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Register a new userHandyNet: A One-stop Solution to Detect, Segment, Localize & Analyze Driver Hands
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Added by
Akshay Rangesh
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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Tasks related to human hands have long been part of the computer vision community. Hands being the primary actuators for humans, convey a lot about activities and intents, in addition to being an alternative form of communication/interaction with other humans and machines. In this study, we focus on training a single feedforward convolutional neural network (CNN) capable of executing many hand related tasks that may be of use in autonomous and semi-autonomous vehicles of the future. The resulting network, which we refer to as HandyNet, is capable of detecting, segmenting and localizing (in 3D) driver hands inside a vehicle cabin. The network is additionally trained to identify handheld objects that the driver may be interacting with. To meet the data requirements to train such a network, we propose a method for cheap annotation based on chroma-keying, thereby bypassing weeks of human effort required to label such data. This process can generate thousands of labeled training samples in an efficient manner, and may be replicated in new environments with relative ease.
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Camouflage is a key defence mechanism across species that is critical to survival. Common strategies for camouflage include background matching, imitating the color and pattern of the environment, and disruptive coloration, disguising body outlines [35]. Camouflaged object detection (COD) aims to segment camouflaged objects hiding in their surroundings. Existing COD models are built upon binary ground truth to segment the camouflaged objects without illustrating the level of camouflage. In this paper, we revisit this task and argue that explicitly modeling the conspicuousness of camouflaged objects against their particular backgrounds can not only lead to a better understanding about camouflage and evolution of animals, but also provide guidance to design more sophisticated camouflage techniques. Furthermore, we observe that it is some specific parts of the camouflaged objects that make them detectable by predators. With the above understanding about camouflaged objects, we present the first ranking based COD network (Rank-Net) to simultaneously localize, segment and rank camouflaged objects. The localization model is proposed to find the discriminative regions that make the camouflaged object obvious. The segmentation model segments the full scope of the camouflaged objects. And, the ranking model infers the detectability of different camouflaged objects. Moreover, we contribute a large COD testing set to evaluate the generalization ability of COD models. Experimental results show that our model achieves new state-of-the-art, leading to a more interpretable COD network.
Most online multi-object trackers perform object detection stand-alone in a neural net without any input from tracking. In this paper, we present a new online joint detection and tracking model, TraDeS (TRAck to DEtect and Segment), exploiting tracking clues to assist detection end-to-end. TraDeS infers object tracking offset by a cost volume, which is used to propagate previous object features for improving current object detection and segmentation. Effectiveness and superiority of TraDeS are shown on 4 datasets, including MOT (2D tracking), nuScenes (3D tracking), MOTS and Youtube-VIS (instance segmentation tracking). Project page: https://jialianwu.com/projects/TraDeS.html.
Accurate segmentation of critical anatomical structures is at the core of medical image analysis. The main bottleneck lies in gathering the requisite expert-labeled image annotations in a scalable manner. Methods that permit to produce accurate anatomical structure segmentation without using a large amount of fully annotated training images are highly desirable. In this work, we propose a novel contribution of Contour Transformer Network (CTN), a one-shot anatomy segmentor including a naturally built-in human-in-the-loop mechanism. Segmentation is formulated by learning a contour evolution behavior process based on graph convolutional networks (GCNs). Training of our CTN model requires only one labeled image exemplar and leverages additional unlabeled data through newly introduced loss functions that measure the global shape and appearance consistency of contours. We demonstrate that our one-shot learning method significantly outperforms non-learning-based methods and performs competitively to the state-of-the-art fully supervised deep learning approaches. With minimal human-in-the-loop editing feedback, the segmentation performance can be further improved and tailored towards the observer desired outcomes. This can facilitate the clinician designed imaging-based biomarker assessments (to support personalized quantitative clinical diagnosis) and outperforms fully supervised baselines.
Driven by Convolutional Neural Networks, object detection and semantic segmentation have gained significant improvements. However, existing methods on the basis of a full top-down module have limited robustness in handling those two tasks simultaneously. To this end, we present a joint multi-task framework, termed IvaNet. Different from existing methods, our IvaNet backwards abstract semantic information from higher layers to augment lower layers using local top-down modules. The comparisons against some counterparts on the PASCAL VOC and MS COCO datasets demonstrate the functionality of IvaNet.
With the advent of the nanosat/cubesat revolution, new opportunities have appeared to develop and launch small ($sim$ts 1000 cm$^3$), low-cost ($sim$ts US$ 1M) experiments in space in very short timeframes ($sim$ 2ts years). In the field of high-energy astrophysics, in particular, it is a considerable challenge to design instruments with compelling science and competitive capabilities that can fit in very small satellite buses such as a cubesat platform, and operate them with very limited resources. Here we describe a hard X-ray (30--200ts keV) experiment, LECX (Localizador de Explos~oes Cosmicas de Raios X -- Locator of X-Ray Cosmic Explosions), that is capable of detecting and localizing within a few degrees events like Gamma-Ray Bursts and other explosive phenomena in a 2U-cubesat platform, at a rate of $sim${bf 5 events year$^{-1}$.} In the current gravitational wave era of astronomy, a constellation or swarm of small spacecraft carrying instruments such as LECX can be a very cost-effective way to search for electromagnetic counterparts of gravitational wave events produced by the coalescence of compact objects.
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