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Depth estimation and semantic segmentation are two fundamental problems in image understanding. While the two tasks are strongly correlated and mutually beneficial, they are usually solved separately or sequentially. Motivated by the complementary properties of the two tasks, we propose a unified framework for joint depth and semantic prediction. Given an(More)
Segmenting semantic objects from images and parsing them into their respective semantic parts are fundamental steps towards detailed object understanding in computer vision. In this paper, we propose a joint solution that tackles semantic object and part segmentation simultaneously, in which higher object-level context is provided to guide part(More)
Parsing human regions into semantic parts, e.g., body, head and arms etc., from a random natural image is challenging while fundamental for computer vision and widely applicable in industry. One major difficulty to handle such a problem is the high flexibility of scale and location of a human instance and its corresponding parts, making the parsing task(More)
Parsing human into semantic parts is crucial to human-centric analysis. In this paper, we propose a human parsing pipeline that uses pose cues, i.e., estimates of human joint locations, to provide pose-guided segment proposals for semantic parts. These segment proposals are ranked using standard appearance cues, deep-learned semantic feature, and a novel(More)
Parsing human body into semantic regions is crucial to human-centric analysis. In this paper, we propose a segment-based parsing pipeline that explores human pose information, i.e. the joint location of a human model, which improves the part proposal, accelerates the inference and regularizes the parsing process at the same time. Specifically , we first(More)
For least squares support vector machine (LS-SVM) classifier to the loss of sparseness and generalization, a pruning modeling method is proposed based on Quadratic Renyi entropy. The kernel principal component is adopted for data pre-processing, and the training set is divided randomly. Then the concept of quadratic Renyi entropy is introduced as the basis(More)
This paper studies the uncertainties in component reliability estimation and their impact on system reliability prediction. Monte Carlo simulations (i.e. sampling-based methods) are used to investigate the correlation between system complexity and component lifetime distributions. We focus on the applications where component lifetimes can be modeled by(More)
This paper introduces an approach to regularize 2.5D surface normal and depth predictions at each pixel given a single input image. The approach infers and reasons about the underlying 3D planar surfaces depicted in the image to snap predicted normals and depths to inferred planar surfaces, all while maintaining fine detail within objects. Our approach(More)
Magnetic field strengths inferred for relativistic outflows including gamma-ray bursts (GRB) and active galactic nuclei (AGN) are larger than naively expected by orders of magnitude. We present three-dimensional relativistic magnetohydrodynamics (MHD) simulations demonstrating amplification and saturation of magnetic field by a macroscopic turbulent dynamo(More)