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The Secrets of Salient Object Segmentation
(Center for Brains, Minds and Machines (CBMM), arXiv, 2014-06-13)
In this paper we provide an extensive evaluation of fixation prediction and salient object segmentation algorithms as well as statistics of major datasets. Our analysis identifies serious design flaws of existing salient ...
Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding
(Center for Brains, Minds and Machines (CBMM), arXiv, 2014-06-15)
Recent trends in image understanding have pushed for holistic scene understanding models that jointly reason about various tasks such as object detection, scene recognition, shape analysis, contextual reasoning, and local ...
A role for recurrent processing in object completion: neurophysiological, psychophysical and computational evidence.
(Center for Brains, Minds and Machines (CBMM), arXiv, 2014-04-26)
Recognition of objects from partial information presents a significant challenge for theories of vision because it requires spatial integration and extrapolation from prior knowledge. We combined neurophysiological recordings ...
Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts
(Center for Brains, Minds and Machines (CBMM), arXiv, 2014-06-10)
Detecting objects becomes difficult when we need to deal with large shape deformation, occlusion and low resolution. We propose a novel approach to i) handle large deformations and partial occlusions in animals (as examples ...
Parsing Semantic Parts of Cars Using Graphical Models and Segment Appearance Consistency
(Center for Brains, Minds and Machines (CBMM), arXiv, 2014-06-13)
This paper addresses the problem of semantic part parsing (segmentation) of cars, i.e.assigning every pixel within the car to one of the parts (e.g.body, window, lights, license plates and wheels). We formulate this as a ...