Multiple Adverse Weather Conditions Adaptation for Object Detection via Causal Intervention.

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.

Segmentation and Pore Structure Estimation in SEM Images of Tissue Engineering Scaffolds Using Genetic Algorithm.

A python computer package is developed to segment and analyze scanning electron microscope (SEM) images of scaffolds for bone tissue engineering. The method requires only a portion of an SEM image to be labeled and used for training. The algorithm is then able to detect the pore characteristics for other SEM images acquired at different ambient conditions from different scaffolds with the same material as the labeled image.

Effect of Mold Geometry on Pore Size in Freeze-Cast Chitosan-Alginate Scaffolds for Tissue Engineering.

Freeze-casting is a popular method to produce biomaterial scaffolds with highly porous structures. The pore structure of freeze-cast biomaterial scaffolds is influenced by processing parameters but has mostly been controlled experimentally. A mathematical model integrating Computational Fluid Dynamics with Population Balance Model was developed to predict average pore size (APS) of 3D porous chitosan-alginate scaffolds and to assess the influence of the geometrical parameters of mold on scaffold pore structure.

Learning Structural Representations via Dynamic Object Landmarks Discovery for Sketch Recognition and Retrieval.

State-of-the-art methods on sketch classification and retrieval are based on deep convolutional neural network to learn representations. Although deep neural networks have the ability to model images with hierarchical representations by convolution kernels, they can not automatically extract the structural representations of object categories in a human-perceptible way. Furthermore, sketch images usually have large scale visual variations caused by the styles of drawing or viewpoints, which make it difficult to develop generalized representations using the fixed computational mode of convolutional kernel.

A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes.

During the last half decade, convolutional neural networks (CNNs) have triumphed over semantic segmentation, which is one of the core tasks in many applications such as autonomous driving and augmented reality. However, to train CNNs requires a considerable amount of data, which is difficult to collect and laborious to annotate. Recent advances in computer graphics make it possible to train CNNs on photo-realistic synthetic imagery with computer-generated annotations.

Sparse One-Grab Sampling with Probabilistic Guarantees.

Sampling is an important and effective strategy in analyzing "big data," whereby a smaller subset of a dataset is used to estimate the characteristics of its entire population. The main goal in sampling is often to achieve a significant gain in the computational time. However, a major obstacle towards this goal is the assessment of the smallest sample size needed to ensure, with a high probability, a faithful representation of the entire dataset, especially when the data set is compiled of a large number of diverse structures (e.

NELasso: Group-Sparse Modeling for Characterizing Relations Among Named Entities in News Articles.

Named entities such as people, locations, and organizations play a vital role in characterizing online content. They often reflect information of interest and are frequently used in search queries. Although named entities can be detected reliably from textual content, extracting relations among them is more challenging, yet useful in various applications (e.

A Context-Driven Extractive Framework for Generating Realistic Image Descriptions.

Automatic image annotation methods are extremely beneficial for image search, retrieval, and organization systems. The lack of strict correlation between semantic concepts and visual features, referred to as the semantic gap, is a huge challenge for annotation systems. In this paper, we propose an image annotation model that incorporates contextual cues collected from sources both intrinsic and extrinsic to images, to bridge the semantic gap.

Computing Cyclotorsion in Refractive Cataract Surgery.

Goal: In refractive surgery, astigmatism-correcting treatments are generally planned with the aid of some diagnostic imaging device and often executed by some computer guided laser system. In the transition from sitting down at a diagnostic device to lying down beneath a laser system, a phenomenon known as cyclotorsion (rotation of the eye within the socket) occurs. Hence, registration between lasers and diagnostic devices is necessary.

Constrained Multi-View Video Face Clustering.

In this paper, we focus on face clustering in videos. To promote the performance of video clustering by multiple intrinsic cues, i.e.

Exploring sparseness and self-similarity for action recognition.

We propose that the dynamics of an action in video data forms a sparse self-similar manifold in the space-time volume, which can be fully characterized by a linear rank decomposition. Inspired by the recurrence plot theory, we introduce the concept of Joint Self-Similarity Volume (Joint-SSV) to model this sparse action manifold, and hence propose a new optimized rank-1 tensor approximation of the Joint-SSV to obtain compact low-dimensional descriptors that very accurately characterize an action in a video sequence. We show that these descriptor vectors make it possible to recognize actions without explicitly aligning the videos in time in order to compensate for speed of execution or differences in video frame rates.

Scene text deblurring using text-specific multiscale dictionaries.

Texts in natural scenes carry critical semantic clues for understanding images. When capturing natural scene images, especially by handheld cameras, a common artifact, i.e.

View-invariant action recognition from point triplets.

We propose a new view-invariant measure for action recognition. For this purpose, we introduce the idea that the motion of an articulated body can be decomposed into rigid motions of planes defined by triplets of body points. Using the fact that the homography induced by the motion of a triplet of body points in two identical pose transitions reduces to the special case of a homology, we use the equality of two of its eigenvalues as a measure of the similarity of the pose transitions between two subjects, observed by different perspective cameras and from different viewpoints.

Phase-shifting for nonseparable 2-D Haar wavelets.

In this paper, we present a novel and efficient solution to phase-shifting 2-D nonseparable Haar wavelet coefficients. While other methods either modify existing wavelets or introduce new ones to handle the lack of shift-invariance, we derive the explicit relationships between the coefficients of the shifted signal and those of the unshifted one. We then establish their computational complexity, and compare and demonstrate the superior performance of the proposed approach against classical interpolation tools in terms of accumulation of errors under successive shifting.

Optimal local shape description for rotationally non-symmetric optical surface design and analysis.

A local optical surface representation as a sum of basis functions is proposed and implemented. Specifically, we investigate the use of linear combination of Gaussians. The proposed approach is a local descriptor of shape and we show how such surfaces are optimized to represent rotationally non-symmetric surfaces as well as rotationally symmetric surfaces.

Application of radial basis functions to shape description in a dual-element off-axis magnifier.

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 degrees field of view.

Extension of phase correlation to subpixel registration.

In this paper, we have derived analytic expressions for the phase correlation of downsampled images. We have shown that for downsampled images the signal power in the phase correlation is not concentrated in a single peak, but rather in several coherent peaks mostly adjacent to each other. These coherent peaks correspond to the polyphase transform of a filtered unit impulse centered at the point of registration.

Autoconfiguration of a dynamic nonoverlapping camera network.

In order to monitor sufficiently large areas of interest for surveillance or any event detection, we need to look beyond stationary cameras and employ an automatically configurable network of nonoverlapping cameras. These cameras need not have an overlapping field of view and should be allowed to move freely in space. Moreover, features like zooming in/out, readily available in security cameras these days, should be exploited in order to focus on any particular area of interest if needed.

Camera calibration using symmetric objects.

This paper proposes a novel method for camera calibration using images of a mirror symmetric object. Assuming unit aspect ratio and zero skew, we show that interimage homographies can be expressed as a function of only the principal point. By minimizing symmetric transfer errors, we thus obtain an accurate solution for the camera parameters.

Subpixel estimation of shifts directly in the Fourier domain.

In this paper, 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 two-dimensional sawtooth signal. Subpixel registration can, thus, be performed directly in the Fourier domain by counting number of cycles of the phase difference matrix along each frequency axis. The subpixel portion is given by the noninteger fraction of the last cycle along each axis.

Pixelwise-adaptive blind optical flow assuming nonstationary statistics.

In this paper, we address some of the major issues in optical flow within a new framework assuming nonstationary statistics for the motion field and for the errors. Problems addressed include the preservation of discontinuities, model/data errors, outliers, confidence measures, and performance evaluation. In solving these problems, we assume that the statistics of the motion field and the errors are not only spatially varying, but also unknown.