J. M. F. Rodrigues

Multi-scale lines and edges in V1 and beyond: Brightness, object categorization and recognition, and consciousness

In this paper we present an improved model for line and edge detection in cortical area V1. This model is based on responses of simple and complex cells, and it is multi-scale with no free parameters. We illustrate the use of the multi-scale line/edge representation in different processes: visual reconstruction or brightness perception, automatic scale selection and object segregation. A two-level object categorization scenario is tested in which pre-categorization is based on coarse scales only and final categorization on coarse plus fine scales. We also present a multi-scale object and face recognition model. Processing schemes are discussed in the framework of a complete cortical architecture. The fact that brightness perception and object recognition may be based on the same symbolic image representation is an indication that the entire (visual) cortex is involved in consciousness.

Visual Cortex Frontend: Integrating Lines, Edges, Keypoints, and Disparity

We present a 3D representation that is based on the processing in the visual cortex by simple, complex and end-stopped cells. We improved multiscale methods for line/edge and keypoint detection, including a method for obtaining vertex structure (i.e. T, L, K etc). We also describe a new disparity model. The latter allows to attribute depth to detected lines, edges and keypoints, i.e., the integration results in a 3D “wire-frame” representation suitable for object recognition.

A cortical framework for invariant object categorization and recognition.

In this paper we present a new model for invariant object categorization and recognition. It is based on explicit multi-scale features: lines, edges and keypoints are extracted from responses of simple, complex and end-stopped cells in cortical area V1, and keypoints are used to construct saliency maps for Focus-of-Attention. The model is a functional but dichotomous one, because keypoints are employed to model the “where” data stream, with dynamic routing of features from V1 to higher areas to obtain translation, rotation and size invariance, whereas lines and edges are employed in the “what” stream for object categorization and recognition. Furthermore, both the “where” and “what” pathways are dynamic in that information at coarse scales is employed first, after which information at progressively finer scales is added in order to refine the processes, i.e., both the dynamic feature routing and the categorization level. The construction of group and object templates, which are thought to be available in the prefrontal cortex with “what” and “where” components in PF46d and PF46v, is also illustrated. The model was tested in the framework of an integrated and biologically plausible architecture.

Multi-scale keypoints in v1 and face detection

End-stopped cells in cortical area V1, which combine outputs of complex cells tuned to different orientations, serve to detect line and edge crossings (junctions) and points with a large curvature. In this paper we study the importance of the multi-scale keypoint representation, i.e. retinotopic keypoint maps which are tuned to different spatial frequencies (scale or Level-of-Detail). We show that this representation provides important information for Focus-of-Attention (FoA) and object detection. In particular, we show that hierarchically-structured saliency maps for FoA can be obtained, and that combinations over scales in conjunction with spatial symmetries can lead to face detection through grouping operators that deal with keypoints at the eyes, nose and mouth, especially when non-classical receptive field inhibition is employed. Although a face detector can be based on feedforward and feedback loops within area V1, such an operator must be embedded into dorsal and ventral data streams to and from higher areas for obtaining translation-, rotation- and scale-invariant face (object) detection.

Augmented Reality and Holograms for the Visualization of Mechanical Engineering Parts

There is an increasing number of students using tablets in schools. Mobile devices gained popularity as an educational tool and there are many schools that use them frequently in educational activities to improve learning. We found that first year students of mechanical engineering in general have difficulties in understanding 3D shapes from 2D views. There are many Augmented Reality (AR) applications available that can be used to create educational contents for these mobile devices. On the other hand, there is an increasing interest in making holograms. In this paper we studied the most popular AR systems and show examples of using an AR system for the visualization of 3D models. We also present the creation of a low cost prototype, the EducHolo, that enables the visualization of holograms supported by tablets. With this prototype students can visualize the hologram of mechanical parts, providing a better perception of the model 3D shape and improving the ability of making the 2D orthographic views and perspectives that they study in the first year of mechanical engineer.

Realtime local navigation for the blind: detection of lateral doors and sound interface

Worldwide there are about 285 million visually impaired persons, of which 39 million are blind and the others have low vision. Almost all systems designed to assist them are quite complex and expensive, but most blind persons do not have advanced technical assistance and they are rather poor. We are therefore developing a low-cost navigation aid which can be afforded by almost all blind persons: basically, the ultimate goal is to use only a mobile phone with a built-in camera. This aid complements the white cane, it is easily portable, and it is not a hindrance when walking with the cane. The system will have an easy and intuitive interface, yet providing assistance in local and global navigation in realtime. In this paper we present the progress concerning local navigation. Path and obstacle detection just beyond the reach of the cane is now supplemented by detection of doors in corridors. This is necessary for localization, i.e., for developing a better impression of the environment and for finding a specific room. A sophisticated sound interface can assist the user for centering on paths like sidewalks and corridors, alerting to looming obstacles for avoiding them.

Indoor Localization and Navigation for Blind Persons using Visual Landmarks and a GIS

In an unfamiliar environment we spot and explore all available information which might guide us to a desired location. This largely unconscious processing is done by our trained sensory and cognitive systems. These recognize and memorize sets of landmarks which allow us to create a mental map of the environment, and this map enables us to navigate by exploiting very few but the most important landmarks stored in our memory. We present a system which integrates a geographic information system of a building with visual landmarks for localizing the user in the building and for tracing and validating a route for the users navigation. Hence, the developed system complements the white cane for improving the users autonomy during indoor navigation. Although designed for visually impaired persons, the system can be used by any person for wayfinding in a complex building.

Computer vision and GIS for the navigation of blind persons in buildings

This paper presents a system which integrates a geographic information system of a building with computer vision. It uses only one camera, for example, the one of a mobile phone. Visual landmarks, such as frontal and lateral doors, stairs, signs, and fire extinguishers, are employed for localizing the user in the building and for tracing and validating a route for the user’s navigation. The developed system clearly improves the autonomy of persons with a very low vision during indoor navigation.

CAMBADA Soccer Team: from Robot Architecture to Multiagent Coordination

Robotic soccer is nowadays a popular research domain in the area of multi-robot systems. RoboCup is an international joint project to promote research in artificial intelligence, robotics and related fields. RoboCup chose soccer as the main problem aiming at innovations to be applied for socially relevant problems. It includes several competition leagues, each one with a specific emphasis, some only at software level, others at both hardware and software, with single or multiple agents, cooperative and competitive. In the context of RoboCup, the Middle Size League (MSL) is one of the most challenging. In this league, each team is composed of up to 5 robots with a maximum size of 50cm× 50cm, 80cm height and a maximumweight of 40Kg, playing in a field of 18m× 12m. The rules of the game are similar to the official FIFA rules, with minor changes required to adapt them for the playing robots CAMBADA, Cooperative Autonomous Mobile roBots with Advanced Distributed Architecture, is the MSL Soccer team from the University of Aveiro. The project started in 2003, coordinated by the Transverse Activity on Intelligent Robotics group of the Institute of Electronic and Telematic Engineering of Aveiro (IEETA). This project involves people working on several areas for building the mechanical structure of the robot, its hardware architecture and controllers (Almeida et al., 2002; Azevedo et al., 2007) and the software development in areas such as image analysis and processing (Caleiro et al., 2007; Cunha et al., 2007; Martins et al., 2008; Neves et al., 2007; 2008), sensor and information fusion (Silva et al., 2008; 2009), reasoning and control (Lau et al., 2008), cooperative sensing approach based on a Real-Time Database (Almeida et al., 2004), communications among robots (Santos et al., 2009; 2007) and the development of an efficient basestation. The main contribution of this chapter is to present the new advances in the areas described above involving the development of an MSL team of soccer robots, taking the example of the CAMBADA team that won the RoboCup 2008 and attained the third place in the last edition of the MSL tournament at RoboCup 2009. CAMBADA also won the last three editions

Fast cortical keypoints for real-time object recognition

Best-performing object recognition algorithms employ a large number features extracted on a dense grid, so they are too slow for real-time and active vision. In this paper we present a fast cortical keypoint detector for extracting meaningful points from images. It is competitive with state-of-the-art detectors and particularly well-suited for tasks such as object recognition. We show that by using these points we can achieve state-of-the-art categorization results in a fraction of the time required by competing algorithms.