Neucimar J. Leite

A flexible software for tracking of markers used in human motion analysis

In this work, we present a software for the tracking of markers used in human motion analysis. This software is based mainly on image sequences captured by video cameras and on image processing and computer vision tools. Unlike the optoelectronic systems, which record only the coordinates of the markers, a video-based system offers more visual information and flexibility which can be exploited in different applications. However, it needs a more complex tracking procedure concerned with the extraction and identification of the used markers. The tracking module presented here is divided into the following three procedures: segmentation, matching and prediction. The segmentation consists in extracting the objects of interest (markers). The matching is used to find the correspondence between the extracted objects in two consecutive frames. The prediction is important to limit the region of processing, thus reducing the execution time. Some results of the automatic tracking are presented together with their application in human motions analysis.

Tracking soccer players aiming their kinematical motion analysis

In this work, we consider the problem of tracking players, during a soccer game, through the use of multiple cameras. The main goal here consists in finding the position of the players on the pitch at each instance of time. The tracking is performed through a graph representation in which the nodes correspond to the blobs obtained by image segmentation and the edges, weighted using the blobs information and trajectory in the image sequence, represent the distance between nodes. We present a new way of trating occlusions by splitting segmented blobs based on morphological operators and a backward and forward graph representation which allows an increasing in the number of frames automatically tracked. Unlike other works in which the analysis of short video sequences is presented, this paper illustrates the tracking results for all the players during a whole game.

White blood cell segmentation using morphological operators and scale-space analysis

This paper proposes a model-based methodology for recognizing and tracking objects in digital image sequences. Objects are represented by attributed relational graphs (or ARGs), which carry both local and relational information about them. The recognition is performed by inexact graph matching, which consists in finding an approximate homomorphism between ARGs derived from an input video and a model image. Searching for a suitable homomorphism is achieved through a tree-search optimization algorithm and the minimization of a pre-defined cost function. Motion smoothness between successive frames is exploited to achieve the recognition over the whole sequence, with improved spatio-temporal coherence.Cell segmentation is a challenging problem due to both the complex nature of the cells and the uncertainty present in video microscopy. Manual methods for this purpose are onerous, imprecise and highly subjective, thus requiring automated methods that perform this task in an objective and efficient way. In this paper, we propose a novel method to segment nucleus and cytoplasm of white blood cells (WBC). WBC composition of the blood provides important information to doctors and plays an important role in the diagnosis of different diseases. We use simple morphological operators and explore the scale-space properties of a toggle operator to improve the segmentation accuracy. The proposed scheme has been successfully applied to a large number of images, showing promising results for varying cell appearance and image quality, encouraging future works.

VISON: VIdeo Summarization for ONline applications

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 to 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. Thus, video summaries are usually produced off-line, penalizing any user interaction. The lack of customization is very critical, as users often have different demands and resources. Since video data are usually available in compressed form, it is desirable to directly process video material without decoding. In this paper, we present VISON, a novel approach for video summarization that works in the compressed domain and allows user interaction. 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 technique produces video summaries with high quality relative to the state-of-the-art solutions and in a computational time that makes it suitable for online usage.

A multiscale directional operator and morphological tools for reconnecting broken ridges in fingerprint images

In this paper, we introduce a multiscale operator which, together with some morphological tools, can be used to reconnect broken components of fingerprint images. This operator, which can be applied to both binary and gray-scale pictures, extracts the orientation field of an image by taking into account information parameters of sets of pixels in a given neighborhood and located in different directions. It has proved to be very robust to noise and outperforms the gradient- and directional mask-based methods, generally used for orientation field definition. Although we illustrate its application in the fingerprint domain, the approach described here can be easily extended to images whose components exhibit well-defined directional information.

T-HOG: An effective gradient-based descriptor for single line text regions

We discuss the use of histogram of oriented gradients (HOG) descriptors as an effective tool for text description and recognition. Specifically, we propose a HOG-based texture descriptor (T-HOG) that uses a partition of the image into overlapping horizontal cells with gradual boundaries, to characterize single-line texts in outdoor scenes. The input of our algorithm is a rectangular image presumed to contain a single line of text in Roman-like characters. The output is a relatively short descriptor that provides an effective input to an SVM classifier. Extensive experiments show that the T-HOG is more accurate than Dalal and Triggss original HOG-based classifier, for any descriptor size. In addition, we show that the T-HOG is an effective tool for text/non-text discrimination and can be used in various text detection applications. In particular, combining T-HOG with a permissive bottom-up text detector is shown to outperform state-of-the-art text detection systems in two major publicly available databases.

Tracking soccer players using the graph representation

In this work, we consider the problem of tracking soccer players during a game by using multiple cameras. The main goal consists in finding the position of the players on the pitch at each instance of time. The occlusion is treated by splitting segmented blobs and the tracking is performed using a graph representation, where nodes correspond to the blobs obtained by image segmentation and edges represent the distance between the blobs.

Video segmentation based on 2D image analysis

The video segmentation problem consists in the identification of the boundary between consecutive shots. The common approach to solve this problem is based on dissimilarity measures between frames. In this work, the video segmentation problem is transformed into a problem of pattern detection, where each video event is transformed into a different pattern on a 2D image, called visual rhythm, obtained by a specific transformation. In our analysis we use topological and morphological tools to detect cuts. Also, we use discrete line analysis and max tree analysis to detect fade transitions and flashes, respectively. We present a comparative analysis of our method for cut detection with respect to some other methods, which shows the better results of our method.

Background recovering in outdoor image sequences: An example of soccer players segmentation

In this work, we consider the problem of background pixels information recovering which can be used, for example, in applications concerning segmentation and tracking of components in video images. Shortly, to recover the background of image sequences representing outdoor scenes, we consider a non-parametric morphological leveling operation, which takes into account the specific problem of lighting changes and the fact that we can have both slow and fast motion in the scene. We illustrate the segmentation of players based on the difference between image sequences and the corresponding recovered background representation. We also discuss the reduction of shadows in digital video of soccer games and show the good results of the whole background recovering and segmentation process.

Online video summarization on compressed domain

Abstract 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 to be accessed in a user-friendly way. Ideally, one would like to understand a video content, without having to watch it entirely. This has been the goal of a quickly evolving research area known as video summarization. In this paper, we present a novel approach for video summarization that works in the compressed domain and allows the progressive generation of a video summary. The proposed method relies on exploiting visual features extracted from the video stream and on using a simple and fast algorithm to summarize the video content. Experiments on a TRECVID 2007 dataset show that our approach presents high quality relative to the state-of-the-art solutions and in a computational time that makes it suitable for online usage.