Recently, cars are equipped with a lot of sensors for safety driving. We have been trying to store the driving-scene video with such sensor data and to detect the change of scenery of streets. Detection results can be used for building historical database of town scenery, automatic landmark updating of maps, and so forth. In order to compare images to detect changes, image retrieval taken at nearly identical locations is required as the first step. Since Global Positioning System (GPS) data essentially contain some noises, we cannot rely only on GPS data for our image retrieval. Therefore, we have developed an image retrieval algorithm employing edge-histogram-based image features in conjunction with hierarchical search. By using edge histograms projected onto the vertical and horizontal axes, the retrieval has been made robust to image variation due to weather change, clouds, obstacles, and so on. In addition, matching cost has been made small by limiting the matching candidates employing the hierarchical search. Experimental results have demonstrated that the mean retrieval accuracy has been improved from 65% to 76% for the front-view images and from 34% to 53% for the side-view images.

In this paper, we propose a method of accelerating test generation for sequential circuits by using the knowledge about the availability of state justification sequences, the bound on the length of state distinguishing sequences, differentiation between valid and invalid states, and the existence of a reset state. We also propose a method of synthesis for testability (SfT) which takes the features of our test generation method into consideration to synthesize sequential circuits from given FSM descriptions. The SfT method guarantees that the test generator will be able to find a state distinguishing sequence. The proposed method extracts the state justification sequence from the FSM produced by the synthesizer to improve the performance of its test generation process. Experimental results show that the proposed method can achieve 100% fault efficiency in relatively short test generation time.

In vector analysis, it is important to classify three flow primitives as translation, rotation and divergence. These three primitives can be detected utilizing line integral and surface integral according to the knowledge of vector analysis. In this paper, we introduce a method for extracting these three primitives utilizing edges in an image based on vector analysis, namely edge field analysis. The edge has the information of inclination. However, the edge has no information of the direction unlike vector. Hence, line integral and surface integral can not be directly applied to detect these three primitives utilizing edges. We firstly formulate the problem and describe the algorithm for detecting the three primitives in vector analysis. We then propose an algorithm for estimating three primitives regarding edge image as pseudo-vector field. For illustration, we apply edge field analysis to quasi-motion extraction and feature extraction. We also show the experimental results in terms of estimating the center of the flowers, the cell body of neuron, the eye of the storm, the center of the explosion and so on.

The notion of correlation intractable function ensembles (CIFEs) was introduced in an attempt to capture the "unpredictability" property of random oracles [12]: If O is a random oracle then it is infeasible to find an input x such that the input-output pair (x,O(x)) has some desired property. In this paper, we observe relationships between zero-knowledge protocols and CIFEs. Specifically, we show that, in the non-uniform model, the existence of CIFEs implies that 3-round auxiliary-input zero-knowledge (AIZK) AM interactive proofs exist only for BPP languages. In the uniform model, we show that 3-round AIZK AM interactive proofs with perfect completeness exist only for easy-to-approximate languages. These conditional triviality results extend to constant-round AIZK AM interactive proofs assuming the existence of multi-input CIFEs, where "multi-input" means that the correlation intractability is satisfied with respect to multiple input-output pairs. Also, as a corollary, we show that any construction of uniform multi-input CIFEs from uniform one-way functions proves unconditionally that constant-round AIZK AM interactive proofs with perfect completeness only for easy-to-approximate languages.

A 3 dimensional (3D) error diffusion method based on edge detection for flat panel display (FPD) is presented. The new method diffuses errors to the neighbor pixels in current frame and the neighbor pixel in the next frame. And the weights of error filters are dynamically adjusted based on the results of edge detection in each pixel's processing, which makes the weights coincide with the local edge feathers of input image. The proposed method can reduce worm artifacts and improve reproduction precision of image details.

Multi-Edge type Low-Density Parity-Check codes (MET-LDPC codes) introduced by Richardson and Urbanke are generalized LDPC codes which can be seen as LDPC codes obtained by concatenating several standard (ir)regular LDPC codes. We prove in this paper that MET-LDPC code ensembles possess a certain symmetry with respect to their Average Coset Weight Distributions (ACWD). Using this symmetry, we drive ACWD of MET-LDPC code ensembles from ACWD of their constituent ensembles.

We consider the problem of dynamically apportioning resources among a set of options in a worst-case online framework. The model we investigate is a generalization of the well studied online learning model. In particular, we allow the learner to see as additional information how high the risk of each option is. This assumption is natural in many applications like horse-race betting, where gamblers know odds for all options before placing bets. We apply Vovk's Aggregating Algorithm to this problem and give a tight performance bound. The results support our intuition that it is safe to bet more on low-risk options. Surprisingly, the loss bound of the algorithm does not depend on the values of relatively small risks.

In the field of industrial manufacturing, visual pattern inspection is an important task to prevent the inclusion of incorrect parts. There have been demands for such methods able to handle factors caused by positional and rotational alignment, and illumination changes. In this paper, we propose a discrimination method called Trinarized broad-edge and Gray-scale Hybrid Matching (TGHM). The method is highly reliable due to gray-scale cross correlation which has a high pattern discrimination efficiency, with high-speed position and rotation alignment using the characteristics of trinarized broad-edge representation which has high data compressibility and illumination-resistant variability. In an example in which the method is applied to mis-collation inspection equipment of a bookbinding machine, it is confirmed that the processing speed is 24,000 sheets/hour, the error detection rate is 100.0%, and the mis-alarm rate is less than 0.002%, and it is verified that the method is practical.

We describe attempts to have robots behave as embodied knowledge media that will permit knowledge to be communicated through embodied interactions in the real world. The key issue here is to give robots the ability to associate interactions with information content while interacting with a communication partner. Toward this end, we present two contributions in this paper. The first concerns the formation and maintenance of joint intention, which is needed to sustain the communication of knowledge between humans and robots. We describe an architecture consisting of multiple layers that enables interaction with people at different speeds. We propose the use of an affordance-based method for fast interactions. For medium-speed interactions, we propose basing control on an entrainment mechanism. For slow interactions, we propose employing defeasible interaction patterns based on probabilistic reasoning. The second contribution is concerned with the design and implementation of a robot that can listen to a human instructor to elicit knowledge, and present the content of this knowledge to a person who needs it in an appropriate situation. In addition, we discuss future research agenda toward achieving robots serving as embodied knowledge media, and fit the robots-as-embodied-knowledge-media view in a larger perspective of Conversational Informatics.

Given a graph G=(V,E), a set of vertices S ⊆ V covers v ∈ V if the edge connectivity between S and v is at least a given number k. Vertices in S are called sources. The source location problem is a problem of finding a minimum-size source set covering all vertices of a given graph. This paper presents a new variation of the problem, called maximum-cover source-location problem, which finds a source set S with a given size p, maximizing the sum of the weight of vertices covered by S. It presents an O(np + m + nlog n)-time algorithm for k=2, where n=|V| and m=|E|. Especially it runs linear time if G is connected. This algorithm uses a subroutine for finding a subtree with the maximum weight among p-leaf trees of a given vertex-weighted tree. For the problem we give a greedy-based linear-time algorithm, which is an extension of the linear-time algorithm for finding a longest path of a given tree presented by E. W. Dijkstra around 1960. Moreover, we show some polynomial solvable cases, e.g., a given graph is a tree or (k-1)-edge-connected, and NP-hard cases, e.g., a vertex-cost function is given or G is a digraph.

For a property π on graphs, the edge-contraction problem with respect to π is defined as a problem of finding a set of edges of minimum cardinality whose contraction results in a graph satisfying the property π. This paper gives a lower bound for the approximation ratio for the problem for any property π that is hereditary on contractions and determined by biconnected components.

The protein threading problem with profiles is known to be efficiently solvable using dynamic programming. In this paper, we consider a variant of the protein threading problem with profiles in which constraints on distances between residues are given. We prove that protein threading with profiles and constraints is NP-hard. Moreover, we show a strong hardness result on the approximation of an optimal threading satisfying all the constraints. On the other hand, we develop two practical algorithms: CLIQUETHREAD and BBDPTHREAD. CLIQUETHREAD reduces the threading problem to the maximum edge-weight clique problem, whereas BBDPTHREAD combines dynamic programming and branch-and-bound techniques. We perform computational experiments using protein structure data in PDB (Protein Data Bank) using simulated distance constraints. The results show that constraints are useful to improve the alignment accuracy of the target sequence and the template structure. Moreover, these results also show that BBDPTHREAD is in general faster than CLIQUETHREAD for larger size proteins whereas CLIQUETHREAD is useful if there does not exist a feasible threading.

Let H be a graph with a designated vertex s, where edges are weighted by nonnegative reals. Splitting edges e={u,s} and e'={s,v} at s is an operation that reduces the weight of each of e and e' by a real δ>0 while increasing the weight of edge {u,v} by δ. It is known that all edges incident to s can be split off while preserving the edge-connectivity of H and that such a complete splitting is used to solve many connectivity problems. In this paper, we give an O(mn+n2log n) time algorithm for finding a complete splitting in a graph with n vertices and m edges.

A topological book embedding of a graph is an embedding in a book that carries the vertices in the spine of the book and the edges in the pages so that edges are allowed to cross the spine. Recently, the author has shown that for an arbitrary graph G with n vertices there exists a d+1-page book embedding of G in which each edge crosses the spine logd n times. This paper improves the result for the case of bipartite graphs and shows that there exists a d+1-page book embedding of a bipartite graph Gn1,n2 having two partite sets with n1 and n2 vertices respectively (n1 ≥ n2) in which each edge crosses the spine logd n2 -1 times.

Group signature schemes with membership revocation have been intensively researched. However, signing and/or verification of some existing schemes have computational costs of O(R), where R is the number of revoked members. Existing schemes using a dynamic accumulator or a similar technique have efficient signing and verifications with O(1) complexity. However, before signing, the signer has to modify his secret key with O(N) or O(R) complexity, where N is the group size. Therefore, for larger groups, signers suffer from enormous costs. On the other hand, an efficient scheme for middle-scale groups with about 1,000 members is previously proposed, where the signer need not modify his secret key. However this scheme also suffers from heavy signing/verification costs for larger groups with more than 10,000 members. In this paper, we adapt the middle-scale scheme to larger groups ranging from 1,000 to 1,000,000 members. At the sacrifice of the group manager's slight cost, our signing/verification is sufficiently efficient.

The k-edge-connectivity augmentation problem for a specified set of vertices of a graph with degree constraints, kECA-SV-DC, is defined as follows: "Given an undirected multigraph G = (V,E), a specified set of vertices S ⊆V and a function g: V → Z+ ∪{∞}, find a smallest set E' of edges such that (V,E ∪ E') has at least k edge-disjoint paths between any pair of vertices in S and such that, for any v ∈ V, E' includes at most g(v) edges incident to v, where Z+ is the set of nonnegative integers." This paper first shows polynomial time solvability of kECA-SV-DC and then gives a linear time algorithm for 2ECA-SV-DC.

Text detection in color images has become an active research area in the past few decades. In this paper, we present a novel approach to accurately detect text in color images possibly with a complex background. The proposed algorithm is based on the combination of connected component and texture feature analysis of unknown text region contours. First, we utilize an elaborate color image edge detection algorithm to extract all possible text edge pixels. Connected component analysis is performed on these edge pixels to detect the external contour and possible internal contours of potential text regions. The gradient and geometrical characteristics of each region contour are carefully examined to construct candidate text regions and classify part non-text regions. Then each candidate text region is verified with texture features derived from wavelet domain. Finally, the Expectation maximization algorithm is introduced to binarize each text region to prepare data for recognition. In contrast to previous approach, our algorithm combines both the efficiency of connected component based method and robustness of texture based analysis. Experimental results show that our proposed algorithm is robust in text detection with respect to different character size, orientation, color and language and can provide reliable text binarization result.

The objective of this paper is to present a decision support system which uses a computer-based procedure to detect tumor blocks or lesions in digitized medical images. The authors developed a simple method with a low computation effort to detect tumors on T2-weighted Magnetic Resonance Imaging (MRI) brain images, focusing on the connection between the spatial pixel value and tumor properties from four different perspectives: 1) cases having minuscule differences between two images using a fixed block-based method, 2) tumor shape and size using the edge and binary images, 3) tumor properties based on texture values using spatial pixel intensity distribution controlled by a global discriminate value, and 4) the occurrence of content-specific tumor pixel for threshold images. Measurements of the following medical datasets were performed: 1) different time interval images, and 2) different brain disease images on single and multiple slice images. Experimental results have revealed that our proposed technique incurred an overall error smaller than those in other proposed methods. In particular, the proposed method allowed decrements of false alarm and missed alarm errors, which demonstrate the effectiveness of our proposed technique. In this paper, we also present a prototype system, known as PCB, to evaluate the performance of the proposed methods by actual experiments, comparing the detection accuracy and system performance.

A network for the detection of an approaching object with simple-shape recognition is proposed based on lower animal vision. The locust can detect an approaching object through a simple process in the descending contralateral movement detector (DCMD) in the locust brain, by which the approach velocity and direction of the object is determined. The frog can recognize simple shapes through a simple process in the tectum and thalamus in the frog brain. The proposed network is constructed of simple analog complementary metal oxide semiconductor (CMOS) circuits. The integrated circuit of the proposed network is fabricated with the 1.2 µm CMOS process. Measured results for the proposed circuit indicate that the approach velocity and direction of an object can be detected by the output current of the analog circuit based on the DCMD response. The shape of moving objects having simple shapes, such as circles, squares, triangles and rectangles, was recognized using the proposed frog-visual-system-based circuit.

Let G = (V,E) be an edge weighted graph with n vertices and m edges. For a given integer p with 1 < p < n, we call a set X V of p vertices a p-maximizer if X has a property that the edge-connectivity of the graph obtained by contracting X into a single vertex is no less than that of the graph obtained by contracting any other subset of p vertices. In this paper, we first show that there always exists an ordering v1,v2,...,vn of vertices in V such that, for each i = 2,3,...,n - 1, set {v1,v2,...,vi} is an i-maximizer. We give an O(mn + n2log n) time algorithm for finding such an ordering and then show an application to the source location problem.