Matthew Turk

Bio: Matthew Turk is an academic researcher from Toyota Technological Institute at Chicago. The author has contributed to research in topics: Augmented reality & Facial recognition system. The author has an hindex of 55, co-authored 198 publications receiving 30972 citations. Previous affiliations of Matthew Turk include Massachusetts Institute of Technology & University of California.

Hybrid orbiting-to-photos in 3D reconstructed visual reality

Abstract: Virtually navigating through photos from a 3D image-based reconstruction has recently become very popular in many applications. In this paper, we consider a particular virtual travel maneuver that is important for this type of virtual navigation---orbiting to photos that can see a point-of-interest (POI). The main challenge with this particular type of orbiting is how to give appropriate feedback to the user regarding the existence and information of each photo in 3D while allowing the user to manipulate three degrees-of-freedom (DoF) for orbiting around the POI. We present a hybrid approach that combines features from two baselines---proxy plane and thumbnail approaches. Experimental results indicate that users rated our hybrid approach more favorably for several qualitative questionnaire statements, and that the hybrid approach is preferred over both baselines for outdoor scenes.

Parameterised structured light imaging for depth edge detection (Image and vision processing and display technology)

Abstract: This reported research features parameterised structured light imaging that is practically useful for detecting depth edges. Given input parameters such as the range of distances of an object from the camera/projector and minimum detectable depth difference, the presented method is capable of computing an optimal pattern width and the number of structured light images that are needed to detect all depth edges in the specified range of distances that have at least the given detectable depth difference. Application of this parameter control to the detection of silhouette edges for visual hull reconstruction shows the effectiveness of the method.

Poster: Investigating viewpoint visualizations for click & go navigation

Abstract: We present an investigation of viewpoint visualizations for “Click & Go” 3D navigation interfaces based on a pre-populated set of viewpoints. These scenarios often occur in 3D navigation systems that are based on sets of photos and possibly an underlying 3D reconstruction. Given these photos (and the 3D reconstruction), how does one most effectively navigate through this environment? Existing systems often employ Click & Go interfaces which allow users to navigate with one click of the mouse or tap of the finger. In this work, we investigate viewpoint visualizations for such Click & Go interfaces, describing a preliminary user study and providing valuable insights into Click & Go and its viewpoint visualizations.

Locating binary features for keypoint recognition using noncooperative games

Abstract: Many applications in computer vision rely on determining the correspondence between two images that share an overlapping region. One way to establish this correspondence is by matching binary keypoint descriptors from both images. Although, these descriptors are efficiently computed with bits produced by an arrangement of binary features (pattern), their matching performance falls short in comparison with other more elaborated descriptors such as SIFT. We present an approach based on noncooperative game theory for computing the locations of every binary feature in a pattern, improving the performance of binary-feature-based matchers. We propose a simultaneous two-player zero-sum game in which a maximizer wants to increase a payoff by selecting the possible locations for the features; a minimizer wants to decrease the payoff by selecting a pair of keypoints to confuse the maximizer; and the payoff matrix is computed from the pixel intensities across the pixel neighborhood of the keypoints. We use the best locations from the obtained maximizer's optimal policy for locating every binary feature in the pattern. Our evaluation of this approach coupled with Ferns shows an improvement in matching keypoints, in particular those with similar texture. Moreover, our approach improves the matching performance when fewer bits are required.

A global geometric framework for nonlinear dimensionality reduction.

Abstract: Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar spectra, or human gene distributions, regularly confront the problem of dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. The human brain confronts the same problem in everyday perception, extracting from its high-dimensional sensory inputs-30,000 auditory nerve fibers or 10(6) optic nerve fibers-a manageably small number of perceptually relevant features. Here we describe an approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set. Unlike classical techniques such as principal component analysis (PCA) and multidimensional scaling (MDS), our approach is capable of discovering the nonlinear degrees of freedom that underlie complex natural observations, such as human handwriting or images of a face under different viewing conditions. In contrast to previous algorithms for nonlinear dimensionality reduction, ours efficiently computes a globally optimal solution, and, for an important class of data manifolds, is guaranteed to converge asymptotically to the true structure.

Eigenfaces vs. Fisherfaces: recognition using class specific linear projection

Abstract: We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space-if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed "Fisherface" method has error rates that are lower than those of the eigenface technique for tests on the Harvard and Yale face databases.

Learning the parts of objects by non-negative matrix factorization

Abstract: Is perception of the whole based on perception of its parts? There is psychological and physiological evidence for parts-based representations in the brain, and certain computational theories of object recognition rely on such representations. But little is known about how brains or computers might learn the parts of objects. Here we demonstrate an algorithm for non-negative matrix factorization that is able to learn parts of faces and semantic features of text. This is in contrast to other methods, such as principal components analysis and vector quantization, that learn holistic, not parts-based, representations. Non-negative matrix factorization is distinguished from the other methods by its use of non-negativity constraints. These constraints lead to a parts-based representation because they allow only additive, not subtractive, combinations. When non-negative matrix factorization is implemented as a neural network, parts-based representations emerge by virtue of two properties: the firing rates of neurons are never negative and synaptic strengths do not change sign.

Robust Face Recognition via Sparse Representation

Abstract: We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.

Learning parts of objects by non-negative matrix factorization

Abstract: Is perception of the whole based on perception of its parts? There is psychological and physiological evidence for parts-based representations in the brain, and certain computational theories of object recognition rely on such representations. But little is known about how brains or computers might learn the parts of objects. Here we demonstrate an algorithm for non-negative matrix factorization that is able to learn parts of faces and semantic features of text. This is in contrast to other methods, such as principal components analysis and vector quantization, that learn holistic, not parts-based, representations. Non-negative matrix factorization is distinguished from the other methods by its use of non-negativity constraints. These constraints lead to a parts-based representation because they allow only additive, not subtractive, combinations. When non-negative matrix factorization is implemented as a neural network, parts-based representations emerge by virtue of two properties: the firing rates of neurons are never negative and synaptic strengths do not change sign.