The quality assessment of sets of features extracted from patterns of epidermal ridges on our fingers is a biometric challenge problem with implications on questions concerning security, privacy and identity fraud. In this work, we introduced a new methodology to analyze the quality of high-resolution fingerprint images containing sets of fingerprint pores. Our approach takes into account the spatial interrelationship between the considered features and some basic transformations involving point process and anisotropic analysis. We proposed two new quality index algorithms following spatial and structural classes of analysis. These algorithms have proved to be effective as a performance predictor and as a filter excluding low-quality features in a recognition process. The experiments using error reject curves show that the proposed approaches outperform the state-of-the-art quality assessment algorithm for high-resolution fingerprint recognition, besides defining a new method for reconstructing their friction ridge phases in a very consistent way.

Pore extraction appears to play an important role in high resolution partial fingerprint recognition and in applications involving large population or high security levels. In this paper, we introduce a novel pore extraction approach which takes into account a new relation concerning their spatial and photometric dependence. This relation is given locally by analyzing distinct pores according to their distance and contrast. We evaluate our approach on high resolution pore extraction database and in an application involving partial fingerprint alignment. The proposed segmentation has proved to be quite general, simple and can accurately extract fingerprint pores in real images with different ridge and valley widths. It is also very robust to noise and according to the considered experiments outperforms well-known state-of-art methods.

This paper approaches novel methods to segment the nucleus and cytoplasm of white blood cells (WBC). This information is the basis to perform higher level tasks such as automatic differential counting, which plays an important role in the diagnosis of different diseases. We explore the image simplification and contour regularization resulting from the application of the Self-Dual Multiscale Morphological Toggle (SMMT), an operator with scale-space properties. To segment the nucleus, the image preprocessing with SMMT has shown to be essential to ensure the accuracy of two well-known image segmentations techniques, namely, watershed transform and Level Set methods. To identify the cytoplasm region, we propose two different schemes, based on granulometric analysis and on morphological transformations. The proposed methods have been successfully applied to a large number of images, showing promising segmentation and classification results for varying cell appearance and image quality, encouraging future works.

Making efficient use of video information requires the development of a video signature and a similarity measure to rapidly identify similar videos in a huge database. Most of existing techniques to address this problem have focused on the uncompressed domain. However, decoding and analyzing of a video sequence are extremely time-consuming tasks. Since video data are usually available in compressed form, it is desirable to directly process video material without decoding. In this paper, we present a novel approach for comparing video sequences that works in the compressed domain. The proposed method is based on recognizing motion patterns extracted from the video stream and their occurrence histogram is proven to be a powerful feature for describing the video content. Experiments on a TRECVID 2010 dataset show that our approach presents high accuracy relative to the state-of-the-art solutions and in a computational time that makes it suitable for large collections.

In this work we introduced SnooperTrack, an algorithm for the automatic detection and tracking of text objects - such as store names, traffic signs, license plates, and advertisements - in videos of out door scenes. The purpose is to improve the performances of text detection process in still images by taking advantage of the temporal coherence in videos. We first propose an efficient tracking algorithm using particle filtering framework with original region descriptors. The second contribution is our strategy to merge tracked regions and new detections. We also propose an improved version of our previously published text detection algorithm in still images. Tests indicate that SnooperTrack is fast, robust, enable false positive suppression, and achieved great performances in complex videos of outdoor scenes.

Similarity search in high-dimensional metric spaces is a key operation in many applications, such as multimedia databases, image retrieval, object recognition, and others. The high dimensionality of the data requires special index structures to facilitate the search. Most of existing indexes are constructed by partitioning the data set using distance-based criteria. However, those methods either produce disjoint partitions, but ignore the distribution properties of the data; or produce non-disjoint groups, which greatly affect the search performance. In this paper, we study the performance of a new index structure, called Ball-and-Plane tree (BP-tree), which overcomes the above disadvantages. BP-tree is constructed by recursively dividing the data set into compact clusters. Distinctive from other techniques, it integrates the advantages of both disjoint and non-disjoint paradigms in order to achieve a structure of tight and low overlapping clusters, yielding significantly improved performance. Results obtained from an extensive experimental evaluation with real-world data sets show that BP-tree consistently outperforms state-of-the-art solutions.

Recent advances in technology have increased the availability of video data, creating a strong requirement for efficient systems to manage those materials. Making efficient use of video information requires that data be accessed in a user-friendly way. This has been the goal of a quickly evolving research area known as video summarization. Most of existing techniques to address the problem of summarizing a video sequence have focused on the uncompressed domain. However, decoding and analyzing of a video sequence are two extremely time-consuming tasks. Since video data are usually available in compressed form, it is desirable to directly process video material without decoding. In this paper, we present a novel approach for video summarization that works in the compressed domain. The proposed method is based on both exploiting visual features extracted from the video stream and on using a simple and fast algorithm to summarize the video content. Results from a rigorous empirical comparison with a subjective evaluation show that our approach produces video summaries with superior quality relative to the state-of-the-art solutions and in a computational time that allows on-the-fly usage.

We describe a robust and accurate algorithm, nicknamed AffTrack, to track selected features of a rigid 3D object in a video recording, given a canonical image of each feature and its position on the object. AffTrack uses a synergistic combination of a multiscale feature finder and a flexible camera calibrator. This synergy between the two modules allows, AffTrack to recover features after occlusions of arbitrary duration. Compared to other solutions to this problem, AffTrack can handle videos with variable zoom, and variable lens distortion, does not require a complete geometric model of the object, and does not require the selection of key frames. Tests indicate that AffTrack is more robust and accurate than the popular object trackers include in H. Kato's ARToolKit and in the OpenCV library.

This paper proposes a new texture descriptor to guide the search and retrieval in image databases. It extracts rich information from global and local primitives of textured images. At a higher level, the global macro-features in textured images are characterized by exploiting the multiresolution properties of the Steerable Pyramid Decomposition. By doing this, the global texture configurations are highlighted. At a finer level, the local arrangements of texture micro-patterns are encoded by the Local Binary Pattern operator.Experiments were carried out on the standard Vistex dataset aiming to compare our descriptors against popular texture extraction methods with regard to their retrieval accuracies. The comparative evaluations allowed us to show the superior descriptive properties of our feature representation methods.

The video segmentation problem can be regarded as a problem of detecting the fundamental video units (shots). Due to different ways of linking two consecutive shots this task turns out to be difficult. In this work, we propose a method to detect both cuts and gradual transitions by image segmentation tools instead of using dissimilarity measures or mathematical models. Firstly, the video is transformed into a 2D image, called visual rhythm by sub-sampling. Afterwards, we apply image processing tools to detect all vertical aligned transitions in this image. The main operator applied here is the morphological multiscale gradient. We also present some experimental results.

This paper presents some procedures for segmenting nautical charts using concepts of mathematical morphology. The method discussed, based on the multi-angled parallelism, is capable of extracting overlapped connected components such as lakes, isolines, railways, canals, small symbols and strings of characters. The problem of connectivity is solved using geodesic operations. The procedures have been applied to a 1/50000 scale nautical chart of the Brazilian Navy scanned at a resolution of 300 dpi.

In this paper we analyze some problems concerned with connectivity on binary images. More precisely, by means of mathematical morphology we describe an algorithm which detects and classifies contours of connected sets as inner or outer contours. The method discussed here is quite parallel and can be directly implemented onto an SIMD (single-instruction multiple-data-stream) massively parallel computer.

A special purpose cellular processor, based on the functional programming approach, is proposed for image processing applications. The basic data flow graph of the processor is defined according to the data structure of a class of nonlinear filters very useful in image processing. The cells of the processor are quite simple and support a certain degree of programmability that allows, for instance, a logical reconfiguration of the network. This flexibility contributes to the execution of a multitude of standard low-level image processing algorithms on the same basic structure.>

The paper concerns computational models and languages for iterative cellular automata for image processing applications. The authors present some formal models, with different computational resources, that can be useful in the solution of certain algorithmic problems. They introduce some concepts such as memory splitting, conditional functions, dynamic neighborhood and supervisor automaton. The models defined lead to a parallel language structure that can express low-level image processing algorithms in a clear and concise way. The language allows a transparent description of the algorithms and can be easily expandable to reflect the needs of people working in different branches of image processing.>