Insect Detection in Rice Crop using Google Code Lab

Herb plants are essential in the medical field today and can help humans. Phyllanthus Elegans Wall is used in this study to analyse and categorize whether it is a safe or unhealthy leaf. At the moment, most insect identification methods rely on physical classification, making it difficult to automatically, quickly, and reliably identify in stored grains. The concept of this research is to ascertain the quality of leaves by combining technology with pesticide classification in the agricultural sector. Picture collection, image processing, and classification are the first steps in enhancing leaf quality analysis. The segmentation using HSV to input RGB image for the colour alteration structure is the most significant image processing method for this section. The colour and shape of a leaf disease image are used to analyse it. Insect detection in complex backgrounds is more versatile with the score map that is decision alternate highly interconnected layer, and our detection speed has upgraded. Finally, the taxonomy approach employs an algorithm that feeds directly that employs formation backwards techniques. The result shows a Many-layer Preceptor and Nonlinear Activation Feature comparison, as well as a percentage of accuracy contrast between MLP and RBF. MLP and RBF are neural network algorithms. Clearly, the Neural Network classifier has a better presentation and precision.

A Fast and Accurate Few-Shot Detector for Objects with Fewer Pixels in Drone Image

Unmanned aerial vehicles (UAVs) are important in modern war, and object detection performance influences the development of related intelligent drone application. At present, the target categories of UAV detection tasks are diversified. However, the lack of training samples of novel categories will have a bad impact on the task. At the same time, many state-of-the-arts are not suitable for drone images due to the particularity of perspective and large number of small targets. In this paper, we design a fast few-shot detector for drone targets. It adopts the idea of anchor-free in fully convolutional one-stage object detection (FCOS), which leads to a more reasonable definition of positive and negative samples and faster speed, and introduces Siamese framework with more discriminative target model and attention mechanism to integrate similarity measures, which enables our model to match the objects of the same categories and distinguish the different class objects and background. We propose a matching score map to utilize the similarity information of attention feature map. Finally, through soft-NMS, the predicted detection bounding boxes for support category objects are generated. We construct a DAN dataset as a collection of DOTA and NWPU VHR-10. Compared with many state-of-the-arts on the DAN dataset, our model is proved to outperform them for few-shot detection tasks of drone images.

Siamese Networks-Based People Tracking Using Template Update for 360-Degree Videos Using EAC Format

Rich information is provided by 360-degree videos. However, non-uniform geometric deformation caused by sphere-to-plane projection significantly decreases tracking accuracy of existing trackers, and the huge amount of data makes it difficult to achieve real-time tracking. Thus, this paper proposes a Siamese networks-based people tracker using template update for 360-degree equi-angular cubemap (EAC) format videos. Face stitching overcomes the problem of content discontinuity of the EAC format and avoids raising new geometric deformation in stitched images. Fully convolutional Siamese networks enable tracking at high speed. Mostly important, to be robust against combination of non-uniform geometric deformation of the EAC format and partial occlusions caused by zero padding in stitched images, this paper proposes a novel Bayes classifier-based timing detector of template update by referring to the linear discriminant feature and statistics of a score map generated by Siamese networks. Experimental results show that the proposed scheme significantly improves tracking accuracy of the fully convolutional Siamese networks SiamFC on the EAC format with operation beyond the frame acquisition rate. Moreover, the proposed score map-based timing detector of template update outperforms state-of-the-art score map-based timing detectors.

Investigation of networks underlying hyperkinetic seizures utilizing ictal SPECT

ObjectiveTo study neural networks involved in hyperkinetic seizures (HKS) using ictal SPECT.MethodsWe retrospectively identified 18 patients with HKS evaluated at the Cleveland Clinic between 2005 and 2015 with video-EEG monitoring and ictal SPECT. Semiology was confirmed by the consensus of 2 epileptologists' independent reviews and classified as type 1, 2, or 3 HKS. SPECT data were analyzed by 2 independent physicians using a z score of 1.5. Ictal hyperperfusion patterns for each group were analyzed visually and with SPM. Spatial normalization to Montreal Neurological Institute space for each patient’s data was performed, followed by flipping of data from patients with left-sided ictal onset to the right side. Finally, an average z score map for each group was calculated.ResultsVisual analysis and SPM identified different patterns of ictal hyperperfusion in the 3 subtypes of HKS. Type 1 seizures showed hyperperfusion in a more anteriorly located network involving the anterior insula, orbitofrontal cortex, cingulate, and anterior perisylvian region and rostral midbrain. Type 2 seizures were associated with hyperperfusion in a more caudally located network involving the orbitofrontal cortex, cingulate (middle and posterior), basal ganglia, thalami, and cerebellum. Type 3 seizures showed a mixed pattern of SPECT hyperperfusion involving the temporal pole and anterior perisylvian region.ConclusionsEach of the 3 different semiologic subtypes of HKS is associated with distinct patterns of hyperperfusion, providing further insight into the neural networks involved. This knowledge may inform placement of invasive EEG electrodes in patients with HKS semiology undergoing presurgical evaluation.

LIDAR Scan Matching in Off-Road Environments

Accurately matching the LIDAR scans is a critical step for an Autonomous Land Vehicle (ALV). Whilst most previous works have focused on the urban environment, this paper focuses on the off-road environment. Due to the lack of a publicly available dataset for algorithm comparison, a dataset containing LIDAR pairs with varying amounts of offsets in off-road environments is firstly constructed. Several popular scan matching approaches are then evaluated using this dataset. Results indicate that global approaches, such as Correlative Scan Matching (CSM), perform best on large offset datasets, whilst local scan matching approaches perform better on small offset datasets. To combine the merits of both approaches, a two-stage fusion algorithm is designed. In the first stage, several transformation candidates are sampled from the score map of the CSM algorithm. Local scan matching approaches then start from these transformation candidates to obtain the final results. Four performance indicators are also designed to select the best transformation. Experiments on a real-world dataset prove the effectiveness of the proposed approach.

rStaple: A Robust Complementary Learning Method for Real-Time Object Tracking

Object tracking is a challenging research task because of drastic appearance changes of the target and a lack of training samples. Most online learning trackers are hampered by complications, e.g., drifting problem under occlusion, being out of view, or fast motion. In this paper, a real-time object tracking algorithm termed “robust sum of template and pixel-wise learners” (rStaple) is proposed to address those problems. It combines multi-feature correlation filters with a color histogram. Firstly, we extract a combination of specific features from the searching area around the target and then merge feature channels to train a translation correlation filter online. Secondly, the target state is determined by a discriminating mechanism, wherein the model update procedure stops when the target is occluded or out of view, and re-activated when the target re-appears. In addition, by calculating the color histogram score in the searching area, a significant enhancement is adopted for the score map. The target position can be estimated by combining the enhanced color histogram score with the correlation filter response map. Finally, a scale filter is trained for multi-scale detection to obtain the final tracking result. Extensive experimental results on a large benchmark dataset demonstrates that the proposed rStaple is superior to several state-of-the-art algorithms in terms of accuracy and efficiency.

Image Cropping with Composition and Saliency Aware Aesthetic Score Map

Aesthetic image cropping is a practical but challenging task which aims at finding the best crops with the highest aesthetic quality in an image. Recently, many deep learning methods have been proposed to address this problem, but they did not reveal the intrinsic mechanism of aesthetic evaluation. In this paper, we propose an interpretable image cropping model to unveil the mystery. For each image, we use a fully convolutional network to produce an aesthetic score map, which is shared among all candidate crops during crop-level aesthetic evaluation. Then, we require the aesthetic score map to be both composition-aware and saliency-aware. In particular, the same region is assigned with different aesthetic scores based on its relative positions in different crops. Moreover, a visually salient region is supposed to have more sensitive aesthetic scores so that our network can learn to place salient objects at more proper positions. Such an aesthetic score map can be used to localize aesthetically important regions in an image, which sheds light on the composition rules learned by our model. We show the competitive performance of our model in the image cropping task on several benchmark datasets, and also demonstrate its generality in real-world applications.

Fast Learning of Temporal Action Proposal via Dense Boundary Generator

Generating temporal action proposals remains a very challenging problem, where the main issue lies in predicting precise temporal proposal boundaries and reliable action confidence in long and untrimmed real-world videos. In this paper, we propose an efficient and unified framework to generate temporal action proposals named Dense Boundary Generator (DBG), which draws inspiration from boundary-sensitive methods and implements boundary classification and action completeness regression for densely distributed proposals. In particular, the DBG consists of two modules: Temporal boundary classification (TBC) and Action-aware completeness regression (ACR). The TBC aims to provide two temporal boundary confidence maps by low-level two-stream features, while the ACR is designed to generate an action completeness score map by high-level action-aware features. Moreover, we introduce a dual stream BaseNet (DSB) to encode RGB and optical flow information, which helps to capture discriminative boundary and actionness features. Extensive experiments on popular benchmarks ActivityNet-1.3 and THUMOS14 demonstrate the superiority of DBG over the state-of-the-art proposal generator (e.g., MGG and BMN).