Visual and Camera Sensors

This book includes 13 papers published in Special Issue ("Visual and Camera Sensors") of the journal Sensors. The goal of this Special Issue was to invite high-quality, state-of-the-art research papers dealing with challenging issues in visual and camera sensors

An effective trajectory-based algorithm for ball detection and tracking with application to the analysis of broadcast sports video

Ph.DDOCTOR OF PHILOSOPH

Towards Visual Localization, Mapping and Moving Objects Tracking by a Mobile Robot: a Geometric and Probabilistic Approach

Dans cette thèse, nous résolvons le problème de reconstruire simultanément une représentation de la géométrie du monde, de la trajectoire de l'observateur, et de la trajectoire des objets mobiles, à l'aide de la vision. Nous divisons le problème en trois étapes : D'abord, nous donnons une solution au problème de la cartographie et localisation simultanées pour la vision monoculaire qui fonctionne dans les situations les moins bien conditionnées géométriquement. Ensuite, nous incorporons l'observabilité 3D instantanée en dupliquant le matériel de vision avec traitement monoculaire. Ceci élimine les inconvénients inhérents aux systèmes stéréo classiques. Nous ajoutons enfin la détection et suivi des objets mobiles proches en nous servant de cette observabilité 3D. Nous choisissons une représentation éparse et ponctuelle du monde et ses objets. La charge calculatoire des algorithmes de perception est allégée en focalisant activement l'attention aux régions de l'image avec plus d'intérêt. ABSTRACT : In this thesis we give new means for a machine to understand complex and dynamic visual scenes in real time. In particular, we solve the problem of simultaneously reconstructing a certain representation of the world's geometry, the observer's trajectory, and the moving objects' structures and trajectories, with the aid of vision exteroceptive sensors. We proceeded by dividing the problem into three main steps: First, we give a solution to the Simultaneous Localization And Mapping problem (SLAM) for monocular vision that is able to adequately perform in the most ill-conditioned situations: those where the observer approaches the scene in straight line. Second, we incorporate full 3D instantaneous observability by duplicating vision hardware with monocular algorithms. This permits us to avoid some of the inherent drawbacks of classic stereo systems, notably their limited range of 3D observability and the necessity of frequent mechanical calibration. Third, we add detection and tracking of moving objects by making use of this full 3D observability, whose necessity we judge almost inevitable. We choose a sparse, punctual representation of both the world and the moving objects in order to alleviate the computational payload of the image processing algorithms, which are required to extract the necessary geometrical information out of the images. This alleviation is additionally supported by active feature detection and search mechanisms which focus the attention to those image regions with the highest interest. This focusing is achieved by an extensive exploitation of the current knowledge available on the system (all the mapped information), something that we finally highlight to be the ultimate key to success

Automatic Detection and Quantification of WBCs and RBCs Using Iterative Structured Circle Detection Algorithm

Segmentation and counting of blood cells are considered as an important step that helps to extract features to diagnose some specific diseases like malaria or leukemia. The manual counting of white blood cells (WBCs) and red blood cells (RBCs) in microscopic images is an extremely tedious, time consuming, and inaccurate process. Automatic analysis will allow hematologist experts to perform faster and more accurately. The proposed method uses an iterative structured circle detection algorithm for the segmentation and counting of WBCs and RBCs. The separation of WBCs from RBCs was achieved by thresholding, and specific preprocessing steps were developed for each cell type. Counting was performed for each image using the proposed method based on modified circle detection, which automatically counted the cells. Several modifications were made to the basic (RCD) algorithm to solve the initialization problem, detecting irregular circles (cells), selecting the optimal circle from the candidate circles, determining the number of iterations in a fully dynamic way to enhance algorithm detection, and running time. The validation method used to determine segmentation accuracy was a quantitative analysis that included Precision, Recall, and F-measurement tests. The average accuracy of the proposed method was 95.3% for RBCs and 98.4% for WBCs

Tracking, Detection and Registration in Microscopy Material Images

Fast and accurate characterization of fiber micro-structures plays a central role for material scientists to analyze physical properties of continuous fiber reinforced composite materials. In materials science, this is usually achieved by continuously crosssectioning a 3D material sample for a sequence of 2D microscopic images, followed by a fiber detection/tracking algorithm through the obtained image sequence. To speed up this process and be able to handle larger-size material samples, we propose sparse sampling with larger inter-slice distance in cross sectioning and develop a new algorithm that can robustly track large-scale fibers from such a sparsely sampled image sequence. In particular, the problem is formulated as multi-target tracking and Kalman filters are applied to track each fiber along the image sequence. One main challenge in this tracking process is to correctly associate each fiber to its observation given that 1) fiber observations are of large scale, crowded and show very similar appearances in a 2D slice, and 2) there may be a large gap between the predicted location of a fiber and its observation in the sparse sampling. To address this challenge, a novel group-wise association algorithm is developed by leveraging the fact that fibers are implanted in bundles and the fibers in the same bundle are highly correlated through the image sequence. Tracking-by-detection algorithms rely heavily on detection accuracy, especially the recall performance. The state-of-the-art fiber detection algorithms perform well under ideal conditions, but are not accurate where there are local degradations of image quality, due to contaminants on the material surface and/or defocus blur. Convolutional Neural Networks (CNN) could be used for this problem, but would require a large number of manual annotated fibers, which are not available. We propose an unsupervised learning method to accurately detect fibers on the large scale, which is robust against local degradations of image quality. The proposed method does not require manual annotations, but uses fiber shape/size priors and spatio-temporal consistency in tracking to simulate the supervision in the training of the CNN. Due to the significant microscope movement during the data acquisition, the sampled microscopy images might be not well aligned, which increases the difficulties for further large-scale fiber tracking. In this dissertation, we design an object tracking system which could accurately track large-scale fibers and simultaneously perform satisfactory image registration. Large-scale fiber tracking task is accomplished by Kalman filters based tracking methods. With the assistance of fiber tracking, the image registration is performed in a coarse-to-fine way. To evaluate the proposed methods, a dataset was collected by Air Force Research Laboratories (AFRL). The material scientists in AFRL used a serial sectioning instrument to cross-section the 3D material samples. During sample preparation, the samples are ground, cleaned, and then imaged. Experimental results on this collected dataset have demonstrated that the proposed methods yield significant improvements in large-scale fiber tracking and detection, together with satisfactory image registration

Model-Based Environmental Visual Perception for Humanoid Robots

The visual perception of a robot should answer two fundamental questions: What? and Where? In order to properly and efficiently reply to these questions, it is essential to establish a bidirectional coupling between the external stimuli and the internal representations. This coupling links the physical world with the inner abstraction models by sensor transformation, recognition, matching and optimization algorithms. The objective of this PhD is to establish this sensor-model coupling

Robust fuzzy clustering for multiple instance regression.

Multiple instance regression (MIR) operates on a collection of bags, where each bag contains multiple instances sharing an identical real-valued label. Only few instances, called primary instances, contribute to the bag labels. The remaining instances are noise and outliers observations. The goal in MIR is to identify the primary instances within each bag and learn a regression model that can predict the label of a previously unseen bag. In this thesis, we introduce an algorithm that uses robust fuzzy clustering with an appropriate distance to learn multiple linear models from a noisy feature space simultaneously. We show that fuzzy memberships are useful in allowing instances to belong to multiple models, while possibilistic memberships allow identification of the primary instances of each bag with respect to each model. We also use possibilistic memberships to identify and ignore noisy instances and determine the optimal number of regression models. We evaluate our approach on a series of synthetic data sets, remote sensing data to predict the yearly average yield of a crop and application to drug activity prediction. We show that our approach achieves higher accuracy than existing methods

Active shape models with focus on overlapping problems applied to plant detection and soil pore analysis

[no abstract