A coarse-to-fine IP-driven registration for pose estimation from single ultrasound image

A fast registration making use of implicit polynomial (IP) models is helpful for the real-time pose estimation from single clinical free-hand Ultrasound (US) image, because it is superior in the areas such as robustness against image noise, fast registration without enquiring correspondences, and fast IP coefficient transformation. However it might lead to the lack of accuracy or failure registration.In this paper, we present a novel registration method based on a coarse-to-fine IP representation. The approach starts from a high-speed and reliable registration with a coarse (of low degree) IP model and stops when the desired accuracy is achieved by a fine (of high degree) IP model. Over the previous IP-to-point based methods our contributions are: (i) keeping the efficiency without requiring pair-wised correspondences, (ii) enhancing the robustness, and (iii) improving the accuracy. The experimental result demonstrates the good performance of our registration method and its capabilities of overcoming the limitations of unconstrained freehand ultrasound data, resulting in fast, robust and accurate registration.     

Color restoration method based on spectral information using normalized cut

This paper proposes a novel method for color restoration that can effectively apply accurate color based on spectral information to a segmented image using the normalized cut technique. Using the proposed method, we can obtain a digital still camera image and spectral information in different environments. Also, it is not necessary to estimate reflectance spectra using a spectral database such as other methods. The synthesized images are accurate and high resolution. The proposed method effectively works in making digital archive contents. Some experimental results are demonstrated in this paper.     

Synthesis of polyamide-hydroxyapatite nanocomposites

Simultaneous one-pot syntheses of PA66 and HAp were carried out by extracting H₂O and CO₂ from PA66 monomers and HAp raw materials, respectively, resulting in the formation of a polyamide (PA) 66-hydroxyapatite (HAp) nanocomposite. During the process, a spherical nano-sized HAp particle was precipitated following dissolution of micro-sized CaHPO₄・2H₂O. The PA66 monomers were subsequently adsorbed onto the generated HAp product. Some of the adsorbed PA66 monomers formed a bound polymer on HAp, and an increase in the adhesiveness of the PA66-HAp interface was observed as the polymerization progressed. During this process, the synthesis of a nanocomposite from a micro-sized raw material and creation of an autonomous strong interface between the matrix and filler was achieved. In addition, the shape of the resultant HAp was controllable and could be modified to needle shape by the addition of F⁻ and Mg²⁺ ions to the raw material. HAp could also be changed to plate shape via octa-calcium phosphate (OCP). Notably, during the synthesis, the filler shape of the nanocomposite could be controlled to 0D (particle), 1D (needle), and 2D (plate).     

Determining surface orientations of transparent objects based on polarization degrees in visible and infrared wavelengths

Techniques for modeling an object through observation are very important in object recognition and virtual reality. A wide variety of techniques have been developed for modeling objects with opaque surfaces, whereas less attention has been paid to objects with transparent surfaces. A transparent surface has only surface reflection; it has little body reflection. We present a new method for obtaining surface orientations of transparent surfaces through analysis of the degree of polarization in surface reflection and emission in visible and far-infrared wavelengths, respectively. This parameter, the polarization degree of reflected light at the visible wavelengths, is used for determining the surface orientation at a surface point. The polarization degree at visible wavelengths provides two possible solutions, and the proposed method uses the polarization degree at far-infrared wavelengths to resolve this ambiguity.     

Illumination normalization with time-dependent intrinsic images for video surveillance

Variation in illumination conditions caused by weather, time of day, etc., makes the task difficult when building video surveillance systems of real world scenes. Especially, cast shadows produce troublesome effects, typically for object tracking from a fixed viewpoint, since it yields appearance variations of objects depending on whether they are inside or outside the shadow. In this paper, we handle such appearance variations by removing shadows in the image sequence. This can be considered as a preprocessing stage which leads to robust video surveillance. To achieve this, we propose a framework based on the idea of intrinsic images. Unlike previous methods of deriving intrinsic images, we derive time-varying reflectance images and corresponding illumination images from a sequence of images instead of assuming a single reflectance image. Using obtained illumination images, we normalize the input image sequence in terms of incident lighting distribution to eliminate shadowing effects. We also propose an illumination normalization scheme which can potentially run in real time, utilizing the illumination eigenspace, which captures the illumination variation due to weather, time of day, etc., and a shadow interpolation method based on shadow hulls. This paper describes the theory of the framework with simulation results and shows its effectiveness with object tracking results on real scene data sets.     

The separation of reflected and transparent layers from real-world image sequence

This paper presents an optimal method for the separation of reflected and transparent layers from real-world scene images. Whereas past research has been applied to indoor environments and static cameras, our technique can be used for outdoor scenes and motion cameras. The method is based on spatio-temporal analysis, especially using epipolar plane images (EPI). The edge and color information of EPI has been used to segment the areas on EPIs efficiently and separate the reflected and transparent layers. This method can be used for refining building textures by removing reflections from captured images for the purpose of city modeling.     

Camera Spectral Sensitivity and White Balance Estimation from Sky Images

Photometric camera calibration is often required in physics-based computer vision. There have been a number of studies to estimate camera response functions (gamma function), and vignetting effect from images. However less attention has been paid to camera spectral sensitivities and white balance settings. This is unfortunate, since those two properties significantly affect image colors. Motivated by this, a method to estimate camera spectral sensitivities and white balance setting jointly from images with sky regions is introduced. The basic idea is to use the sky regions to infer the sky spectra. Given sky images as the input and assuming the sun direction with respect to the camera viewing direction can be extracted, the proposed method estimates the turbidity of the sky by fitting the image intensities to a sky model. Subsequently, it calculates the sky spectra from the estimated turbidity. Having the sky \(RGB\) values and their corresponding spectra, the method estimates the camera spectral sensitivities together with the white balance setting. Precomputed basis functions of camera spectral sensitivities are used in the method for robust estimation. The whole method is novel and practical since, unlike existing methods, it uses sky images without additional hardware, assuming the geolocation of the captured sky is known. Experimental results using various real images show the effectiveness of the method.     

Shape from regular patterns

This paper describes an algorithm to obtain local surface orientation from the apparent surface-pattern distortion in an image.We propose a spherical projection to model perspective imaging. A mapping is defined based on the measurement of the local distortions of a repeated known texture pattern due to the image projection. This mapping maps an apparent shape on the image sphere to a locus of possible surface orientations on the Gaussian sphere.An iterative constraint propagation algorithm with the orientations at occluding boundaries reduces possible surface orientations to a unique orientation. This algorithm can recover local surface orientation as well as interpolate surface orientations where no information is available. This algorithm is applied to a real image to demonstrate its performance.     

A note on two-dimensional probabilistic finite automata

This note introduces two-dimensional probabilistic finite automata (2-pfa's), and investigates several properties of them. We first show that the class of sets recognized by 2-pfa's with bounded error probability, 2-PFA, is incomparable with the class of sets accepted by two-dimensional alternating finite automata. We then show that 2-PFA is not closed under row catenation, column catenation, row +, and column + operations in Siromoney et al. (G. Siromoney, R. Siromoney, K. Krithivasan, Inform. and Control 22 (1973) 447).