234,782 research outputs found
Fine-Grained Product Class Recognition for Assisted Shopping
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
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
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
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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