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Fruit detection is essential for harvesting robot platforms. However, complicated environmental attributes such as illumination variation and occlusion have made fruit detection a challenging task. In this study, a Transformer-based mask region-based convolution neural network (R-CNN) model for tomato detection and segmentation is proposed to address these difficulties. Swin Transformer is used as the backbone network for better feature extraction. Multi-scale training techniques are shown to yield significant performance gains. Apart from accurately detecting and segmenting tomatoes, the method effectively identifies tomato cultivars (normal-size and cherry tomatoes) and tomato maturity stages (fully-ripened, half-ripened, and green). Compared with existing work, the method has the best detection and segmentation performance for these tomatoes, with mean average precision (mAP) results of 89.4% and 89.2%, respectively. [ABSTRACT FROM AUTHOR]

Vein pattern-based methods have powerfully promoted the performance of finger vein recognition. However, it is not easy to precisely extract vein patterns from images, especially from low-quality images, and the non-vein area have been proved to be helpful for recognition. This paper proposes to use low-rank representation to extract as much noiseless discriminative information as possible from finger vein images. However, image deformation and image quality variations weaken the correlation of genuine images, and therefore damage the low-rank linear representation. To further deal with this problem, the class labels of training images and the local geometric structure between testing images and training images, reflected by sparse reconstruction errors of testing images, are used as constraints of low-rank coefficients. In particular, vein backbone decomposition based sparse representation is proposed to fast compute the deformation-robust reconstruction errors of each testing image. The reconstruction error on sub-backbones of one training image are used and modified as the constraint of the low-rank coefficient on this training image. We evaluate the proposed method on three widely used finger vein databases, and experimental results show that the proposed method performs well in finger vein recognition. [ABSTRACT FROM AUTHOR]

• GNMF method is used to integrate the ECG data information, geometry and label information. • GNMF also achieves dimensionality reduction and primary feature extraction. • Sparse representation is utilized to obtain final features to perform ECG matching. • Our method shows robust recognition performance on two public datasets. As a vital sign, Electrocardiogram (ECG) has highly discriminative characteristics in the field of biometrics. This paper aims to propose a novel robust ECG biometric method based on graph regularized non-negative matrix factorization (GNMF) and sparse representation. First, after ECG signal pre-processing and heartbeat segmentation, GNMF is used to reduce the dimensions of each heartbeat. In GNMF, an affinity graph is constructed to encode the geometrical information and label information in order to obtain more discriminative features. Second, in order to seek highly discriminability of ECG, the sparse representation is utilized to perform final feature extraction. We evaluate the method on two public datasets: ECG-ID and MIT-BIH Arrhythmia (MITDB). When fusing three heartbeats as a test sample, the accuracy achieves 98.03% and 100% on the ECG-ID dataset and the MITDB dataset, respectively. Experimental results show that the proposed method is robust for within-session and across-session of the ECG signal. [ABSTRACT FROM AUTHOR]

Vein pattern-based methods powerfully boost the recognition accuracy of finger veins, but real-time recognition cannot be guaranteed, especially in large-scale applications. Moreover, previous studies focused on either the matching task to enhance the accuracy or the indexing task to improve the efficiency. This paper proposes a finger vein code indexing method and combines it with a finger vein pattern matching method into an integration framework for improving both accuracy and efficiency. With the extracted vein patterns, the direction of each vein segment is detected and represented by the elliptical direction map as a feature for indexing, which will be encoded into a binary code by the angle K-means. The similarity between vein direction codes is measured by the grouped hamming distance in indexing, and further weighted by the overlap degree of the corresponding vein patterns to return the candidates for the probe. In addition, based on the above distance measurement, only vein segments with the same direction code are considered in following probe-to-candidate matching. Experimental results indicate that our indexing method outperforms the state-of-the-art methods and has competitive potential in performing the matching task. The results also indicate that the integration framework highly improves the identification efficiency with a slight improvement on the accuracy. [ABSTRACT FROM AUTHOR]

We propose a new multiscale rotation-invariant convolutional neural network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography. MRCNN employs Gabor-local binary pattern that introduces a good property in image analysis—invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches. Experimental results on a public interstitial lung disease database show a superior performance of the proposed method to state of the art. [ABSTRACT FROM AUTHOR]

Biometric identification is the study of physiological and behavioral attributes of an individual to overcome security problems. Finger vein recognition is a biometric technique used to analyze finger vein patterns of persons for proper authentication. This paper presents a detailed review on finger vein recognition algorithms. Such tools include image acquisition, preprocessing, feature extraction and matching methods to extract and analyze object patterns. In addition, we list some novel findings after the critical comparative analysis of the highlighted techniques. The comparative studies indicate that the accuracy of finger vein identification methods is up to the mark. [ABSTRACT FROM AUTHOR]

Minutiae based matching methods are the most intensively investigated matching methods in fingerprint recognition. However, the discriminative information contained in the extracted minutiae cannot be effectively used in recent related techniques. This paper proposes to analyze and use the discriminative information contained in the unsuccessfully matched minutiae in order to improve the recognition accuracy of the automatic fingerprint recognition systems. Specifically, we extract seven features from the unmatched minutiae, and for each of them, we calculate an auxiliary matching score. The combination of our seven auxiliary matching scores with the matching score of the successfully matched minutiae enables our matching method to get boosted recognition accuracy. The discrimination ability of the unmatched minutiae and the effectiveness of the proposed method are validated by the experimental results obtained using the FVC2000, FVC2002 and FVC2004 databases for international fingerprint verification competition. In the experiments, the fingerprint recognition accuracy is significantly promoted by the proposed method. [ABSTRACT FROM AUTHOR]