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Top-down class-specific knowledge is crucial for accurate image segmentation, as low-level color and texture cues alone are insufficient to identify true object boundaries. However, existing methods such as conditional random field models (CRFs) generally impose the class-specific knowledge only at the “node” level, evaluating class membership probabilities at the (super)pixels that define the random...
This paper introduces a new segmentation-based approach for disparity optimization in stereo vision. The main contribution is a significant enhancement of the matching quality at occlusions and textureless areas by segmenting either the left color image or the calculated texture image. The local cost calculation is done with a Census-based correlation method and is compared with standard sum of absolute...
The application of digital technologies to culture history preservation and interpretation is a rapidly growing field that has captured the imagination of many. In this work, we explore the application of image classification systems for use in the reconstruction of archaeologically excavated 18th and 19th-century ceramic fragments. In specific, we investigate the classification of thin-shell ceramics...
The segmentation of tissues in whole-slide histology images is a necessary step for the morphological analyses of tissues and cellular structures. Previous works have demonstrated the potential of two-point correlation functions (TPCF) as features for tissue segmentation, however the feature space is not yet well understood and computational methods are lacking. This paper illustrates several fundamental...
Structured Light is a well-known method for acquiring 3D surface data. Single-shot methods are restricted to the use of only one pattern, but make it possible to measure even moving objects with simple and compact hardware setups. However, they typically operate at lower resolutions and are less robust than multi-shot approaches. This paper presents an algorithm for decoding images of a scene illuminated...
In this paper, we present a new perceptual grouping algorithm using sparse semi-supervised learning (SSSL). In SSSL, KD-tree is used for effective representation and efficient retrieval. SSSL performs both transductive and inductive inference with a new dynamic graph concept. The perceptual grouping problem is tackled using SSSL to group different patterns into one object and separate similar patterns...
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