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Keyword spotting techniques are becoming cost-effective solutions for information retrieval in handwritten documents. We explore the extension of the single-word, line-level probabilistic indexing approach described in our previous works to allow for page-level search of queries consisting in Boolean combinations of several single-keywords. We propose heuristic rules to combine the single-word relevance probabilities into probabilistically consistent confidence scores of the multi-word boolean combinations. An empirical study, also presented in this paper, evaluates the search performance of word-pair queries involving AND and OR Boolean operations. Results of this study support the proposed approach and clearly show its effectiveness. Finally, a web-based demonstration system based on the proposed methods is presented. [ABSTRACT FROM AUTHOR]

The Euler number of a binary image is an important topological feature for many image processing, image analysis, pattern recognition, and computer vision applications. This paper proposes a new run-based Euler number computation algorithm. The conventional run-based algorithm processes rows of the given image one-by-one from top to bottom in a single phase. For each row, it finds the runs in the row and records the start and end locations of each run to compute neighbor runs. In contrast, our algorithm calculates the Euler number of an image in two phases. In the first phase, we process odd rows alternately to find runs and only record its end location. In the second phase, we process each of the remaining even rows to find runs and calculate neighboring runs between the current row and the rows immediately above and below using the recorded run data. Using this method, the number of accesses required to compute the Euler number decreases in almost all cases. Analysis of the time complexity and experimental results demonstrate that our algorithm outperforms conventional Euler number computation algorithms. [ABSTRACT FROM AUTHOR]

Several feature-based pattern recognition algorithms have been developed during the past decade. These algorithms rely on identifying keypoints in an image and assigning a descriptor to each point based on the composition of their surrounding region. Comparison of the descriptors of keypoints found in two images enables these algorithms to match similar objects within those images. The dependence of these algorithms' performance on the similarity of the internal structure of objects makes them susceptible to modifications that change this internal structure. In this paper, we first compare the relative performance of some major feature-based algorithms in finding similar objects surrounded by geometrical noise. Next, we add several noise and transformation types to target objects and re-evaluate the performance of these algorithms under the resulting structural changes. Our results provide insights on the relative strengths of these algorithms in the presence and absence of several noise and transformation types. In addition, these findings allow us to identify modification types that can better inhibit the performance of these algorithms. The resulting insight can be used in applications that need to build resistance against such algorithms, e.g., in developing CAPTCHAs that need to be resistant to recognition attacks. [ABSTRACT FROM AUTHOR]

The dangerous, overgrown vegetation/trees under high voltage transmission lines right-of-ways (ROWs) have caused severe blackouts/flashovers due to interference with power lines which leads to short circuiting among the conductors. Therefore, these dangerous encroachments are monitored periodically along the electrical distribution networks ROWs through visual inspection, or by airborne system. Each of these methods has its own attributes and limitations and have proved to be costly, time consuming and not much accurate. In these circumstances, it is necessary for the electrical utilities to review their vegetation management practices so as to avoid incidents of unintended encroachments. In tropical countries, overgrown vegetation is a common cause for power line failure. This paper proposes an innovative concept of utilizing a single camera for monitoring dangerous vegetation (trees, shrubs and plants, etc.) under transmission lines ROWs. The main focus is on using an imaging device (camera) integrated on each transmission pole to automate inspection for the vegetation encroachments endangering the transmission lines. These cameras are envisioned to be connected wirelessly to each other, forming a series of wireless camera networks that can be monitored remotely. A single camera mounted on power poles acquires images and sends them wirelessly to the base station. At base station, algorithm (software) trained by image processing and pattern recognition techniques is used to identify (height, depth, and width of encroached vegetation, etc.) excess vegetation encroachments within and outside ROWs. The performance evaluation of a real time developed test-bed scenario proves the feasibilities of integrating the method for transmission line maintenance. [ABSTRACT FROM AUTHOR]