An accurate and reliable image-based quantification system for blueberries may be useful for the automation of harvest management. It may also serve as the basis for controlling robotic harvesting systems. Quantification of blueberries from images is a challenging task due to occlusions, differences in size, illumination conditions and the irregular amount of blueberries that can be present in an image. This paper proposes the quantification per image and per batch of blueberries in the wild, using high definition images captured using a mobile device. In order to quantify the number of berries per image, a network based on Mask R-CNN for object detection and instance segmentation was proposed. Several backbones such as ResNet101, ResNet50 and MobileNetV1 were tested. The performance of the algorithm was evaluated using the Intersection over Union Error (IoU) and the competitive mean Average Precision (mAP) per images and per batch. The best detection result was obtained with the ResNet50 backbone achieving a mIoU score of 0.595 and mAP scores of 0.759 and 0.724 respectively (for IoU thresholds 0.5 and 0.7). For instance segmentation, the best results obtained were 0.726 for the mIoU metric and 0.909 and 0.774 for the mAP metric using thresholds of 0.5 and 0.7 respectively.
Selfie soft biometrics has great potential for various applications ranging from marketing, security and online banking. However, it faces many challenges since there is limited control in data acquisition conditions. This chapter presents a Super-Resolution-Convolutional Neural Networks (SRCNNs) approach that increases the resolution of low quality periocular iris images cropped from selfie images of subject's faces. This work shows that increasing image resolution (2x and 3x) can improve the sex-classification rate when using a Random Forest classifier. The best sex-classification rate was 90.15% for the right and 87.15% for the left eye. This was achieved when images were upscaled from 150×150 to 450×450 pixels. These results compare well with the state of the art and show that when improving image resolution with the SRCNN the sex-classification rate increases. Additionally, a novel selfie database captured from 150 subjects with an iPhone X was created (available upon request).
Semantic segmentation has been widely used for several applications, including the detection of eye structures. This is used in tasks such as eye-tracking and gaze estimation, which are useful techniques for human-computer interfaces, salience detection, and Virtual reality (VR), amongst others. Most of the state of the art techniques achieve high accuracy but with a considerable number of parameters. This paper explores alternatives to improve the efficiency of the state of the art method, namely DenseNet Tiramisu, when applied to NIR image segmentation. This task is not trivial; the reduction of block and layers also affects the number of feature maps. The growth rate (k) of the feature maps regulates how much new information each layer contributes to the global state, therefore the trade-off amongst grown rate (k), IOU, and the number of layers needs to be carefully studied. The main goal is to achieve a lightweight and efficient network with fewer parameters than traditional architectures in order to be used for mobile device applications. As a result, a DenseNet with only three blocks and ten layers is proposed (DenseNet10). Experiments show that this network achieved higher IOU rates when comparing with Encoder-Decoder, DensetNet56-67-103, MaskRCNN, and DeeplabV3+ models in the Facebook database. Furthermore, this method reached 8th place in The Facebook semantic segmentation challenge with 0.94293 mean IOU and 202.084 parameters with a final score of 0.97147. This score is only 0,001 lower than the first place in the competition. The sclera was identified as the more challenging structure to be segmented.
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