Crowdsourcing in Computer Vision

Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to capture human knowledge and understanding, for a vast number of visual perception tasks. In this survey, we describe the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. We begin by discussing data collection on both classic (e.g., object recognition) and recent (e.g., visual story-telling) vision tasks. We then summarize key design decisions for creating effective data collection interfaces and workflows, and present strategies for intelligently selecting the most important data instances to annotate. Finally, we conclude with some thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in Computer Graphics and Vision, 201

A Portable Active Binocular Robot Vision Architecture for Scene Exploration

We present a portable active binocular robot vision archi- tecture that integrates a number of visual behaviours. This vision archi- tecture inherits the abilities of vergence, localisation, recognition and si- multaneous identification of multiple target object instances. To demon- strate the portability of our vision architecture, we carry out qualitative and comparative analysis under two different hardware robotic settings, feature extraction techniques and viewpoints. Our portable active binoc- ular robot vision architecture achieved average recognition rates of 93.5% for fronto-parallel viewpoints and, 83% percentage for anthropomorphic viewpoints, respectively

Vision systems with the human in the loop

The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed

Towards Active Event Recognition

Directing robot attention to recognise activities and to anticipate events like goal-directed actions is a crucial skill for human-robot interaction. Unfortunately, issues like intrinsic time constraints, the spatially distributed nature of the entailed information sources, and the existence of a multitude of unobservable states affecting the system, like latent intentions, have long rendered achievement of such skills a rather elusive goal. The problem tests the limits of current attention control systems. It requires an integrated solution for tracking, exploration and recognition, which traditionally have been seen as separate problems in active vision.We propose a probabilistic generative framework based on a mixture of Kalman filters and information gain maximisation that uses predictions in both recognition and attention-control. This framework can efficiently use the observations of one element in a dynamic environment to provide information on other elements, and consequently enables guided exploration.Interestingly, the sensors-control policy, directly derived from first principles, represents the intuitive trade-off between finding the most discriminative clues and maintaining overall awareness.Experiments on a simulated humanoid robot observing a human executing goal-oriented actions demonstrated improvement on recognition time and precision over baseline systems

Cognitive visual tracking and camera control

Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision

View-Invariant Object Category Learning, Recognition, and Search: How Spatial and Object Attention Are Coordinated Using Surface-Based Attentional Shrouds

Air Force Office of Scientific Research (F49620-01-1-0397); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

The Whole World in Your Hand: Active and Interactive Segmentation

Object segmentation is a fundamental problem in computer vision and a powerful resource for development. This paper presents three embodied approaches to the visual segmentation of objects. Each approach to segmentation is aided by the presence of a hand or arm in the proximity of the object to be segmented. The first approach is suitable for a robotic system, where the robot can use its arm to evoke object motion. The second method operates on a wearable system, viewing the world from a human's perspective, with instrumentation to help detect and segment objects that are held in the wearer's hand. The third method operates when observing a human teacher, locating periodic motion (finger/arm/object waving or tapping) and using it as a seed for segmentation. We show that object segmentation can serve as a key resource for development by demonstrating methods that exploit high-quality object segmentations to develop both low-level vision capabilities (specialized feature detectors) and high-level vision capabilities (object recognition and localization)

Time course of information processing in visual and haptic object classification

Peer reviewedPublisher PD

Multi-Modal Human-Machine Communication for Instructing Robot Grasping Tasks

A major challenge for the realization of intelligent robots is to supply them with cognitive abilities in order to allow ordinary users to program them easily and intuitively. One way of such programming is teaching work tasks by interactive demonstration. To make this effective and convenient for the user, the machine must be capable to establish a common focus of attention and be able to use and integrate spoken instructions, visual perceptions, and non-verbal clues like gestural commands. We report progress in building a hybrid architecture that combines statistical methods, neural networks, and finite state machines into an integrated system for instructing grasping tasks by man-machine interaction. The system combines the GRAVIS-robot for visual attention and gestural instruction with an intelligent interface for speech recognition and linguistic interpretation, and an modality fusion module to allow multi-modal task-oriented man-machine communication with respect to dextrous robot manipulation of objects.Comment: 7 pages, 8 figure