Stacked Features Based CNN for Rotation Invariant Digit Classification
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Numerical digit
A novel bimodal method for face recognition under low-level lighting conditions is proposed. It fuses an enhanced gray level imageand an illumination-invariant geometric image at the feature-level. To further improve the recognition performance under large variations in attributions such as poses and expressions, discriminant features are extracted from source images using the wavelet transform-based method. Features are adaptively fused to reconstruct the final face sample. Then FLD is used to generate a supervised discriminant space for the classification task. Experiments show that the bimodal method outperforms conventional methods under complex conditions.
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It has been reported in the literature on computational neuroscience that a rat's uncanny ability to dash back to a home position in the absence of any visual clues (or in total darkness, for that matter) may stem from its distinctive method of position representation. More specifically, it is hypothesized that the rat uses a multimodular method akin to residue number system (RNS), but with continuous residues or digits, to encode position information. After a brief review of the evidence in support of this hypothesis, and how it relates to RNS, we discuss the properties of continuous-digit RNS, and derive results on the dynamic range, representational accuracy and factors affecting the choice of the moduli, which are themselves real numbers. We conclude with suggestions for further research on important open problems concerning the process of selection, or evolutionary refinement, of the set of moduli in such a representation.
Numerical digit
Representation
Uncanny
Numeral system
ENCODE
Mental arithmetic
Position (finance)
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Scale-invariant feature transform
Three-dimensional face recognition
Object-class detection
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The recognition of frontal facial appearance with illumination is a difficult task for face recognition.In this paper,a novel illumination invariant extraction method was proposed to deal with the illumination problem based on wavelet transform and denoising model.The illumination invariant was extracted in wavelet domain by using wavelet-based denoising techniques.Through manipulating the high frequency wavelet coefficient combined with denoising model,the edge features of the illumination invariants were enhanced and more useful information was restored in illumination invariants,which could lead to an excellent face recognition performance.The experimental results on Yale face database B and CMU PIE face database show that satisfactory recognition rate can be achieved by the proposed method.
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Object recognition is basically invariant to the dramatic changes caused in objects' appearance such as location, size, viewpoint, illumination, occlusion and more by the variability in viewing conditions. In this paper, we employ an efficient approach for object recognition using invariant features and machine learning technique. The invariant features namely color, shape and texture invariant features of the objects are extracted separately with the aid of suitable feature extraction techniques. In the proposed approach, we integrate the color, shape and texture invariant features of the objects at the feature level, so as to improve the recognition performance. The fused feature set serves as the pattern for the forthcoming processes involved in the proposed approach. We employed the pattern recognition algorithms, like Discriminative Canonical Correlation (DCC) and attain distinct or identical results concerned with false positives. Our proposed approach is evaluated on the ALOI collection, a large collection of object images consists of 1000 objects recorded under various imaging circumstances. The experiments clearly demonstrate that our proposed approach significantly outperforms the state-of-the- art methods for combining color, shape and texture features. The proposed method is shown to be effective under a wide variety of imaging conditions.
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In this paper, we look at various arithmetic properties of the set of those positive integers n whose sum of digits in a fixed base b>1 is a fixed positive integers s . For example, we prove that such integers can have many prime factors, that they are not very smooth, and that most such integers have a large prime factor dividing the value of their Euler \phi function.
Numerical digit
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A blurred palmprint recognition method based on Relative Invariant Structure Feature (RISF) is proposed in this paper to improve the low recognition accuracy of blurred palmprint. Firstly, the OSV decomposition model is used to obtain stable feature from blurred images. Next, a non-overlapping sampling method based on Structure Ratio (SR) for RISF is used to further improve the effectiveness of feature. Finally, Structural Similarity Index Measurement (SSIM) is introduced to measure the similarity of palmprints and judge the palmprint category for classification. Numerical experiments show that the proposed method is effective and better than some other classical algorithms.
Feature (linguistics)
Similarity (geometry)
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Different methods of handling the summing process for the geometric series are shown to give results indicating widely differing significances when carried out in a machine incorporating “significant-digit” arithmetic.
Numerical digit
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Numerical digit
Value (mathematics)
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In pattern recognition, it is often necessary to deal with problems to classify a transformed pattern. A neural pattern recognition system which is insensitive to rotation of input pattern by various degrees is proposed. The system consists of a fixed invariance network with many slabs and a trainable multilayered network. The system was used in a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin. The results show that the approach works well for variable rotation pattern recognition.
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