Object recognition by grasping

In this paper, we describe a method for recognizing both the three-dimensional pattern and the size of objects by grasping them with multijointed fingers equipped with tactile sensors. First, the bending data of the joint is sorted out by analyzing the distribution pattern of the sensors in contact. Then the appropriate pattern is determined by calculating the values of the several linear discriminant functions provided for each contact pattern. Classification of the size of an object is achieved by applying a linear machine with a maximum-value selector to the bending form of the joints. The results of the experiments show that the most useful discriminant function is a linear one. Its percentage of total correctness is highest for the pattern recognition of an object.     

Object recognition by three-dimensional curve matching

Experimental results for the recognition of general curves in three-space using registered range and intensity images are presented. the matching algorithm uses fast Fourier transforms to determine the least-squares difference between sequences of points sampled at equal intervals along two piecewise linear approximations of curves in three-space and returns the rotation and translation required to bring one of the curves into closest juxtaposition with the other. Performance of the algorithm is demonstrated by matching curves in three-space which are the boundaries of regions of contrasting reflectivity on curved surfaces. the experiments use a recently developed range sensor which is able to generate a 512 × 460 × 12 bit range image (with registered intensity image) in 40 s.     

Object recognition using shape-from-shading

Investigates whether surface topography information extracted from intensity images using a shape-from-shading (SFS) algorithm can be used for the purposes of 3D object recognition. We consider how curvature and shape-index information delivered by this algorithm can be used to recognize objects based on their surface topography. We explore two contrasting object recognition strategies. The first of these is based on a low-level attribute summary and uses histograms of curvature and orientation measurements. The second approach is based on the structural arrangement of constant shape-index maximal patches and their associated region attributes. We show that region curvedness and a string ordering of the regions according to size provides recognition accuracy of about 96 percent. By polling various recognition schemes, including a graph matching method, we show that a recognition rate of 98-99 percent is achievable     

Object delineation in noisy images by a modified policy-iterationmethod

The contours of isolated objects in noisy images may be detected with a minimal cost contour detection algorithm. An algorithm that is based on the policy-iteration method for locating the closed minimal cost path is introduced. Computational results indicate that it is computationally more efficient than the dynamic programming approach. The method is applied to left ventricular contours in scintigraphic images, although it is applicable to any domain where a closed minimal cost path is to be computed in a matrix of cost coefficients     

Fuzzy system based human behavior recognition by combining behavior prediction and recognition

With the development of intelligent surveillance systems, human behavior recognition has been extensively researched. Most of the previous methods recognized human behavior based on spatial and temporal features from (current) input image sequences, without the behavior prediction from previously recognized behaviors. Considering an example of behavior prediction, “punching” is more probable in the current frame when the previous behavior is “standing” as compared to the previous behavior being “lying down.” Nevertheless, there has been little study regarding the combination of currently recognized behavior information with behavior prediction. Therefore, we propose a fuzzy system based behavior recognition technique by combining both behavior prediction and recognition. To perform behavior recognition during daytime and nighttime, a dual camera system of visible light and thermal (far infrared light) cameras is used to capture 12 datasets including 11 different human behaviors in various surveillance environments. Experimental results along with the collected datasets and open database showed that the proposed method achieved higher accuracy of behavior recognition when compared to conventional methods.     

Object panorama construction using large-parallax images

Multimedia Tools and Applications - Conventional panorama techniques create a wide-angle image by stitching images taken from the same viewpoint. In contrast, the method proposed in this work...     

Object recognition using discriminative parts

The existing object recognition methods can be classified into two categories: interest-point-based and discriminative-part-based. The interest-point-based methods do not perform well if the interest points cannot be selected very carefully. The performance of the discriminative-part-base methods is not stable if viewpoints change, because they select discriminative parts from the interest points. In addition, the discriminative-part-based methods often do not provide an incremental learning ability. To address these problems, we propose a novel method that consists of three phases. First, we use some sliding windows that are different in scale to retrieve a number of local parts from each model object and extract a feature vector for each local part retrieved. Next, we construct prototypes for the model objects by using the feature vectors obtained in the first phase. Each prototype represents a discriminative part of a model object. Then, we establish the correspondence between the local parts of a test object and those of the model objects. Finally, we compute the similarity between the test object and each model object, based on the correspondence established. The test object is recognized as the model object that has the highest similarity with the test object. The experimental results show that our proposed method outperforms or is comparable with the compared methods in terms of recognition rates on the COIL-100 dataset, Oxford buildings dataset and ETH-80 dataset, and recognizes all query images of the ZuBuD dataset. It is robust enough for distortion, occlusion, rotation, viewpoint and illumination change. In addition, we accelerate the recognition process using the C4.5 decision tree technique, and the proposed method has the ability to build prototypes incrementally.     

Efficient fall activity recognition by combining shape and motion features

Computational Visual Media - This paper presents a vision-based system for recognizing when elderly adults fall. A fall is characterized by shape deformation and high motion. We represent shape...     

Object recognition and articulated object learning by accumulative Hopfield matching

In this paper, a novel object recognition method based on attributed relational graph matching is proposed, which is called accumulative Hopfield matching. We first divide the scene graph into many sub-graphs, and a modified Hopfield network is then constructed to obtain the sub-graph isomorphism between each sub-scene graph and model graph. The final result is deduced by accumulating the solutions of all small sub-networks. Comparing to the traditional Hopfield network, the proposed system has the advantage of finding homomorphic mappings between two graphs. Furthermore, the system can be applied for articulated object recognition and visual model learning, which is considered as a difficult topic till now. The proposed method has been evaluated with real images.     

Object recognition by spectral feature derived from canonical shape representation

In this paper, we introduce a new spectral shape feature that can be used in content-based object recognition. We explain a new canonical string representation for a polygonal shape approximation from which the proposed spectral feature is derived. This spectral feature is a composition of Fourier coefficients of the shape function that is derived from the canonical representation. We applied the proposed feature in classification of lung nodules by means of our hierarchical learning scheme proposed in another study. The results show that the spectral feature is promising for lung nodule recognition.     

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.     

基于音高与端点联合检测的音符识别算法

结合次谐波与谐波比率(SHR)音高提取算法与基于短时能量及过零率的端点检测算法,实现了一种音高、端点联合检测算法.该方法以短时能量及过零率为音符切分的基础,进而利用音高跳变来检测音符的变化.仿真实验表明,采用SHR音高提取算法,该联合检测提高了音符识别的精度,尤其在噪声环境下,其表现优于传统端点检测算法.     

Human activity recognition in RGB-D videos by dynamic images

Human Activity Recognition in RGB-D videos has been an active research topic during the last decade. However, only a few efforts have been made, for recognizing human activity in RGB-D videos where several performers are performing simultaneously. In this paper we introduce such a challenging dataset with several performers performing the activities simultaniously. We present a novel method for recognizing human activities performed simultaniously in the same videos. The proposed method aims in capturing the motion information of the whole video by producing a dynamic image corresponding to the input video. We use two parallel ResNet-101 architectures to produce the dynamic images for the RGB video and depth video separately. The dynamic images contain only the motion information of the whole frame, which is the main cue for analyzing the motion of the performer during action. Hence, dynamic images help recognizing human action by concentrating only on the motion information appeared on the frame. We send the two dynamic images through a fully connected layer for classification of activity. The proposed dynamic image reduces the complexity of the recognition process by extracting a sparse matrix from a video, while preserving the motion information required for activity recognition, and produces comparable results with respect to the state-of-the-art.

     

Object segmentation within microscope images of palynofacies

Identification of fossil material under a microscope is the basis of micropalentology. Our task is to locate and count the pieces of inertinite and vitrinite in images of sieve sampled rock. The classical watershed algorithm oversegments the objects because of their irregular shapes. In this paper we propose a method for locating multiple objects in a black and white image while accounting for possible overlapping or touching. The method, called Centre Supported Segmentation (CSS), eliminates oversegmentation and is robust against differences in size and shape of the objects.     

Object recognition based on critical nodes

Pattern Analysis and Applications - In recent decades, the need for efficient and effective image search from large databases has increased. In this paper, we present a novel shape matching...     

Face recognition in hyperspectral images

Hyperspectral cameras provide useful discriminants for human face recognition that cannot be obtained by other imaging methods. We examine the utility of using near-infrared hyperspectral images for the recognition of faces over a database of 200 subjects. The hyperspectral images were collected using a CCD camera equipped with a liquid crystal tunable filter to provide 31 bands over the near-infrared (0.7 /spl mu/m-1.0 /spl mu/m). Spectral measurements over the near-infrared allow the sensing of subsurface tissue structure which is significantly different from person to person, but relatively stable over time. The local spectral properties of human tissue are nearly invariant to face orientation and expression which allows hyperspectral discriminants to be used for recognition over a large range of poses and expressions. We describe a face recognition algorithm that exploits spectral measurements for multiple facial tissue types. We demonstrate experimentally that this algorithm can be used to recognize faces over time in the presence of changes in facial pose and expression.