In this work, we present an adaptive switching filter for noise reduction and sharpness preservation in depth maps
provided by Time-of-Flight (ToF) image sensors. Median filter and bilateral filter are commonly used in cost-sensitive
applications where low computational complexity is needed. However, median filter blurs fine details and edges in depth
map while bilateral filter works poorly with impulse noise present in the image. Since the variance of depth is inversely
proportional to amplitude, we suggest an adaptive filter that switches between median filter and bilateral filter based on
the level of amplitude. If a region of interest has low amplitude indicating low confidence level of measured depth data,
then median filter is applied on the depth at the position while regions with high level of amplitude is processed with
bilateral filter using Gaussian kernel with adaptive weights. Results show that the suggested algorithm performs surface
smoothing and detail preservation as well as median filter and bilateral filter, respectively. By using the suggested
algorithm, significant gain in visual quality is obtained in depth maps while low computational cost is maintained.
This paper proposes a depth up-sampling method using the confidence map for a fusion of a high resolution color sensor
and low resolution time-of-flight depth sensor. The confidence map represents the accuracy of depth depending on the
reflectance of a measured object and is estimated with amplitude, offset, and reconstructed error of a received signal.
The proposed method suppresses the depth artifacts that are caused by difference between low and high reflective
materials on an object at a distance. Although the surface of an object is located at the same distance, the reflectance of
small regions within the surface depends on constituent materials. Weighted filter generated by confidence map is added
to the modified noise-aware filter for depth up-sampling that is proposed by Derek et al., and is adaptively selected. The
proposed method consists of followings; the normalization, the reconstruction, the confidence map estimation, and the
modified noise-aware filtering. In the normalization, amplitudes and offsets of received signals are calculated and
received signals are normalized by those. The phase shifts are measured between transmitted and received signals. In the
reconstruction, received signals are reconstructed using only the values of phase shifts and the reconstruction errors are
calculated. The confidence map is estimated with amplitudes, offsets, and reconstruction errors. The coefficients of a
modified noise-aware filter are adaptively selected by referring to the confidence map. The proposed method shows the
enhanced results of removing depth artifacts in the experiments.
We propose a novel method using a single catadioptric omnidirectional camera to track 3-D head positions in nonintrusive biometric systems. Existing methods have not been able to deal with cases in which the ground plane is nonorthogonal to the optical axis of the camera. To overcome this problem, our proposed method presents the following three improvements: (i) By using 1-D tracking of the feet and head positions combined with a circular constraint in images instead of conventional 2-D tracking methods, the accuracy and computational efficiency of the tracking process is significantly improved, (ii) based on the detected 2-D head position in images with the precalibration information of the camera, the 3-D head positions can be obtained, and (iii) the 3-D head positions can be obtained without having the optical axis of the camera orthogonal to the ground plane. This makes the proposed method feasible in practical environments that use omnidirectional camera systems. Experimental results showed that the proposed method was able to track 3-D head positions at real-time speed.
The purpose of fake iris detection is to discriminate between real and fake iris images and to defeat fake (forged) iris images. A robust fake iris detection method should be able to detect various types of fake iris images obtained from a fake printed iris, an artificial eye, or a fake contact lens, correctly and nonintrusively. To solve the problem, we propose a new fake iris detection method. We measure distinctive physiological multifeatures [the first and second features refer to the reflectance ratios of the iris to the sclera (RRIS) at 750 and 850 nm, respectively, and the third feature refers to the thickness of the corneoscleral limbus], and classify those features extracted from live irises and fake irises using a support vector machine (SVM). Using the proposed method, we can discriminate various types of fake iris images without inconveniencing users by shining visible light. To measure the performance of the method, three types of fake irises are made: a printed iris, an artificial eye, and a fake contact lens. Our experimental results show that it is possible to detect those fake iris images with high accuracy.
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