Automatic hand or head gesture interface for individuals with motor impairments, senior citizens and young children

Senior citizens, very young children and users with different kinds of impairments are often prevented from enjoying the benefits of latest technologies for assistance, accessibility and usability, often due to unfriendly or expensive interfaces. We present a friendly and inexpensive system that allows one to interact with a computer, using only a few distinct but intuitive gestures which are translated into mouse actions. The gestures can be carried out by the head or by the hand, which are automatically selected, without any kind of prior calibration or special environment. For each kind of user a different frontend is presented, adapted to specific needs, nevertheless access to all the functionalities of the operating systems can be given if requested

Accurate Calibration Scheme for a Multi-Camera Mobile Mapping System

Mobile mapping systems (MMS) are increasingly used for many photogrammetric and computer vision applications, especially encouraged by the fast and accurate geospatial data generation. The accuracy of point position in an MMS is mainly dependent on the quality of calibration, accuracy of sensor synchronization, accuracy of georeferencing and stability of geometric configuration of space intersections. In this study, we focus on multi-camera calibration (interior and relative orientation parameter estimation) and MMS calibration (mounting parameter estimation). The objective of this study was to develop a practical scheme for rigorous and accurate system calibration of a photogrammetric mapping station equipped with a multi-projective camera (MPC) and a global navigation satellite system (GNSS) and inertial measurement unit (IMU) for direct georeferencing. The proposed technique is comprised of two steps. Firstly, interior orientation parameters of each individual camera in an MPC and the relative orientation parameters of each cameras of the MPC with respect to the first camera are estimated. In the second step the offset and misalignment between MPC and GNSS/IMU are estimated. The global accuracy of the proposed method was assessed using independent check points. A correspondence map for a panorama is introduced that provides metric information. Our results highlight that the proposed calibration scheme reaches centimeter-level global accuracy for 3D point positioning. This level of global accuracy demonstrates the feasibility of the proposed technique and has the potential to fit accurate mapping purposes

Data base manipulation for assessment of multiresource suitability and land change

Progress is reported in three tasks which support the overall objectives of renewable resources inventory task of the AgRISTARS program. In the first task, the geometric correction algorithms of the Master Data Processor were investigated to determine the utility of data corrected by this processor for U.S. Forest Service uses. The second task involved investigation of logic to form blobs as a precursor step to automatic change detection involving two dates of LANDSAT data. Some routine procedures for selecting BLOB (spatial averaging) parameters were developed. In the third task, a major effort was made to develop land suitability modeling approches for timber, grazing, and wildlife habitat in support of resource planning efforts on the San Juan National Forest

Systems and image database resources for UAV search and rescue applications

Aerial search and rescue applications using unmanned aerial vehicles (UAVs) can incorporate image processing technologies to locate targets faster and with a higher degree of accuracy. The task of developing such systems involves the development of both hardware and software components. Examples of both hardware and software approaches in the design process for an aerial imaging system are shown for small UAV applications. This project contains resources to help facilitate the incorporation of UAV applications into educational ventures and design projects. A power supply design is done for a lightweight imaging system and a large database of aerial images is assembled for image recognition development. The image set is all based in a small subset of geography, mainly lightly wooded pastureland, with a variety of lost-hiker target objects. The work describes an imaging system design, a power distribution design, target image collection and categorization, and basic image processing approaches --Abstract, page iii

Vision based indoor positioning in a retail environment

Modern smart phones often come with a significant amount of computational power and an integrated digital camera making them an ideal platform for intelligents assistants. This work is restricted to retail environments, where users could be provided with for example navigational instructions to desired products or information about special offers within their close proximity. This kind of applications usually require information about the user's current location in the domain environment, which in our case corresponds to a retail store. We propose a vision based positioning approach that recognizes products the user's mobile phone's camera is currently pointing at. The products are related to locations within the store, which enables us to locate the user by pointing the mobile phone's camera to a group of products. The first step of our method is to extract meaningful features from digital images. We use the Scale- Invariant Feature Transform SIFT algorithm, which extracts features that are highly distinctive in the sense that they can be correctly matched against a large database of features from many images. We collect a comprehensive set of images from all meaningful locations within our domain and extract the SIFT features from each of these images. As the SIFT features are of high dimensionality and thus comparing individual features is infeasible, we apply the Bags of Keypoints method which creates a generic representation, visual category, from all features extracted from images taken from a specific location. A category for an unseen image can be deduced by extracting the corresponding SIFT features and by choosing the category that best fits the extracted features. We have applied the proposed method within a Finnish supermarket. We consider grocery shelves as categories which is a sufficient level of accuracy to help users navigate or to provide useful information about nearby products. We achieve a 40% accuracy which is quite low for commercial applications while significantly outperforming the random guess baseline. Our results suggest that the accuracy of the classification could be increased with a deeper analysis on the domain and by combining existing positioning methods with ours

Face Detection And Lip Localization

Integration of audio and video signals for automatic speech recognition has become an important field of study. The Audio-Visual Speech Recognition (AVSR) system is known to have accuracy higher than audio-only or visual-only system. The research focused on the visual front end and has been centered around lip segmentation. Experiments performed for lip feature extraction were mainly done in constrained environment with controlled background noise. In this thesis we focus our attention to a database collected in the environment of a moving car which hampered the quality of the imagery. We first introduce the concept of illumination compensation, where we try to reduce the dependency of light from over- or under-exposed images. As a precursor to lip segmentation, we focus on a robust face detection technique which reaches an accuracy of 95%. We have detailed and compared three different face detection techniques and found a successful way of concatenating them in order to increase the overall accuracy. One of the detection techniques used was the object detection algorithm proposed by Viola-Jones. We have experimented with different color spaces using the Viola-Jones algorithm and have reached interesting conclusions. Following face detection we implement a lip localization algorithm based on the vertical gradients of hybrid equations of color. Despite the challenging background and image quality, success rate of 88% was achieved for lip segmentation