122735646
Segmentation using multiscale cues
Edges at multiple scales provide complementary grouping cues for image segmentation. These cues are reliable within different ranges. The larger the scale of an edge, the longer range the grouping cues it designates, and the greater impact it has on the final segmentation. A good segmentation respects grouping cues at each scale. These intuitions are formulated in a graph-theoretic framework where multiscale edges define pairwise pixel affinity at multiple grids, each captured in one graph. A novel criterion called average cuts of normalized affinity is proposed to evaluate a simultaneous segmentation through all these graphs. Its near-global optima can be solved efficiently. With a sparse yet complete characterization of pairwise pixel affinity, this graph-cuts approach leads to a hierarchy of coarse to fine segmentations that naturally take care of textured regions and weak contours.
344050
What do V1 neurons tell us about saccadic suppression?
Abstract A series of neurophysiological experiments on the responses of V1 neurons during saccadic eye movements were carried out. Strong suppression followed by rebound was observed in post-saccadic neural activities. These results showed that although the reduction in perceptual sensitivity during rapid eye movements was largely due to the smearing of visual stimuli during saccades, there exist saccade-related extra-retinal signals mediating saccadic suppression at the neuronal level.
2791772
Segmentation induced by scale invariance
Perceptual organization is scale-invariant. In turn, a segmentation that separates features consistently at all scales is the desired one that reveals the underlying structural organization of an image. Addressing cross-scale correspondence with interior pixels, we develop this intuition into a general segmenter that handles texture and illusory contours through edges entirely without any explicit characterization of texture or curvilinearity. Experimental results demonstrate that our method not only performs on par with either texture segmentation or boundary completion methods on their specialized examples, but also works well on a variety of real images.
2196967
Multiclass spectral clustering
We propose a principled account on multiclass spectral clustering. Given a discrete clustering formulation, we first solve a relaxed continuous optimization problem by eigen-decomposition. We clarify the role of eigenvectors as a generator of all optimal solutions through orthonormal transforms. We then solve an optimal discretization problem, which seeks a discrete solution closest to the continuous optima. The discretization is efficiently computed in an iterative fashion using singular value decomposition and nonmaximum suppression. The resulting discrete solutions are nearly global-optimal. Our method is robust to random initialization and converges faster than other clustering methods. Experiments on real image segmentation are reported.
1761618
Concurrent Object Recognition and Segmentation by Graph Partitioning
Segmentation and recognition have long been treated as two separate processes. We propose a mechanism based on spectral graph partitioning that readily combine the two processes into one. A part-based recognition system detects object patches, supplies their partial segmentations as well as knowledge about the spatial configurations of the object. The goal of patch grouping is to find a set of patches that conform best to the object configuration, while the goal of pixel grouping is to find a set of pixels that have the best low-level feature similarity. Through pixel-patch interactions and between-pateh competition encoded in the solution space, these two processes are realized in one joint optimization problem. The globally optima] partition is obtained by solving a constrained eigenvalue problem. We demonstrate that the resulting object segmentation eliminates false positives for the part detection, while overcoming occlusion and weak contours for the low-level edge detection.
14253919
Object-Specific Figure-Ground Segregation
We consider the problem of segmenting an image into foreground and background, with foreground containing solely objects of interest known a priori. We propose an integration model that incorporates both edge detection and object part detection results. It consists of two parallel processes: low-level pixel grouping and high-level patch grouping. We seek a solution that optimizes a joint grouping criterion in a reduced space enforced by grouping correspondence between pixels and patches. Using spectral graph partitioning, we show that a near global optimum can be found by solving a constrained eigenvalue problem. We report promising experimental results on a dataset of 15 objects under clutter and occlusion.
329765
Unsupervised segmentation of color images
A novel technique for unsupervised learning in feature space is presented. The feature space is considered as composed of two distinct sources, "mode" and "valley", in the point of view of information theory. An entropy-based thresholding is taken to distinguish the discretized cells in the feature space. The cells labeled as "mode" are then chained to form mode areas. Thereafter a modified Akaike's information criterion is proposed to solve the cluster validity problem. After all the parameters are estimated, a labeling algorithm is developed based on the majority game theory. The method is applied to color image segmentation. The segmentation process is completely autonomous.
1105581
Class-is-type is inadequate for object reuse
It is well known that class and type are two different concepts in object-oriented programming languages (OOPLs). However, in many popular OOPLs, classes are used as types. In this paper, we argue that the class-is-type principle is a major obstacle to software reuse, especially to object reuse. The concepts of the basic entities, i.e., objects, object classes, object types, and object kinds, in the type hierarchy of OOPLs are revisited. The notion of object reuse is defined and elaborated. In addition, we show that parameterized types and generic functions are better served by used kind-bounded quantification than universal quantification and other mechanisms.
46077383
A Formal Study Of Practical Regular Expressions
Regular expressions are used in many practical applications. Practical regular expressions are commonly called "regex". It is known that regex are different from regular expressions. In this paper, we give regex a formal treatment. We make a distinction between regex and extended regex; while regex represent regular languages, extended regex represent a family of languages larger than regular languages. We prove a pumping lemma for the languages expressed by extended regex. We show that the languages represented by extended regex are incomparable with context-free languages and a proper subset of context-sensitive languages. Other properties of the languages represented by extended regex are also studied.
1058977
Block-based web search
Multiple-topic and varying-length of web pages are two negative factors significantly affecting the performance of web search. In this paper, we explore the use of page segmentation algorithms to partition web pages into blocks and investigate how to take advantage of block-level evidence to improve retrieval performance in the web context. Because of the special characteristics of web pages, different page segmentation method will have different impact on web search performance. We compare four types of methods, including fixed-length page segmentation, DOM-based page segmentation, vision-based page segmentation, and a combined method which integrates both semantic and fixed-length properties. Experiments on block-level query expansion and retrieval are performed. Among the four approaches, the combined method achieves the best performance for web search. Our experimental results also show that such a semantic partitioning of web pages effectively deals with the problem of multiple drifting topics and mixed lengths, and thus has great potential to boost up the performance of current web search engines.
38593151
Performance Comparison of a Deterministic and a Stochastic Method for Image Classification
In this paper, we compare the performances of two methods that we proposed for image classification. The first method is a stochastic approach which uses a Markov random field modelization. An optimization technique based on the game theory is used for the maximization of the a posteriori probability of the classes. The second method is a deterministic one which uses a feed-forward neural network. A novel learning method called projection learning is employed to train the neural network. The performances of these two methods are compared through their applications to image classification. The advantages and disadvantages of both methods, as well as possibilities to improve them are discussed.