Zig-Zag Based Single-Pass Connected Components Analysis

Deep Learning Based Tongue Prickles Detection in Traditional Chinese Medicine

Tongue diagnosis is a convenient and noninvasive clinical practice of traditional Chinese medicine (TCM), having existed for thousands of years. Prickle, as an essential indicator in TCM, appears as a large number of red thorns protruding from the tongue. The term “prickly tongue” has been used to describe the flow of qi and blood in TCM and assess the conditions of disease as well as the health status of subhealthy people. Different location and density of prickles indicate different symptoms. As proved by modern medical research, the prickles originate in the fungiform papillae, which are enlarged and protrude to form spikes like awn. Prickle recognition, however, is subjective, burdensome, and susceptible to external factors. To solve this issue, an end-to-end prickle detection workflow based on deep learning is proposed. First, raw tongue images are fed into the Swin Transformer to remove interference information. Then, segmented tongues are partitioned into four areas: root, center, tip, and margin. We manually labeled the prickles on 224 tongue images with the assistance of an OpenCV spot detector. After training on the labeled dataset, the super-resolutionfaster-RCNN extracts advanced tongue features and predicts the bounding box of each single prickle. We show the synergy of deep learning and TCM by achieving a 92.42% recall, which is 2.52% higher than the previous work. This work provides a quantitative perspective for symptoms and disease diagnosis according to tongue characteristics. Furthermore, it is convenient to transfer this portable model to detect petechiae or tooth-marks on tongue images.

Union-Retire for Connected Components Analysis on FPGA

The Union-Retire CCA (UR-CCA) algorithm started a new paradigm for connected components analysis. Instead of using directed tree structures, UR-CCA focuses on connectivity. This algorithmic change leads to a reduction in required memory, with no end-of-row processing overhead. In this paper we describe a hardware architecture based on UR-CCA and its realisation on an FPGA. The memory bandwidth and pipelining challenges of hardware UR-CCA are analysed and resolved. It is shown that up to 36% of memory resources can be saved using the proposed architecture. This translates directly to a smaller device for an FPGA implementation.

Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation

With the increase in the digitization efforts of herbarium collections worldwide, dataset repositories such as iDigBio and GBIF now have hundreds of thousands of herbarium sheet images ready for exploration. Although this serves as a new source of plant leaves data, herbarium datasets have an inherent challenge to deal with the sheets containing other non-plant objects such as color charts, barcodes, and labels. Even for the plant part itself, a combination of different overlapping, damaged, and intact individual leaves exist together with other plant organs such as stems and fruits, which increases the complexity of leaf trait extraction and analysis. Focusing on segmentation and trait extraction on individual intact herbarium leaves, this study proposes a pipeline consisting of deep learning semantic segmentation model (DeepLabv3+), connected component analysis, and a single-leaf classifier trained on binary images to automate the extraction of an intact individual leaf with phenotypic traits. The proposed method achieved a higher F1-score for both the in-house dataset (96%) and on a publicly available herbarium dataset (93%) compared to object detection-based approaches including Faster R-CNN and YOLOv5. Furthermore, using the proposed approach, the phenotypic measurements extracted from the segmented individual leaves were closer to the ground truth measurements, which suggests the importance of the segmentation process in handling background noise. Compared to the object detection-based approaches, the proposed method showed a promising direction toward an autonomous tool for the extraction of individual leaves together with their trait data directly from herbarium specimen images.

Identification of QR Code Perspective Distortion Based on Edge Directions and Edge Projections Analysis

QR (quick response) Codes are one of the most popular types of two-dimensional (2D) matrix codes currently used in a wide variety of fields. Two-dimensional matrix codes, compared to 1D bar codes, can encode significantly more data in the same area. We have compared algorithms capable of localizing multiple QR Codes in an image using typical finder patterns, which are present in three corners of a QR Code. Finally, we present a novel approach to identify perspective distortion by analyzing the direction of horizontal and vertical edges and by maximizing the standard deviation of horizontal and vertical projections of these edges. This algorithm is computationally efficient, works well for low-resolution images, and is also suited to real-time processing.

Image Processing Using FPGAs

Nine articles have been published in this Special Issue on image processing using field programmable gate arrays (FPGAs). The papers address a diverse range of topics relating to the application of FPGA technology to accelerate image processing tasks. The range includes: Custom processor design to reduce the programming burden; memory management for full frames, line buffers, and image border management; image segmentation through background modelling, online K-means clustering, and generalised Laplacian of Gaussian filtering; connected components analysis; and visually lossless image compression.