Personalised placement in networked video

Personalised video can be achieved by inserting objects into a video play-out according to the viewer's profile. Content which has been authored and produced for general broadcast can take on additional commercial service features when personalised either for individual viewers or for groups of viewers participating in entertainment, training, gaming or informational activities. Although several scenarios and use-cases can be envisaged, we are focussed on the application of personalised product placement. Targeted advertising and product placement are currently garnering intense interest in the commercial networked media industries. Personalisation of product placement is a relevant and timely service for next generation online marketing and advertising and for many other revenue generating interactive services. This paper discusses the acquisition and insertion of media objects into a TV video play-out stream where the objects are determined by the profile of the viewer. The technology is based on MPEG-4 standards using object based video and MPEG-7 for metadata. No proprietary technology or protocol is proposed. To trade the objects into the video play-out, a Software-as-a-Service brokerage platform based on intelligent agent technology is adopted. Agencies, libraries and service providers are represented in a commercial negotiation to facilitate the contractual selection and usage of objects to be inserted into the video play-out.

Content Based Video Retrieval using SURF Descriptor

This paper presents a Robust Content Based Video Retrieval (CBVR) system. This system retrieves similar videos based on a local feature descriptor called SURF (Speeded Up Robust Feature). The higher dimensionality of SURF like feature descriptors causes huge storage consumption during indexing of video information. To achieve a dimensionality reduction on the SURF feature descriptor, this system employs a stochastic dimensionality reduction method and thus provides a model data for the videos. On retrieval, the model data of the test clip is classified to its similar videos using a minimum distance classifier. The performance of this system is evaluated using two different minimum distance classifiers during the retrieval stage. The experimental analyses performed on the system shows that the system has a retrieval performance of 78%. This system also analyses the performance efficiency of the low dimensional SURF descriptor.

Object-based three-dimensional X-ray imaging

A form of three-dimensional X-ray imaging, called Object 3-D, is introduced, where the relevant subject material is represented as discrete ‘objects’. The surface of each such object is derived accurately from the projections of its outline, and of its other discontinuities, in about ten conventional X-ray views, distributed in solid angle. This technique is suitable for many applications, and permits dramatic savings in radiation exposure and in data acquisition and manipulation. It is well matched to user-friendly interactive displays.

A comparison of pixel and object-based land cover classification. A case study of the Asmara region, Eritrea

In this paper we compare the performance of two image classification paradigms (object- and pixel-based) for creating a land cover map of Asmara, the capital of Eritrea and its surrounding areas using a Landsat ETM+ imagery acquired in January 2000. The image classification methods used were maximum likelihood for the pixel-based approach and Bhattacharyya distance for the object-oriented approach available in, respectively, ArcGIS and SPRING software packages. Advantages and limitations of both approaches are presented and discussed. Classifications outputs were assessed using overall accuracy and Kappa indices. Pixel- and object-based classification methods result in an overall accuracy of 78% and 85%, respectively. The Kappa coefficient for pixel- and object-based approaches was 0.74 and 0.82, respectively. Although pixel-based approach is the most commonly used method, assessment and visual interpretation of the results clearly reveal that the object-oriented approach has advantages for this specific case-study.

Informative State-Based Video Communication

We study state-based video communication where a client simultaneously informs the server about the presence status of various packets in its buffer. In sender-driven transmission, the client periodically sends to the server a single acknowledgement packet that provides information about all packets that have arrived at the client by the time the acknowledgment is sent. In receiver-driven streaming, the client periodically sends to the server a single request packet that comprises a transmission schedule for sending missing data to the client over a horizon of time. We develop a comprehensive optimization framework that enables computing packet transmission decisions that maximize the end-to-end video quality for the given bandwidth resources, in both prospective scenarios. The core step of the optimization comprises computing the probability that a single packet will be communicated in error as a function of the expected transmission redundancy (or cost) used to communicate the packet. Through comprehensive simulation experiments, we carefully examine the performance advances that our framework enables relative to state-of-the-art scheduling systems that employ regular acknowledgement or request packets. Consistent gains in video quality of up to 2B are demonstrated across a variety of content types. We show that there is a direct analogy between the error-cost efficiency of streaming a single packet and the overall rate-distortion performance of streaming the whole content. In the case of sender-driven transmission, we develop an effective modeling approach that accurately characterizes the end-to-end performance as a function of the packet loss rate on the backward channel and the source encoding characteristics.

Object-Based Customization of Learning Content: Potentials and Pitfalls

Specification consortia and standardization bodies concentrate on e-Learning objects to en-sure reusability of content. Learning objects may be collected in a library and used for deriv-ing course offerings that are customized to the needs of different learning communities. How-ever, customization of courses is possible only if the logical dependencies between the learn-ing objects are known. Metadata for describing object relationships have been proposed in several e-Learning specifications. This paper discusses the customization potential of e-Learning objects but also the pitfalls that exist if content is customized inappropriately.

Increasing awareness of colorectal cancer screening through targeted exam room based video messages

Colorectal cancer (CRC) is the third largest cause of cancer death in the United States. While the disease burden is high, there are proven methods to screen for CRC and detect it at a stage that is amenable to cure. Patients with low health literacy have difficulty navigating the health care system and are at increased risk to not receive preventive care services such as colorectal cancer screening (CRCS). To address this need, an exam-room based video was developed to be played for patients in the privacy of the exam room, while they are waiting to be seen by their medical provider. In roughly 2 minutes, the video informs the patient about CRC and CRCS and how they can successfully complete CRCS. One of the key barriers to completing CRCS is the need to increase patients' knowledge and improve attitudes surrounding CRCS. This study examines the impact of the video on patients' knowledge and attitudes about CRC and CRCS in a medically underserved patient population in Houston, Texas. ^ Sixty-one patients presenting for routine medical care were enrolled in the study. Depending on their randomization, the patients either received routine information about CRC and CRCS or they watched the video. We found that the patients who did watch the video did have improvements in their knowledge and improved attitudes about CRC and CRCS. Future studies will be needed to examine whether the video improves the patients' completion of CRCS.^

Icebergs in the Amundsen Sea (Antarctica) determined from ENVISAT ASAR WSM data using object-based image analysis approach, link to ESRI GDB files

ENVISAT ASAR WSM images with pixel size 150 × 150 m, acquired in different meteorological, oceanographic and sea ice conditions were used to determined icebergs in the Amundsen Sea (Antarctica). An object-based method for automatic iceberg detection from SAR data has been developed and applied. The object identification is based on spectral and spatial parameters on 5 scale levels, and was verified with manual classification in four polygon areas, chosen to represent varying environmental conditions. The algorithm works comparatively well in freezing temperatures and strong wind conditions, prevailing in the Amundsen Sea during the year. The detection rate was 96% which corresponds to 94% of the area (counting icebergs larger than 0.03 km**2), for all seasons. The presented algorithm tends to generate errors in the form of false alarms, mainly caused by the presence of ice floes, rather than misses. This affects the reliability since false alarms were manually corrected post analysis.

Benthic cover map of Heron Reef derived from a high-spatial-resolution multi-spectral satellite image using object based image analysis

Providing accurate maps of coral reefs where the spatial scale and labels of the mapped features correspond to map units appropriate for examining biological and geomorphic structures and processes is a major challenge for remote sensing. The objective of this work is to assess the accuracy and relevance of the process used to derive geomorphic zone and benthic community zone maps for three western Pacific coral reefs produced from multi-scale, object-based image analysis (OBIA) of high-spatial-resolution multi-spectral images, guided by field survey data. Three Quickbird-2 multi-spectral data sets from reefs in Australia, Palau and Fiji and georeferenced field photographs were used in a multi-scale segmentation and object-based image classification to map geomorphic zones and benthic community zones. A per-pixel approach was also tested for mapping benthic community zones. Validation of the maps and comparison to past approaches indicated the multi-scale OBIA process enabled field data, operator field experience and a conceptual hierarchical model of the coral reef environment to be linked to provide output maps at geomorphic zone and benthic community scales on coral reefs. The OBIA mapping accuracies were comparable with previously published work using other methods; however, the classes mapped were matched to a predetermined set of features on the reef.

Toward Multi-Scale Object-Based Data Fusion

This paper proposes a new methodology for object based 2-D data fu- sion, with a multiscale character. This methodology is intended to be use in agriculture, specifically in the characterization of the water status of different crops, so as to have an appropriate water management at a farm-holding scale. As a first approach to its evaluation, vegetation cover vigor data has been integrated with texture data. For this purpose, NDVI maps have been calculated using a multispectral image and Lacunarity maps from the panchromatic image. Preliminary results show this methodology is viable in the integration and management of large volumes of data, which characterize the behavior of agricultural covers at farm-holding scale.

Multiscale object-based classification of satellite images merging multispectral information with panchromatic textural features

Once admitted the advantages of object-based classification compared to pixel-based classification; the need of simple and affordable methods to define and characterize objects to be classified, appears. This paper presents a new methodology for the identification and characterization of objects at different scales, through the integration of spectral information provided by the multispectral image, and textural information from the corresponding panchromatic image. In this way, it has defined a set of objects that yields a simplified representation of the information contained in the two source images. These objects can be characterized by different attributes that allow discriminating between different spectral&textural patterns. This methodology facilitates information processing, from a conceptual and computational point of view. Thus the vectors of attributes defined can be used directly as training pattern input for certain classifiers, as for example artificial neural networks. Growing Cell Structures have been used to classify the merged information.

Spatial and thematic assessment of object- based forest stands delineation using OFA-matrix

Validación de la cartografía generada del terreno a partir de una nuevo sistema de validación propuesto