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The high-level feature representation of deep convo-lutional neural networks (ConvNets) has proven to be superior to hand-crafted low-level features. Thus, this study investigates the effect of fusing such high-level features from multi-deep ConvNets under an application of visual object/scene categorization. In which, three pre-trained ConvNets are exploited as feature extractors, a single hidden...
Physical library collections are valuable and long standing resources for knowledge and learning. However, managing and finding books or other volumes on a large collection of bookshelves often leads to tedious manual work, especially for large collections where books or others might be missing or misplaced. Recently, deep neural-based models have been successful in detecting and recognizing text...
Embedded dictation, i.e. recognizing vocal commands in noisy environments, with good accuracy and using low complexity implementations is a desirable task with many applications. Such applications include automotive infotainment solutions particularly when no connectivity is available, personal assistants including embedded dictation solutions for disabled people, and so on. This paper reports our...
Compared to the traditional Gabor transform, the circularly symmetrical Gabor transform (CSGT) not only retains the characteristics of local and multi-resolution analysis, but also has the remarkable advantages of less redundancy and rotational invariance. Simultaneously, the collaborative representation-based classification with regularized least square (CRC-RLS) overcomes the shortcoming of the...
Palmprint identification is a popular biometric technology used for personal characterization. Traditional palmprint recognition methods are mostly based on acquisition devices with contact, and this, may affect their user friendliness. In this paper, a toucheless palmprint identification method based on Scale Invariant Feature Transform (SIFT) descriptors and sparse representation method is proposed,...
This paper reports a new approach based on convolutional neural networks (CNNs), which uses spatial transformer networks (STNs). The approach, referred to as Tied Spatial Transformer Networks (TSTNs), consists of training a system which combines a localization CNN and a classification CNN whose weights are shared. The localization CNN is used for predicting an affine transform for the input image,...
Face recognition (FR) is an interesting topic in recent pattern recognition investigation. Especially, the accuracy of FR is the foremost concern for practical applications. Linear regression classification (LRC) is one of the most famous and effective methods in the FR area. However, it could perform inaccuracy under variant situations such as few training samples, lighting changes, and partial occlusions...
In this paper, we propose a combined feature approach which takes full advantages of local structure information and the more global one for improving texture image classification results. In this way, Local Binary Pattern is used for extracting local features, whilst the Scattering Transform feature plays the role of a global descriptor. Intensive experiments conducted on many texture benchmarks...
Fetal electrocardiogram (FECG) monitoring has become essential due to the current increase in the relative number of cardiac patients worldwide. This paper proposes to use a deep learning approach to compress/recover FECG signals, improving the computation speed in a telemonitoring system. The problem is analogous to the reconstruction of a non-sparse signal in compressive sensing (CS) framework....
Here, we propose a method for recognition of handwritten English digit utilizing discrete cosine space-frequency transform known as the Discrete Cosine S-Transform (DCST). Experiments have been conducted on the publicly availabe standard MNIST handwritten digit database. The DCST features along with an Artificial Neural Network (ANN) classifier is utilized for solving the classification issues of...
Iris Recognition technology the most sought after biometric technology these days uses statistically independent patterns generated from human iris that are unique to every human being, reliable and has greater user acceptance. A robust and foolproof iris recognition system is expected to have zero FAR (False Acceptance Rate) and FRR (False Rejection Rate). In this work the impact of number of outsiders...
Now a days the palm print is one of the physiological characteristics which is believed that it is unique to individuals. Palm print is the great deal of different texture, which is consists of principal lines, regions, datum points, geometry, wrinkles, ridges, delta points and also minutiae features. So many methods of palm images have been applied by several researchers. Different types of image...
This paper investigates how we can achieve object recognition in an image by looking at some examples of training images. Scale Invariant Feature Transform (SIFT) is one proposal method to detect features in an image and then can use those features to distinguish between different objects. Therefore, my aim was to implement SIFT code to do recognition tasks using simple thresholding and evaluating...
As a new biometric feature, finger vein has attracted more attention from researchers. In this paper, we propose a new method to improve the performance of finger vein identification systems. Our proposed method includes the following steps: (1) At first, images of finger veins are cropped to have regions of interest (ROI's). (2) Then, local invariant orientation features are extracted by using MFRAT...
In this paper we propose an improvement of a human action recognition method that uses a string-based representation and a string edit distance to compare the observed action with reference actions in the training set. In particular, the original improvement is based on a specific formulation of the string edit distance that is more suited to take into account the problems related to noise and to...
In this paper, a supervised facial recognition system is presented. In the feature extraction step, a Two Dimensional Discrete Multiwavelet Transform (2D DMWT) is used to extract useful information from the face images. The 2D DMWT is followed by a Two-Dimensional Fast Independent Component Analysis (2D FastICA) and eigendecomposition to obtain discriminating and independent features. The resulting...
Previous sparse representation (SR) methods are constructed on the assumption that the test sample can be approximately expressed by a linear combination of all original training samples. However, in most real-world applications samples are not subject to this assumption. Consequently, it is significant to explore a new way to improve SR. In this paper, we propose two directional transform based sparse...
We focus on the feature transform approach as one methodology for biometric template protection, where the template consists of the features extracted from the biometric trait. This paper considers some properties of the unitary transform-based template protection in particular. It is known that the Euclidean distance between the templates protected by a unitary transform is the same as that between...
There has long been a concern to improve the techniques of image processing for PCB's inspection. However, it is noticed the focus is mainly on the inspection of PCB in mass production. Even with a low volume of production, the small series production has become increasingly relevant. This work presents an image processing pipeline for mounted PCB inspection in small series production, strongly based...
Missing values are a common problem in many real world databases. A common way to cope with this problem is to use imputation methods to fill missing values with plausible values. Genetic programming-based multiple feature construction (GPMFC) is a filter approach to multiple feature construction for classifiers using Genetic programming. The GPMFC algorithm has been demonstrated to improve classification...
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