Exploiting eye–hand coordination to detect grasping movements

Human beings are very skillful at reaching for and grasping objects under multiple conditions, even when faced with an object's wide variety of positions, locations, structures and orientations. This natural ability, controlled by the human brain, is called eye–hand coordination. To understand this behavior it is necessary to study both eye and hand movements simultaneously. This paper proposes a novel approach to detect grasping movements by means of computer vision techniques. This solution fuses two viewpoints, one viewpoint which is obtained from an eye-tracker capturing the user's perspective and a second viewpoint which is captured by a wearable camera attached to a user's wrist. Utilizing information from these two viewpoints it is possible to characterize multiple hand movements in conjunction with eye-gaze movements through a Hidden–Markov Model framework. This paper shows that combining these two sources makes it possible to detect hand gestures using only the objects contained in the scene even without markers on the surface of the objects. In addition, it is possible to detect which is the desired object before the user can actually grasp said object.     

Reshaping 3D facial scans for facial appearance modeling and 3D facial expression analysis

3D face scans have been widely used for face modeling and analysis. Due to the fact that face scans provide variable point clouds across frames, they may not capture complete facial data or miss point-to-point correspondences across various facial scans, thus causing difficulties to use such data for analysis. This paper presents an efficient approach to representing facial shapes from face scans through the reconstruction of face models based on regional information and a generic model. A new approach for 3D feature detection and a hybrid approach using two vertex mapping algorithms, displacement mapping and point-to-surface mapping, and a regional blending algorithm are proposed to reconstruct the facial surface detail. The resulting models can represent individual facial shapes consistently and adaptively, establishing facial point correspondences across individual models. The accuracy of the generated models is evaluated quantitatively. The applicability of the models is validated through the application of 3D facial expression recognition using the static 3DFE and dynamic 4DFE databases. A comparison with the state of the art has also been reported.     

Combining classifiers under probabilistic models: experimental comparative analysis of methods

This work will present a review of the concept of classifier combination based on the combined discriminant function. We will present a Bayesian approach, in which the discriminant function assumes the role of the posterior probability. We will propose a probabilistic interpretation of expert rules and conditions of knowledge consistency for expert rules and learning sets. We will suggest how to measure the quality of learning materials and we will use the measure mentioned above for an algorithm that eliminates contradictions in the rule set. In this work several recognition algorithms will be described, based on either: (i) pure rules, or; (ii) rules together with learning sets. Furthermore, the original concept of information unification, which enables the formation of rules on the basis of learning set or learning set on the basis of rules will be proposed. The obtained conclusions will serve as a spring‐board for the formulation of new project guidelines for this type of decision‐making system. At the end, experimental results of the proposed algorithms will be presented, both from computer generated data and for a real problem from the medical diagnostics field.     

A method using long digital straight segments for fingerprint recognition

In this paper, we proposed a new method using long digital straight segments (LDSSs) for fingerprint recognition based on such a discovery that LDSSs in fingerprints can accurately characterize the global structure of fingerprints. Different from the estimation of orientation using the slope of the straight segments, the length of LDSSs provides a measure for stability of the estimated orientation. In addition, each digital straight segment can be represented by four parameters: x-coordinate, y-coordinate, slope and length. As a result, only about 600 bytes are needed to store all the parameters of LDSSs of a fingerprint, as is much less than the storage orientation field needs. Finally, the LDSSs can well capture the structural information of local regions. Consequently, LDSSs are more feasible to apply to the matching process than orientation fields. The experiments conducted on fingerprint databases FVC2002 DB3a and DB4a show that our method is effective.     

2D representation of facial surfaces for multi-pose 3D face recognition

The increasing availability of 3D facial data offers the potential to overcome the intrinsic difficulties faced by conventional face recognition using 2D images. Instead of extending 2D recognition algorithms for 3D purpose, this letter proposes a novel strategy for 3D face recognition from the perspective of representing each 3D facial surface with a 2D attribute image and taking the advantage of the advances in 2D face recognition. In our approach, each 3D facial surface is mapped homeomorphically onto a 2D lattice, where the value at each site is an attribute that represents the local 3D geometrical or textural properties on the surface, therefore invariant to pose changes. This lattice is then interpolated to generate a 2D attribute image. 3D face recognition can be achieved by applying the traditional 2D face recognition techniques to obtained attribute images. In this study, we chose the pose invariant local mean curvature calculated at each vertex on the 3D facial surface to construct the 2D attribute image and adopted the eigenface algorithm for attribute image recognition. We compared our approach to state-of-the-art 3D face recognition algorithms in the FRGC (Version 2.0), GavabDB and NPU3D database. Our results show that the proposed approach has improved the robustness to head pose variation and can produce more accurate 3D multi-pose face recognition.     

基于机器视觉的零件颜色识别系统

以Visual C＋＋6.0作为软件开发平台,以开源的OpenCV作为辅助的图像处理库,组建系统的硬件系统,实现了对工业自动生产线上的零件图像的采集和处理.该系统能对不同颜色的零件进行识别,为后续零件的筛选和分类奠定了基础.实验证明,采用机器视觉对零件颜色进行在线识别,不但检测精度高,而且能够满足生产线上实时性的要求.