Traditional pattern recognition methods face challenges in pedestrian localization. In complex motion environments, failure to consider diversity, continuity and randomness of pedestrian gait can cause inaccurate localization. In this article, a feedback feature learning system is designed to improve human motion recognition and localization accuracy. First, advanced features are extracted using out-of-bag (OOB) evaluation, and optimal motion patterns are extracted using frequency pie probability, with an average recognition accuracy as high as 99.8%. Then, to enhance the accuracy of zero-velocity update (ZUPT) at different step frequencies, a novel peak detection method is developed to extract real-time stride frequency, and an adaptive step frequency-based ZUPT optimal threshold function is obtain-ed. Next, a motion constraint model is designed to overcome the influence of non-Gaussian noise, and the 3-D position information in different motion modes solved by Extended Kalman filter (EKF) is optimized. Finally, the obtained more accurate 3-D position information is used as the new feature input of the classifier to further improve the pattern recognition accuracy. After three feature iteration feedbacks, the localization accuracy in relatively complex random motion is 0.1% to 1.69%.

Abstract Image denoising is one of the hottest topics in image restoration area, it has achieved great progress both in terms of quantity and quality in recent years, especially after the wide and intensive application of deep neural networks. In many deep learning based image denoising models, the performance can greatly benefit from the prepared clean/noisy image pairs used for model training, however, it also limits the application of these models in real denoising scenes. Therefore, more and more researchers tend to develop models that can be learned without image pairs, namely the denoising models that can be well generalised in real‐world denoising tasks. This motivates to make a survey on the recent development of image denoising methods. In this paper, the typical denoising methods from the perspective of model training are reviewed, the reviewed methods are categorised into four classes: the models need clean/noisy image pairs to train, the models trained on multiple noisy images, the models can be learned from a single noisy image, and the visual transformer based models. The denoising results of different denoisers were compared on some public datasets to discover the performance and advantages. The challenges and future directions in image denoising area are also discussed.

Accuracy and reliability are two important issues in computer assisted breast cancer diagnosis. In this paper, a new cascade Random Subspace ensembles scheme with reject options is proposed for automatic breast cancer diagnosis. The diagnosis system is built as a serial fusion of two different Random Subspace classifier ensembles with rejection options to enhance the classification reliability. The first ensemble consists of a set of Support Vector Machine (SVM) classifiers that converts the original K-class classification problem into a number of K 2-class problems. The second ensemble consists of a Multi-Layer Perceptron (MLP) ensemble, that focuses on the rejected samples from the first ensemble. For both of the ensembles, the reject option is implemented by relating the consensus degree from majority voting to a confidence measure, and abstaining to classify ambiguous samples if the consensus degree is lower than some threshold. Using a microscopic breast biopsy image dataset from Israel Institute of Technology and benchmark datasets from UCI, promising results are obtained using the proposed system.

Source separation of musical signals is an appealing but difficult problem, especially in the single-channel case. In this paper, an unsupervised single-channel music source separation algorithm based on average harmonic structure modeling is proposed. Under the assumption of playing in narrow pitch ranges, different harmonic instrumental sources in a piece of music often have different but stable harmonic structures; thus, sources can be characterized uniquely by harmonic structure models. Given the number of instrumental sources, the proposed algorithm learns these models directly from the mixed signal by clustering the harmonic structures extracted from different frames. The corresponding sources are then extracted from the mixed signal using the models. Experiments on several mixed signals, including synthesized instrumental sources, real instrumental sources, and singing voices, show that this algorithm outperforms the general nonnegative matrix factorization (NMF)-based source separation algorithm, and yields good subjective listening quality. As a side effect, this algorithm estimates the pitches of the harmonic instrumental sources. The number of concurrent sounds in each frame is also computed, which is a difficult task for general multipitch estimation (MPE) algorithms.

Separation of voice and music is an interesting but difficult problem. It is useful for many other researches such as audio content analysis. In this paper, the difference between voice and music signals is carefully studied. It is proposed that the harmonic structure stability is the key difference between them. A separation algorithm based on this theory is proposed. The main idea is to learn the average harmonic structure of the music, and then separate signals by using it to distinguish voice and music harmonic structures. Experimental results show that the algorithm can separate mixed signals and obtains not only a very high signal-to-noise ratio (SNR) but also a rather good subjective audio quality

Separation of music signals is an interesting but difficult problem. It is helpful for many other music researches such as audio content analysis. In this paper, a new music signal separation method is proposed, which is based on harmonic structure modeling. The main idea of harmonic structure modeling is that the harmonic structure of a music signal is stable, so a music signal can be represented by a harmonic structure model. Accordingly, a corresponding separation algorithm is proposed. The main idea is to learn a harmonic structure model for each music signal in the mixture, and then separate signals by using these models to distinguish harmonic structures of different signals. Experimental results show that the algorithm can separate signals and obtain not only a very high Signalto-Noise Ratio (SNR) but also a rather good subjective audio quality.