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Most state-of-the-art object detection methods have achieved impressive perfomrace on several public benchmarks, which are trained with high definition images. However, existing detectors are often sensitive to the visual variations and out-of-distribution data due to the domain gap caused by various confounders, e.g. the adverse weathre conditions. To bridge the gap, previous methods have been mainly exploring domain alignment, which requires to collect an amount of domain-specific training samples. In this paper, we introduce a novel domain adaptation model to discover a weather condition invariant feature representation. Specifically, we first employ a memory network to develop a confounder dictionary, which stores prototypes of object features under various scenarios. To guarantee the representativeness of each prototype in the dictionary, a dynamic item extraction strategy is used to update the memory dictionary. After that, we introduce a causal intervention reasoning module to explore the invariant representation of a specific object under different weather conditions. Finally, a categorical consistency regularization is used to constrain the similarities between categories in order to automatically search for the aligned instances among distinct domains. Experiments are conducted on several public benchmarks (RTTS, Foggy-Cityscapes, RID, and BDD 100K) with state-of-the-art performance achieved under multiple weather conditions.

This paper proposes a new approach to subpixel registration, under local/global shifts or rotation, using the phase-difference matrix. We establish the exact relationship between the continuous and the discrete phase difference of two shifted images and show that their discrete phase difference is a 2-dimensional sawtooth signal. As a result, the exact shifts or rotations can be determined to subpixel or subangle accuracy by counting the number of cycles of the phase-difference matrix along the frequency axes. The subpixel portion is represented by a fraction of a cycle corresponding to the noninteger part of the shift or rotation. The rotation angle is estimated by applying our method using a polar coordinate system. The problem is formulated as an overdetermined system of equations and is solved by imposing a regularity constraint. The tradeoff for imposing the constraint is determined by exploiting the rank constraint leading to a closed-form expression for the optimal regularization parameter.

Abstract In this paper, we describe how geometrically correct and visually realistic shadows may be computed for objects composited into a single view of a target scene. Compared to traditional single view compositing methods, which either do not deal with the shadow effects or manually create the shadows for the composited objects, our approach efficiently utilizes the geometric and photometric constraints extracted from a single target image to synthesize the shadows consistent with the overall target scene for the inserted objects. In particular, we explore (i) the constraints provided by imaged scene structure, e.g. vanishing points of orthogonal directions, for camera calibration and thus explicit determination of the locations of the camera and the light source; (ii) the relatively weaker geometric constraint, the planar homology, that models the imaged shadow relations when explicit camera calibration is not possible; and (iii) the photometric constraints that are required to match the color characteristics of the synthesized shadows with those of the original scene. For each constraint, we demonstrate the working examples followed by our observations. To show the accuracy and the applications of the proposed method, we present the results for a variety of target scenes, including footage from commercial Hollywood movies and 3D video games.

We previously demonstrated that radial basis functions may be preferred as a descriptor of free-form shape for a single mirror magnifier when compared to other conventional descriptions such as polynomials [Opt. Express 16, 1583 (2008)]. A key contribution is the application of radial basis functions to describe and optimize the shape of a free-form mirror in a dual-element magnifier with the specific goal of optimizing the pupil size given a 20° field of view. We demonstrate a 12 mm exit pupil, 20° diagonal full field of view, 15.5 mm eye clearance, 1.5 arcmin resolution catadioptric dual-element magnifier design operating across the photopic visual regime. A second contribution is the explanation of why it is possible to approximate any optical mirror shape using radial basis functions.