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Pattern recognition

Automatic speech recognition for under-resourced languages: A survey

A DNA-based color image encryption method is proposed by using three 1D chaotic systems with excellent performance and easy implementation.The key streams used for encryption are related to both the secret keys and the plain-image.To improve the security and sensitivity, a division-shuffling process is introduced.Transforming the plain-image and the key streams into the DNA matrices randomly can further enhance the security of the cryptosystem.The presented scheme has a good robustness for some common image processing operations and geometric attack. This paper proposes a new encryption scheme for color images based on Deoxyribonucleic acid (DNA) sequence operations and multiple improved one-dimensional (1D) chaotic systems with excellent performance. Firstly, the key streams are generated from three improved 1D chaotic systems by using the secret keys and the plain-image. Transform randomly the key streams and the plain-image into the DNA matrices by the DNA encoding rules, respectively. Secondly, perform the DNA complementary and XOR operations on the DNA matrices to get the scrambled DNA matrices. Thirdly, decompose equally the scrambled DNA matrices into blocks and shuffle these blocks randomly. Finally, implement the DNA XOR and addition operations on the DNA matrices obtained from the previous step and the key streams, and then convert the encrypted DNA matrices into the cipher-image by the DNA decoding rules. Experimental results and security analysis show that the proposed encryption scheme has a good encryption effect and high security. Moreover, it has a strong robustness for the common image processing operations and geometric attack.

A new color image encryption scheme based on DNA sequences and multiple improved 1D chaotic maps

Color image DNA encryption using NCA map-based CML and one-time keys

Color image encryption using skew tent map and hyper chaotic system of 6th-order CNN

Detecting small targets in infrared (IR) image sequences is an important task in IR guidance systems. The clutter of complex backgrounds often submerges small targets, making detection difficult. Achieving high detection and low false alarm rates with complex backgrounds is a primary problem. We propose an IR small target detection method using our new homogeneity-weighted local contrast measure (HWLCM). Inspired by the ability of the human visual system (HVS) to determine saliency characteristics, we implement our method to use the local contrast features of the central and surrounding regions and the weighted homogeneity characteristics of the surrounding regions to enhance the target while suppressing the complex background. Our method divides each image into blocks with a sliding window for which the HWLCM is calculated. The HWLCM enhances the actual target and suppresses interference simultaneously. We apply an adaptive threshold to target region extraction to further refine the results. Our experimental results show that our proposed method is more effective than six comparable methods, especially in terms of the signal-to-clutter gain (SCRG) and background suppression factor (BSF) indicators.

Infrared Small Target Detection Using Homogeneity-Weighted Local Contrast Measure

Effectiveness and false alarm (FA) suppression are key issues in infrared (IR) moving small-target detection. In this letter, we propose a novel spatial–temporal local difference measure (STLDM) algorithm to detect a moving IR small target. The method we propose involves three steps. First, we block off three frames in a certain range of time (temporal) domain. Next, in a 3-D spatial–temporal domain, we analyze the local grayscale intensity difference of the small targets moving between the three frames and calculate the difference between the grayscale intensity in the center area and the grayscale intensity in the eight directions in the area surrounding the target. Finally, we segment the small target in a detection result map. The results of our experiment demonstrate that our proposed STLDM method has a higher rate of detection of IR moving small targets, as well as fewer FAs than other existing methods.

Infrared Moving Small-Target Detection Using Spatial–Temporal Local Difference Measure

Uyghur language is an agglutinative language in which words are formed by suffixes attaching to a stem (or root). Because of the explosive nature in vocabulary of the agglutinative languages, several morpheme-based language models are built and experiments are implemented. Morpheme is the smallest meaning bearing unit. In this research, morpheme is referred to any of prefix, stem, or suffix. As a result, a large vocabulary ASR system is built on the basis of Julius system. Several ASR results on language models based on different units (word, morpheme, and syllable) are compared.

Uyghur morpheme-based language models and ASR

Many techniques have been reported for handwriting-based writer identification, but none of these techniques presented that the written text is in Uyghur. In this paper, we propose a technique for off-line writer identification for Uyghur handwriting. Texture features were extracted for wide range of frequency and orientation because of the nature of the Uyghur handwriting. 144 features are extracted from the handwriting sample using 2-D Gabor filters.By applying feature selection and extraction methods on this set of features, subsets of lower dimensionality are obtained. The most discriminant features were selected with a model for feature selection using genetic algorithm techniques. Three classification techniques were used: support vector machine (SVM), weighted Euclidean distance (WED), and the K nearest neighbours (K_NN) classifier. Experiments were performed using Uyghur handwriting samples from 23 different people and very promising results of 88.0% correct identification rate were achieved.

Askar Hamdulla

Papers

Color image encryption using skew tent map and hyper chaotic system of 6th-order CNN

Infrared Small Target Detection Using Homogeneity-Weighted Local Contrast Measure

Infrared Moving Small-Target Detection Using Spatial–Temporal Local Difference Measure

Uyghur morpheme-based language models and ASR

Research on Uyghur off-line handwriting-based writer identification