David R. Martin

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This paper presents a database containing ‘ground truth’ segmentations produced by humans for images of a wide variety of natural scenes. We define an error measure which quantifies the consistency between segmentations of differing granularities and find that different human segmentations of the same image are highly consistent. Use of this dataset is(More)
The goal of this work is to accurately detect and localize boundaries in natural scenes using local image measurements. We formulate features that respond to characteristic changes in brightness, color, and texture associated with natural boundaries. In order to combine the information from these features in an optimal way, we train a classifier using human(More)
An Empirical Approach to Grouping and Segmentation by David Royal Martin Doctor of Philosophy in Computer Science University of California, Berkeley Professor Jitendra Malik, Co-Chair Professor David Patterson, Co-Chair This thesis presents a novel dataset of 12,000 segmentations of 1,000 natural images by 30 human subjects. The subjects marked the(More)
This paper presents a database of " ground truth " segmentations produced by humans for images of a wide variety of natural scenes. We define an error measure which quantifies the consistency between segmentations of differing granularities and find that different human segmentations of the same image are highly consistent. One of many uses of this dataset(More)
This paper studies the problem of combining region and boundary cues for natural image segmentation. We employ a large database of manually segmented images in order to learn an optimal affinity function between pairs of pixels. These pairwise affinities can then be used to cluster the pixels into visually coherent groups. Region cues are computed as the(More)
The goal of this work is to accurately detect and localize boundaries in natural scenes using local image measurements. We formulate features that respond to characteristic changes in brightness and texture associated with natural boundaries. In order to combine the information from these features in an optimal way, a classifier is trained using human(More)
Figure-ground organization refers to the visual perception that a contour separating two regions belongs to one of the regions. Recent studies have found neural correlates of figure-ground assignment in V2 as early as 10-25 ms after response onset, providing strong support for the role of local bottom-up processing. How much information about figure-ground(More)
We propose a novel global pose estimation method to detect body parts of articulated objects in images based on non-tree graph models. There are two kinds of edges defined in the body part relation graph: Strong (tree) edges corresponding to the body plan that can enforce any type of constraint, and weak (non-tree) edges that express exclusion constraints(More)
The goal of Intelligent RAM (IRAM) is to design a cost-effective computer by designing a processor in a memory fabrication process, instead of in a conventional logic fabrication process, and include memory on-chip. To design a processor in a DRAM process one must learn about the business and culture of the DRAMs, which is quite different from(More)
Many architectural ideas that appear to be useful from a hardware standpoint fail to achieve wide acceptance due to lack of compiler support. In this paper we explore the design of the VIRAM architecture from the perspective of compiler writers, describing some of the code generation problems that arise in VIRAM and their solutions in the VIRAM compiler.(More)