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Hybrid rice row detection at the pollination stage is critical for the automation of in-field pollination agricultural vehicles. The parental crops of hybrid rice are planted at intervals in seed production fields with narrow inter-row spacing. During the advance of the pollination vehicle, in addition to the centerline of the crop row, information on the crop region boundaries is required to guide the vehicle and prevent it from running over the crop. For complete crop row detection, a novel machine vision-based method was presented to identify each of the individual regions of the crop rows, more than the centerlines, by line-shaped mask scanning combined with the vanishing point of the crop rows. The approach consisted of grayscale transformation, vanishing point detection, crop region identification, boundary position fine-tuning and crop region segmentation. Its region detection performance outperformed the convolutional neural network-based (CNN-based) methods with an intersection over union (IoU) of 0.832, an accuracy of 90.48%, a recall of 86.36%, a precision of 98.96% and an f1-Score of 92.23%. Its centerline extraction ability was compared with Hough Transform-based and SegNet-based methods on the basis of average lateral distance (ALD) between the ground truth line and the detected line. The proposed method resulted in an ALD of 1.943 pixels in a 640*360 resolution image, which was superior to the Hough Transform-based (5.704) and the SegNet-based (3.555) methods. [ABSTRACT FROM AUTHOR]

Vehicle re-identification (Re-ID) aims to search for a vehicle of interest in a large video corpus captured by different surveillance cameras. The identification process considers both coarse-grained similarity (e.g., vehicle Model/color) and fine-grained similarity (e.g., windshield stickers/decorations) among vehicles. Coarse-grained and fine-grained similarity comparisons usually attend to very different visual regions, which implies that two different attention modules are required to handle different granularity comparisons. In this paper, we propose a dual attention granularity network (DAG-Net) for Vehicle Re-ID. The DAG-Net consists of three main components: (1) A convolutional neural network with a dual-branch structure is proposed as the backbone feature extractor for coarse-grained recognition (i.e., vehicle Model) and fine-grained recognition (i.e., vehicle ID); (2) the self-attention model is added to each branch, which enables the DAG-Net to detect different regions of interest (ROIs) at both coarse-level and fine-level with the assistance of the part-positioning block; (3) finally, we obtain refined regional features of the ROIs from the sub-networks ROIs. As a result, the proposed DAG-Net is able to selectively attend to the most discriminative regions for coarse/fine-grained recognition. We evaluate our method on two Vehicle Re-ID datasets: VeRi-776 and VehicleID. Experiments show that the proposed method can bring substantial performance improvement and achieve state-of-the-art accuracy. In addition, we focus on the different effects of regional features and global features. We conduct experiments to verify it in the PKU dataset and discuss the effectiveness. [ABSTRACT FROM AUTHOR]

Many medical image processing applications rely on targeted regions of interest within a larger volumetric image. Whole-body scans represent an extreme case in which large volumes must be broken into smaller sub-volumes for regional analysis. In this work, we sought automatic solutions to divide medical X-ray computed tomography (CT) images into six main anatomical regions: head, neck, chest, abdomen, pelvis and legs. We implemented and compared three methods: (1) an analytical approach which does not require training and solely relies on utilizing critical points in image intensity profiles to derive cut-planes that divide the scan into the mentioned regions, (2) a classical convolutional neural network (CNN) approach, which classifies each transaxial 2D plane independently and then concatenates classification results, and (3) CNN followed by a context-based correction algorithm (CBCA) which improves the CNN classification using positional relationships between all CT slices. The analytical approach achieved acceptable accuracy for anatomical region segmentation without the need for explicit data labeling and was effective for batch labeling whole-body CTs, greatly reducing manual labeling efforts. CNNs achieved superior accuracy and allowed for rapid development and training, but required labeled data and were susceptible to produce discontinuous anatomical regions and therefore ambiguous anatomical boundaries. Post hoc correction of CNN results using CBCA overcame these limitations, achieving nearly perfect CT slice labeling and anatomical region segmentation. [ABSTRACT FROM AUTHOR]

In this paper, a new multiphase segmentation which has two stages is proposed. At the first stage, independent from the kind of image, k equidistant real numbers within [0, 1] interval are set as the labels of k main regions. At the second stage, two factors are used, which consist of the intensity function for distinguishing similar regions, as well as the eigenvector of Hessian matrix for distinguishing similar ridges with an auxiliary parameter for controlling the influence of this factor in different images. Also, an iterative algorithm is applied for gradually growing the region. Numerical experiences are given to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Linear gradients are commonly applied in non-photographic artwork for shading and other artistic effects. It is sometimes necessary to generate a vector graphics form of raster images comprising such artwork with the expectation to obtain a simple output and plug it into a traditional workflow, to be further edited and arranged. Many such workflows support only linear gradients and our goal is to generate a standard vector form of the image that can fit such workflow. This vectorization process should be automatic with minimal user intervention. We present a simple image vectorization algorithm that detects regions of linear gradient in potentially noisy images and reconstructs the vector definition on the basis of that information. It uses a novel interval gradient optimization scheme to derive large regions of uniform gradient. We also demonstrate the technique on noisy and hand-drawn portraits. [ABSTRACT FROM AUTHOR]

Our aim is to detect photometric invariant regions in multispectral images robust against sensor noise. Therefore, different polar angle representations of a spectrum are examined for invariance using the dichromatic reflection model. These invariant representations take advantage of white balancing. Based on the camera sensitivity, a theoretical expression is obtained of the certainty associated with the polar angular representations under the influence of noise. The expression is employed by the segmentation technique to ensure robustness against sensor noise. [ABSTRACT FROM AUTHOR]

A new segmentation algorithm for lumen region detection and boundary extraction from gastro-intestinal (GI) images is presented. The proposed algorithm consists of two steps. First, a preliminary region of interest (ROI) representing the GI lumen is segmented by an adaptive progressive thresholding (APT) technique. Then, an adaptive filter, the Iris filter, is applied to the ROI to determine the actual region. It has been observed that the combined APT-Iris filter technique can enhance and detect the unclear boundaries in the lumen region of GI images and thus produces a more accurate lumen region, compared with the existing techniques. Experiments are carried out to determine the maximum error on the extracted boundary with respect to an expert-annotated boundary technique. Investigations show that, based on the experimental results obtained from 50 endoscopic images, the maximum error is reduced by up to 72 pixels for a 256 x 256 image representation compared with other existing techniques. In addition, a new boundary extraction algorithm, based on a heuristic search on the neighbourhood pixels, is employed to obtain a connected single pixel width outer boundary using two preferential sequence windows. Experimental results are also presented to justify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]