Searching for "OBJ CUT." – sorted by Relevance.
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Torr Learning Class-specific Edges for Object Detection and Segmentation
- for object detection based on chamfer matching in §3, and then for object segmentation in §4 using the ObjCut
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Shape-Based Object Localization for Descriptive Classification
- ://ai.stanford.edu/ ∼ gaheitz/Research/Loops. 4LOOPS OBJ CUT kAS Detector Figure 3: Randomly selected outlines produced by LOOPS
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Learning to combine bottom-up and top-down segmentation
- distribution for a data-driven Monte-Carlo sampling over possible segmentations. In the OBJ-CUT algorithm [6] a
- Cited by 23 (0 self) – Add To MetaCart
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The layout consistent random field for recognizing and segmenting partially occluded objects
- ) [10] to achieve segmentation [9] or sparse part-based detection [14]. The OBJ CUT work of Kumar et al
- Cited by 17 (3 self) – Add To MetaCart
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INFORMATIONAL RETRIEVAL ASSISTED OBJECT SEGMENTATION IN VIDEO
- detection to aid segmentation has been demonstrated in the “Obj-Cut” system, [9]. Configurations of known
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The Layout Consistent Random Field for Recognizing and Segmenting Partially
- ) [10] to achieve segmentation [9] or sparse part-based detection [14]. The OBJ CUT work of Kumar et al
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Constructing semantic representations using the MDL principle
- -prep put put on verb-prep he he on subj-prep cut cut with verb-prep he he with subj-prep put put blade verb-obj
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Located hidden random fields: Learning discriminative parts for object detection
- ] or sparse part-based detection [11]. The OBJ CUT work of Kumar et al. [12] uses a discriminative model
- Cited by 5 (2 self) – Add To MetaCart
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Multiple class segmentation using a unified framework over mean-shift patches
- was proposed in [6] for visual object recognition. Kumar et al. [13] presented the OBJ CUT algorithm which
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Example Based Image Processing
- [191] and OBJ CUT [131], objects are described in terms of their shape and intensity, the Magic Wand
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