Multimedia Tools and Applications - Color information has been acknowledged for its important role in object recognition and scene classification. How to describe the color characteristics and...     

Robust histogram construction from color invariants for object recognition

An effective object recognition scheme is to represent and match images on the basis of histograms derived from photometric color invariants. A drawback, however, is that certain color invariant values become very unstable in the presence of sensor noise. To suppress the effect of noise for unstable color invariant values, in this paper, histograms are computed by variable kernel density estimators. To apply variable kernel density estimation in a principled way, models are proposed for the propagation of sensor noise through color invariant variables. As a result, the associated uncertainty is obtained for each color invariant value. The associated uncertainty is used to derive the parameterization of the variable kernel for the purpose of robust histogram construction. It is empirically verified that the proposed density estimator compares favorably to traditional histogram schemes for the purpose of object recognition.     

空中回转体目标识别算法的研究

为加强空中回转体目标识别的智能化程度,提高目标识别率,对目标图像的不变矩特征与BP神经网络相结合的空中回转体日标识别方法进行了研究.以给出权值的方式引入Relief算法对目标图像的7个Hu氏不变矩、3个仿射不变矩的识别性能进行科学的评估.选出权值较大的特征量作为BP神经网络的输入特征来训练网络,识别样本.模拟实验表明:引入Relief算法对空中回转体目标不变矩选择的目标识别方案是有效的,应用Relief算法选择出的特征项作为神经网络的输入特征不但减少了特征量提取的采集次数,降低了算法的计算量,而且,可使网络更易于收敛.且提高了目标物的识别率.     

Combining spatial and colour information for content based image retrieval

Colour is one of the most important features in content based image retrieval. However, colour is rarely used as a feature that codes local spatial information, except for colour texture. This paper presents an approach to represent spatial colour distributions using local principal component analysis (PCA). The representation is based on image windows which are selected by two complementary data driven attentive mechanisms: a symmetry based saliency map and an edge and corner detector. The eigenvectors obtained from local PCA of the selected windows form colour patterns that capture both low and high spatial frequencies, so they are well suited for shape as well as texture representation. Projections of the windows selected from the image database to the local PCs serve as a compact representation for the search database. Queries are formulated by specifying windows within query images. System feedback makes both the search process and the results comprehensible for the user.     

Using an ICA representation of local color histograms for object recognition

This paper applies a Bayesian classification scheme to the problem of object recognition through probabilistic modeling of local color histograms. In this context, the density estimation is generally performed via nonparametric kernel methods and the high dimensionality does not allow precision in the results. We propose a local independent component analysis (ICA) representation of the data. Within this representation, the components can be assumed statistically independent and, for this particular problem, sparsity of the independent components is observed. We show how these two characteristics simplify and add accuracy to the density estimation and develop a Bayesian decision scheme within this representation. We propose a set of possible density estimations for supergaussian densities, the density type associated with a sparse representation. Two experiments were performed. The first one illustrates the properties of the ICA representation for local color histograms. The second experiment tests the ICA classification model for a large set of pharmaceutical products and compares this scheme with a nonparametric technique based on Gaussian Kernels, two nearest-neighbor techniques and global histogram approach.     

融合目标特征和空间信息的粒子滤波跟踪

传统的基于颜色直方图的粒子滤波跟踪算法不能很好地利用跟踪对象的空间结构信息,因此在邻域颜色相似或目标模型微小变化时,不能取得良好的跟踪效果。提出一种融合目标特征和目标空间位置信息的粒子滤波跟踪算法,该算法鉴于目标空间位置包含跟踪对象一定的结构信息,可以和目标特征互为补充,利用定义的融合目标特征和目标空间位置的度量函数来进行跟踪对象相似度度量,以提高跟踪算法的稳健性和精确性。同时针对粒子滤波计算粒子相似度时可并行的特点,运用OpenMP共享存储并行计算进行粒子滤波跟踪的加速。实验表明,基于融合目标特征和空间信息的粒子滤波跟踪算法能得到更鲁棒的跟踪效果,可以有效地提高目标跟踪的速度。     

Combining monoSLAM with object recognition for scene augmentation using a wearable camera

In wearable visual computing, maintaining a time-evolving representation of the 3D environment along with the pose of the camera provides the geometrical foundation on which person-centred processing can be built. In this paper, an established method for the recognition of feature clusters is used on live imagery to identify and locate planar objects around the wearer. Objects’ locations are incorporated as additional 3D measurements into a monocular simultaneous localization and mapping process, which routinely uses 2D image measurements to acquire and maintain a map of the surroundings, irrespective of whether objects are present or not. Augmenting the 3D maps with automatically recognized objects enables useful annotations of the surroundings to be presented to the wearer. After demonstrating the geometrical integrity of the method, experiments show its use in two augmented reality applications.     

一种改进的类人足球机器人彩色目标识别算法

针对类人足球机器人视觉需求,提出一种结合区域生长和基于形状判别的阈值自适应更新的彩色目标识别算法。该算法在HSI空间基于S分量把图像分为高饱和区域和低饱和区域,在高饱和区域基于H分量采用区域生长算法识别目标;通过目标形状判别自适应更新阈值,并用新阈值更新区域生长中原来的阈值,以稳定准确地识别彩色目标。在类人足球机器人系统中的成功应用表明,该算法能在不同光照条件下稳定地识别出彩色目标,对光照环境有良好的适应性和鲁棒性,具有良好的识别效果。     

Accurate quaternion radial harmonic Fourier moments for color image reconstruction and object recognition

Pattern Analysis and Applications - Orthogonal moments have become a powerful tool for object representation and image analysis. Radial harmonic Fourier moments (RHFMs) are one of such image...     

颜色共生矩阵的Fisher信息度量及识别应用

基于多项流形的黎曼几何,提出一个在矩阵流形框架下度量颜色共生矩阵信息差异并将其应用于目标识别的新方法。对于给定的颜色量化水平和每个像素局部邻域,该方法将一幅彩色图像的任意两个颜色通道中共生的颜色建模为一个潜在的多项分布的概率实现。通过基于紧化的共生频率嵌入,可将每幅图像等同为一个积矩阵流形上的一点,其中每个因子流形被赋予了从对应的多项流形上诱导的Fisher信息距离度量。对于一个识别任务,测试样本与训练样本间的匹配通过先在每个因子流形上使用最近邻分类器进行标签预测然后在积流形上进行多数投票完成。在GT彩色人脸库和COIL-100目标库上获得的出色的识别效果验证了该方法的有效性。     

A color object recognition scheme: application to cellular sorting

This paper presents a color object recognition scheme which proceeds in three sequential steps: segmentation, features extraction and classification. We mainly focus on the first and the third steps here. A color watershed using global and local criteria is first described. A color contrast value is defined to select the best color space for segmenting color objects. Then, an architecture of binary neural networks is described. Its properties relies on the simplification of the recognition problem, leading to a noticeable increase in the classification rate. We conclude with the abilities of such a recognition scheme and present an automated cell sorting system. Received: 15 June 2001 / Accepted: 1 July 2002 Published online: 23 April 2003 Correspondence to: O. Lezoray (e-mail: Olivier.Lezoray@info.unicaen.fr)