A New Stereo Correspondence Method for Snake-Based Object Segmentation

ABSTRACT In this paper, we propose a new method for generating excellent external energy for snake-based object segmentation methods in stereo images. Our method first generates an edge-based disparity map by performing stereo correspondence between multi-level edge maps of the stereo image pair. Only edges of similar strength are considered for matching. To filter the disparity map for edges of the object of interest, the method estimates the object's disparity value by matching the pattern of edges of the region of interest in the left image against candidate patterns in the right image. The filtered edge map is then used to generate external energy for the snake. The proposed method has been tested on two snake models and results show a noticeable enhancement on performance of the snake when compared with other methods

Detección de la saliencia visual dinámica: hacia el modelado de la atención

The evaluation of the features of an image analysing the eye movements is important for developing computer vision applications, as well as the understanding of how biological systems explore the environment. For this reason, there are numerous models that seek to predict where the human visual attention will be focused when watching an image, i.e. its visual saliency. In this project, a dynamic saliency model has been modified towards the attention modeling. This has been done by adding a face detector and a center-bias to an existing saliency model. We will start explaining some saliency and face detection algorithms, delving into the ones that have been used in order to carry out our objective. Then, the description of the developed model is given, together with the decisions that have been taken. Finally, we included some experiments and its results to evaluate the performance of the implemented system. A budget showing the costs of improving the algorithm is given in chapter 6. Nowadays, most saliency models are focused on the extraction of bottom-up information, like color, contrast or motion, to predict the eye fixations of an observer. Nevertheless, human visual attention focuses on high-level features of the image, which provide relevant information in order to understand the scene –top-down attention–. A small amount of visual saliency algorithms include this top-down attention, so our purpose is to improve the performance of an existing bottom-up saliency model, by adding some high-level features: the tendency of looking to the center of the screen and the fact that we pay more attention to faces. These two top-down cues were included, although the proposed problem was to implement only the existence of faces. In order to build this hybrid model, we are going to start from a dynamic bottom-up saliency algorithm, developed by Carlos Ruiz in his Final Year Project (in Spanish, Trabajo Fin de Grado, TFG) –explained in section 3.1–. So, the main objective is to improve its performance. The obtained results and the comparison between both models can be seen in section 4.4. On the other hand, enlarging the eye tracking database was proposed as future work in his TFG, so other objective is to accomplish this task. Although a large database formed by 60 videos with eye fixations data was found, only 8 of them have been used due to the time restrictions, as it is detailed in section 4.1.Ingeniería de Sistemas Audiovisuale

Improved facial feature fitting for model based coding and animation

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Automatic human face detection in color images

Automatic human face detection in digital image has been an active area of research over the past decade. Among its numerous applications, face detection plays a key role in face recognition system for biometric personal identification, face tracking for intelligent human computer interface (HCI), and face segmentation for object-based video coding. Despite significant progress in the field in recent years, detecting human faces in unconstrained and complex images remains a challenging problem in computer vision. An automatic system that possesses a similar capability as the human vision system in detecting faces is still a far-reaching goal. This thesis focuses on the problem of detecting human laces in color images. Although many early face detection algorithms were designed to work on gray-scale Images, strong evidence exists to suggest face detection can be done more efficiently by taking into account color characteristics of the human face. In this thesis, we present a complete and systematic face detection algorithm that combines the strengths of both analytic and holistic approaches to face detection. The algorithm is developed to detect quasi-frontal faces in complex color Images. This face class, which represents typical detection scenarios in most practical applications of face detection, covers a wide range of face poses Including all in-plane rotations and some out-of-plane rotations. The algorithm is organized into a number of cascading stages including skin region segmentation, face candidate selection, and face verification. In each of these stages, various visual cues are utilized to narrow the search space for faces. In this thesis, we present a comprehensive analysis of skin detection using color pixel classification, and the effects of factors such as the color space, color classification algorithm on segmentation performance. We also propose a novel and efficient face candidate selection technique that is based on color-based eye region detection and a geometric face model. This candidate selection technique eliminates the computation-intensive step of window scanning often employed In holistic face detection, and simplifies the task of detecting rotated faces. Besides various heuristic techniques for face candidate verification, we developface/nonface classifiers based on the naive Bayesian model, and investigate three feature extraction schemes, namely intensity, projection on face subspace and edge-based. Techniques for improving face/nonface classification are also proposed, including bootstrapping, classifier combination and using contextual information. On a test set of face and nonface patterns, the combination of three Bayesian classifiers has a correct detection rate of 98.6% at a false positive rate of 10%. Extensive testing results have shown that the proposed face detector achieves good performance in terms of both detection rate and alignment between the detected faces and the true faces. On a test set of 200 images containing 231 faces taken from the ECU face detection database, the proposed face detector has a correct detection rate of 90.04% and makes 10 false detections. We have found that the proposed face detector is more robust In detecting in-plane rotated laces, compared to existing face detectors. +D2