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We present a novel method for recognizing an object in an image using full pixel matching between a reference image and an input image without advance segmentation of the image. A method called two-dimensional continuous dynamic programming (2DCDP) is adopted to optimally calculate the accumulated local distances of all corresponding pixels in nonlinearly matched areas in an input image and a reference...
Human identification by recognizing the spontaneous gait recorded in real-world setting is a tough and not yet fully resolved problem in biometrics research. Several issues have contributed to the difficulties of this task. They include various poses, different clothes, moderate to large changes of normal walking manner due to carrying diverse goods when walking, and the uncertainty of the environments...
An integrated silhouette with perfect appearance is helpful for gait recognition. However, the silhouettes often have holes or missing parts, because the commonly used motion detection and extraction methods are not always suitable for every case. A robust post-processing strategy is proposed here to refine the raw silhouettes. First, an individual silhouette model which represents the mutual characteristic...
In this paper, we present a method for automatically classifying/recognizing the shoeprint images based on the outsole pattern. Shoeprints are distinctive patterns often found at crime scenes that can provide valuable forensic evidence. Directionality is the most obvious feature in these shoeprints. For extracting features corresponding to the directionality, co-occurrence matrices, Fourier transform,...
This paper presented a new gait identification and authentication method based on Haar wavelet and Radon transform. This method consists of two stages, gait modeling and recognition. In the first stage, images extracted from video sequences are pre-processed into binary silhouette. In terms of gait cycle, they are divided into 4 states, in each of which the distinct images are selected. The horizontal...
Illumination variation brings in significant difficulties to pattern recognition and consequent robot operations. In this paper an illumination factor for real-world images taken under a wide variety of lighting conditions in ICA subspace was extracted. After normalizing with the combination coefficient of the illumination factors, all the images that originated from the same object but with different...
Indoor scene recognition is a challenging open problem in high level vision. Most scene recognition models that work well for outdoor scenes perform poorly in the indoor domain. The main difficulty is that while some indoor scenes (e.g. corridors) can be well characterized by global spatial properties, others (e.g, bookstores) are better characterized by the objects they contain. More generally, to...
Empirical mode decomposition (EMD) developed by Huang et al. is a nonlinear data analysis method for nonstationary real-valued time series. It has been applied extensively in many research areas. Recently, several generalized EMD methods for complex-valued data analysis was proposed. Since a plane closed curve comprises many two-dimensional (2D) space data points, one can imagine that the boundary...
Radiologists that analyze screening mammographic images miss the 10-20% of the diagnosis since this kind of images are very difficult to interpret. In this paper, we present the first step of a CADx (computer aided diagnosis) system that, from the original mammogram, extracts suspicious regions on which the radiologists have to focus their attention. The procedure often successes also in case of very...
Nowadays, sign language is commonly used as a communication language for auditory handicapped people. In addition to voice and controller pads, hand gestures can also be an effective way of communication between humans and robots or even between auditory handicapped people and robots. To be an effective sign recognition system, it should be glove-free, fast, small database and accurate. In this project,...
One of the biggest challenges in person recognition using biometric systems is the variability in the acquired data. In this paper, we evaluate the effects of an increasing time lapse between reference and test biometric data consisting of static images of handwritten signatures and texts. We use for our experiments two recognition approaches exploiting information at the global and local levels,...
The authors present a new algorithm for iris recognition. Segmentation is based on local statistics, and after segmentation, the image is subjected to contrast-limited, adaptive histogram equalization. Feature extraction is then conducted using two directional filters (vertically and horizontally oriented). The presence (or absence) of ridges and their dominant directions are determined, based on...
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