We propose a method for understanding a room from a single spherical image, i.e., reconstructing and identifying structural planes forming the ceiling, the floor, and the walls in a room. A spherical image records the light that falls onto a single viewpoint from all directions and does not require correlating geometrical information from multiple images, which facilitates robust and precise reconstruction of the room structure. In our method, we detect line segments from a given image, and classify them into two groups: segments that form the boundaries of the structural planes and those that do not. We formulate this problem as a higher-order energy minimization problem that combines the various measures of likelihood that one, two, or three line segments are part of the boundary. We minimize the energy with graph cuts to identify segments forming boundaries, from which we estimate structural the planes in 3D. Experimental results on synthetic and real images confirm the effectiveness of the proposed method.

We present an approach for the detection of buildings in multispectral satellite images. Unlike 3-channel RGB images, satellite imagery contains additional channels corresponding to different wavelengths. Approaches that do not use all channels are unable to fully exploit these images for optimal performance. Furthermore, care must be taken due to the large bias in classes, e.g., most of the Earth is covered in water and thus it will be dominant in the images. Our approach consists of training a Convolutional Neural Network (CNN) from scratch to classify multispectral image patches taken by satellites as whether or not they belong to a class of buildings. We then adapt the classification network to detection by converting the fully-connected layers of the network to convolutional layers, which allows the network to process images of any resolution. The dataset bias is compensated by subsampling negatives and tuning the detection threshold for optimal performance. We have constructed a new dataset using images from the Landsat 8 satellite for detecting solar power plants and show our approach is able to significantly outperform the state-of-the-art. Furthermore, we provide an indepth evaluation of the seven different spectral bands provided by the satellite images and show it is critical to combine them to obtain good results.

We propose a HOG-based hand gesture recognition system running on a mobile device. Input data is a video of hand gesture taken by a mobile device. The input data is compared with a database storing hand gesture images, which was synthesized with rotation variation. The comparison is done based on their HOG features and the gesture corresponding to the best-matched image is returned as the result. The recognition algorithm is implemented on a client-server system. The proposed system is applied to American Sign Language (ASL) alphabet recognition problem. The experimental results show that the proposed recognition algorithm improves HOG's robustness under rotation change and compare processing time with different network configurations.

Super resolution is a technique to recover a high resolution image from a low resolution image. We develop a variational super resolution method for the subpixel accurate optical flow computation using variational optimisation. We combine variational super resolution and the variational optical flow computation for the super resolution optical flow computation.

Introduction In this paper, we develop an algorithm for the detection of circles from an image captured by a monocular omnidirectional camera system. We assume that an image captured by an omnidirectional camera system is normalised to a spherical image, the image on the unit sphere. Using this geometrical property of the omnidirectional images, we introduce a method for marker-based positioning and navigation of autonomous mobile robots which mounts a monocular omnidirectional camera system. We first clarify the geometric properties of the spherical image of a circle marker placed on the ground plane, and we show that, for the detection of a plane from circle markers, we are required to capture at least two coplanar circle markers. We prove that the image of a circle on the spherical image is a fourth-order algebraic curve, which is the intersection of a sphere and an oblique elliptic cone. For the detection of marker images on the spherical images, we introduce a method for transforming the detection of this fourth-order algebraic curve to the detection of a spatial conic, which is quadric. Second, we develop a voting method for the extraction of images of planar circle markers on a spherical image, using the spatial-quadric detection strategy. Finally, using the assumption for the geometrical configuration of the camera system and the circle markers on the plane on which the robot moves, we derive a positioning algorithm. This positioning method for the robot mounting a monocular omnidirectional camera system allows us to navigate a robot using our circle-detection algorithm. We show some numerical examples both for synthetic and real images.