Texture as pixel feature for video object segmentation

As texture represents one of the key perceptual attributes of any object, integrating textural information into existing video object segmentation frameworks affords the potential to achieve semantically improved performance. While object segmentation is fundamentally pixel-based classification, texture is normally defined for the entire image, which raises the question of how best to directly specify and characterise texture as a pixel feature. Introduced is a generic strategy for representing textural information so it can be seamlessly incorporated as a pixel feature into any video object segmentation paradigm. Both numerical and perceptual results upon various test sequences reveal considerable improvement in the object segmentation performance when textural information is embedded

Step-by-step description of lateral interaction in accumulative computation

In this paper we present a method for moving objects detection and labeling denominated Lateral Interaction in Accumulative Computation (LIAC). The LIAC method usefulness in the general task of motion detection may be appreciated by means of some step-by-step descriptions of significant examples of object detection in video sequences of synthetic and real images

Step-by-Step Description of Lateral Interaction in Accumulative Computation

Abstract. In this paper we present a method for moving objects detection and labeling denominated Lateral Interaction in Accumulative Computation (LIAC). The LIAC method usefulness in the general task of motion detection may be appreciated by means of some step-by-step descriptions of significant examples of object detection in video sequences of synthetic and real images

Recognition System for Person Analysis by Image Signal Processing

Import 22/07/2015Diplomová práce se zabývá rozpoznáním osob na základě obrazového signálu. Je zde rozebrána problematika práce s obrazovým signálem - předzpracování signálu, digitalizace, segmentace, prahování. Na základě prahování obrazu jsou z obrazového signálu detekovány oči a ústa. Dalším parametrem, který je měřen je šířka a výška obličeje. K rozpoznávání osob je vytvořeno grafické uživatelské rozhraní, přes které lze jak rozpoznávat osoby již v databázi uložené, tak přidávat nové záznamy.This graduation thesis deals with recognition of persons by image signal processing. It analyzes issues with image signal - signal preprocessing, digitalization, segmentation, thresholding. Based on thresholded image are detected eyes and mouth. Another parameter that is measured is the width and height of face. For recognizing of persons is designed graphical user interface, which allows recognition of person from stored database and also add new records.450 - Katedra kybernetiky a biomedicínského inženýrstvívelmi dobř

Video-based situation assessment for road safety

In recent decades, situational awareness (SA) has been a major research subject in connection with autonomous vehicles and intelligent transportation systems. Situational awareness concerns the safety of road users, including drivers, passengers, pedestrians and animals. Moreover, it holds key information regarding the nature of upcoming situations. In order to build robust automatic SA systems that sense the environment, a variety of sensors, such as global positioning systems, radars and cameras, have been used. However, due to the high cost, complex installation procedures and high computational load of automatic situational awareness systems, they are unlikely to become standard for vehicles in the near future. In this thesis, a novel video-based framework for the automatic assessment of risk of collision in a road scene is proposed. The framework uses as input the video from a monocular video camera only, avoiding the need for additional, and frequently expensive, sensors. The framework has two main parts: a novel ontology tool for the assessment of risk of collision, and semantic feature extraction based on computervision methods. The ontology tool is designed to represent the various relations between the most important risk factors, such as risk from object and road environmental risk. The semantic features related to these factors iii Abstract iv are based on computer vision methods, such as pedestrian detection and tracking, road-region detection and road-type classification. The quality of these methods is important for achieving accurate results, especially with respect to video segmentation. This thesis, therefore, proposes a new criterion of high-quality video segmentation: the inclusion of temporal-region consistency. On the basis of the new criteria, an online method for the evaluation of video segmentation quality is proposed. This method is more consistent than the state-of-the-art method in terms of perceptual-segmentation quality, for both synthetic and real video datasets. Furthermore, using the Gaussian mixture model for video segmentation, one of the successful video segmentation methods in this area, new online methods for both road-type classification and road-region detection are proposed. The proposed vision-based road-type classification method achieves higher classification accuracy than the state-of-the-art method, for each road type individually. Consequently, it achieves higher overall classi- fication accuracy. Likewise, the proposed vision-based road-region detection method achieves high performance accuracy compared to the state-of-the-art methods, according to two measures: pixel-wise percentage accuracy and area under the receiver operating characteristic (ROC) curve (AUC). Finally, the evaluation performance of the automatic risk-assessment framework is measured. At this stage, the framework includes only the assessment of pedestrian risk in the road scene. Using the semantic information obtained via computer-vision methods, the framework's performance is assessed for two datasets: first, a new dataset proposed in Chapter 7, which comprises six videos, and second, a dataset comAbstract v prising five examples selected from an established, publicly available dataset. Both datasets consist of real-world videos illustrating pedestrian movement. The experimental results show that the proposed framework achieves high accuracy in the assessment of risk resulting from pedestrian behaviour in road scenes

Detecting Red Blood Cells Morphological Abnormalities Using Genetic Algorithm and Kmeans

Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. Computer-aided diagnosis is another important application of pattern recognition, aiming at assisting doctors in making diagnostic decisions. Many diseases which are not blood diseases in origin have hematological abnormalities and manifestation (have symptoms appeared on the blood). CBC (cell blood count test) for instance, is still the first test to be requested by the physicians or become in their mind. Blood abnormality can be in white blood cells, red blood cells and plasma. In this thesis, red blood cells are the suggested for detecting it is abnormality. The abnormality of blood cells shapes can't be detected easily, where the CBC (cell blood count) device give a count number and percentages not a description of the shapes of the blood cells, when the blood cells shapes wanted to be known, hematologist asked to view the blood films under the microscope which is time consuming task besides that the human error risk is high. Since the number of abnormal cells to normal cells in a given blood sample give a measure of the disease severity, detecting one cell with potential abnormality can give premature warning for future illness that can be avoided or treated earlier. This case can't be detected by hematologist. Computer involved in such task to save time and effort besides minimizing human error. This thesis name is "DETECTING RED BLOOD CELLS MORPHOLOGICAL ABNORMALITIES USING GENETIC ALGORITHM AND KMEANS". In this thesis, the thesis divided into four phases. First phase data collection where blood samples was drawn from healthy and sick people and then blood films made and viewed under microscope and an images captured for these blood films. Second phase preprocessing phase where the images prepared for the next phase. Third phase feature extraction was executed where these features are spatial domain and frequency domain features. Fourth phase is the classification phase where the features fed into the classifier to be classified. An acceptable detection rate is achieved by the proposed system. The genetic algorithm classifier success rate was 92.31% and the K-means classifier success rate was 94.00%

Détection et suivi d'objets par vision fondés sur segmentation par contour actif base région

La segmentation et le suivi d'objets sont des domaines de recherche compétitifs dans la vision par ordinateur. Une de leurs applications importantes réside dans la robotique où la capacité à segmenter un objet d'intérêt du fond de l'image, d'une manière précise, est cruciale particulièrement dans des images acquises à bord durant le mouvement du robot. Segmenter un objet dans une image est une opération qui consiste à distinguer la région objet de celle du fond suivant un critère défini. Suivre un objet dans une séquence d'images est une opération qui consiste à localiser la région objet au fil du temps dans une vidéo. Plusieurs techniques peuvent être utilisées afin d'assurer ces opérations. Dans cette thèse, nous nous sommes intéressés à segmenter et suivre des objets en utilisant la méthode du contour actif en raison de sa robustesse et son efficacité à pouvoir segmenter et suivre des objets non rigides. Cette méthode consiste à faire évoluer une courbe à partir d'une position initiale, entourant l'objet à détecter, vers la position de convergence qui correspond aux bords de cet objet d'intérêt. Nous utilisons des critères qui dépendent des régions de l'image ce qui peut imposer certaines contraintes sur les caractéristiques de ces régions comme une hypothèse d'homogénéité. Cette hypothèse ne peut pas être toujours vérifiée du fait de l'hétérogénéité souvent présente dans les images. Dans le but de prendre en compte l'hétérogénéité qui peut apparaître soit sur l'objet d'intérêt soit sur le fond dans des images bruitées et avec une initialisation inadéquate du contour actif, nous proposons une technique qui combine des statistiques locales et globales pour définir le critère de segmentation. En utilisant un rayon de taille fixe, un demi-disque est superposé sur chaque point du contour actif afin de définir les régions d'extraction locale. Lorsque l'hétérogénéité se présente à la fois sur l'objet d'intérêt et sur le fond de l'image, nous développons une technique basée sur un rayon flexible déterminant deux demi-disques avec deux rayons de valeurs différentes pour extraire l'information locale. Le choix de la valeur des deux rayons est déterminé en prenant en considération la taille de l'objet à segmenter ainsi que de la distance séparant l'objet d'intérêt de ses voisins. Enfin, pour suivre un objet mobile dans une séquence vidéo en utilisant la méthode du contour actif, nous développons une approche hybride du suivi d'objet basée sur les caractéristiques de la région et sur le vecteur mouvement des points d'intérêt extraits dans la région objet. En utilisant notre approche, le contour actif initial à chaque image sera ajusté suffisamment d'une façon à ce qu'il soit le plus proche possible au bord réel de l'objet d'intérêt, ainsi l'évolution du contour actif basée sur les caractéristiques de la région ne sera pas piégée par de faux contours. Des résultats de simulations sur des images synthétiques et réelles valident l'efficacité des approches proposées.Object segmentation and tracking is a challenging area of ongoing research in computer vision. One important application lies in robotics where the ability to accurately segment an object of interest from its background is crucial and particularly on images acquired onboard during robot motion. Object segmentation technique consists in separating the object region from the image background according to a pre-defined criterion. Object tracking is a process of determining the positions of moving objects in image sequences. Several techniques can be applied to ensure these operations. In this thesis, we are interested to segment and track objects in video sequences using active contour method due to its robustness and efficiency to segment and track non-rigid objects. Active contour method consists in making a curve converge from an initial position around the object to be detected towards this object boundary according to a pre-defined criterion. We employ criteria which depend on the image regions what may impose certain constraints on the characteristics of these regions as a homogeneity assumption. This assumption may not always be verified due to the heterogeneity often present in images. In order to cope with the heterogeneity that may appear either in the object of interest or in the image background in noisy images using an inadequate active contour initialization, we propose a technique that combines local and global statistics in order to compute the segmentation criterion. By using a radius with a fixed size, a half-disk is superposed on each point of the active contour to define the local extraction regions. However, when the heterogeneity appears on both the object of interest and the image background, we develop a new technique based on a flexible radius that defines two half-disks with two different radius values to extract the local information. The choice of the value of these two radii is determined by taking into consideration the object size as well as the distance separating the object of interest from its neighbors. Finally, to track a mobile object within a video sequence using the active contour method, we develop a hybrid object tracking approach based on region characteristics and on motion vector of interest points extracted on the object region. Using our approach, the initial active contour for each image will be adequately adjusted in a way that it will be as close as possible to the actual boundary of the object of interest so that the evolution of active contour based on characteristics of the region will not be trapped by false contours. Simulation results on synthetic and real images validate the effectiveness of the proposed approaches

Generative models for image segmentation and representation

This PhD. Thesis consists of two well differentiated parts, each of them focusing on one particular field of Computer Vision. The first part of the document considers the problem of automatically generating image segmentations in video sequences in the absence of any kind of semantic knowledge or labeled data. To that end, a blind spatio-temporal segmentation algorithm is proposed that fuses motion, color and spatial information to produce robust segmentations. The approach follows an iterative splitting process in which well known probabilistic techniques such as Gaussian Mixture Models are used as a core technique. At each iteration of the segmentation process, some regions are split into new ones, so that the number of mixture components is automatically set depending on the image content. Furthermore, in order to keep in memory valuable information from previous iterations, prior distributions are applied to the mixture components so that areas of the image that remain unchanged are fixed during the learning process. Additionally, in order to make decisions about whether or not to split regions at the end of one iteration, we propose the use of novel spatio-temporal mid-level features. These features model properties that are usually found in real-world objects so that the resulting segmentations are closer to the human perception. Examples of spatial mid-level features are regularity or adjacency, whereas the temporal ones relate to well known motion patterns such as translation or rotation. The proposed algorithm has been assessed in comparison to some state-of-the-art spatio-temporal segmentation algorithms, taking special care of showing the influence of each of the original contributions. The second part of the thesis studies the application of generative probabilistic models to the image representation problem. We consider “image representation” as a concurrent process that helps to understand the contents in an image and covers several particular tasks in computer vision as image recognition, object detection or image segmentation. Starting from the well-known bag-of-words paradigm we study the application of Latent Topic Models. These models were initially proposed in the text retrieval field, and consider a document as generated by a mixture of latent topics that are hopefully associated to semantic concepts. Each topic generates in turn visual local descriptors following a specific distribution. Due to the bag-of-words representation, Latent Topic Models exhibit an important limitation when applied to vision problems: they do not model the distribution of topics along the images. The benefits of this spatial modeling are twofold: first, an improved performance of these models in tasks such as image classification or topic discovery; and second, an enrichment of such models with the capability of generating robust image segmentations. However, modeling the spatial location of visual words under this framework is not longer straightforward since one must ensure that both appearance and spatial models are jointly trained using the same learning algorithm that infers the latent topics. We have proposed two Latent Topic Models, Region-Based Latent Topic Model and Region-Based Latent Dirichlet Allocation that extend basic approaches to model the spatial distribution of topics along images. For that end, previous blind segmentations provide a geometric layout of an image and are included in the model through cooperative distributions that allow regions to influence each other. In addition, our proposals tackle several other aspects in topic models that enhance the image representation. It is worth to mention one contribution that explores the use of advanced appearance models, since it has shown to notably improve the performance in several tasks. In particular, a distribution based on the Kernel Logistic Regression has been proposed that takes into account the nonlinear relations of visual descriptors that lie in the same image region. Our proposals have been evaluated in three important tasks towards the total scene understanding: image classification, category-based image segmentation and unsupervised topic discovery. The obtained results support our developments and compare well with several state-of-the-art algorithms and, even more, with more complex submissions to international challenges in the vision field