Results 261 to 270 of about 5,097,408 (330)
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Linear Multilayer ICA Generating Hierarchical Edge Detectors
Neural Computation, 2007In this letter, a new ICA algorithm, linear multilayer ICA (LMICA), is proposed. There are two phases in each layer of LMICA. One is the mapping phase, where a two-dimensional mapping is formed by moving more highly correlated (nonindependent) signals closer with the stochastic multidimensional scaling network.
Matsuda, Yoshitatsu, Yamaguchi, Kazunori
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arXiv.org
This study presents a detailed analysis of the YOLOv8 object detection model, focusing on its architecture, training techniques, and performance improvements over previous iterations like YOLOv5.
M. Yaseen
semanticscholar +1 more source
This study presents a detailed analysis of the YOLOv8 object detection model, focusing on its architecture, training techniques, and performance improvements over previous iterations like YOLOv5.
M. Yaseen
semanticscholar +1 more source
arXiv.org
This study provides a comprehensive analysis of the YOLOv9 object detection model, focusing on its architectural innovations, training methodologies, and performance improvements over its predecessors.
M. Yaseen
semanticscholar +1 more source
This study provides a comprehensive analysis of the YOLOv9 object detection model, focusing on its architectural innovations, training methodologies, and performance improvements over its predecessors.
M. Yaseen
semanticscholar +1 more source
PG-RCNN: Semantic Surface Point Generation for 3D Object Detection
IEEE International Conference on Computer Vision, 2023One of the main challenges in LiDAR-based 3D object detection is that the sensors often fail to capture the complete spatial information about the objects due to long distance and occlusion.
Inyong Koo +5 more
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Generative And Encoded Anomaly Detectors
2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2019We present two fully unsupervised deep learning approaches for hyperspectral anomaly detection. In one approach we formulate the anomaly detection problem as an adversarial game where a generator network learns the distribution of the hyperspectral background pixels comprising a single hyperspectral image and the output of the corresponding ...
Tegan H. Emerson +4 more
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Photon-Counting Detector CT for Musculoskeletal Imaging: A Clinical Perspective.
AJR. American journal of roentgenology, 2022Photon-counting detector (PCD) CT has emerged as a novel imaging modality that represents a fundamental shift in the way that CT systems detect X-rays.
Francis I Baffour +6 more
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Detectors: General Characteristics
2016This chapter firstly introduces the properties of detectors and presents a synoptic discussion of the organization of the following chapters. The single detectors will be the building blocks of the astroparticle instrumentation discussed later. Detectors for high energy photons and particles show a great difference compared to optical and radio ...
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Two dimensional generalized edge detector
Proceedings 10th International Conference on Image Analysis and Processing, 2003Detecting edges in images is one of the most challenging issues in computer vision and image processing due to lack of a robust detector. Gokmen and Jain (1997) have obtained an edge detector called the generalized edge detector (GED), capable of producing most of the existing edge detectors.
B. Kurt, M. Gokmen
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Digital Single-Tone Generator-Detectors
Bell System Technical Journal, 1976A class of digital, linear generator-detectors, based upon cyclotomic polynomials, which have simple implementation and operate without roundoff errors, is proposed. It is shown how these filters are optimal among all linear generator-detectors which have no roundoff required in the feedback loop.
R. P. Kurshan, B. Gopinath
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Joint 3D Proposal Generation and Object Detection from View Aggregation
IEEE/RJS International Conference on Intelligent RObots and Systems, 2017We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios. The proposed neural network architecture uses LIDAR point clouds and RGB images to generate features that are shared by two subnetworks: a region proposal ...
Jason Ku +4 more
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