http://cvssp-data.eps.surrey.ac.uk/Personal/AdrianHilton/Publications_files/survey_techrep.pdf

A survey of advances in vision-based human motion capture and analysis

Cell phones and other portable devices are equipped with a variety of technologies by which existing functionality can be improved, and new functionality can be provided. Some relate to visual search capabilities, and determining appropriate actions responsive to different image inputs. Others relate to processing of image data. Still others concern metadata generation, processing, and representation. Yet others relate to coping with fixed focus limitations of cell phone cameras, e.g., in reading digital watermark data. Still others concern user interface improvements. A great number of other features and arrangements are also detailed.

Methods and Systems for Content Processing

A mobile terminal including a body; a touchscreen provided to a front and extending to side of the body and configured to display content; and a controller configured to detect one side of the body comes into contact with one side of an external terminal, display a first area on the touchscreen corresponding to a contact area of the body and the external terminal and a second area including the content, receive an input of moving the content displayed in the second area to the first area, display the content in the first area, and share the content in the first area with the external terminal.

Mobile terminal and method for controlling the same

A smart phone senses audio, imagery, and/or other stimulus from a user's environment, and acts autonomously to fulfill inferred or anticipated user desires. In one aspect, the detailed technology concerns phone-based cognition of a scene viewed by the phone's camera. The image processing tasks applied to the scene can be selected from among various alternatives by reference to resource costs, resource constraints, other stimulus information (e.g., audio), task substitutability, etc. The phone can apply more or less resources to an image processing task depending on how successfully the task is proceeding, or based on the user's apparent interest in the task. In some arrangements, data may be referred to the cloud for analysis, or for gleaning. Cognition, and identification of appropriate device response(s), can be aided by collateral information, such as context. A great number of other features and arrangements are also detailed.

Intuitive computing methods and systems

Map-matching is the process of aligning a sequence of observed user positions with the road network on a digital map. It is a fundamental pre-processing step for many applications, such as moving object management, traffic flow analysis, and driving directions. In practice there exists huge amount of low-sampling-rate (e.g., one point every 2--5 minutes) GPS trajectories. Unfortunately, most current map-matching approaches only deal with high-sampling-rate (typically one point every 10--30s) GPS data, and become less effective for low-sampling-rate points as the uncertainty in data increases. In this paper, we propose a novel global map-matching algorithm called ST-Matching for low-sampling-rate GPS trajectories. ST-Matching considers (1) the spatial geometric and topological structures of the road network and (2) the temporal/speed constraints of the trajectories. Based on spatio-temporal analysis, a candidate graph is constructed from which the best matching path sequence is identified. We compare ST-Matching with the incremental algorithm and Average-Frechet-Distance (AFD) based global map-matching algorithm. The experiments are performed both on synthetic and real dataset. The results show that our ST-matching algorithm significantly outperform incremental algorithm in terms of matching accuracy for low-sampling trajectories. Meanwhile, when compared with AFD-based global algorithm, ST-Matching also improves accuracy as well as running time.

/pdf/map-matching-for-low-sampling-rate-gps-trajectories-4hstm6zk4l.pdf

Map-matching for low-sampling-rate GPS trajectories

Systems, methods, devices and computer program products which relate to utilizing a camera of a mobile terminal as a user interface for search applications and online services to perform visual searching are provided. The system consists of an apparatus that includes a processor that is configured to capture an image of one or more objects and analyze data of the image to identify an object(s) of the image. The processor is further configured to receive information that is associated with at least one object of the images and display the information that is associated with the image. In this regard, the apparatus is able to simplify access to location based services and improve a user's experience. The processor of the apparatus is configured to combine results of robust visual searches with online information resources to enhance location based services.

Method, Device, Mobile Terminal, and Computer Program Product for a Point of Interest Based Scheme for Improving Mobile Visual Searching Functionalities

A device for switching between code-based searching, optical character recognition (OCR) searching and visual searching is provided. The device includes a media content input for receiving media content from a camera or other element of the device and transferring this media content to a switch. Additionally, the device includes a meta-information input capable of receiving meta- information from an element of the device and transferring the meta-information to the switch. The switch is able to utilize the received media content and the meta- information to select and/or switch between a visual search algorithm, an OCR algorithm and a code-based algorithm.

Method, device and computer program product for integrating code-based and optical character recognition technologies into a mobile visual search

We present a new adaptive algorithm for automatic detection of text from a natural scene. The initial cues of text regions are first detected from the captured image/video. An adaptive color modeling and searching algorithm is then utilized near the initial text cues, to discriminate text/non-text regions. EM optimization algorithm is used for color modeling, under the constraint of text layout relations for a specific language. The proposed algorithm combines the advantages of several previous approaches for text detection, and utilizes a focus-of-attention approach for text finding. The whole algorithm is applied in a prototype system that can automatically detect and recognize sign input from a video camera, and translate the signs into English text or voice streams. We present evaluation results of our algorithm on this system.

/pdf/an-adaptive-algorithm-for-text-detection-from-natural-scenes-3cp2y4g0ak.pdf

An adaptive algorithm for text detection from natural scenes

Previous research in automatic facial expression recognition has been limited to recognition of gross expression categories (e.g., joy or anger) in posed facial behavior under well-controlled conditions (e.g., frontal pose and minimal out-of-plane head motion). We developed a system that detects discrete and important facial actions, (e.g., eye blinking), in spontaneously occurring facial behavior with non-frontal pose, moderate out-of-plane head motion, and occlusion. The system recovers 3D motion parameters, stabilizes facial regions, extracts motion and appearance information, and recognizes discrete facial actions in spontaneous facial behavior. We tested the system in video data from a 2-person interview. Subjects were ethnically diverse, action units occurred during speech, and out-of-plane motion and occlusion from head motion and glasses were common. The video data were originally collected to answer substantive questions in psychology, and represent a substantial challenge to automated AU recognition. In the analysis of 335 single and multiple blinks and non-blinks, the system achieved 98% accuracy.

/pdf/automatic-recognition-of-eye-blinking-in-spontaneously-2yv4veshu7.pdf

Automatic recognition of eye blinking in spontaneously occurring behavior

Pervasive activity monitoring in a skilled-nursing facility helps capture a continuous audio and video record. The CareMedia project analyzes this video information by automatically tracking people, helping to efficiently label individuals, and characterizing selected activities and actions.

Jiang Gao

Papers

Method, Device, Mobile Terminal, and Computer Program Product for a Point of Interest Based Scheme for Improving Mobile Visual Searching Functionalities

Method, device and computer program product for integrating code-based and optical character recognition technologies into a mobile visual search

An adaptive algorithm for text detection from natural scenes

Automatic recognition of eye blinking in spontaneously occurring behavior

Automated analysis of nursing home observations