Active Object Recognition Integrating Attention and Viewpoint Control

We present an active object recognition strategy which combines the use of an attention mechanism for focusing the search for a 3D object in a 2D image, with a viewpoint control strategy for disambiguating recovered object features. The attention mechanism consists of a probabilistic search through a hierarchy of predicted feature observations, taking objects into a set of regions classified according to the shapes of their bounding contours. We motivate the use of image regions as a focus-feature and compare their uncertainty in inferring objects with the uncertainty of more commonly used features such as lines or corners. If the features recovered during the attention phase do not provide a unique mapping to the 3D object being searched, the probabilistic feature hierarchy can be used to guide the camera to a new viewpoint from where the object can be disambiguated. The power of the underlying representation is its ability to unify these object recognition behaviors within a single framework. We present the approach in detail and evaluate its performance in the context of a project providing robotic aids for the disabled.     

基于残差双注意力与跨级特征融合模块的静态手势识别

为解决卷积神经网络提取特征遗漏、手势多特征提取不充分问题,本文提出基于残差双注意力与跨级特征融合模块的静态手势识别方法.设计了一种残差双注意力模块,该模块对ResNet50网络提取的低层特征进行增强,能够有效学习关键信息并更新权重,提高对高层特征的注意力,然后由跨级特征融合模块对不同阶段的高低层特征进行融合,丰富高级特征图中不同层级之间的语义和位置信息,最后使用全连接层的Softmax分类器对手势图像进行分类识别.本文在ASL美国手语数据集上进行实验,平均准确率为99.68%,相比基础ResNet50网络准确率提升2.52%.结果验证本文方法能充分提取与复用手势特征,有效提高手势图像的识别精度.     

一种结合非局部和多区域注意力机制的细粒度图像识别方法

细粒度图像识别的目标是对细粒度级别的物体子类进行分类,由于不同子类间的差异非常细微,使得细粒度图像识别具有非常大的挑战性.目前细粒度图像识别算法的难度在于如何定位细粒度目标中具有分辨性的部位以及如何更好地提取细粒度级别的细微特征.为此,提出了一种结合非局部和多区域注意力机制的细粒度识别方法.Navigator只利用图像...     

Automatic recognition of flower species in the natural environment

In this study, we propose a method for the recognition and retrieval of a flower species in the natural environment based on a multi-layer technique, and we also suggest novel applications. First, the study suggests how to capture a flower object that is blooming in the natural environment, as well as the corresponding background. Secondly, an experimental analysis is conducted for the purpose of improving the optimal method of feature extraction for color, texture, and shape. Thirdly, the study developed a flower-image automatic-recognition technology that can be utilized in a mobile environment. We performed experiments on 29,463 images of 300 species of blooming flowers that were collected in South Korea between 2011 and 2014. We found image recognition to be 91.26% for the 1st-ranking recognition of the flower image and 97.40% for the 6th-ranking recognition. These results show that the color–texture–shape features of the flower pictures are the most effective; furthermore, the effectiveness and validity of this suggested method for demonstration services are verified in this paper.     

隐马尔可夫模型下基于SIFT特征的局部遮挡目标识别

提出一种利用隐马尔可夫模型建立目标特征匹配库来识别图像中局部遮挡目标的新方法。该方法首先通过SIFT算法提取目标SIFT特征,然后采用隐马尔可夫模型对目标所有的SIFT特征进行训练,得到目标SIFT特征对应的模型输出概率范围,将该概率范围作为目标特征匹配库。在对图像中的目标进行识别时,利用目标特征匹配库可以把目标特征从图像所有特征中识别出来,即使目标遮挡比例为60%时,该方法仍能识别出目标。实验结果表明,新方法可以精准地识别出图像中被遮挡目标,能够很好地解决遮挡情况下的目标识别问题。与现有局部遮挡目标识别算法相比,新方法所取得的目标识别率均有所提高。     

特征平衡的无人机航拍图像目标检测算法

无人机航拍图像目标较小、图像视角变化大,导致目标检测效果不佳。针对此问题,设计了一种适用于无人机小目标检测的网络。该网络中的可变形卷积模块可以提高多视角目标的特征提取能力,以解决航拍图像目标视角变化剧烈致使目标特征难以提取的问题;特征平衡金字塔模块可以增强网络中底层小目标特征,以解决航拍图像中的小目标因特征易丢失而造成其检测效果差的问题;同时利用像素重组构建底层大尺度特征以解决特征平衡金字塔模块的底层特征卷积运算量大的问题;交叉自注意力机制获取目标上下文信息,改善严苛条件下的漏检错检问题。公开数据集上的仿真结果表明,在保证实时检测的情况下所提算法的平均准确度优于主流检测算法。     

Object recognition based on image sequences by using inter-feature-line consistencies

An image sequence-based framework for appearance-based object recognition is proposed in this paper. Compared with the methods of using a single view for object recognition, inter-frame consistencies can be exploited in a sequence-based method, so that a better recognition performance can be achieved. We use the nearest feature line (NFL) method (IEEE Trans. Neural Networks 10 (1999) 439) to model each object. The NFL method is extended in this paper by further integrating motion-continuity information between features lines in a probabilistic framework. The associated recognition task is formulated as maximizing an a posteriori probability measure. The recognition problem is then further transformed to a shortest-path searching problem, and a dynamic-programming technique is used to solve it.     

Leaf recognition based on PCNN

Plant is closely related to humans. How to quickly recognize an unknown plant without related professional knowledge is a huge challenge. With the development of image processing and pattern recognition, it is available for plant recognition based on the technique of image processing. Pulse-coupled neural network is a powerful tool for image processing. It is widely applied in the field of image segmentation, image fusion, feature extraction, etc. Support vector machine is an excellent classifier, which can finish the complex task of data exploration. Based on these two techniques, a novel plant recognition method is proposed in this paper. The key feature is the entropy sequence obtained by pulse-coupled neural network. Other ancillary features can be computed directly by mathematical and morphological methods. Both key feature and ancillary features are employed to represent the unique feature of one plant. Support vector machine in our method is taken as the classifier, which can implement the multi-class classification. Experimental results show that the proposed method can finish the task of plant recognition effectively. Compared with the existing methods, our proposed method has better recognition rate.     

A feature construction method for general object recognition

This paper presents a novel approach for object detection using a feature construction method called Evolution-COnstructed (ECO) features. Most other object recognition approaches rely on human experts to construct features. ECO features are automatically constructed by uniquely employing a standard genetic algorithm to discover series of transforms that are highly discriminative. Using ECO features provides several advantages over other object detection algorithms including: no need for a human expert to build feature sets or tune their parameters, ability to generate specialized feature sets for different objects, and no limitations to certain types of image sources. We show in our experiments that ECO features perform better or comparable with hand-crafted state-of-the-art object recognition algorithms. An analysis is given of ECO features which includes a visualization of ECO features and improvements made to the algorithm.     

Human Face Recognition and the Face Image Set′s Topology

If we consider an n × n image as an n²-dimensional vector, then images of faces can be considered as points in this n²-dimensional image space. Our previous studies of physical transformations of the face, including translation, small rotations, and illumination changes, showed that the set of face images consists of relatively simple connected subregions in image space. Consequently linear matching techniques can be used to obtain reliable face recognition. However, for more general transformations, such as large rotations or scale changes, the face subregions become highly non-convex. We have therefore developed a scale-space matching technique that allows us to take advantage of knowledge about important geometrical transformations and about the topology of the face subregion in image space. While recognition of faces is the focus of this paper, the algorithm is sufficiently general to be applicable to a large variety of object recognition tasks     

Efficient Pose Clustering Using a Randomized Algorithm

Pose clustering is a method to perform object recognition by determining hypothetical object poses and finding clusters of the poses in the space of legal object positions. An object that appears in an image will yield a large cluster of such poses close to the correct position of the object. If there are m model features and n image features, then there are O(m ³ n ³ ) hypothetical poses that can be determined from minimal information for the case of recognition of three-dimensional objects from feature points in two-dimensional images. Rather than clustering all of these poses, we show that pose clustering can have equivalent performance for this case when examining only O(mn) poses, due to correlation between the poses, if we are given two correct matches between model features and image features. Since we do not usually know two correct matches in advance, this property is used with randomization to decompose the pose clustering problem into O(n ² ) problems, each of which clusters O(mn) poses, for a total complexity of O(mn ³ ) . Further speedup can be achieved through the use of grouping techniques. This method also requires little memory and makes the use of accurate clustering algorithms less costly. We use recursive histograming techniques to perform clustering in time and space that is guaranteed to be linear in the number of poses. Finally, we present results demonstrating the recognition of objects in the presence of noise, clutter, and occlusion.     

Learning to Recognize Objects with Little Supervision

This paper shows (i) improvements over state-of-the-art local feature recognition systems, (ii) how to formulate principled models for automatic local feature selection in object class recognition when there is little supervised data, and (iii) how to formulate sensible spatial image context models using a conditional random field for integrating local features and segmentation cues (superpixels). By adopting sparse kernel methods, Bayesian learning techniques and data association with constraints, the proposed model identifies the most relevant sets of local features for recognizing object classes, achieves performance comparable to the fully supervised setting, and obtains excellent results for image classification.     

机器视觉表面缺陷检测综述

目的 工业产品的表面缺陷对产品的美观度、舒适度和使用性能等带来不良影响,所以生产企业对产品的表面缺陷进行检测以便及时发现并加以控制。机器视觉的检测方法可以很大程度上克服人工检测方法的抽检率低、准确性不高、实时性差、效率低、劳动强度大等弊端,在现代工业中得到越来越广泛的研究和应用。方法 以机器视觉表面缺陷检测为研究对象,在广泛调研相关文献和发展成果的基础上,对基于机器视觉在表面缺陷检测领域的应用进行了综述。分析了典型机器视觉表面缺陷检测系统的工作原理和基本结构,阐述了表面缺陷视觉检测的研究现状、现有视觉软件和硬件平台,综述了机器视觉检测所涉及到的图像预处理算法、图像分割算法、图像特征提取及其选择算法、图像识别等相关理论和算法研究,并对每种主要方法的基本思想、特点和存在的局限性进行了总结,对未来可能的发展方向进行展望。结果 机器视觉表面缺陷检测系统中,图像处理和分析算法是重要内容,算法各有优缺点和其适应范围。如何提高算法的准确性、实时性和鲁棒性,一直是研究者们努力的方向。结论 机器视觉是对人类视觉的模拟,机器视觉表面检测涉及众多学科和理论,如何使检测进一步向自动化和智能化方向发展,还需要更深入的研究。     

Affine invariant comparison of point-sets using convex hulls and hausdorff distances

Many object recognition or identification applications involve comparing features associated with point-sets. This paper presents an affine invariant point-set matching technique which measures the similarity between two point-sets by embedding them into an affine invariant feature space. The developed technique assumes no a priori knowledge of reference points, as is the case in many identification problems. Reference points of a point-set are obtained based on its convex hull. An enhanced version of the Modified Hausdorff Distance is also introduced and used in the feature space for comparing two point-sets. It should be noted that the technique does not attempt to obtain correspondences between the point-sets. The introduced technique is applied to two real databases and its performance is found favorable as compared to three other affine invariant matching techniques.     

结合兴趣点和边缘的建筑物和物体识别方法

提出了多种图像特征相结合的建筑物和物体识别方法．使用尺度不变特征描述器描述的Harris-Laplace兴趣点以及边缘颜色直方图描述的边缘特征表示图像．边缘和兴趣点包含图像的重要信息．对2种特征的抽取同时进行：基于Harris检测器可以直接得到边缘特征;在多个尺度下进行Harris兴趣点检测,利用Laplace公式得到Harris-Laplace兴趣点．进行物体识别时,根据兴趣点的数目自适应地改变兴趣点和边缘特征的相似性权重．与同类方法相比较表明,该方法具有更高的识别正确率,在视点变化、光照条件变化等情况下具有较好的性能．     

Image segmentation in the presence of uncertainty

This article incorporates fuzzy set theory into the task of image segmentation. the basic concept is to allow the fuzzy membership function to model the uncertainty and vagueness of definition of objects in digital images. We define a fuzzy segmentation as a fuzzy c-partition of an image and incorporate this definition and fuzzy criteria into several image segmentation techniques including segmentation by clustering, region growing, and relaxation labelling. the algorithms are tested on digital forward looking infrared (FLIR) images and digital subtraction angiographic images. These techniques are shown to perform at least as well as their crisp or probabilistic counterparts when converted to a crisp partition. However, the real advantage to a fuzzy methodology is that the degree of membership provides a model of uncertainty and can subsequently be used by feature extraction and object recognition algorithms to increase the amount of information available in decision processes.     

基于模块2DPCA的人脸识别方法

提出了模块2DPCA（two-dimensional principal component analysis）的人脸识别方法。模块2DPCA方法先对图像矩阵进行分块，将分块得到的子图像矩阵直接用于构造总体散布矩阵，然后利用总体散布矩阵的特征向量进行图像特征抽取。与基于图像向量的鉴别方法（比如PCA）相比，该方法在特征抽取之前不需要将子图像矩阵转化为图像向量，能快速地降低鉴别特征的维数，可以完全避免使用矩阵的奇异值分解，特征抽取方便；此外，模块2DPCA是2DPCA的推广。在ORL和NUST603人脸库上的试验结果表明，模块2DPCA方法在识别性能上优于PCA，比2DPCA更具有鲁棒性。     

基于Gabor系数分块统计和自适应特征选择的人脸描述与识别

提出一种新的人脸描述及识别方法,首先对归一化后的人脸图像进行多方向多尺度Gabor变换;然后对人脸区域进行分块,以块为单位统计Gabor系数的均值和方差,求得块特征矢量(block feature vector,BFV),按先行后列的顺序将各块的BFV拼接,构成整幅人脸图像特征矢量(face feature vector,FFV).在分类器设计阶段,引入两两比对和投票机制,用多个两类分类器组合成多类分类器.在训练某个具体的两类分类器时,根据隶属训练样本计算FFV中每项的分辨力,以分辨力大小为依据选出最优特征子集(best subset feature vector,BSFV).基于Yale人脸数据集展开实验,与已发表的算法和结果进行对比,证明了该方法的有效性.     

On the verification of hypothesized matches in model-basedrecognition

Model-based recognition methods generally use ad hoc techniques to decide whether or not a model of an object matches a given scene. The most common such technique is to set an empirically determined threshold on the fraction of model features that must be matched to data features. Conditions under which to accept a match as correct are rigorously derived. The analysis is based on modeling the recognition process as a statistical occupancy problem. This model makes the assumption that pairings of object and data features can be characterized as a random process with a uniform distribution. The authors present a number of examples illustrating that real image data are well approximated by such a random process. Using a statistical occupancy model, they derive an expression for the probability that a randomly occurring match will account for a given fraction of the features of a particular object. This expression is a function of the number of model features, the number of data features, and bounds on the degree of sensor noise. It provides a means of setting a threshold such that the probability of a random match is very small     

Bi-branch deconvolution-based convolutional neural network for image classification

With the rise of deep neural network, convolutional neural networks show superior performances on many different computer vision recognition tasks. The convolution is used as one of the most efficient ways for extracting the details features of an image, while the deconvolution is mostly used for semantic segmentation and significance detection to obtain the contour information of the image and rarely used for image classification. In this paper, we propose a novel network named bi-branch deconvolution-based convolutional neural network (BB-deconvNet), which is constructed by mainly stacking a proposed simple module named Zoom. The Zoom module has two branches to extract multi-scale features from the same feature map. Especially, the deconvolution is borrowed to one of the branches, which can provide distinct features differently from regular convolution through the zoom of learned feature maps. To verify the effectiveness of the proposed network, we conduct several experiments on three object classification benchmarks (CIFAR-10, CIFAR-100, SVHN). The BB-deconvNet shows encouraging performances compared with other state-of-the-art deep CNNs.