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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]

Bad quality spots should be filtered out at early steps in microarray analysis to avoid noisy data. In this paper we implement quality control of individual spots from real microarray images. First of all, we consider the binary classification problem of detecting bad quality spots. We propose the use of ensemble algorithms to perform detection and obtain improved accuracies over previous studies in the literature. Next, we analyze the untackled problem of identifying specific spot defects. One spot may have several faults simultaneously (or none of them) yielding a multi-label classification problem. We propose several extra features in addition to those used for binary classification, and we use three different methods to perform the classification task: five independent binary classifiers, the recent Convex Multi-task Feature Learning (CMFL) algorithm and Convex Multi-task Independent Learning (CMIL). We analyze the Hamming loss and areas under the receiver operating characteristic (ROC) curves to quantify the accuracies of the methods. We find that the three strategies achieve similar results leading to a successful identification of particular defects. Also, using a Random forest-based analysis we show that the newly introduced features are highly relevant for this task. [ABSTRACT FROM AUTHOR]

Anomaly detection in time-series data is a relevant problem in many fields such as stochastic data analysis, quality assurance, and predictive modeling. Markov models are an effective tool for time-series data analysis. Previous approaches utilizing Markov models incorporate transition matrices (TMs) at varying dimensionalities and resolutions. Other analysis methods treat TMs as vectors for comparison using search algorithms such as the nearest neighbors comparison algorithm, or use TMs to calculate the probability of discrete subsets of time-series data. We propose an analysis method that treats the elements of a TM as random variables, parameterizing them hierarchically. This approach creates a metric for determining the 'normalcy' of a TM generated from a subset of time-series data. The advantages of this novel approach are discussed in terms of computational efficiency, accuracy of anomaly detection, and robustness when analyzing sparse data. Unlike previous approaches, this algorithm is developed with the expectation of sparse TMs. Accounting for this sparseness significantly improves the detection accuracy of the proposed method. Detection rates in a variety of time-series data types range from (97 % TPR, 2.1 % FPR) to (100 % TPR, <0.1 % FPR) with very small sample sizes (20-40 samples) in data with sparse transition probability matrices. [ABSTRACT FROM AUTHOR]

Sign language is the most important means of communication for deaf people. Given the lack of familiarity of non-deaf people with the language of deaf people, designing a translator system which facilitates the communication of deaf people with the surrounding environment seems to be necessary. The system of translating the sign language into spoken languages should be able to identify the gestures in sign language videos. Consequently, this study provides a system based on machine vision to recognize the signs in continuous Persian sign language video. This system generally consists of two main phases of sign words extraction and their classification. Several stages, including tracking and separating the sign words, are conducted in the sign word extraction phase. The most challenging part of this process is separation of sign words from video sequences. To do this, a new algorithm is presented which is capable of detecting accurate boundaries of words in the Persian sign language video. This algorithm decomposes sign language video into the sign words using motion and hand shape features, leading to more favorable results compared to the other methods presented in the literature. In the classification phase, separated words are classified and recognized using hidden Markov model and hybrid KNN-DTW algorithm, respectively. Due to the lack of proper database on Persian sign language, the authors prepared a database including several sentences and words performed by three signers. Simulation of proposed words boundary detection and classification algorithms on the above database led to the promising results. The results indicated an average rate of 93.73 % for accurate words boundary detection algorithm and the average rate of 92.4 and 92.3 % for words recognition using hands motion and shape features, respectively. [ABSTRACT FROM AUTHOR]