Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd the perception of the visual world to read.

The Perception of the Visual World. By James J. Gibson. U.S.A.: Houghton Mifflin Company, 1950 (George Allen & Unwin, Ltd., London). Price 35s.

Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights

Robotics and autonomous systems

https://www.sussex.ac.uk/lifesci/insectnavigation/documents/collettchittkacollett-currbiolreview2013.pdf

Spatial memory in insect navigation.

Visual homing: an insect perspective.

Panoramic image differences can be used for view-based homing under natural outdoor conditions, because they increase smoothly with distance from a reference location (Zeil et al., J Opt Soc Am A 20(3):450–469, 2003). The particular shape, slope and depth of such image difference functions (IDFs) recorded at any one place, however, depend on a number of factors that so far have only been qualitatively identified. Here we show how the shape of difference functions depends on the depth structure and the contrast of natural scenes, by quantifying the depth- distribution of different outdoor scenes and by comparing it to the difference functions calculated with differently processed panoramic images, which were recorded at the same locations. We find (1) that IDFs and catchment areas become systematically wider as the average distance of objects increases, (2) that simple image processing operations—like subtracting the local mean, difference-of-Gaussian filtering and local contrast normalization—make difference functions robust against changes in illumination and the spurious effects of shadows, and (3) by comparing depth-dependent translational and depth-independent rotational difference functions, we show that IDFs of contrast-normalized snapshots are predominantly determined by the depth-structure and possibly also by occluding contours in a scene. We propose a model for the shape of IDFs as a tool for quantitative comparisons between the shapes of these functions in different scenes.

/pdf/depth-contrast-and-view-based-homing-in-outdoor-scenes-3fe7i0g2wg.pdf

Depth, contrast and view-based homing in outdoor scenes

How wasps acquire and use views for homing

Honeybees turn their thorax and thus their flight motor to change direction or to fly sideways. If the bee's head were fixed to its thorax, such movements would have great impact on vision. Head movements independent of thorax orientation can stabilize gaze and thus play an important and active role in shaping the structure of the visual input the animal receives. Here, we investigate how gaze and flight control interact in a homing task. We use high-speed video equipment to record the head and body movements of honeybees approaching and departing from a food source that was located between three landmarks in an indoor flight arena. During these flights, the bees' trajectories consist of straight flight segments combined with rapid turns. These short and fast yaw turns (‘saccades’) are in most cases accompanied by even faster head yaw turns that start about 8 ms earlier than the body saccades. Between saccades, gaze stabilization leads to a behavioural elimination of rotational components from the optical flow pattern, which facilitates depth perception from motion parallax.

/pdf/the-fine-structure-of-honeybee-head-and-body-yaw-movements-2t286v9c34.pdf

The fine structure of honeybee head and body yaw movements in a homing task

SUMMARY Visual landmarks guide humans and animals including insects to a goal location. Insects, with their miniature brains, have evolved a simple strategy to find their nests or profitable food sources; they approach a goal by finding a close match between the current view and a memorised retinotopic representation of the landmark constellation around the goal. Recent implementations of such a matching scheme use raw panoramic images (‘image matching’) and show that it is well suited to work on robots and even in natural environments. However, this matching scheme works only if relevant landmarks can be detected by their contrast and texture. Therefore, we tested how honeybees perform in localising a goal if the landmarks can hardly be distinguished from the background by such cues. We recorded the honeybees9 flight behaviour with high-speed cameras and compared the search behaviour with computer simulations. We show that honeybees are able to use landmarks that have the same contrast and texture as the background and suggest that the bees use relative motion cues between the landmark and the background. These cues are generated on the eyes when the bee moves in a characteristic way in the vicinity of the landmarks. This extraordinary navigation performance can be explained by a matching scheme that includes snapshots based on optic flow amplitudes (‘optic flow matching’). This new matching scheme provides a robust strategy for navigation, as it depends primarily on the depth structure of the environment.

/pdf/goal-seeking-in-honeybees-matching-of-optic-flow-snapshots-3w9td61o4z.pdf

Wolfgang Stürzl

Papers

Depth, contrast and view-based homing in outdoor scenes

How wasps acquire and use views for homing

The fine structure of honeybee head and body yaw movements in a homing task

Goal seeking in honeybees: matching of optic flow snapshots?

Efficient visual homing based on Fourier transformed panoramic images