Integrating shape and texture for hand verification

Author name used in this publication: Ajay KumarAuthor name used in this publication: David ZhangRefereed conference paper2004-2005 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Shape and deformation measurement using heterodyne range imaging technology

Range imaging is emerging as a promising alternative technology for applications that require non-contact visual inspection of object deformation and shape. Previously, we presented a solid-state full-field heterodyne range imaging device capable of capturing three-dimensional images with sub-millimetre range resolution. Using a heterodyne indirect time-of-flight configuration, this system simultaneously measures distance (and intensity), for each pixel in a cameras field of view. In this paper we briefly describe our range imaging system, and its principle of operation. By performing measurements on several metal objects, we demonstrate the potential capabilities of this technology for surface profiling and deformation measurement. In addition to verifying system performance, the reported examples highlight some important system limitations. With these in mind we subsequently discuss the further developments required to enable the use of this device as a robust and practical tool in non-destructive testing and measurement applications

Direct Measurement of the Out-of-Plane Spin Texture in the Dirac Cone Surface State of a Topological Insulator

We have performed spin- and angle-resolved photoemission spectroscopy of Bi2Te3 and present the first direct evidence for the existence of the out-of-plane spin component on the surface state of a topological insulator. We found that the magnitude of the out-of-plane spin polarization on a hexagonally deformed Fermi surface (FS) of Bi2Te3 reaches maximally 25% of the in-plane counterpart while such a sizable out-of-plane spin component does not exist in the more circular FS of TlBiSe2, indicating that the hexagonal deformation of the FS is responsible for the deviation from the ideal helical spin texture. The observed out-of-plane polarization is much smaller than that expected from existing theory, suggesting that an additional ingredient is necessary for correctly understanding the surface spin polarization in Bi2Te3.Comment: 4 pages, 3 figure

A survey of visual preprocessing and shape representation techniques

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)

From Multiview Image Curves to 3D Drawings

Reconstructing 3D scenes from multiple views has made impressive strides in recent years, chiefly by correlating isolated feature points, intensity patterns, or curvilinear structures. In the general setting - without controlled acquisition, abundant texture, curves and surfaces following specific models or limiting scene complexity - most methods produce unorganized point clouds, meshes, or voxel representations, with some exceptions producing unorganized clouds of 3D curve fragments. Ideally, many applications require structured representations of curves, surfaces and their spatial relationships. This paper presents a step in this direction by formulating an approach that combines 2D image curves into a collection of 3D curves, with topological connectivity between them represented as a 3D graph. This results in a 3D drawing, which is complementary to surface representations in the same sense as a 3D scaffold complements a tent taut over it. We evaluate our results against truth on synthetic and real datasets.Comment: Expanded ECCV 2016 version with tweaked figures and including an overview of the supplementary material available at multiview-3d-drawing.sourceforge.ne

Classification of Humans into Ayurvedic Prakruti Types using Computer Vision

Ayurveda, a 5000 years old Indian medical science, believes that the universe and hence humans are made up of five elements namely ether, fire, water, earth, and air. The three Doshas (Tridosha) Vata, Pitta, and Kapha originated from the combinations of these elements. Every person has a unique combination of Tridosha elements contributing to a person’s ‘Prakruti’. Prakruti governs the physiological and psychological tendencies in all living beings as well as the way they interact with the environment. This balance influences their physiological features like the texture and colour of skin, hair, eyes, length of fingers, the shape of the palm, body frame, strength of digestion and many more as well as the psychological features like their nature (introverted, extroverted, calm, excitable, intense, laidback), and their reaction to stress and diseases. All these features are coded in the constituents at the time of a person’s creation and do not change throughout their lifetime. Ayurvedic doctors analyze the Prakruti of a person either by assessing the physical features manually and/or by examining the nature of their heartbeat (pulse). Based on this analysis, they diagnose, prevent and cure the disease in patients by prescribing precision medicine. This project focuses on identifying Prakruti of a person by analysing his facial features like hair, eyes, nose, lips and skin colour using facial recognition techniques in computer vision. This is the first of its kind research in this problem area that attempts to bring image processing into the domain of Ayurveda