234,782 research outputs found

    Fine-Grained Product Class Recognition for Assisted Shopping

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    Assistive solutions for a better shopping experience can improve the quality of life of people, in particular also of visually impaired shoppers. We present a system that visually recognizes the fine-grained product classes of items on a shopping list, in shelves images taken with a smartphone in a grocery store. Our system consists of three components: (a) We automatically recognize useful text on product packaging, e.g., product name and brand, and build a mapping of words to product classes based on the large-scale GroceryProducts dataset. When the user populates the shopping list, we automatically infer the product class of each entered word. (b) We perform fine-grained product class recognition when the user is facing a shelf. We discover discriminative patches on product packaging to differentiate between visually similar product classes and to increase the robustness against continuous changes in product design. (c) We continuously improve the recognition accuracy through active learning. Our experiments show the robustness of the proposed method against cross-domain challenges, and the scalability to an increasing number of products with minimal re-training.Comment: Accepted at ICCV Workshop on Assistive Computer Vision and Robotics (ICCV-ACVR) 201

    Discriminating land cover types using SPOT 4 imagery in the mixed grassland ecosystem

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    Non-Peer ReviewedThis study was conducted at a mixed grass prairie in southern Saskatchewan. The main objective of this study is to evaluate spectral differentiations of different land cover types and grassland communities, and thus find the way of improving the utility of the land cover data product with remote sensing techniques for the pasture insurance program. Data for this study were field measurements and two SPOT 4 images acquired in the summer of 2005. Using discriminant analysis, the study extracted and analyzed spectral signals from different land cover types. The results showed that a hierarchical classification method was necessary as different level of classification uses different spectral properties. Both June and July imagery can separate the seven major classes with high accuracy and July image is 1% more accuracy than June image. The images from different dates have different advantages for separating classes based on their discriminant functions. It is easier to differentiate land cover types, such as vegetation covered area and non-vegetation covered area, however, the accuracy will be lower when separating the classes of crop and shrub, and fallow and badland due to their similar spectral properties. Three common grassland management practices (cropland, grazed grassland, and ungrazed grassland) can be spectrally discriminated, but it is difficult to separate grasslands from different topography patterns because they have similar spectral features

    Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning

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    We demonstrate the feasibility of a fully automatic computer-aided diagnosis (CAD) tool, based on deep learning, that localizes and classifies proximal femur fractures on X-ray images according to the AO classification. The proposed framework aims to improve patient treatment planning and provide support for the training of trauma surgeon residents. A database of 1347 clinical radiographic studies was collected. Radiologists and trauma surgeons annotated all fractures with bounding boxes, and provided a classification according to the AO standard. The proposed CAD tool for the classification of radiographs into types "A", "B" and "not-fractured", reaches a F1-score of 87% and AUC of 0.95, when classifying fractures versus not-fractured cases it improves up to 94% and 0.98. Prior localization of the fracture results in an improvement with respect to full image classification. 100% of the predicted centers of the region of interest are contained in the manually provided bounding boxes. The system retrieves on average 9 relevant images (from the same class) out of 10 cases. Our CAD scheme localizes, detects and further classifies proximal femur fractures achieving results comparable to expert-level and state-of-the-art performance. Our auxiliary localization model was highly accurate predicting the region of interest in the radiograph. We further investigated several strategies of verification for its adoption into the daily clinical routine. A sensitivity analysis of the size of the ROI and image retrieval as a clinical use case were presented.Comment: Accepted at IPCAI 2020 and IJCAR

    DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders

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    This paper presents a novel deep learning-based method for learning a functional representation of mammalian neural images. The method uses a deep convolutional denoising autoencoder (CDAE) for generating an invariant, compact representation of in situ hybridization (ISH) images. While most existing methods for bio-imaging analysis were not developed to handle images with highly complex anatomical structures, the results presented in this paper show that functional representation extracted by CDAE can help learn features of functional gene ontology categories for their classification in a highly accurate manner. Using this CDAE representation, our method outperforms the previous state-of-the-art classification rate, by improving the average AUC from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates on input images that were downsampled significantly with respect to the original ones to make it computationally feasible
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