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This paper describes a method for automatic detection of contractions in the small bowel through analyzing Wireless Capsule Endoscopic images. Based on the characteristics of contraction images, a coherent procedure that includes analyzes of the temporal and spatial features is proposed. For temporal features, the image sequence is examined to detect candidate contractions through the changing number of edges and an evaluation of similarities between the frames of each possible contraction to eliminate cases of low probability. For spatial features, descriptions of the directions at the edge pixels are used to determine contractions utilizing a classification method. The experimental results show the effectiveness of our method that can detect a total of 83% of cases. Thus, this is a feasible method for developing tools to assist in diagnostic procedures in the small bowel. [ABSTRACT FROM AUTHOR]

This paper describes a new method to track people walking by matching their gait image sequences in the frequency domain. When a person walks at a distance from a camera, that person often appears and disappears due to being occluded by other people and/or objects, or by going out of the field of view. Therefore, it is important to track the person by taking correspondence of the image sequences between before and after the disappearance. In the case of tracking, the computational time is more crucial factor than that in the case of identification. We create a three-dimensional volume by piling up an image sequence of human walking. After using Fourier analysis to extract the frequency characteristics of the volume, our method computes the similarity of two volumes. We propose a method to compute their correlation of the amplitude of the principal frequencies to improve the cost of comparison. Finally, we experimentally test our method and validate that the amplitude of principal frequencies and spatial information are important to discriminate gait image sequences. [ABSTRACT FROM AUTHOR]

Gait analyses have recently gained attention as methods of identification of individuals at a distance from a camera. However, appearance changes due to view direction changes cause difficulties for gait recognition systems. Here, we propose a method of gait recognition from various view directions using frequency-domain features and a view transformation model. We first construct a spatio-temporal silhouette volume of a walking person and then extract frequency-domain features of the volume by Fourier analysis based on gait periodicity. Next, our view transformation model is obtained with a training set of multiple persons from multiple view directions. In a recognition phase, the model transforms gallery features into the same view direction as that of an input feature, and so the features match each other. Experiments involving gait recognition from 24 view directions demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Examination inside an intestine by an endoscope is difficult and time consuming, because the whole image of the intestine cannot be taken at one time due to the limited field of view. Thus, it is necessary to generate a dissection image, which can be obtained by extending the image of an intestine. We acquire an annular image sequence with an omnidirectional or wide-angle camera, and then generate the dissection image by mosaicing the image sequence. Though usual mosaicing techniques transform an image by perspective or affine transformations, these are not suitable for our situation because the target object is a generalized cylinder and the camera motion is unknown a priori. Therefore, we propose a novel approach for image registration that deforms images by a two-dimensional-polynomial function which parameters are estimated from optical flow. We evaluated our method by registering annular image sequences and we successfully generated dissection images, as presented in this paper. [ABSTRACT FROM AUTHOR]