219 research outputs found
Review: Object vision in a structured world
In natural vision, objects appear at typical locations, both with respect to visual space (e.g., an airplane in the upper part of a scene) and other objects (e.g., a lamp above a table). Recent studies have shown that object vision is strongly adapted to such positional regularities. In this review we synthesize these developments, highlighting that adaptations to positional regularities facilitate object detection and recognition, and sharpen the representations of objects in visual cortex. These effects are pervasive across various types of high-level content. We posit that adaptations to real-world structure collectively support optimal usage of limited cortical processing resources. Taking positional regularities into account will thus be essential for understanding efficient object vision in the real world
The Impact of Shape on the Perception of Euler Diagrams
Euler diagrams are often used for visualizing data collected into sets. However, there is a significant lack of guidance regarding graphical choices for Euler diagram layout. To address this deficiency, this paper asks the question `does the shape of a closed curve affect a user's comprehension of an Euler diagram?' By empirical study, we establish that curve shape does indeed impact on understandability. Our analysis of performance data indicates that circles perform best, followed by squares, with ellipses and rectangles jointly performing worst. We conclude that, where possible, circles should be used to draw effective Euler diagrams. Further, the ability to discriminate curves from zones and the symmetry of the curve shapes is argued to be important. We utilize perceptual theory to explain these results. As a consequence of this research, improved diagram layout decisions can be made for Euler diagrams whether they are manually or automatically drawn
Object Vision in a Structured World
In natural vision, objects appear at typical locations, both with respect to visual space (e.g., an airplane in the upper part of a scene) and other objects (e.g., a lamp above a table). Recent studies have shown that object vision is strongly adapted to such positional regularities. In this review we synthesize these developments, highlighting that adaptations to positional regularities facilitate object detection and recognition, and sharpen the representations of objects in visual cortex. These effects are pervasive across various types of high-level content. We posit that adaptations to real-world structure collectively support optimal usage of limited cortical processing resources. Taking positional regularities into account will thus be essential for understanding efficient object vision in the real world
Integration and Segmentation Conflict During Ensemble Coding of Aspect Ratio
The visual system often integrates information that goes together . Once information has been integrated, summary information (e.g., average emotion or average size) can be extracted; this occurs during ensemble coding. Integration thus allows for fast and efficient generalizations about sets to be made. In contrast, the visual system sometimes segments input that does not go together. For example, the perception of objects can be exaggerated away from natural category boundaries (e.g., a perfect circle is a category boundary; it is neither flat nor tall ). Segmentation allows the visual system to make quick categorical distinctions. Much of the time, integration and segmentation work in parallel, and they have most often been studied in isolation. However, investigating how these two processes operate together, and potentially even conflict, was the purpose of this dissertation. I examined the ensemble coding of aspect ratio, which is a visual feature roughly equivalent to tallness/flatness . Aspect ratio has a category boundary (e.g., a circle or square), and the perception of aspect ratio tends to be exaggerated -segmented - away from that boundary. Thus, I predicted that observers\u27 ability to integrate aspect ratio information that spanned the category boundary would be disrupted, since in those instances, integration and segmentation would be at odds. To test this prediction, observers were asked about the average aspect ratio of a set of ellipses. In two experiments, observers were less sensitive to the mean of sets that included both tall and flat ellipses, compared to sets that only included tall or flat ellipses. A third experiment confirmed that segmentation perceptually distorted the appearance of ellipses near the category boundary away from that boundary; shapes were perceived to be more extreme than they actually were. Segmentation thus made sets that included both flat and tall ellipses appear more heterogeneous than they really were, which disrupted ensemble coding. In general, these experiments provide a deeper understanding of how the visual system summarizes large sets of information, by investigating how integration interacts with, and even conflicts with, segmentation
The State-of-the-Art of Set Visualization
Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net
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SLEDGE: Sequential Labeling of Image Edges for Boundary Detection
Our goal is to detect boundaries of objects or surfaces
occurring in an arbitrary image. We present a new approach
that discovers boundaries by sequential labeling of
a given set of image edges. A visited edge is labeled as
on or off a boundary, based on the edge’s photometric and
geometric properties, and evidence of its perceptual grouping
with already identified boundaries. We use both local
Gestalt cues (e.g., proximity and good continuation), and
the global Helmholtz principle of non-accidental grouping.
A new formulation of the Helmholtz principle is specified
as the entropy of a layout of image edges. For boundary
discovery, we formulate a new, policy iteration algorithm,
called SLEDGE. Training of SLEDGE is iterative. In each
training image, SLEDGE labels a sequence of edges, which
induces loss with respect to the ground truth. These sequences
are then used as training examples for learning
SLEDGE in the next iteration, such that the total loss is
minimized. For extracting image edges that are input to
SLEDGE, we use our new, low-level detector. It finds salient
pixel sequences that separate distinct textures within the image.
On the benchmark Berkeley Segmentation Datasets
300 and 500, our approach proves robust and effective. We
outperform the state of the art both in recall and precision
for different input sets of image edges
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