Elliptic Object Detection Based on Shape Preserving and Active Contour

本文通过形状约束方程(组)与一般主动轮廓模型结合,将目标形状与主动轮廓模型融合到统一能量泛函模型中,提出了一种形状保持主动轮廓模型即曲线在演化过程中保持为某一类特定形状。模型通过参数化水平集函数的零水平集控制演化曲线形状,不仅达到了分割即目标的目的,而且能够给出特定目标的定量描述。根据形状保持主动轮廓模型,建立了一个用于椭圆状目标检测的统一能量泛函模型,导出了相应的Euler-Lagrange常微分方程并用水平集方法实现了椭圆状目标检测。此模型可以应用于眼底乳头分割,虹膜检测及相机标定。实验结果表明,此模型不仅能够准确的检测出给定图像中的椭圆状目标,而且有很强的抗噪、抗变形及遮挡性能。

Resolution deterioration in emission electron microscopy due to object roughness

In the present paper a general analytic expression has been obtained and confirmed by a computer simulation which links the surface roughness of an object under study in an emission electron microscope and it's resolution. A quantitative derivation was made for the model case when there is a step on the object surface. It was shown that the resolution is deteriorated asymmetrically relative to the step. The effect sets a practical limit to the ultimate lateral resolution obtainable in an emission electron microscope.

维持性血液透析患者认知功能特点研究

Objective: To study the episodic memory, semantic memory, cognitive planning ability and inhibition ability in MHD patients. Method: Neuropsychological research methods such as Action memory of verb-object phrase, Trail Making Test (A and B), Verbal Fluency Test, Go-No/Go test and Stroop Color Naming Task were used to investigate Episodic Memory 、Semantic Memory、Executive Function of 40 MHD and 40 NC. Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale(SDS), Social Support Scale, Life Satisfaction Scale, and biochemical examination were applied and their relationships with cognitive function were analized. The mean age and education level of MHD group and NC group have no significant difference. Result: 1.Action memory of verb-object phrase differed significantly between MHD group and NC group. 2.Two tests of Verbal Fluency differed significantly between MHD group and NC group. 3.Trail Making Test A, Trail Making Test B, the baseline condition of Go-No/Go Test and Stroop Color Naming Test differed significantly between MHD group and NC group. 4.There is no significant difference between MHD group and NC group on the correct rate of No/Go Test and the baseline condition. Both groups showed Stroop Effect in Go-No/Go test, but MHD group performed significantly worse. 5.In Stroop Color Naming Task Test, NC group showed Stroop Effect, significant Repeated Distraction Promotion Effect and significant Negative Priming Effect,while MHD group showed only Stroop Effect and no Repeated Distraction Promotion Effect and no Negative Priming Effect. There is significant difference in Stroop Effect between MHD group and NC group. Conclusion: 1.Comparing with NC group, episodic memory, semantic memory, cognitive planning ability, and inhibition ability of MHD group were impaired significantly. 2.The pathological aging of Executive Function in MHD group showed: executive Function should be a unitary system. 3.Cognitive impairment is negatively correlated with serum creatinine, blood pressure and anxiety score in MHD patients; and is related with hemoglobin, hematocrit, social support and life satisfaction. Keyword: maintenance hemodialysis, episodic memory, semantic memory, cognitive planning, inhibition ability.

注意对驾驶场景客体-位置联系内隐学习的影响

The purpose of this study is to investigate the influence of attention resourse requirement and allocation on implicit memory and explicit memory for object-location associations in driving situation based on Adams theory on the function of implicit knowledge in the Situation Awareness(SA). This study adopted Musen’s implicit learning of object-location associations, sysmemtly manipulated the type and difficuty of the naming task. This research includes three studies and ten experiments. Their aim are separately to explore the influence of attention on implicit and explicit memory for object-loction assocaitons in simple stimulus and the driving situation. And it is needed to confirme the condition and the influencing factors of implicit memory for car-location association in different condition. It is also our aim to explore the feasibility of introduce of implicit learning methods in SA measurement. The results indicted that: ⑴ The influence of attention resourse allocation ,the difficulty of naming task , the deepness of processing on on implicit memory for object-location associations in driving situation are different . the dissociated results support the standpoint that there are two independent knowledge system; ⑵ The type of naming task more influenced the implicit and explicit memory for object-location associations than the difficulty of the naming task. The attention resourse requirement of the different type can not be compared; ⑶ The implicit memory seldom appears in the location naming task resulted from the defiency of processing on object-location association, and not as a results of the overtaxed; ⑷ The reaction time methods in the implicit learning could be used in SA measurement , it is a complementarity of the existing explicit SA measurement. These findings not only contribute to resolve ongoing debates about the process of cognition and mechanism of SA structure, but also have significant practical application in traffic safety.

Feature Matching for Object Localization in the Presence of Uncertainty

We consider the problem of matching model and sensory data features in the presence of geometric uncertainty, for the purpose of object localization and identification. The problem is to construct sets of model feature and sensory data feature pairs that are geometrically consistent given that there is uncertainty in the geometry of the sensory data features. If there is no geometric uncertainty, polynomial-time algorithms are possible for feature matching, yet these approaches can fail when there is uncertainty in the geometry of data features. Existing matching and recognition techniques which account for the geometric uncertainty in features either cannot guarantee finding a correct solution, or can construct geometrically consistent sets of feature pairs yet have worst case exponential complexity in terms of the number of features. The major new contribution of this work is to demonstrate a polynomial-time algorithm for constructing sets of geometrically consistent feature pairs given uncertainty in the geometry of the data features. We show that under a certain model of geometric uncertainty the feature matching problem in the presence of uncertainty is of polynomial complexity. This has important theoretical implications by demonstrating an upper bound on the complexity of the matching problem, an by offering insight into the nature of the matching problem itself. These insights prove useful in the solution to the matching problem in higher dimensional cases as well, such as matching three-dimensional models to either two or three-dimensional sensory data. The approach is based on an analysis of the space of feasible transformation parameters. This paper outlines the mathematical basis for the method, and describes the implementation of an algorithm for the procedure. Experiments demonstrating the method are reported.

The Effect of Indexing on the Complexity of Object Recognition

Many current recognition systems use constrained search to locate objects in cluttered environments. Previous formal analysis has shown that the expected amount of search is quadratic in the number of model and data features, if all the data is known to come from a sinlge object, but is exponential when spurious data is included. If one can group the data into subsets likely to have come from a single object, then terminating the search once a "good enough" interpretation is found reduces the expected search to cubic. Without successful grouping, terminated search is still exponential. These results apply to finding instances of a known object in the data. In this paper, we turn to the problem of selecting models from a library, and examine the combinatorics of determining that a candidate object is not present in the data. We show that the expected search is again exponential, implying that naﶥ approaches to indexing are likely to carry an expensive overhead, since an exponential amount of work is needed to week out each of the incorrect models. The analytic results are shown to be in agreement with empirical data for cluttered object recognition.

Viewpoint-Specific Representations in Three-Dimensional Object Recognition

We report a series of psychophysical experiments that explore different aspects of the problem of object representation and recognition in human vision. Contrary to the paradigmatic view which holds that the representations are three-dimensional and object-centered, the results consistently support the notion of view-specific representations that include at most partial depth information. In simulated experiments that involved the same stimuli shown to the human subjects, computational models built around two-dimensional multiple-view representations replicated our main psychophysical results, including patterns of generalization errors and the time course of perceptual learning.

Object Recognition By Alignment Using Invariant Projections of Planar Surfaces

In order to recognize an object in an image, we must determine the best transformation from object model to the image. In this paper, we show that for features from coplanar surfaces which undergo linear transformations in space, there exist projections invariant to the surface motions up to rotations in the image field. To use this property, we propose a new alignment approach to object recognition based on centroid alignment of corresponding feature groups. This method uses only a single pair of 2D model and data. Experimental results show the robustness of the proposed method against perturbations of feature positions.

View-Based Models of 3D Object Recognition and Class-Specific Invariances

This paper describes the main features of a view-based model of object recognition. The model tries to capture general properties to be expected in a biological architecture for object recognition. The basic module is a regularization network in which each of the hidden units is broadly tuned to a specific view of the object to be recognized.

Viewer-Centered Object Recognition in Monkeys

How does the brain recognize three-dimensional objects? We trained monkeys to recognize computer rendered objects presented from an arbitrarily chosen training view, and subsequently tested their ability to generalize recognition for other views. Our results provide additional evidence in favor of with a recognition model that accomplishes view-invariant performance by storing a limited number of object views or templates together with the capacity to interpolate between the templates (Poggio and Edelman, 1990).

Linear Object Classes and Image Synthesis from a Single Example Image

The need to generate new views of a 3D object from a single real image arises in several fields, including graphics and object recognition. While the traditional approach relies on the use of 3D models, we have recently introduced techniques that are applicable under restricted conditions but simpler. The approach exploits image transformations that are specific to the relevant object class and learnable from example views of other "prototypical" objects of the same class. In this paper, we introduce such a new technique by extending the notion of linear class first proposed by Poggio and Vetter. For linear object classes it is shown that linear transformations can be learned exactly from a basis set of 2D prototypical views. We demonstrate the approach on artificial objects and then show preliminary evidence that the technique can effectively "rotate" high- resolution face images from a single 2D view.

Spatial Reference Frames for Object Recognition: Tuning for Rotations in Depth

The inferior temporal cortex (IT) of monkeys is thought to play an essential role in visual object recognition. Inferotemporal neurons are known to respond to complex visual stimuli, including patterns like faces, hands, or other body parts. What is the role of such neurons in object recognition? The present study examines this question in combined psychophysical and electrophysiological experiments, in which monkeys learned to classify and recognize novel visual 3D objects. A population of neurons in IT were found to respond selectively to such objects that the monkeys had recently learned to recognize. A large majority of these cells discharged maximally for one view of the object, while their response fell off gradually as the object was rotated away from the neuron"s preferred view. Most neurons exhibited orientation-dependent responses also during view-plane rotations. Some neurons were found tuned around two views of the same object, while a very small number of cells responded in a view- invariant manner. For five different objects that were extensively used during the training of the animals, and for which behavioral performance became view-independent, multiple cells were found that were tuned around different views of the same object. No selective responses were ever encountered for views that the animal systematically failed to recognize. The results of our experiments suggest that neurons in this area can develop a complex receptive field organization as a consequence of extensive training in the discrimination and recognition of objects. Simple geometric features did not appear to account for the neurons" selective responses. These findings support the idea that a population of neurons -- each tuned to a different object aspect, and each showing a certain degree of invariance to image transformations -- may, as an assembly, encode complex 3D objects. In such a system, several neurons may be active for any given vantage point, with a single unit acting like a blurred template for a limited neighborhood of a single view.