Penjejakan Fitur Berbasis Koherensi Temporal dalam Sistem Animasi Ekspresi Wajah

AbstrakTingginya permintaan produktivitas industri animasi di Indonesia menuntut adanya perubahandi sektor produksi. Teknologi motion capture merupakan penerapan prinsip visi komputeryang mengadaptasi indera mata manusia untuk mengenali fenomena gerakan yang tertangkapkamera dan memetakannya dalam pola gerak virtual. Tulisan ilmiah ini akan membahas metodepenjejakan fitur penanda di wajah manusia untuk mendapatkan informasi mengenai ekspresiwajah. Teknik penjejakan menggunakan penerapan prinsip koherensi temporal. Asumsi yangdigunakan pada penelitian ini berargumentasi bahwa dengan menggunakan pendekatankoherensi temporal, maka proses penjejakan fitur di citra sekuensial dapat disederhanakandengan perhitungan nilai kedekatan pada penanda di setiap frame-nya. Hasil yang didapatmenunjukkan bahwa proses penjejakan fitur yang diusulkan memiliki hasil yang handal untukmenangani banyak frame. Komputasi yang digunakan juga sangat efisien dan hemat karenaprosesnya tidak memerlukan tahap pembelajaran terlebih dahulu. Kumpulan hasil penjejakanparameter fitur penanda secara sekuensial akan membentuk sebuah basis data ekspresi visualdari wajah manusia. AbstractTemporal Coherence Based Feature Tracking in the Animation System of Facial Expression.High demand on the productivity of the animation industry in Indonesia requires a changein the existing production process. Motion capture technology is the implementation of acomputer vision principle to adopt the human eye senses to understand the phenomenon ofmotion results from a camera and to map the virtual movement patterns. This paper willdiscuss a method for tracking marker features in the human face to obtain information aboutfacial expressions. The tracking technique is using implementation of temporal coherenceprinciple. This research assumes that by using temporal coherence approach, the trackingprocess in sequential images can be simplified by calculating similarity on markers in eachframe. The result shows that this feature-tracking process have reliable result to handle alot of frames. The computation used is very efficient and cheap because it does not requirea learning process in advance. The precision accuracy of tracking parameters generated adatabase of good visual expression

Statistical Shape Spaces for 3D Data: A Review

International audienceMethods and systems for capturing 3D geometry are becoming increasingly commonplace–and with them a plethora of 3D data. Much of this data is unfortunately corrupted by noise, missing data, occlusions or other outliers. However, when we are interested in the shape of a particular class of objects, such as human faces or bodies, we can use machine learning techniques, applied to clean, registered databases of these shapes, to make sense of raw 3D point clouds or other data. This has applications ranging from virtual change rooms to motion and gait analysis to surgical planning depending on the type of shape. In this chapter, we give an overview of these techniques, a brief review of the literature, and comparative evaluation of two such shape spaces for human faces

Edge-Aware Spatial Denoising Filtering Based on a Psychological Model of Stimulus Similarity

Noise reduction is a fundamental operation in image quality enhancement. In recent years, a large body of techniques at the crossroads of statistics and functional analysis have been developed to minimize the blurring artifact introduced in the denoising process. Recent studies focus on edge-aware filters due to their tendency to preserve image structures. In this study, we adopt a psychological model of similarity based on Shepard’s generalization law and introduce a new signal-dependent window selection technique. Such a focus is warranted because blurring is essentially a cognitive act related to the human perception of physical stimuli (pixels). The proposed windowing technique can be used to implement a wide range of edge-aware spatial denoising filters, thereby transforming them into nonlocal filters. We employ simulations using both synthetic and real image samples to evaluate the performance of the proposed method by quantifying the enhancement in the signal strength, noise suppression, and structural preservation measured in terms of the Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity (SSIM) index, respectively. In our experiments, we observe that incorporating the proposed windowing technique in the design of mean, median, and nonlocalmeans filters substantially reduces the MSE while simultaneously increasing the PSNR and the SSIM

Registration and Recognition in 3D

The simplest Computer Vision algorithm can tell you what color it sees when you point it at an object, but asking that computer what it is looking at is a much harder problem. Camera and LiDAR (Light Detection And Ranging) sensors generally provide streams pixel of values and sophisticated algorithms must be engineered to recognize objects or the environment. There has been significant effort expended by the computer vision community on recognizing objects in color images; however, LiDAR sensors, which sense depth values for pixels instead of color, have been studied less. Recently we have seen a renewed interest in depth data with the democratization provided by consumer depth cameras. Detecting objects in depth data is more challenging in some ways because of the lack of texture and increased complexity of processing unordered point sets. We present three systems that contribute to solving the object recognition problem from the LiDAR perspective. They are: calibration, registration, and object recognition systems. We propose a novel calibration system that works with both line and raster based LiDAR sensors, and calibrates them with respect to image cameras. Our system can be extended to calibrate LiDAR sensors that do not give intensity information. We demonstrate a novel system that produces registrations between different LiDAR scans by transforming the input point cloud into a Constellation Extended Gaussian Image (CEGI) and then uses this CEGI to estimate the rotational alignment of the scans independently. Finally we present a method for object recognition which uses local (Spin Images) and global (CEGI) information to recognize cars in a large urban dataset. We present real world results from these three systems. Compelling experiments show that object recognition systems can gain much information using only 3D geometry. There are many object recognition and navigation algorithms that work on images; the work we propose in this thesis is more complimentary to those image based methods than competitive. This is an important step along the way to more intelligent robots

Tracking multiple mice

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 59-62).Monitoring mouse social behaviors over long periods of time is essential for neurobehavioral analysis of social mouse phenotypes. Currently, the primary method of social behavioral plienotyping utilizes human labelers, which is slow and costly. In order to achieve the high throughput desired for scientific studies, social behavioral phenotyping must be automated. The problem of automation can be divided into two tasks; tracking and phenotyping. First, individual body parts of mice must be accurately tracked. This is achieved using shape context descriptors to obtain precise point to point correspondences between templates and mice in any frame of a video. This method provides for greater precision and accuracy than current state of the art techniques. We propose a means by which this tracking information can be used to classify social behaviors between mice.by Stav Braun.M.Eng

3D analysis of tooth surfaces to aid accurate brace placement

Master'sMASTER OF ENGINEERIN

Object recognition using multi-view imaging

Single view imaging data has been used in most previous research in computer vision and image understanding and lots of techniques have been developed. Recently with the fast development and dropping cost of multiple cameras, it has become possible to have many more views to achieve image processing tasks. This thesis will consider how to use the obtained multiple images in the application of target object recognition. In this context, we present two algorithms for object recognition based on scale- invariant feature points. The first is single view object recognition method (SOR), which operates on single images and uses a chirality constraint to reduce the recognition errors that arise when only a small number of feature points are matched. The procedure is extended in the second multi-view object recognition algorithm (MOR) which operates on a multi-view image sequence and, by tracking feature points using a dynamic programming method in the plenoptic domain subject to the epipolar constraint, is able to fuse feature point matches from all the available images, resulting in more robust recognition. We evaluated these algorithms using a number of data sets of real images capturing both indoor and outdoor scenes. We demonstrate that MOR is better than SOR particularly for noisy and low resolution images, and it is also able to recognize objects that are partially occluded by combining it with some segmentation techniques

Image Segmentation and Analysis for Automated Classification of Traumatic Pelvic Injuries

In the past decades, technical advances have allowed for the collection and storage of more types and larger quantities of medical data. The increase in the volume of existing medical data has increased the need for processing and analyzing such data. Medical data holds information that is invaluable for diagnostic as well as treatment planning purposes. Presently, a large portion of the data is not optimally used towards medical decisions because information contained in the data is inaccessible through simple human inspection, or traditional computational methods. In the field of trauma medicine, where caregivers are frequently confronted with situations where they need to make rapid decisions based on large amounts of information, the need for reliable, fast and automated computational methods for decision support systems is stringent. Such methods could process and analyze, in a timely fashion, all available medical data and provide caretakers with recommendations/predictions for both patient diagnostic and treatment planning. Presently however, even extracting features that are known to be useful for diagnosis, like presence and location of hemorrhage and fracture, is not easily achievable in automatic manner. Trauma is the main cause of death among Americans age 40 and younger; hence, it has become a national priority. A computer-aided decision making system capable of rapidly analyzing all data available for a patient and forming reliable recommendations for physicians can greatly impact the quality of care provided to patients. Such a system would also reduce the overall costs involved in patient care as it helps in optimizing the decisions, avoiding unnecessary procedures, and customizing treatments for individual patients. Among different types of trauma with a high impact on the lives of Americans, traumatic pelvic injuries, which often occur in motor vehicle accidents and in falls, have had a tremendous toll on both human lives and healthcare costs in the United States. The present project has developed automated computational methods and algorithms to analyze pelvic CT images and extract significant features describing the severity of injuries. Such a step is of great importance as every CT scan consists of tens of slices that need to be closely examined. This method can automatically extract information hidden in CT images and therefore reduce the time of the examination. The method identifies and signals areas of potential abnormality and allows the user to decide upon the action to be taken (e.g. further examination of the image and/or area and neighboring images in the scan). The project also initiates the design of a system that combines the features extracted from biomedical signals and images with information such as injury scores, injury mechanism and demographic information in order to detect the presence and the severity of Traumatic Pelvic Injuries and to provide recommendations for diagnosis and treatment. The recommendations are provided in form of grammatical rules, allowing physicians to explore the reasoning behind these assessments