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Register a new userA Global Approach for Solving Edge-Matching Puzzles
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Added by
Shahar Kovalsky
Publication date
2014
fields
Informatics Engineering
and research's language is
English
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We consider apictorial edge-matching puzzles, in which the goal is to arrange a collection of puzzle pieces with colored edges so that the colors match along the edges of adjacent pieces. We devise an algebraic representation for this problem and provide conditions under which it exactly characterizes a puzzle. Using the new representation, we recast the combinatorial, discrete problem of solving puzzles as a global, polynomial system of equations with continuous variables. We further propose new algorithms for generating approximate solutions to the continuous problem by solving a sequence of convex relaxations.
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The paper proposes a solution based on Generative Adversarial Network (GAN) for solving jigsaw puzzles. The problem assumes that an image is cut into equal square pieces, and asks to recover the image according to pieces information. Conventional jigsaw solvers often determine piece relationships based on the piece boundaries, which ignore the important semantic information. In this paper, we propose JigsawGAN, a GAN-based self-supervised method for solving jigsaw puzzles with unpaired images (with no prior knowledge of the initial images). We design a multi-task pipeline that includes, (1) a classification branch to classify jigsaw permutations, and (2) a GAN branch to recover features to images with correct orders. The classification branch is constrained by the pseudo-labels generated according to the shuffled pieces. The GAN branch concentrates on the image semantic information, among which the generator produces the natural images to fool the discriminator with reassembled pieces, while the discriminator distinguishes whether a given image belongs to the synthesized or the real target manifold. These two branches are connected by a flow-based warp that is applied to warp features to correct order according to the classification results. The proposed method can solve jigsaw puzzles more efficiently by utilizing both semantic information and edge information simultaneously. Qualitative and quantitative comparisons against several leading prior methods demonstrate the superiority of our method.
We propose a new deep learning model for goal-driven tasks that require intuitive physical reasoning and intervention in the scene to achieve a desired end goal. Its modular structure is motivated by hypothesizing a sequence of intuitive steps that humans apply when trying to solve such a task. The model first predicts the path the target object would follow without intervention and the path the target object should follow in order to solve the task. Next, it predicts the desired path of the action object and generates the placement of the action object. All components of the model are trained jointly in a supervised way; each component receives its own learning signal but learning signals are also backpropagated through the entire architecture. To evaluate the model we use PHYRE - a benchmark test for goal-driven physical reasoning in 2D mechanics puzzles.
Recently, records on stereo matching benchmarks are constantly broken by end-to-end disparity networks. However, the domain adaptation ability of these deep models is quite poor. Addressing such problem, we present a novel domain-adaptive pipeline called AdaStereo that aims to align multi-level representations for deep stereo matching networks. Compared to previous methods for adaptive stereo matching, our AdaStereo realizes a more standard, complete and effective domain adaptation pipeline. Firstly, we propose a non-adversarial progressive color transfer algorithm for input image-level alignment. Secondly, we design an efficient parameter-free cost normalization layer for internal feature-level alignment. Lastly, a highly related auxiliary task, self-supervised occlusion-aware reconstruction is presented to narrow down the gaps in output space. Our AdaStereo models achieve state-of-the-art cross-domain performance on multiple stereo benchmarks, including KITTI, Middlebury, ETH3D, and DrivingStereo, even outperforming disparity networks finetuned with target-domain ground-truths.
The black hole binary properties inferred from the LIGO gravitational wave signal GW150914 posed several serious problems. The high masses and low effective spin of black hole binary can be explained if they are primordial (PBH) rather than the products of the stellar binary evolution. Such PBH properties are postulated ad hoc but not derived from fundamental theory. We show that the necessary features of PBHs naturally follow from the slightly modified Affleck-Dine (AD) mechanism of baryogenesis. The log-normal distribution of PBHs, predicted within the AD paradigm, is adjusted to provide an abundant population of low-spin stellar mass black holes. The same distribution gives a sufficient number of quickly growing seeds of supermassive black holes observed at high redshifts and may comprise an appreciable fraction of Dark Matter which does not contradict any existing observational limits. Testable predictions of this scenario are discussed.
Every GRB model where the progenitor is assumed to be a highly relativistic hadronic jet whose pions, muons and electron pair secondaries are feeding the gamma jets engine, necessarily (except for very fine-tuned cases) leads to a high average neutrino over photon radiant exposure (radiance), a ratio well above unity, though the present observed average IceCube neutrino radiance is at most comparable to the gamma in the GRB one. Therefore no hadronic GRB, fireball or hadronic thin precessing jet, escaping exploding star in tunneled or penetrarting beam, can fit the actual observations. A new model is shown here, based on a purely electronic progenitor jet, fed by neutrons (and relics) stripped from a neutron star (NS) by tidal forces of a black hole or NS companion, showering into a gamma jet. Such thin precessing spinning jets explain unsolved puzzles such as the existence of the X-ray precursor in many GRBs. The present pure electron jet model, disentangling gamma and (absent) neutrinos, explains naturally why there is no gamma GRB correlates with any simultaneous TeV IceCube astrophysical neutrinos. Rare unstable NS companion stages while feeding the jet may lead to an explosion simulating a SN event. Recent IceCube-160731A highest energy muon neutrino event with absent X-gamma traces confirms the present model expectations.
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