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This paper deals with classification algorithms as one of the basic principles of pattern recognition. We analyze their effect to a feature space and compare the type and the shape of the separating and decision surface, respectively. We proposed a novel classification approach based on Cumulative Fuzzy Membership Function that creates a decision surface in a different way as an MF ARTMAP neural network...
This work proposes an off-line handwritten signature identification system using the Histogram of Symbolic Representation (HSR). The HSR is considered as one-class classifier which has the ability to generate a model for each writer using only its own reference signatures. This method allows also modeling the writing style of each writer by taking into account the variability of signatures. To evaluate...
Person re-identification in public areas (such as airports, train stations and shopping malls) has recently received increased attention within computer vision research due, in part, to the demand for enhanced levels of security. Re-identifying subjects within non-overlapped camera networks can be considered as a challenging task. Illumination changes in different scenes, variations in camera resolutions,...
This paper addresses the problem of object-mask registration, which aligns a shape mask to a target object instance. Prior work typically formulate the problem as an object segmentation task with mask prior, which is challenging to solve. In this work, we take a transformation based approach that predicts a 2D non-rigid spatial transform and warps the shape mask onto the target object. In particular,...
In this paper, we propose an indoor video-based feature recognition method to detect the fall behaviors of people. We firstly establish and update the video background using Gaussian mixture model, and apply background subtraction to extract the moving targets. To remove the shadow interference on these extracted moving targets, we eliminate these shadows by integrating color and gradient features...
Learning automatically the structure of object categories remains an important open problem in computer vision. In this paper, we propose a novel unsupervised approach that can discover and learn landmarks in object categories, thus characterizing their structure. Our approach is based on factorizing image deformations, as induced by a viewpoint change or an object deformation, by learning a deep...
Face alignment has witnessed substantial progress in the last decade. One of the recent focuses has been aligning a dense 3D face shape to face images with large head poses. The dominant technology used is based on the cascade of regressors, e.g., CNNs, which has shown promising results. Nonetheless, the cascade of CNNs suffers from several drawbacks, e.g., lack of end-to-end training, handcrafted...
We propose a data-driven method for recovering missing parts of 3D shapes. Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network. The global structure inference network incorporates a long short-term memorized context fusion module (LSTM-CF) that infers the global structure of the shape based...
Because there are many false targets in the seabed imaging of SAS (synthetic aperture sonar), it is difficult for the automatic alarm of buried column targets. An automatic alarm method for buried targets based on membership classification is proposed in this paper. Firstly, the mean-standard deviation maximum entropy segmentation is used to segment seabed image. Then the area and posture ratio of...
Inshore ship detection in remote sensing images is a challenging task because of the connectivity and similarity between ships and backgrounds. The usual shape feature is not always applicable because sometimes it is hard to be extracted. In this paper, deep features extracted from a convolutional neural network (CNN) are used for inshore ship detection. In order to feed the CNN with exclusively positive...
The image retrieval from multimedia databases is a very challenging problem nowadays. Not only it requires the proper query form, but also efficient methods of data storage. The problem is important, because nowadays there are many different systems which needs image retrieval. As an example web searching engines may be given, which had to store a very huge amount of images and needs fast image retrieval...
Analysis of electrocardiogram and heart rate provides useful information about health condition of a patient. The North Sea Bicycle Race is an annual competition in Norway. Examination of ECG recordings collected from participants of this race may allow defining and evaluating the relationship between physical endurance exercises and heart electrophysiology. Parameters reflecting potentially alarming...
We present hierarchical multi-feature classification (HMC) system for multiclass fruit recognition problem. Our approach to HMC exploits the advantages of combining multimodal features and the fruit hierarchy property. In the construction of hybrid features, we take the advantage of using color feature in the fruit recognition problem and combine it with 3D shape feature of depth channel of RGBD (Red,...
We propose a new fully automatic spike sorting algorithm that is able to match, or even improve, the performance of semiautomatic solutions with supervised intervention from expert users. We achieved this by incorporating: 1) a set of heuristic criteria inspired by the expert actions following the solution from semiautomatic algorithms, and 2) an improved feature selection method that increases the...
Novel human gesture recognition and classification technique is suggested and experimentally studied. Suggested strategy is based on exploiting the interactions of human gestures with high-frequency electromagnetic field. Extracting of classification features contained in the wireless radio signal modulated by human gestures is proposed by utilizing bispectrum-based processing of the signal envelope...
Recent studies show that eyebrows can be used as a biometric or soft biometric for recognition. In some scenarios such as partially occluded or covered faces, they can be used for recognition. In this paper, we study eyebrow recognition using texture-based features. We apply features which have not been used before for eyebrow recognition such as 3-patch local binary pattern and WLD (Weber local descriptor)...
Image over-segmentation, as a pre-processing step of image segmentation, splits the input image into superpixels. Those are small compact regions with irregular shapes. The majority of existing methods for texture feature extraction are not suitable for arbitrarily shaped regions. Therefore, only color information can be used to classify and merge superpixels to create final image segmentation. We...
Usually the static or dynamic characteristics of the flame are extracted for flame detection. But the relationship between the various features of flame could not be distinguished by the human eye. the Gradient Boost Decision Tree (GBDT) is thus proposed to combine and optimize the flame shape and texture features, so as to mine the relationship of flame features. Then the more discriminant new flame...
Although acoustical features can be extracted directly from time series, more relevant and more precise features can be collected from a higher processing level, namely, the frequency domain. Physicians prefer thin peaks in the frequency space, which can be usually achieved by windowing and wavelet analysis. In a bio-inspirited way, the human brain can determine an object from the area of the region...
The aim of our research is a development of the sign language recognition (SLR) system for smooth communication between hearing disability people and hearing people. Sign language is consisted of three elements; hand position, hand movement, and hand shape. The proposed method is reflected all elements. Multi-stream HMM is used for the learning and recognition processes. In the experiment, two datasets:...
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