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Occlusions are common in real world scenes and are a major obstacle to robust object detection. In this paper, we present a method to coherently reason about occlusions on many types of detectors. Previous approaches primarily enforced local coherency ...

In this paper, we present our recent sensor fusion approaches to obtain high-quality 3D information. We first discuss two fusion methods that combine geometric and photometric information. The first method, multiview photometric stereo, reconstructs the ...

This paper mainly introduces the techniques required for a future system, called Magic Mirror. Imagine when you wake up in the morning and prepare for the coming day, the Magic Mirror will automatically recommend to you the most appropriate styles of ...

Real-world data sets are highly complicated. They can contain a lot of features, and may involve multiple learning tasks with intrinsically or explicitly represented task relationships. In this paper, we briefly discuss several recent approaches that ...

Gait recognition is a method of biometric person authentication from his/her unconscious walking manner. Unlike the other biometrics such as DNA, fingerprint, vein, and iris, the gait can be recognized even at a distance from a camera without subjects' ...

Recently, the Fisher vector representation of local features has attracted much attention because of its effectiveness in both image classification and image retrieval. Another trend in the area of image retrieval is the use of binary feature such as ...

In this paper, we present a very fast method for performing content-based image retrieval of JPEG compressed images. Our method works directly in the compressed domain of JPEG but in contrast to previous techniques does not require to undo the entropy ...

Image tagging plays a critical role in image indexing and retrieval and it has gained more and more attention along with the increasing availability of large quantities of web images. However, most of current tagging methods only utilize single feature ...

This paper reports on experiments for indoor image-based location recognition. The basic method makes use of three stages: visual bag-of-words for ranking, a voting method, and a final verification method, if the voting method does not produce a ...

Object-level view image retrieval for robot vision applications has been actively studied recently, as they can provide semantic and compact method for efficient scene matching. In existing frameworks, landmark objects are extracted from an input view ...

This paper proposes a method to localize a mobile camera by searching corresponding points between the mobile camera image and aerial images yielded by GIS database. A same object differently appears in the two images, because the mobile camera images ...

Aircraft detection is a difficult task in high-resolution remote sensing images, due to the variable sizes, colors, orientations and complex backgrounds. In this paper, an effective aircraft detection method is proposed which exactly locates the object ...

Finite mixture modeling has been widely used for image segmentation. However, since it takes no account of the spatial correlation among pixels in its standard form, its segmentation accuracy can be heavily deteriorated by noise in images. To improve ...

Extracting planar surfaces from 3D point clouds is an important and challenging step for generating building models as the obtained data are always noisy, missing and unorganised. In this paper, we present a novel graph Laplacian regularized K-planes ...

In the detection of human from image using statistical learning methods, the labor cost of collecting training samples and the time cost for retraining to match the target scene are major issues. One method to reduce the work involved in sample ...

This paper presents a novel method, namely nuclear norm based 2DPCA (N-2DPCA), for image feature extraction. Unlike the conventional 2DPCA, N-2DPCA uses a nuclear norm based reconstruction error criterion. The criterion is minimized by converting the ...

In order to efficiently select a discriminative and complementary subset from a large feature pool, we propose a two-stage learning strategy considering both samples and their features simultaneously, namely sample selection and feature selection. The ...