Detecting moving objects based on the camera attached in mobile robot is not trivial since both background and object are moving independently. For moving object detection the movement of moving object needs to be extracted by considering the background which has also changed by the ego-motion of mobile robot. Affine transformation is widely used to estimate the background transformation between images. However when using omnidirectional camera, the mixed motion of scaling, rotation and translation appears in local areas and single affine transformation is not sufficient to describe those mixed nonlinear motions. In this paper, the proposed method divides the image as grid windows and obtains each affine transform for each window. This method can obtain stable background transformation when the background has few corner features. The area of moving objects can be obtained from the background transformation-compensated frame difference using every local affine transformation for each local window. The experimental results demonstrate the proposed method is very efficient in moving object detection in mobile robot environment.

We present a new way of dual interactions between multi-display and smartphone for collaborative design review and sharing among multi-users. It provides individual and cooperative interactions in immersive and non-immersive environments to decouple private and public spaces between multi-display and smartphone. Thus, it can provide different views among multiusers and make them communicate with each other more adaptively according to the shared contents. We will show implementation results by demonstrating digital product review and multimedia interactions among multi-users.

At present, vibration feedback is widely used to improve the limited user interface of a mobile device. Despite recent advances of miniature actuator technology, a vibration motor is still a dominant actuator for commercial mobile devices. In this paper, we present a new vibration rendering method which can enhance the identification of mobile device vibrations produced by a vibration motor. Whereas the traditional method separates vibrations in voltage applied to the motor, we partition vibrations in their perceived intensity using perceptually transparent rendering. An empirical evaluation using absolute identification showed that our rendering method can improve perception performance in terms of correct identification rate and the amount of information transfer. The results suggest that perceptually transparent rendering can contribute to increasing the number of discrete vibrations that can be used for information delivery via a mobile device, e.g., for the priorities of phone calls.

Most research on image decomposition, e.g. image segmentation and image parsing, has predominantly focused on the low-level visual clues within single image and neglected the contextual information across different images. In this paper, we present a new perspective to image decomposition piloted by the multi-labels associated with individual images. Observing that the context information (i.e., local label representations of the same label are similar while those from different labels are dissimilar) exists across different images, we propose to perform image decomposition in a collective way, and then the image decomposition problem is formulated as an optimization which maximizes inter-label difference and at the same time minimizes intra-label difference of the target label representations. Such contextual image decomposition has a wide variety of applications, among which the two exemplary ones are: 1) multi-label image annotation in which the sparse coding of a query image over the bases consisting of all learned label representations naturally produces the multi-label annotation, and 2) label ranking in which the annotated labels are re-ordered according to the sparse coding coefficients on those learned label representations. It is worth noting that these two applications can be performed simultaneously via the label propagation process in sparse coding.

Categorizing visual elements is fundamentally important for autonomous mobile robots to get intelligence such as new object acquisition and topological place classification. The main problem of visual categorization is how to reduce the large intra-class variations, especially surface markings of man-made objects. In this paper, we present a robust method by introducing intermediate blurring and entropy-guided codebook selection in a bag-of-words framework. Intermediate blurring can filter out the high frequency of surface markings and provide dominant shape information. Entropy of a hypothesized codebook can provide the necessary measure for the semantic parts among training exemplars. From the first step, a generative optimal codebook for each category is learned using the MDL (minimum description length) principle guided by entropy information. From the second step, a final set of codebook is learned using the discriminative method guided by the inter-category entropy of the codebook. We select the necessary parameters through various evaluations and validate the effect of the surface marking reduction method using a Caltech-101 DB, which has large intra-class variations. Finally, we briefly introduce the impact of the method to the object categorization and segmentation problem

This paper presents a new method for real-time gesture recognition and its application. The key idea of the method is that we use multiple template models for specific feature parts of body such as arms and estimate the pose of this part by considering similarities of an input image to the models. To calculate the similarity for the part, we developed maskable template model (MTM) with which both masking and template matching are done simultaneously. As an application of this method, we realize a real-time interactive system, gesture game system, which can activate the characters of video game by gestures in real-time.

The paper presents a new idea for detecting an unknown human face in input imagery and recognizing his/her facial expression represented in the deformation of the two dimensional net, called potential net. The method deals with the facial information, faceness and expressions, as an overall pattern of the net activated by edges in a single input image of face, rather than from changes in the shape of the facial organs or their geometrical relationships. We build models of facial expressions from the deformation patterns in the potential net for face images in the training set of different expressions and then project them into emotion space. Expression of an unknown subject can be recognized from the projection of the net for the image into the emotion space. The potential net is further used to model the common human face. The mosaic method representing energy in the net is used as a template for finding candidates for the face area and the candidates are verified their faceness by projecting them into emotion space in order to select the finalist. Precise location of the face is determined by the histogram analysis of vertical and horizontal projections of edges.>

This paper presents a new method for pose estimation of the human face. The virtue of the method is the use a very simple calculation; correlation among multiple model images. It does not use any facial features such as organs or face gestalts. It is an improvement of the conventional template matching method, but to cope with deformation of facial pattern caused by its rotation or translation, some temporary model images are synthesized by using the generic S-dimensional shape model of the human face. A region of a face with various view directions is detected and then accumulated as a model image. It is arranged in "pose space", according to its estimated view direction, and the pose of the face is estimated by a simple calculation of the correlation between multiple model images accumulated by that time. An experimental result using real moving images of various human faces is shown.>

This paper presents a new method for detecting a human face, and estimating its pose while tracking it in real image sequences. The virtue of the method is that parameterized qualitative features derived from a lot of sampled facial images are introduced in the detection process, and in the face tracking process, some temporary model images of the face with various poses are synthesized by a texture mapping technique and utilized. While tracking the detected face, many model images are accumulated and the pose of the human face is estimated as a linear combination of correlations between the models.

Describes a parallel computing system and a software algorithm for realtime interaction between a human user and a synthesized moving humanlike image or agent. The realistic human-like agent appearing on a display can recognize the palm position and finger motion (finger sign) of the human user, then tracks and gazes at the hand position and changes his/her facial expression in response to the recognized finger sign in realtime. This kind of interactive agent is expected to play an important role in advanced human interfaces. To realize realtime image recognition and image synthesis, a parallel image processing and synthesis system named 'TN-VIT' has been developed.>